[{"data":1,"prerenderedAt":793},["ShallowReactive",2],{"/en-us/blog/quick-vulnerability-remediation-with-gitlab-advanced-sast-duo-ai":3,"navigation-en-us":41,"banner-en-us":441,"footer-en-us":451,"blog-post-authors-en-us-Fernando Diaz":692,"blog-related-posts-en-us-quick-vulnerability-remediation-with-gitlab-advanced-sast-duo-ai":706,"assessment-promotions-en-us":745,"next-steps-en-us":783},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":28,"isFeatured":12,"meta":29,"navigation":12,"path":30,"publishedDate":20,"seo":31,"stem":36,"tagSlugs":37,"__hash__":40},"blogPosts/en-us/blog/quick-vulnerability-remediation-with-gitlab-advanced-sast-duo-ai.yml","Quick Vulnerability Remediation With Gitlab Advanced Sast Duo Ai",[7],"fernando-diaz",null,"ai-ml",{"slug":11,"featured":12,"template":13},"quick-vulnerability-remediation-with-gitlab-advanced-sast-duo-ai",true,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"Quick vulnerability remediation with GitLab Advanced SAST + Duo AI ","Shorten your mean time to remediation by pairing Advanced SAST and artificial intelligence. This detailed demo shows you how.",[18],"Fernando Diaz","https://res.cloudinary.com/about-gitlab-com/image/upload/v1750098458/Blog/Hero%20Images/Blog/Hero%20Images/blog-image-template-1800x945_24mPf16vAPHORs3d9y62q_1750098458538.png","2024-10-22","With GitLab 17.4, we’ve made [GitLab Advanced SAST generally available](https://about.gitlab.com/blog/gitlab-advanced-sast-is-now-generally-available/). [GitLab Advanced SAST](https://docs.gitlab.com/ee/user/application_security/sast/gitlab_advanced_sast.html) is a static application security testing scanner designed to discover vulnerabilities by performing cross-function and cross-file taint analysis. By following the paths user inputs take, the analyzer identifies potential points where untrusted data can influence the execution of your application in unsafe ways, ensuring the vulnerabilities are detected even when they span multiple functions and files.\n\nGitLab Advanced SAST can be used together with [GitLab Duo Vulnerability Explanation](https://docs.gitlab.com/ee/user/application_security/vulnerabilities/#explaining-a-vulnerability) in order to reduce the mean time to remediation (MTTR). GitLab Duo can provide practical, AI-powered examples of how threat actors can exploit vulnerabilities and offer light-weight remediation guidance, which can be used with cross-file analysis to enhance application security (AppSec) efficiency.\n\nThis tutorial will show you how to:\n* enable GitLab Advanced SAST\n* read results from the scanner\n* review the code flow of a vulnerability\n* use GitLab AI to quickly remediate the vulnerability\n\n## Enable GitLab Advanced SAST\n\nFollow the instructions below to enable GitLab Advanced SAST. You can also view this video to get started:\n\n\u003C!-- blank line -->\n\u003Cfigure class=\"video_container\">\n  \u003Ciframe src=\"https://www.youtube.com/embed/xDa1MHOcyn8?si=5SYuKgP-BdBryqcU\" frameborder=\"0\" allowfullscreen=\"true\"> \u003C/iframe>\n\u003C/figure>\n\u003C!-- blank line -->\n\n## Run GitLab Advanced SAST on each code commit\n\nBefore using Advanced SAST, the following prerequisites must be met:\n\n- GitLab Ultimate Subscription ([free trial](https://gitlab.com/-/trials/new?glm_content=default-saas-trial&glm_source=about.gitlab.com%2F))\n- GitLab SaaS or GitLab Self-managed (running Version 17.4)\n\nTo enable the GitLab Advanced SAST scanner:\n\n- On the left sidebar, select **Search** or **Go to** and find your project.\n- Add or edit the `.gitlab-ci.yml` to include the following:\n    - Test stage\n    - `Jobs/SAST.gitlab-ci.yml` template\n    - `GITLAB_ADVANCED_SAST_ENABLED` variable set to true\n- Apply the change.\n\nYour newly merged `.gitlab-ci.yml` should contain the following:\n\n```yaml\nstages:\n  - test\n\ninclude:\n  - template: Jobs/SAST.gitlab-ci.yml\n\nvariables:\n  GITLAB_ADVANCED_SAST_ENABLED: 'true'\n\n```\n\nThis will now run the `gitlab-advances-sast` job within the test stage of your application along with all the other jobs you have defined. Advanced SAST will replace the semgrep SAST scanner for the [supported programming languages](https://docs.gitlab.com/ee/user/application_security/sast/gitlab_advanced_sast.html#supported-languages).\n\n![Running `gitlab-advances-sast` job within the test stage of your application](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750098466/Blog/Content%20Images/Blog/Content%20Images/1_aHR0cHM6_1750098466629.png)\n\n\u003Ccenter>\u003Ci>GitLab Advanced SAST job in pipeline\u003C/i>\u003C/center>\n\n\u003Cbr>\u003C/br>\n\n**Note:** You can fully configure the job as you would any job in GitLab. For more information, see the [CI/CD YAML syntax documentation](https://docs.gitlab.com/ee/ci/yaml/).