[{"data":1,"prerenderedAt":815},["ShallowReactive",2],{"/en-us/blog/how-the-security-culture-committee-is-strengthening-gitlab-values":3,"navigation-en-us":36,"banner-en-us":446,"footer-en-us":456,"blog-post-authors-en-us-Heather Simpson":698,"blog-related-posts-en-us-how-the-security-culture-committee-is-strengthening-gitlab-values":712,"blog-promotions-en-us":753,"next-steps-en-us":805},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":24,"isFeatured":12,"meta":25,"navigation":26,"path":27,"publishedDate":20,"seo":28,"stem":32,"tagSlugs":33,"__hash__":35},"blogPosts/en-us/blog/how-the-security-culture-committee-is-strengthening-gitlab-values.yml","How The Security Culture Committee Is Strengthening Gitlab Values",[7],"heather-simpson",null,"security",{"slug":11,"featured":12,"template":13},"how-the-security-culture-committee-is-strengthening-gitlab-values",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"How the Security Culture Committee is strengthening GitLab values","Learn how this group of team members works to preserve and reinforce GitLab values in the Security department and beyond.",[18],"Heather Simpson","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749670879/Blog/Hero%20Images/Sec-Culture-Committee-blog.png","2021-05-07","\n\nTransparency is a core value here at GitLab and we strive to be [\"open about as many things as possible\"](https://handbook.gitlab.com/handbook/values/#transparency), but as any security practitioner knows, this can, at times, feel as though it conflicts with the work we do within security. That feeling of conflict is one of the main drivers behind the creation of a [Security Culture Committee](https://handbook.gitlab.com/handbook/security/security-culture.html) here at GitLab. The other is to ensure the Security department, and all of GitLab, lives up to our [company values](https://handbook.gitlab.com/handbook/values/), especially as we continue to scale. The [mission and goals of the Security Culture Committee](https://handbook.gitlab.com/handbook/security/security-culture.html#mission-statement) were developed by the committee members themselves, with an eye on our GitLab values and also to ensure representation of our fellow team members.\n\n## How does the committee work?\n\nOur first group of team members, five of us, were peer nominated (thanks, team 😉) back in August of 2020 and include: [Dominic Couture](/company/team/#dcouture), [Mark Loveless](/company/team/#mloveless), [Joern Schneeweisz](/company/team/#joernchen), [Heather Simpson](/company/team/#heather), and [Steve Truong](/company/team/#sttruong). We meet monthly via Zoom (meetings are recorded and viewable internally for GitLab team members) to discuss candidate initiatives or process improvements where GitLab values could be better represented. Between meetings, we work async through GitLab issues and in a dedicated, public-to-GitLab Slack channel (#security-culture).\n\nFellow team members can bring suggestions for initiatives we should tackle via #security-culture channel, an issue or a Slack DM if that's more comfortable. Candidate initiatives are anything where [collaboration](https://handbook.gitlab.com/handbook/values/#collaboration), [results](https://handbook.gitlab.com/handbook/values/#results), [efficiency](https://handbook.gitlab.com/handbook/values/#efficiency), [diversity, inclusion & belonging](https://handbook.gitlab.com/handbook/values/#diversity-inclusion), [iteration](https://handbook.gitlab.com/handbook/values/#iteration) and/or [transparency](https://handbook.gitlab.com/handbook/values/#transparency) (all GitLab values), could be strengthened and improved.\n\n## Where has the committee focused our efforts so far?\n\nOne of the first things we tried to do was determine how we would define \"success\". We weren't sure, so reached out to the Security department via an anonymous feedback form asking the following questions:\n\n* Do you think the Security Culture Committee is strengthening the GitLab values within the Security department?\n* Do you think the Security Culture Committee should continue its efforts for at least another quarter?\n* Do you have anything to share what the committee could do in the future? Any ideas for opportunities are welcome.\n* Anything else you'd like to mention to the committee?