[{"data":1,"prerenderedAt":806},["ShallowReactive",2],{"/en-us/blog/secure-compliant-and-ai-powered-get-to-know-3-new-gitlab-features":3,"navigation-en-us":40,"banner-en-us":450,"footer-en-us":460,"blog-post-authors-en-us-Jessica Hurwitz":699,"blog-related-posts-en-us-secure-compliant-and-ai-powered-get-to-know-3-new-gitlab-features":713,"blog-promotions-en-us":744,"next-steps-en-us":796},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":27,"isFeatured":12,"meta":28,"navigation":12,"path":29,"publishedDate":20,"seo":30,"stem":35,"tagSlugs":36,"__hash__":39},"blogPosts/en-us/blog/secure-compliant-and-ai-powered-get-to-know-3-new-gitlab-features.yml","Secure Compliant And Ai Powered Get To Know 3 New Gitlab Features",[7],"jessica-hurwitz",null,"product",{"slug":11,"featured":12,"template":13},"secure-compliant-and-ai-powered-get-to-know-3-new-gitlab-features",true,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"Secure, compliant, and AI-powered: Get to know 3 new GitLab features","Enhance security, leverage new AI capabilities, and protect sensitive data with our latest platform improvements.",[18],"Jessica Hurwitz","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749664458/Blog/Hero%20Images/Gartner_AI_Code_Assistants_Blog_Post_Cover_Image_1800x945.png","2025-01-27","AI capabilities are rapidly reshaping how teams build, secure, and deploy applications. As part of our ongoing commitment to helping you navigate the evolving marketplace, GitLab has introduced more than 440 improvements in the past three releases. We're excited to spotlight three standout features making an immediate impact on how teams approach AI-powered DevSecOps. In addition, we announced we are partnering with AWS to launch [GitLab Duo with Amazon Q](https://about.gitlab.com/blog/gitlab-duo-with-amazon-q-devsecops-meets-agentic-ai/), combining our strengths to transform software development. We're creating an experience, together, that makes AI-powered development feel seamless and upholds the security, compliance, and reliability that enterprises require.\n\n> Learn how GitLab can [deliver 483% ROI over the next three years](https://about.gitlab.com/blog/gitlab-ultimates-total-economic-impact-483-roi-over-3-years/), according to Forrester Consulting.\n\n\u003C!-- blank line -->\n\u003Cfigure class=\"video_container\">\n  \u003Ciframe src=\"https://player.vimeo.com/video/1056012314?badge=0\" frameborder=\"0\" allow=\"autoplay; fullscreen; picture-in-picture; clipboard-write; encrypted-media\" title=\"GitLab 17.6-17.8 Quarterly Release Overview\"> \u003C/iframe>\n\u003C/figure>\n\u003C!-- blank line -->\n\n## 1. Vulnerability Resolution: Streamline security remediation\n\nGitLab’s 2024 [Global DevSecOps Report](https://about.gitlab.com/developer-survey/) found that 66% of companies are releasing software twice as fast — or faster — than in previous years, as businesses strive to deliver more value to their customers than competitors. However, speed introduces risk. With security teams [outnumbered by dev teams 80:1](https://www.opentext.com/assets/documents/en-US/pdf/developer-driven-appsec-security-at-the-speed-of-devops-pp-en.pdf), threat actors are able to exploit applications at a record pace. Last year alone, [80% of the top data breaches](https://www.crowdstrike.com/2024-state-of-application-security-report/) stemmed from attacks at the application layer.\n\n[GitLab Duo Vulnerability Resolution](https://docs.gitlab.com/ee/user/application_security/vulnerabilities/#vulnerability-resolution) addresses this challenge head-on. When vulnerabilities are detected in your code, you can now access detailed information right from the vulnerability report and invoke GitLab Duo to automatically create a merge request that updates your code and mitigates the risk. While developers must review these auto-generated merge requests before merging to verify the changes, this automation significantly streamlines the remediation process. Vulnerability Resolution pairs with [Vulnerability Explanation](https://about.gitlab.com/the-source/ai/understand-and-resolve-vulnerabilities-with-ai-powered-gitlab-duo/), which also recently became generally available. Vulnerability Explanation gives developers a detailed description of the vulnerability infecting their code, real-world examples of how attackers can exploit the vulnerable code, and practical suggestions for remediation.\n\nBy expediting the vulnerability remediation process, your teams can focus on delivering software faster while maintaining strong security practices. With less time spent researching and remediating vulnerabilities, developers can concentrate on building features that drive business value.