\n\n## Remediate vulnerabilities in merge request (pre-production)\n\nJust like our previous SAST scanner, Advanced SAST allows you to scan source code in the diff of a feature branch. This allows us to address any incoming vulnerabilities before they make it into production. Here we can see the scanner results for the diff within a merge request:\n\n![Advanced SAST scanner results for the diff within a merge request](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750098467/Blog/Content%20Images/Blog/Content%20Images/2_aHR0cHM6_1750098466630.png)\n\nWhen selecting a newly detected vulnerability, we get the following details to assist with remediation:\n\n- **Status:** The status of the vulnerability (Needs triage, Confirmed, Dismissed, Resolved)\n- **Description:** Detailed information on the detected vulnerability\n- **Detection time:** Time vulnerability was detected\n- **Location:** Line of code where vulnerability is detected\n- **Severity:** Severity of vulnerability from CVE database\n- **Training:** Gamified training from our partners\n- **Solutions:** Information on how to remediate or resolve a vulnerability\n- **Identifiers:** Relevant links showcasing detailed description, exploitation, and remediation\n\n![Merge request with vulnerability insights](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750098467/Blog/Content%20Images/Blog/Content%20Images/MR_with_vulnerability_insights_aHR0cHM6_1750098466632.png)\n\n\u003Ccenter>\u003Ci>Merge request with vulnerability insights\u003C/i>\u003C/center>\n\n\u003Cbr>\u003C/br>\nVulnerabilities detected within an MR are actionable, meaning they can be dismissed or an issue can be created and populated with relevant vulnerability information.\n\nDismissing an issue saves AppSec teams time, because they can see relevant developer information when reviewing an MR. Creating a confidential issue allows developers and AppSec teams to further collaborate on resolving a vulnerability where a fix is not straightforward. Confidential issues have limited permissions and can be used with confidential merge requests to prevent possible malicious actors from exploiting.\n\nTo further support separation of duties and prevent vulnerable code from making it into production, you can require approval from certain people (for example, the security team) in order to merge vulnerable code.\n\n![GitLab security policies in action](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750098467/Blog/Content%20Images/Blog/Content%20Images/security_policies_in_action_aHR0cHM6_1750098466634.png)\n\n\u003Ccenter>\u003Ci>Security policies in action\u003C/i>\u003C/center>\n\n\u003Cbr>\u003C/br>\n\n**Note:** Learn more about Security Policies and how to implement them in the [Security Policy documentation](https://docs.gitlab.com/ee/user/application_security/policies/).\n\n## Manage vulnerabilities in production\n\nWhile preventing vulnerabilities from making it into production is crucial for application security, it is equally as important to manage vulnerabilities in production. When security scanners are run on a default or production-level branch, a [vulnerability report](https://docs.gitlab.com/ee/user/application_security/vulnerability_report/) will be populated with the latest vulnerability data which can be used to triage and manage vulnerabilities.\n\n![GitLab Vulnerability Report sorted by Advanced SAST](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750098467/Blog/Content%20Images/Blog/Content%20Images/5_aHR0cHM6_1750098466636.png)\n\n\u003Ccenter>\u003Ci>GitLab Vulnerability Report sorted by Advanced SAST\u003C/i>\u003C/center>\n\u003Cbr>\u003C/br>\n\nWhen selecting a vulnerability you get similar vulnerability details as seen in a merge request, making for a single source of truth for developers and AppSec teams.\n\n![Vulnerability page with vulnerability insights](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750098467/Blog/Content%20Images/Blog/Content%20Images/Vuln_page_with_vulnerability_insights_aHR0cHM6_1750098466637.png)\n\n\u003Ccenter>\u003Ci>Vulnerability page with vulnerability insights\u003C/i>\u003C/center>\n\n\u003Cbr>\u003C/br>\n\nAppSec teams can triage a vulnerability by changing its status and adding relevant details on the status change. Issues can be created to track the progress of a fix. From here, a developer can be assigned.\n\n## Examine vulnerable code flow\n\nFor vulnerabilities detected with Advanced SAST, we can see a \"Code flow\" tab on the Vulnerability page.\n\n![Advanced SAST - image 7](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750098467/Blog/Content%20Images/Blog/Content%20Images/7_aHR0cHM6_1750098466638.png)\n\n\u003Ccenter>\u003Ci>GitLab Advanced SAST code flow\u003C/i>\u003C/center>\n\u003Cbr>\u003C/br>\n\nIn this example, you can see that a vulnerability is traced across multiple functions, giving deeper insight into the best practices we should put in place to not only resolve the vulnerability, but prevent similar vulnerabilities in the future.\n\n## Use GitLab Duo Vulnerability Explanation\n\nGitLab Duo can help you mitigate or remediate a vulnerability by using a large language model to:\n\n- Summarize the vulnerability\n- Help developers and security analysts understand the vulnerability\n- Show how the vulnerability can be exploited\n- Provide a suggested remediation or mitigation\n\nTo use Vulnerability Explanation, the following is required:\n\n- GitLab Ultimate subscription\n- GitLab Duo Enterprise seat\n- GitLab Duo must be enabled for your group or instance\n\nFrom the vulnerability report, you can select a SAST vulnerability and go to its Vulnerability page. From the Vulnerability page, you can do any of the following to explain the vulnerability:\n\n- Select the text below the vulnerability description\n- You can use AI by asking GitLab Duo Chat to explain this vulnerability and offer a suggested fix.\n- In the upper right, from the \"Resolve with merge request\" dropdown list, select **Explain Vulnerability**, then select **Explain vulnerability**.\n- Open GitLab Duo Chat and use the explain a vulnerability command: `/vulnerability_explain`.\n\nThen the vulnerable code will be processed by Anthropic’s Claude 3 Haiku model and provide the following data:\n\n![GitLab Duo Vulnerability Explanation](https://res.cloudinary.com/about-gitlab-com/image/upload/v1750098467/Blog/Content%20Images/Blog/Content%20Images/vuln_explain_2_aHR0cHM6_1750098466640.png)\n\n## Putting it all together\n\nNow, let's put it all together with a concrete example. I will use the [OWASP Juice Shop](https://owasp.org/www-project-juice-shop/) as my demo application and run GitLab Advanced SAST to detect a vulnerability in production. Then I will use the vulnerability code flow and GitLab Duo to investigate vulnerability exploitation, and remediation. You can [follow along with this demo](https://gitlab.com/gitlab-da/tutorials/security-and-governance/owasp/juice-shop) and see this workflow in action by watching:\n\n\u003C!-- blank line -->\n\u003Cfigure class=\"video_container\">\n  \u003Ciframe src=\"https://www.youtube.com/embed/H1S43oM44k0?si=2LYorTjByOHbCAko\" frameborder=\"0\" allowfullscreen=\"true\"> \u003C/iframe>\n\u003C/figure>\n\u003C!-- blank line -->\n\nThe detection and remediation workflow is as follows:\n\n- Enable GitLab Advanced SAST and run it on the project’s default branch.\n- Open the Vulnerability Report and sort by **Tool:GitLab Advanced SAST**.\n- Select the **Improper neutralization of special elements in data query logic** vulnerability found in `Basket.ts`.\n- Use the vulnerability code flow to understand the vulnerable paths.\n- Run **Explain this vulnerability** to see exploit information.\n- Run the application locally to attempt exploitation.\n- Change vulnerability status to \"Confirmed\" and provide relevant info.\n- Determine remediation path using all relevant data:\n    - Vulnerability page insights, Code Flow, Vulnerability Explanation results\n- Create a new branch and apply remediation.\n- Run the remediated application locally and try to exploit again.\n- Create a merge request with the fix.\n- Code change will be tested using CI to assure we don’t break the application.\n- Validate and merge MR.\n- Test exploit in deployed environment.\n- Change vulnerability status to \"Resolved\" on the Vulnerability page.\n\n**Note:** There are many ways to triage and remediate vulnerabilities, make sure to follow best practices set by your organization.\n\n# Useful links\n\nTo learn more about GitLab and how you can get started with enhancing your organization’s application security posture, check out the following resources.\n\n* [GitLab Ultimate](https://about.gitlab.com/pricing/ultimate/)\n* [GitLab Duo](https://about.gitlab.com/gitlab-duo/)\n* [GitLab Security and Compliance Solutions](https://about.gitlab.com/solutions/application-security-testing/)\n* [GitLab Software Supply Chain Security Solutions](https://about.gitlab.com/solutions/supply-chain/)\n* [GitLab Continuous Software Compliance](https://about.gitlab.com/solutions/software-compliance/)\n* [JuiceShop Demo Application](https://gitlab.com/gitlab-da/tutorials/security-and-governance/owasp/juice-shop)\n* [GitLab AppSec documentation](https://docs.gitlab.com/ee/user/application_security/)\n* [Advanced SAST documentation](https://docs.gitlab.com/ee/user/application_security/sast/gitlab_advanced_sast.html)\n* [Explain this Vulnerability documentation](https://docs.gitlab.com/ee/user/application_security/vulnerabilities/#explaining-a-vulnerability)\n* [Code Flow documentation](https://docs.gitlab.com/ee/user/application_security/vulnerabilities/#vulnerability-code-flow)\n* [Security Policy documentation](https://docs.gitlab.com/ee/user/application_security/policies/)\n* [OWASP Juice Shop documentation](https://owasp.org/www-project-juice-shop/)\n",[23,24,25,26,27],"AI/ML","security","tutorial","features","DevSecOps platform","yml",{},"/en-us/blog/quick-vulnerability-remediation-with-gitlab-advanced-sast-duo-ai",{"title":15,"description":16,"ogTitle":15,"ogDescription":16,"noIndex":32,"ogImage":19,"ogUrl":33,"ogSiteName":34,"ogType":35,"canonicalUrls":33},false,"https://about.gitlab.com/blog/quick-vulnerability-remediation-with-gitlab-advanced-sast-duo-ai","https://about.gitlab.com","article","en-us/blog/quick-vulnerability-remediation-with-gitlab-advanced-sast-duo-ai",[38,24,25,26,39],"aiml","devsecops-platform","_VfKZrESeo4uuuY-TF9rQx5Lt0hhxkx0-8DIZxIDiis",{"data":42},{"logo":43,"freeTrial":48,"sales":53,"login":58,"items":63,"search":371,"minimal":402,"duo":421,"pricingDeployment":431},{"config":44},{"href":45,"dataGaName":46,"dataGaLocation":47},"/","gitlab logo","header",{"text":49,"config":50},"Get free 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AI prompts to speed your team’s software delivery","Eliminate review backlogs, security delays, and coordination overhead with ready-to-use AI prompts covering every stage of the software lifecycle.",[712],"Chandler Gibbons","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772632341/duj8vaznbhtyxxhodb17.png","2026-03-04","AI-assisted coding tools are helping developers generate code faster than ever. So why aren’t teams _shipping_ faster?\n\nBecause coding is only 20% of the software delivery lifecycle, the remaining 80% becomes the bottleneck: code review backlogs grow, security scanning can’t keep pace, documentation falls behind, and manual coordination overhead increases.