\n\n_GitLab Duo Vulnerability Resolution is available as a [GitLab Duo Enterprise add-on](https://about.gitlab.com/sales/?type=free-trial&toggle=gitlab-duo-pro)._\n\n\u003C!-- blank line -->\n\u003Cfigure class=\"video_container\">\n  \u003Ciframe src=\"\nhttps://www.youtube.com/embed/VJmsw_C125E?si=W7n1ESS63xkPyH4H\" frameborder=\"0\" title=\"GitLab Vulnerability Resolution\" allowfullscreen=\"true\"> \u003C/iframe>\n\u003C/figure>\n\u003C!-- blank line -->\n\n## 2. Model Registry: Breaking down silos between Data Science and Development teams\n\nFor organizations building AI-powered applications, bridging the gap between data science and software development teams has been a persistent challenge. Data scientists and developers often work in disconnected tools and workflows, leading to friction, delays, and potential errors when deploying models to production.\n\n[GitLab Model Registry](https://docs.gitlab.com/ee/user/project/ml/model_registry/) directly addresses this challenge by providing a centralized hub where data science and development teams can collaborate seamlessly within their existing GitLab workflow. Built with [MLflow](https://docs.gitlab.com/ee/user/project/ml/experiment_tracking/mlflow_client.html#model-registry) native integration, the registry allows data scientists to continue using their preferred tools while making models and artifacts instantly accessible to the broader development team.\nThis unified approach transforms team collaboration. Data scientists can version models, store artifacts, and document model behavior through comprehensive model cards, while developers can easily integrate these models into their applications using GitLab CI/CD pipelines for automated testing and deployment.\n\nAdditionally, the Model Registry's semantic versioning and GitLab API integration enables teams to implement robust governance and automate production deployments, creating a streamlined environment where data scientists and developers can work together effectively to deliver AI-powered innovation.\n\n_Model Registry is available across all tiers for SaaS and self-managed customers. See the [release blog for 17.6](https://about.gitlab.com/releases/2024/11/21/gitlab-17-6-released/#model-registry-now-generally-available) and [documentation](https://docs.gitlab.com/ee/user/project/ml/model_registry/) for more._\n\n## 3. Secret Push Protection: Shift security left with proactive secret detection\n\nTeams often face a critical security challenge: Developers may hardcode sensitive information like API keys, tokens, and credentials as plain text in source code repositories, sometimes without even realizing it. This creates an easy target for threat actors and puts your organization at risk.\n\n[Secret Push Protection](https://about.gitlab.com/blog/prevent-secret-leaks-in-source-code-with-gitlab-secret-push-protection/) directly addresses this problem by blocking developers from pushing code that contains secrets, significantly reducing the likelihood of a breach. It works by leveraging customizable rules to identify high-confidence secrets before they ever reach your repository.\n\nWhat makes this solution particularly powerful is its integration with our pipeline secret detection, creating a comprehensive defense strategy.\n\n_Secret Push Protection is now generally available for all [GitLab Ultimate tier](https://about.gitlab.com/pricing/ultimate/) and [GitLab Dedicated](https://about.gitlab.com/dedicated/) customers._\n\n\u003C!-- blank line -->\n\u003Cfigure class=\"video_container\">\n  \u003Ciframe src=\"\nhttps://www.youtube.com/embed/SFVuKx3hwNI?si=aV_3Lazs2AiDH3Jf\" title=\"Introduction to Secret Push Protection\" frameborder=\"0\" title=\"GitLab Vulnerability Resolution\" allowfullscreen=\"true\"> \u003C/iframe>\n\u003C/figure>\n\u003C!-- blank line -->\n\n## Put these features to work today\n\nAt GitLab, we’re committed to making it easier for teams to build software, faster. Capabilities like GitLab Duo Vulnerability Resolution, Model Registry, and Secret Push Protection are just a few of the recent innovations we’ve delivered to help developers and security teams level up their DevSecOps workflows. To learn more, check out our [releases page](https://about.gitlab.com/releases/categories/releases/).\n\n> Get started with these new features today with [a free trial of GitLab Ultimate](https://about.gitlab.com/free-trial/).