\n\nThe good news is that the same AI capabilities that accelerate individual coding can eliminate these team-level delays. You just need to apply AI across your entire software lifecycle, not only during the coding phase.\n\nBelow are 10 ready-to-use prompts from the [GitLab Duo Agent Platform Prompt Library](https://about.gitlab.com/gitlab-duo/prompt-library/) that help teams overcome common obstacles to faster software delivery. Each prompt addresses a specific slowdown that emerges when individual productivity increases without corresponding improvements in team processes.\n\n## How do you move code review from bottleneck to accelerator?\nDevelopers generate merge requests faster with AI assistance, but human reviewers can quickly become overwhelmed as code review cycles stretch from hours to days. AI can handle routine review tasks, freeing reviewers to focus on architecture and business logic instead of catching basic logical errors and API contract violations.\n\n### Review MR for logical errors\n**Complexity**: Beginner\n\n**Category**: Code Review\n\n**Prompt from library**:\n\n\n```text\nReview this MR for logical errors, edge cases, and potential bugs: [MR URL or paste code]\n```\n\n**Why it helps**: Automated linters catch syntax issues, but logical errors require understanding intent. This prompt catches bugs before human reviewers even look at the code, reducing review cycles from multiple rounds to often just one approval.\n\n### Identify breaking changes in MR\n**Complexity**: Beginner\n\n**Category**: Code Review\n\n**Prompt from library**:\n\n\n```text\nDoes this MR introduce any breaking changes?\n\nChanges:\n[PASTE CODE DIFF]\n\nCheck for:\n1. API signature changes\n2. Removed or renamed public methods\n3. Changed return types\n4. Modified database schemas\n5. Breaking configuration changes\n```\n\n**Why it helps**: Breaking changes discovered during deployment can cause rollbacks and incidents. This prompt shifts that discovery left to the MR stage, when fixes are faster and less expensive.\n\n## How can you shift security left without slowing down?\nSecurity scans generate hundreds of findings. Security teams manually triage each one while developers wait for approval to deploy. Most findings are false positives or low-risk issues, but identifying the real threats requires expertise and time. AI can prioritize findings by actual exploitability and auto-remediate common vulnerabilities, allowing security teams to focus on the threats that matter.\n\n### Analyze security scan results\n**Complexity**: Intermediate\n\n**Category**: Security\n\n**Agent**: Duo Security Analyst\n\n**Prompt from library**:\n\n\n```text\n@security_analyst Analyze these security scan results:\n\n[PASTE SCAN OUTPUT]\n\nFor each finding:\n1. Assess real risk vs false positive\n2. Explain the vulnerability\n3. Suggest remediation\n4. Prioritize by severity\n```\n\n**Why it helps**: Most security scan findings are false positives or low-risk issues. This prompt helps security teams focus on the findings that actually matter, reducing remediation time from weeks to days.\n\n### Review code for security issues\n**Complexity**: Intermediate\n\n**Category**: Security\n\n**Agent**: Duo Security Analyst\n\n**Prompt from library**:\n\n```text\n@security_analyst Review this code for security issues:\n\n[PASTE CODE]\n\nCheck for:\n1. Injection vulnerabilities\n2. Authentication/authorization flaws\n3. Data exposure risks\n4. Insecure dependencies\n5. Cryptographic issues\n```\n\n**Why it helps**: Traditional security reviews happen after code is written. This prompt enables developers to find and fix security issues before creating an MR, eliminating the back and forth that delays deployments.\n\n## How do you keep documentation current as code changes?\nCode changes faster than documentation. Onboarding new developers takes weeks because docs are outdated or missing. Teams know documentation is important, but it always gets deferred when deadlines approach. Automating documentation generation and updates as part of your standard workflow ensures docs stay current without adding manual work.\n\n### Generate release notes from MRs\n**Complexity**: Beginner\n\n**Category**: Documentation\n\n**Prompt from library**:\n\n```text\nGenerate release notes for these merged MRs:\n[LIST MR URLs or paste titles]\n\nGroup by:\n1. New features\n2. Bug fixes\n3. Performance improvements\n4. Breaking changes\n5. Deprecations\n```\n\n**Why it helps**: Manual release note compilation takes hours and often includes errors or omissions. Automated generation ensures every release has comprehensive notes without adding work to your release process.\n\n### Update documentation after code changes\n**Complexity**: Beginner\n\n**Category**: Documentation\n\n**Prompt from library**:\n\n```text\nI changed this code:\n\n[PASTE CODE CHANGES]\n\nWhat documentation needs updating? Check:\n1. README files\n2. API documentation\n3. Architecture diagrams\n4. Onboarding guides\n```\n\n**Why it helps**: Documentation drift happens because teams forget which docs need updates after code changes. This prompt makes documentation maintenance part of your development workflow, not a separate task that gets deferred.\n\n## How do you break down planning complexity?\nLarge features get stuck in planning. Teams spend weeks in meetings trying to scope work and identify dependencies. The complexity feels overwhelming, and it's hard to know where to start. AI can systematically decompose complex work into concrete, implementable tasks with clear dependencies and acceptance criteria, transforming weeks of planning into focused implementation.\n\n### Break down epic into issues\n**Complexity**: Intermediate\n\n**Category**: Documentation\n\n**Agent**: Duo Planner\n\n**Prompt from library**:\n\n```text\nBreak down this epic into implementable issues:\n\n[EPIC DESCRIPTION]\n\nConsider:\n1. Technical dependencies\n2. Reasonable issue sizes\n3. Clear acceptance criteria\n4. Logical implementation order\n```\n\n**Why it helps**: This prompt transforms a week of planning meetings into 30 minutes of AI-assisted decomposition followed by team review. Teams start implementation sooner with clearer direction.\n\n## How can you expand test coverage without expanding effort?\nDevelopers are writing code faster, but if testing doesn't keep pace, test coverage decreases and bugs slip through. Writing comprehensive tests manually is time-consuming, and developers often miss edge cases under deadline pressure. Generating tests automatically means developers can review and refine rather than write from scratch, maintaining quality without sacrificing velocity.\n\n### Generate unit tests\n**Complexity**: Beginner\n\n**Category**: Testing\n\n**Prompt from library**:\n\n```text\nGenerate unit tests for this function:\n\n[PASTE FUNCTION]\n\nInclude tests for:\n1. Happy path\n2. Edge cases\n3. Error conditions\n4. Boundary values\n5. Invalid inputs\n```\n\n**Why it helps**: Writing tests manually is time consuming, and developers often miss edge cases. This prompt generates thorough test suites in seconds, which developers can review and adjust rather than write from scratch.\n\n### Review test coverage gaps\n**Complexity**: Beginner\n\n**Category**: Testing\n\n**Prompt from library**:\n\n```text\nAnalyze test coverage for [MODULE/COMPONENT]:\n\nCurrent coverage: [PERCENTAGE]\n\nIdentify:\n1. Untested functions/methods\n2. Uncovered edge cases\n3. Missing error scenario tests\n4. Integration points without tests\n5. Priority areas to test next\n```\n\n**Why it helps**: This prompt reveals blind spots in your test suite before they cause production incidents. Teams can systematically improve coverage where it matters most.\n\n## How do you reduce mean time to resolution when debugging?\nProduction incidents take hours to diagnose. Developers wade through logs and stack traces while customers experience downtime. Every minute of debugging is a minute of lost productivity and potential revenue. AI can accelerate root cause analysis by parsing complex error messages and suggesting specific fixes, cutting diagnostic time from hours to minutes.\n\n### Debug failing pipeline\n**Complexity**: Beginner\n\n**Category**: Debugging\n\n**Prompt from library**:\n\n```text\nThis pipeline is failing:\n\nJob: [JOB NAME]\nStage: [STAGE]\nError: [PASTE ERROR MESSAGE/LOG]\n\nHelp me:\n1. Identify the root cause\n2. Suggest a fix\n3. Explain why it started failing\n4. Prevent similar issues\n```\n\n**Why it helps**: CI/CD failures block entire teams. This prompt diagnoses failures in seconds instead of the 15-30 minutes developers typically spend investigating, keeping deployment velocity high.\n\n## Moving from individual gains to team acceleration\nThese prompts represent a shift in how teams apply AI to software delivery. Rather than focusing solely on individual developer productivity, they address the coordination, quality, and knowledge-sharing challenges that actually constrain team velocity.\n\nThe [complete prompt library](https://about.gitlab.com/gitlab-duo/prompt-library/) contains more than 100 prompts across all stages of the software lifecycle: planning, development, security, testing, deployment, and operations. Each prompt is tagged by complexity level (Beginner, Intermediate, Advanced) and categorized by use case, making it easy to find the right starting point for your team.\n\nStart with prompts tagged “Beginner” that address your team’s most pressing obstacles. As your team builds confidence, explore intermediate and advanced prompts that enable more sophisticated workflows. The goal is not just faster coding — it's faster, safer, higher-quality software delivery from planning through production.",[23,717],"DevOps platform",{"featured":32,"template":13,"slug":719},"10-ai-prompts-to-speed-your-teams-software-delivery",{"content":721,"config":730},{"title":722,"description":723,"heroImage":724,"authors":725,"date":727,"body":728,"category":9,"tags":729},"AI can detect vulnerabilities, but who governs risk?","AI-assisted vulnerability detection is developing fast, but the harder challenges of enforcement, governance, and supply chain security require a holistic platform.","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772195014/ooezwusxjl1f7ijfmbvj.png",[726],"Omer Azaria","2026-02-27","Anthropic recently announced Claude Code Security, an AI system that detects vulnerabilities and proposes fixes. The market reacted immediately, with security stocks dipping as investors questioned whether AI might replace traditional AppSec tools. The question on everyone's mind: If AI can write code and secure it, is application security about to become obsolete?\n\nIf security only meant scanning code, the answer might be yes. But enterprise security has never been about detection alone.