\n",[23,24,25,9,26],"DevSecOps","DevSecOps 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18.11: Budget guardrails for GitLab Credits","Learn how new spending caps and per-user credit limits give organizations the budget guardrails to scale GitLab Duo Agent Platform.",[719],"Bryan Rothwell","https://res.cloudinary.com/about-gitlab-com/image/upload/v1776259080/cakqnwo5ecp255lo8lzo.png","2026-04-16","Teams using GitLab Duo Agent Platform with on-demand GitLab Credits are shipping faster, catching bugs earlier, and automating tasks that used to take entire sprints. But as adoption grows, so does oversight from finance, procurement, and platform teams to prove that AI spending is bounded, predictable, and controllable.\n\nOne of the greatest barriers to broader AI adoption isn't skepticism about the technology. It's uncertainty about managing spend. Without budget caps, a busy month could produce unexpected expenses. Without per-user limits, a handful of power users could burn through the team's credits before the month is over. And without either, engineering leaders who want to expand their use of agentic AI for software development have to jump through more hoops for budget approval.\n\nSince its [general availability](https://about.gitlab.com/blog/gitlab-duo-agent-platform-is-generally-available/), GitLab Duo Agent Platform has provided usage governance and visibility. With GitLab 18.11, we're introducing usage controls for [GitLab Credits](https://about.gitlab.com/blog/introducing-gitlab-credits/): spending caps and budget guardrails that give your organization even more control and transparency over how credits are consumed.\n\n## Managing GitLab Credits\n\nGitLab 18.11 adds three layers of control over GitLab Credits consumption: a subscription-level spending cap, per-user credit limits, and visibility into cap status and enforcement.\n\n### Subscription-level spending cap\n\nBilling account managers can now set a hard monthly ceiling for on-demand GitLab Credits consumption for their entire subscription.\n\nHere's how it works:\n\n* **Set a cap** in the `Customers Portal` under your subscription's GitLab Credits settings.  \n* **Enforce spend limits automatically.**  When on-demand usage reaches the cap, DAP access is paused for all users on that subscription until the next monthly period begins.  \n* **Make adjustments as you go.** Raise or disable the cap mid-month to restore access.\n\nThe cap resets each monthly period and your configured limit carries forward unless you change it. Because usage data is synchronized periodically rather than in real time, a small amount of additional usage may occur after the cap is reached before enforcement takes effect. See the [GitLab Credits documentation](https://docs.gitlab.com/subscriptions/gitlab_credits/) for details.\n\n### User-level spending caps\n\nNot every user consumes credits at the same rate, and that's expected. But when one or two power users account for a disproportionate share of the pool, the rest of the team can lose access before the month is over.\n\nPer-user credit caps prevent any single user from consuming more than their fair share:\n\n* **Flat per-user cap.** Set a uniform credit limit that applies equally to every user on the subscription through the GitLab GraphQL API. Unlike the subscription-level cap, the per-user cap applies to a user's total consumption across all credit sources.  \n* **Custom per-user overrides.** For organizations that need differentiated limits, you can set individual credit caps for specific users through the GraphQL API. For example, you could give your staff engineers a higher allocation while applying a standard limit to the broader team.  \n* **Individual enforcement.** When a user reaches their cap, they retain full access to GitLab. Only their Duo Agent Platform credit usage is paused until the next billing cycle. Everyone else keeps working uninterrupted until they hit their own limit or the subscription-level cap is reached, whichever comes first.\n\n### Visibility and notifications\n\nWhen a subscription-level cap is reached, GitLab sends an email notification to billing account managers so they can take action: raise the cap, wait for the next period, or redistribute credits.\n\nWithin GitLab, group owners (GitLab.com) and instance administrators (Self-Managed) can view which users have been blocked due to reaching their per-user cap and restore access by adjusting the cap through the GraphQL API. \n\n## How budget guardrails help organizations scale AI usage\n\nGuardrails are essential as organizations ramp up their AI adoption. Here's why:\n\n### Predictable AI budgets\n\nUsage controls for GitLab Duo Agent Platform turn AI into a bounded, predictable budget item using on-demand GitLab Credits. That makes it easier to deploy agents across the software development lifecycle and get sign-off from finance, justify renewals, and plan quarterly spend.