\n\nOrganizations are not asking whether AI can find vulnerabilities. They are asking three much harder questions: \n\n* Is what we are about to ship safe?  \n* Has our risk posture changed as environments evolve and dependencies, third-party services, tools, and infrastructure continuously shift?  \n* How do we govern a codebase that is increasingly assembled by AI and third-party sources, and that we are still accountable for? \n\nThose questions require a platform answer: Detection surfaces risk, but governance determines what happens next. \n\n[GitLab](https://about.gitlab.com/) is the orchestration layer built to govern the software lifecycle end-to-end. It gives teams the enforcement, visibility, and auditability they need to keep pace with the speed of AI-assisted development.\n\n## Trusting AI requires governing risk\n\nAI systems are rapidly getting better at identifying vulnerabilities and suggesting fixes. This is a meaningful and welcome advancement, but analysis is not accountability.\n\nAI cannot enforce company policy or define acceptable risk on its own. Humans must set the boundaries, policies, and guardrails that agents operate within, establishing separation of duties, ensuring audit trails, and maintaining consistent controls across thousands of repositories and teams. Trust in agents comes not from autonomy alone, but from clearly defined governance set by people. \n\nIn an [agentic world](https://about.gitlab.com/topics/agentic-ai/), where software is increasingly written and modified by autonomous systems, governance becomes more important, not less. The more autonomy organizations grant to AI, the stronger the governance must be.\n\nGovernance is not friction. It is the foundation that makes AI-assisted development trustworthy at scale.\n\n## LLMs see code, but platforms see context\n\nA large language model ([LLM](https://about.gitlab.com/blog/what-is-a-large-language-model-llm/)) evaluates code in isolation. An enterprise application security platform understands context. This difference matters because risk decisions are contextual:\n\n* Who authored the change?  \n* How critical is the application to the business?  \n* How does it interact with infrastructure and dependencies?  \n* Does the vulnerability exist in code that is actually reachable in production, or is it buried in a dependency that never executes?  \n* Is it actually exploitable in production, given how the application runs, its APIs, and the environment around it?\n\nSecurity decisions depend on this context. Without it, detection produces noisy alerts that slow down development rather than reducing risk. With it, organizations can triage quickly and manage risk effectively. Context evolves continuously as software changes, which means governance cannot be a one-time decision. \n\n## Static scans can’t keep up with dynamic risk\n\nSoftware risk is dynamic. Dependencies change, environments evolve, and systems interact in ways no single analysis can fully predict. A clean scan at one moment does not guarantee safety at release.\n\nEnterprise security depends on continuous assurance: controls embedded directly into development workflows that evaluate risk as software is built, tested, and deployed.\n\nDetection provides insight. Governance provides trust. Continuous governance is what allows organizations to ship safely at scale.\n\n## Governing the agentic future\n\nAI is reshaping how software is created. The question is no longer whether teams will use AI, but how safely they can scale it.\n\nSoftware today is assembled as much as it is written, from AI-generated code, open-source libraries, and third-party dependencies that span thousands of projects. Governing what ships across all of those sources is the hardest and most consequential part of application security, and it is the part that no developer-side tool is built to address. \n\nAs an intelligent orchestration platform, GitLab is built to address this problem. GitLab Ultimate embeds governance, policy enforcement, security scanning, and auditability directly into the workflows where software is planned, built, and shipped, so security teams can govern at the speed of AI. \n\nAI will accelerate development dramatically. The organizations that benefit most from AI will not be those with the smartest assistants alone, but those that build trust through strong governance.\n\n> To learn how GitLab helps organizations [govern and ship AI-generated code](https://about.gitlab.com/solutions/software-compliance/?utm_medium=blog&utm_campaign=eg_global_x_x_security_en_) safely, [talk to our team today](https://about.gitlab.com/sales/?utm_medium=blog&utm_campaign=eg_global_x_x_security_en_)\n\n\n ## Related reading\n\n - [Integrating AI with DevOps for enhanced security](https://about.gitlab.com/topics/devops/ai-enhanced-security/)\n - [The GitLab AI Security Framework for security leaders](https://about.gitlab.com/blog/the-gitlab-ai-security-framework-for-security-leaders/)\n - [Improve AI security in GitLab with composite identities](https://about.gitlab.com/blog/improve-ai-security-in-gitlab-with-composite-identities/)",[23,24],{"featured":12,"template":13,"slug":731},"ai-can-detect-vulnerabilities-but-who-governs-risk",{"content":733,"config":743},{"title":734,"description":735,"authors":736,"category":9,"tags":738,"date":740,"heroImage":741,"body":742},"Secure and fast deployments to Google Agent Engine with GitLab","Follow this step-by-step guide to build an AI agent with Google's Agent Development Kit and deploy to Agent Engine using GitLab.",[737],"Regnard Raquedan",[23,739,110,557],"google","2026-02-26","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772111172/mwhgbjawn62kymfwrhle.png","In this tutorial, you'll learn how to deploy an AI agent built with Google's Agent Development Kit ([ADK](https://google.github.io/adk-docs/)) to [Agent Engine](https://cloud.google.com/vertex-ai/generative-ai/docs/agent-engine/overview) using GitLab's native Google Cloud integration and CI/CD pipelines. We'll cover IAM configuration, pipeline setup, and testing your deployed agent.\n\n## What is Agent Engine and why does it matter?\n\nAgent Engine is Google Cloud's managed runtime specifically designed for AI agents. Think of it as the production home for your agents — where they live, run, and scale without you having to manage the underlying infrastructure. Agent Engine handles infrastructure, scaling, session management, and memory storage so you can focus on building your agent — not managing servers. It also integrates natively with Google Cloud's logging, monitoring, and IAM.\n\n## Why use GitLab to deploy to Agent Engine?\n\nAI agent deployment is typically difficult to configure correctly. Security considerations, CI/CD orchestration, and cloud permissions create friction that slows down development cycles.\n\nGitLab streamlines this entire process while enhancing security:\n\n- **Built-in security scanning** — Every deployment is automatically scanned for vulnerabilities without additional configuration.\n- **Native Google Cloud integration** — Workload Identity Federation eliminates the need for service account keys.\n- **Simplified CI/CD** — GitLab's templates handle complex deployment logic.\n\n## Prerequisites\n\nBefore you begin, ensure you have:\n\n- A Google Cloud project with the following APIs enabled:\n  - Cloud Storage API\n  - Vertex AI API\n- A GitLab project for your source code and CI/CD pipeline\n- A Google Cloud Storage bucket for staging deployments\n- Google Cloud IAM integration configured in GitLab (see Step 1)\n\nHere are the steps to follow.\n\n## 1. Configure IAM integration\n\nThe foundation of secure deployment is proper IAM configuration between GitLab and Google Cloud using Workload Identity Federation.\n\nIn your GitLab project:\n\n1. Navigate to **Settings > Integrations**.\n2. Locate the **Google Cloud IAM** integration.\n3. Provide the following information:\n   - **Project ID**: Your Google Cloud project ID\n   - **Project Number**: Found in your Google Cloud console\n   - **Workload Identity Pool ID**: A unique identifier for your identity pool\n   - **Provider ID**: A unique identifier for your identity provider\n\nGitLab generates a script for you. Copy and run this script in Google Cloud Shell to establish the Workload Identity Federation between platforms.\n\n**Important:** Add these additional roles to your service principal for Agent Engine deployment:\n\n- `roles/aiplatform.user`\n- `roles/storage.objectAdmin`\n\nYou can add these roles using gcloud commands:\n\n```bash\nGCP_PROJECT_ID=\"\u003Cyour-project-id>\"\nGCP_PROJECT_NUMBER=\"\u003Cyour-project-number>\"\nGCP_WORKLOAD_IDENTITY_POOL=\"\u003Cyour-pool-id>\"\n\ngcloud projects add-iam-policy-binding ${GCP_PROJECT_ID} \\\n  --member=\"principalSet://iam.googleapis.com/projects/${GCP_PROJECT_NUMBER}/locations/global/workloadIdentityPools/${GCP_WORKLOAD_IDENTITY_POOL}/attribute.developer_access/true\" \\\n  --role='roles/aiplatform.user'\n\ngcloud projects add-iam-policy-binding ${GCP_PROJECT_ID} \\\n  --member=\"principalSet://iam.googleapis.com/projects/${GCP_PROJECT_NUMBER}/locations/global/workloadIdentityPools/${GCP_WORKLOAD_IDENTITY_POOL}/attribute.developer_access/true\" \\\n  --role='roles/storage.objectAdmin'\n```\n\n## 2. Create the CI/CD pipeline\n\nNow for the core of the deployment — the CI/CD pipeline. Create a `.gitlab-ci.yml` file in your project root:\n\n```yaml\nstages:\n  - test\n  - deploy\n\ncache:\n  paths:\n    - .cache/pip\n  key: ${CI_COMMIT_REF_SLUG}\n\nvariables:\n  GCP_PROJECT_ID: \"\u003Cyour-project-id>\"\n  GCP_REGION: \"us-central1\"\n  STORAGE_BUCKET: \"\u003Cyour-staging-bucket>\"\n  AGENT_NAME: \"Canada City Advisor\"\n  AGENT_ENTRY: \"canada_city_advisor\"\n\nimage: google/cloud-sdk:slim\n\n# Security scanning templates\ninclude:\n  - template: Jobs/Dependency-Scanning.gitlab-ci.yml\n  - template: Jobs/SAST.gitlab-ci.yml\n  - template: Jobs/Secret-Detection.gitlab-ci.yml\n\ndeploy-agent:\n  stage: deploy\n  identity: google_cloud\n  rules:\n    - if: $CI_COMMIT_BRANCH == \"main\"\n  before_script:\n    - gcloud config set core/disable_usage_reporting true\n    - gcloud config set component_manager/disable_update_check true\n    - pip install -q --no-cache-dir --upgrade pip google-genai google-cloud-aiplatform -r requirements.txt --break-system-packages\n  script:\n    - gcloud config set project $GCP_PROJECT_ID\n    - adk deploy agent_engine \n        --project=$GCP_PROJECT_ID \n        --region=$GCP_REGION \n        --staging_bucket=gs://$STORAGE_BUCKET \n        --display_name=\"$AGENT_NAME\" \n        $AGENT_ENTRY\n```\n\nThe pipeline consists of two stages:\n\n**Test stage** — GitLab's security scanners run automatically. The included templates provide dependency scanning, static application security testing (SAST), and secret detection without additional configuration.