\n\n### Governance and chargeback\n\nLarge organizations often need to align AI consumption with internal budgets, cost centers, or departmental policies. Per-user caps give platform teams a straightforward mechanism to allocate credits fairly and track consumption at the individual level. The API import options make it practical to manage caps at enterprise scale. Combined with per-user usage data from the GitLab Credits dashboard, organizations can track consumption patterns to inform their own internal chargeback or budget allocation processes.\n\n### Confidence to scale\n\nMany customers start GitLab Duo Agent Platform with a small pilot group. Usage controls remove risks associated with expanding that pilot across the organization. You can roll out Duo Agent Platform to hundreds or thousands of developers knowing there's a hard ceiling protecting your budget. If usage grows faster than expected, you'll hit the cap, not an unexpected invoice.\n\n## Addressing the seat-based and visibility conundrum\n\nMany AI coding tools take a seat-based approach to cost management. You buy a fixed number of seats at a flat per-user price, and that's your budget. It's simple, but rigid. You pay the same whether a developer uses the tool ten times a day or never touches it. And as vendors introduce premium models and usage-based overages on top of seat pricing, the cost predictability that seat-based licensing promised starts to erode.\n\n\nGitLab takes a different approach. Usage-based pricing with hard caps and a single governance dashboard. You get the flexibility of paying for what your teams actually use, with the budget predictability of enforced spending limits.\n\n## Real-world usage controls\n\n**One example is a mid-size SaaS customer that wants to protect their monthly budget.** A 200-person engineering organization sets a subscription-level cap equal to their expected on-demand usage. Their VP of Engineering can confidently tell finance that GitLab Duo Agent Platform spend will never exceed the approved amount, even as they onboard new teams. If they approach the cap mid-month, the billing account manager gets a notification and can decide whether to raise the limit or wait for the next period.\n\n**At GitLab, we also work with large enterprises that want to keep usage fair across teams.** A global financial services company with 2,000 developers uses per-user caps to ensure equitable access. Staff engineers working on complex refactoring projects get a higher individual allocation via API, while most developers receive a standard flat cap. No single user can exhaust the pool, and the platform team uses the per-user usage data in the GitLab Credits dashboard to track consumption patterns and inform quarterly budget planning.\n\n## Getting started\n\nUsage controls are available for both GitLab.com and Self-Managed customers running GitLab 18.11. Different controls are configured in different places depending on the scope and your role.\n\n**Subscription-level cap**\n\nBilling account managers set the subscription-level on-demand cap in the Customers Portal:\n\n1. Sign in to the `Customers Portal`.  \n2. On your subscription card, navigate to **GitLab Credits** settings.  \n3. Enable the monthly on-demand credits cap and enter your desired limit.\n\n**Flat per-user cap**\n\nThe flat per-user cap can be set through the GitLab GraphQL API by namespace owners (GitLab.com) or instance administrators (Self-Managed). Check the [GitLab Credits documentation](https://docs.gitlab.com/subscriptions/gitlab_credits/) for the latest on available configuration surfaces.\n\n**Custom per-user overrides**\n\nFor differentiated limits, namespace owners (GitLab.com) and instance administrators (Self-Managed) can set individual caps programmatically. This is useful for automation and infrastructure-as-code workflows.\n\n**Monitor usage and cap status**\n\n* **Customers Portal:** View detailed usage and cap status.  \n* **GitLab.com:** Group owners can view blocked users under **Settings > GitLab Credits**.  \n* **Self-Managed:** Instance administrators can view cap status and blocked users under **Admin > GitLab Credits**.\n\n## GitLab Duo Agent Platform is ready to scale\n\nUsage controls are available now in GitLab 18.11. If you've been waiting for the right guardrails before expanding GitLab Duo Agent Platform across your organization, this is your moment. Set your caps, roll out Duo Agent Platform to more teams, and start shipping faster!\n\n> [Learn more about GitLab Credits and usage controls](https://docs.gitlab.com/subscriptions/gitlab_credits/).",[9,724,725],"AI/ML","news",{"featured":31,"template":13,"slug":727},"gitlab-18-11-budget-guardrails-for-gitlab-credits",{"content":729,"config":732},{"title":730,"heroImage":720,"description":731,"date":721,"category":9},"GitLab 18.11 release","This release includes Agentic SAST Vulnerability Resolution, Data Analyst Foundational Agent, CI Expert Agent, and more.",{"featured":31,"template":13,"externalUrl":733},"https://docs.gitlab.com/releases/18/gitlab-18-11-released/",{"content":735,"config":742},{"title":736,"description":737,"authors":738,"heroImage":720,"date":721,"body":740,"category":9,"tags":741},"GitLab 18.11: CI Expert and Data Analyst AI agents target development gaps","Set up CI and query your software development lifecycle data with two new GitLab Duo Agent Platform foundational agents available in GitLab 18.11.",[739],"Corinne Dent","AI-generated code moves faster than the systems around it can keep up with. More code means more merge requests queued, more pipelines to configure, more questions about delivery that nobody has time to answer — and most of the tooling teams rely on wasn't built for this pace.\n\nIn GitLab 18.11, two new foundational agents for Duo Agent Platform address specific gaps in the development lifecycle that AI has largely left untouched:\n* CI Expert Agent (now in beta) focuses on the gap between writing code and getting it into a running pipeline\n* Data Analyst Agent (now generally available) focuses on the gap between shipping code and being able to answer basic questions about how that delivery is actually going.\n\n\nThese are problem areas that couldn't be solved by a general-purpose assistant. A tool running outside GitLab can generate a YAML file or answer a question, but it has no awareness of how your pipelines have historically performed, where failures cluster, or what your actual MR cycle times look like. That context lives in GitLab. These agents do too.\n## Fast CI setup with CI Expert Agent\n\nAI has made it easier than ever to write code. Getting that code into a running pipeline is still something most teams do days, or weeks, later — if at all. The blank-page problem isn't in the editor anymore. The blank page is now in `.gitlab-ci.yml`.\n\nDevelopers who have never configured CI don't know what language detection looks like in YAML, what their test commands should be, or how to validate the result before pushing. Teams either copy a config from a previous project that may not fit, stitch together examples from documentation, or wait for the one person who's done it before. If that person isn't available, CI becomes the thing you'll \"get to later.\" Later becomes never.\n\nWhen CI never happens, the impact shows up everywhere else. Changes ship without a reliable safety net, regressions surface in production instead of in pipelines, and work piles up in bigger, riskier batches because no one wants to be the person who “breaks the build.” Over time, teams normalize working in the dark, often relying on undocumented institutional knowledge and ad-hoc testing, instead of having a fast, predictable feedback loop baked into every change.\n\nCI Expert Agent, now available in beta, removes that friction. It inspects your repository, identifies your language and framework, and proposes a working build and test pipeline tailored to what's actually there — then explains every decision in plain language. The target: a running pipeline in minutes, with no YAML written by hand.\n\nWhat CI Expert Agent does:\n\n* Repo-aware pipeline generation detects language, framework, and test setup \n* Generates valid, runnable build and test configurations   \n* Guided first-pipeline flow with plain-language explanation of each step in Agentic Chat  \n* Native GitLab CI semantics with no config translation required\n\nBecause it runs inside GitLab and sees real pipeline behavior over time, each improvement can build on how teams actually work, not just on static examples.\n\u003Ciframe src=\"https://player.vimeo.com/video/1183458036?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=\"CI/CD Expert Agent\">\u003C/iframe>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\u003Cbr>\u003C/br>\n\nCI Expert Agent is available on GitLab.com, Self-Managed, Dedicated; Free, Premium, Ultimate Editions with Duo Agent Platform enabled.