\n\n**Deploy stage** — Uses the ADK CLI to deploy your agent directly to Agent Engine. The staging bucket temporarily holds your application workload before Agent Engine picks it up for deployment.\n\n### Key configuration notes\n\n- The `identity: google_cloud` directive enables keyless authentication via Workload Identity Federation.\n- Security scanners are included as templates, meaning they run by default with no setup required.\n- The `adk deploy agent_engine` command handles all the complexity of packaging and deploying your agent.\n- Pipeline caching speeds up subsequent deployments by preserving pip dependencies.\n\n## 3. Deploy and verify\n\nWith your pipeline configured:\n\n1. Commit your agent code and `.gitlab-ci.yml` to GitLab.\n2. Navigate to **Build > Pipelines** to monitor execution.\n3. Watch the test stage complete security scans.\n4. Observe the deploy stage push your agent to Agent Engine.\n\nOnce the pipeline succeeds, verify your deployment in the Google Cloud Console:\n\n1. Navigate to **Vertex AI > Agent Engine**.\n2. Locate your deployed agent.\n3. Note the **resource name** — you'll need this for testing.\n\n## 4. Test your deployed agent\n\nTest your agent using a curl command. You'll need three pieces of information:\n\n- **Agent ID**: From the Agent Engine console (the resource name's numeric identifier)\n- **Project ID**: Your Google Cloud project\n- **Location**: The region where you deployed (e.g., `us-central1`)\n\n```bash\nPROJECT_ID=\"\u003Cyour-project-id>\"\nLOCATION=\"us-central1\"\nAGENT_ID=\"\u003Cyour-agent-id>\"\nTOKEN=$(gcloud auth print-access-token)\n\ncurl -X POST \\\n  -H \"Authorization: Bearer $TOKEN\" \\\n  -H \"Content-Type: application/json\" \\\n  \"https://${LOCATION}-aiplatform.googleapis.com/v1/projects/${PROJECT_ID}/locations/${LOCATION}/reasoningEngines/${AGENT_ID}:streamQuery\" \\\n  -d '{\n    \"input\": {\n      \"message\": \"I make $85,000 per year and I prefer cities with mild winters and a vibrant cultural scene. I also want to be near the coast if possible. What Canadian cities would you recommend?\",\n      \"user_id\": \"demo-user\"\n    }\n  }' | jq -r '.content.parts[0].text'\n```\n\nIf everything is configured correctly, your agent will respond with personalized city recommendations based on the budget and lifestyle preferences provided.\n\n## Security benefits of this approach\n\nThis deployment pattern provides several security advantages:\n\n- **No long-lived credentials**: Workload Identity Federation eliminates service account keys entirely.\n- **Automated vulnerability scanning**: Every deployment is scanned before reaching production.\n- **Complete audit trail**: GitLab maintains full visibility of who deployed what and when.\n- **Principle of least privilege**: Fine-grained IAM roles limit access to only what's needed.\n\n## Summary\n\nDeploying AI agents to production doesn't have to be complex. By combining GitLab's DevSecOps platform with Google Cloud's Agent Engine, you get:\n\n- A managed runtime that handles scaling and infrastructure\n- Built-in security scanning without additional tooling\n- Keyless authentication via native cloud integration\n- A streamlined deployment process that fits modern AI development workflows\n\nWatch the full demo:\n\n\n\u003Cfigure class=\"video_container\"> \u003Ciframe src=\"https://www.youtube.com/embed/sxVFa2Mk-x4?si=Oi3cUjhgd7FT2yEd\" frameborder=\"0\" allowfullscreen=\"true\" title=\"Deploy AI Agents to Agent Engine with GitLab\"> \u003C/iframe> \u003C/figure>\n\n> Ready to try it yourself? Use this tutorial's [complete code example](https://gitlab.com/gitlab-partners-public/google-cloud/demos/agent-engine-demo) to get started now. Not a GitLab customer yet? Explore the DevSecOps platform with [a free trial](https://about.gitlab.com/free-trial/).\n",{"featured":32,"template":13,"slug":744},"secure-and-fast-deployments-to-google-agent-engine-with-gitlab",{"promotions":746},[747,760,772],{"id":748,"categories":749,"header":750,"text":751,"button":752,"image":757},"ai-modernization",[9],"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":245},"/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":764,"text":751,"button":765,"image":769},"devops-modernization",[763,560],"product","Are you just managing tools or shipping innovation?",{"text":766,"config":767},"Get your DevOps maturity score",{"href":768,"dataGaName":756,"dataGaLocation":245},"/assessments/devops-modernization-assessment/",{"config":770},{"src":771},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":773,"categories":774,"header":775,"text":751,"button":776,"image":780},"security-modernization",[24],"Are you trading speed for security?",{"text":777,"config":778},"Get your security maturity score",{"href":779,"dataGaName":756,"dataGaLocation":245},"/assessments/security-modernization-assessment/",{"config":781},{"src":782},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"header":784,"blurb":785,"button":786,"secondaryButton":791},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":787,"config":788},"Get your free trial",{"href":789,"dataGaName":52,"dataGaLocation":790},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":497,"config":792},{"href":56,"dataGaName":57,"dataGaLocation":790},1772652086930]