\n\n## Query GitLab data in plain language with Data Analyst Agent\n\nAI has sped up how teams ship. Answering basic questions about how that work is going has gotten harder, not easier.\n\nHow long are MRs sitting in review? Which pipelines are slowing teams down? Are deployment targets actually being hit? These questions used to be answerable by glancing at a dashboard. Now, with more code, more teams, and more complexity, the data exists — it's in GitLab — but accessing it still means waiting on an analytics team, filing a dashboard request, or learning GLQL.\n\nData Analyst Agent targets that gap. Ask a natural-language question and get an instant visualization in Agentic Chat. No query language, no dashboard request, no waiting for the answers to be assembled by someone else.\n\nFor example, the agent can answer questions about the following topics for these roles:\n\n* Engineering managers: MR cycle time, throughput by project, where reviews get stuck  \n* Developers: Contribution patterns, flaky tests blocking their MRs, pipeline speed trends  \n* DevOps and platform engineers: Pipeline success/failure rates, runner utilization, deployment frequency  \n* Engineering leadership: Cross-portfolio deployment frequency, project health metrics, lead time comparisons\n\nNow generally available in 18.11, the agent covers MRs, issues, projects, pipelines, and jobs — full software development lifecycle coverage, expanded from the beta scope. Because Data Analyst Agent queries what's already in GitLab, the context is always current, and there's no pipeline to maintain or third-party tool to keep synchronized. Generated GitLab Query Language queries can be copied and used anywhere GitLab Flavored Markdown is supported, with direct export to work items and dashboards on the roadmap.\n\n\u003Ciframe src=\"https://player.vimeo.com/video/1183094817?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=\"Data Analyst agent demo\">\u003C/iframe>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\u003Cbr>\u003C/br>\n\nData Analyst Agent is available on GitLab.com, Self-Managed, Dedicated; Free, Premium and Ultimate Edition with Duo Agent Platform enabled.\n\n## One platform, connected context\n\nBoth agents run inside GitLab, with access to the code, pipelines, issues, and merge requests already there. That's what separates platform-native AI from a disconnected assistant: the context is always current, and it only gets more useful over time. CI Expert Agent and Data Analyst Agent represent two concrete steps toward a platform where AI doesn't just help you write code faster; it helps you understand, ship, and maintain what gets built.\n\n> [Start a free trial of GitLab Duo Agent Platform](https://about.gitlab.com/gitlab-duo/) to experience these foundational AI agents.",[724,25,9],{"featured":12,"template":13,"slug":743},"ci-expert-and-data-analyst-ai-agents-target-development-gaps",{"promotions":745},[746,760,771,782],{"id":747,"categories":748,"header":750,"text":751,"button":752,"image":757},"ai-modernization",[749],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":753,"config":754},"Get your AI maturity score",{"href":755,"dataGaName":756,"dataGaLocation":244},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":758},{"src":759},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":761,"categories":762,"header":763,"text":751,"button":764,"image":768},"devops-modernization",[9,37],"Are you just managing tools or shipping innovation?",{"text":765,"config":766},"Get your DevOps maturity score",{"href":767,"dataGaName":756,"dataGaLocation":244},"/assessments/devops-modernization-assessment/",{"config":769},{"src":770},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":772,"categories":773,"header":774,"text":751,"button":775,"image":779},"security-modernization",[26],"Are you trading speed for security?",{"text":776,"config":777},"Get your security maturity score",{"href":778,"dataGaName":756,"dataGaLocation":244},"/assessments/security-modernization-assessment/",{"config":780},{"src":781},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":783,"paths":784,"header":787,"text":788,"button":789,"image":794},"github-azure-migration",[785,786],"migration-from-azure-devops-to-gitlab","integrating-azure-devops-scm-and-gitlab","Is your team ready for GitHub's Azure move?","GitHub is already rebuilding around Azure. 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