\n\nFor the first two questions, team members had to rate their agreement with the statements on a scale of one (strongly disagree) to five (strongly agree) and 91% of answers were four or above. The other two questions generated interesting ideas to improve transparency in the department and better ways to communicate important news and initiatives across GitLab through Slack updates and entries in our Engineering department's week-in-review newsletter. There's definitely opportunity to improve and strengthen communication within GitLab around Security work and initiatives, and the value these efforts bring to the rest of the organization\n\n### Public profiles for transparency and collaboration\n\nAnother early initiative for our group was to encourage more GitLab team members to adopt public profiles to increase transparency across the company. The use of open, public profiles enables company-wide visibility into projects, plans, statuses, and updates. Public profiles ensure efficiency and fosters greater collaboration when there is visibility into the ongoing efforts of GitLab teams and team members. Public profiles also allow any visitor to see the work team members are doing in public projects. See Heather's profile: [https://gitlab.com/heather](https://gitlab.com/heather) as an example.\n\n![Screenshot of Heather Simpson's public GitLab profile](https://about.gitlab.com/images/blogimages/sec-culture-blog/heathersimpson_publicprofile.png){: .shadow.medium.center}\nPublic profiles foster collaboration through greater visibility into the work GitLab team members are doing.\n\n\n\nTo encourage public profile use, we held a Slack campaign where we communicated the value of public profiles and shared our progress toward the goal of making all GitLab profiles public by default.\n\n![Public GitLab profiles Slack campaign](https://about.gitlab.com/images/blogimages/sec-culture-blog/public_profile_msg.png){: .shadow.medium.center}\nAn example of our internal Slack campaign to encourage GitLab team members to switch their profiles from private to public.\n\n\nWe also [added language](https://gitlab.com/gitlab-com/www-gitlab-com/-/merge_requests/60262/diffs) to the [values page of the GitLab Handbook](https://handbook.gitlab.com/handbook/values/#transparency) encouraging the use of public profiles:\n\n> In line with our value of transparency and being public by default, all GitLab team member [profiles](https://docs.gitlab.com/ee/user/profile/#user-profile) should be public. Public profiles also enable broader collaboration and efficiencies between teams. To do so, please make sure that the checkbox under the [Private profile](https://docs.gitlab.com/ee/user/profile/#private-profile) option is unchecked in your profile settings. If you do not feel comfortable with your full name or location on your profile, please change it to what feels appropriate to you as these are displayed even on private profiles.\n\nAnd we added [clarification](https://gitlab.com/gitlab-com/people-group/people-operations/employment-templates/-/merge_requests/465/diffs) to our [onboarding template](https://gitlab.com/gitlab-com/people-group/people-operations/employment-templates/-/blob/c80404ffc53b143bfc393ab69b7ce482de3efdad/.gitlab/issue_templates/onboarding.md#L422) around why we use public profiles to ensure new team members understand how they contribute to GitLab's value of transparency and being [public by default](https://handbook.gitlab.com/handbook/values/#public-by-default).\n\nOur Security Culture Committee will continue to revisit this topic and educate team members on the value of public profiles, but we're proud of our team members commitment to transparency and the results we've achieved, together, to-date:\n\n**As of May 5, 2021:** 🎉\n* All of GitLab: 2.18% private profiles (28 out of 1307)\n* Security department: 2.22% private profiles (1 out of 48)\n\n### Increase transparency in department leadership meetings\n\nBeyond ensuring our GitLab profiles are public, the Security Culture Committee, in partnership with [Security department](https://handbook.gitlab.com/handbook/security/#security-department) leadership, has also advocated for several department and sub-department meeting notes and recordings to be made available internally. By making notes and recordings available, all team members can stay informed about what's going on at the Security leadership level and follow meeting notes and recordings [asynchronously](https://handbook.gitlab.com/handbook/values/#bias-towards-asynchronous-communication). Besides providing more transparency, this also supports our collaboration and results values, as information is made available for all to read and contribute to.\n\n### Strengthen the employee experience\n\nOn a bi-annual cadence, GitLab conducts an organization-wide [Team Member Engagement Survey](https://handbook.gitlab.com/handbook/people-group/engagement/) to give team members an opportunity to provide feedback related to their experience within GitLab across multiple elements, including culture. The results from this survey are aggregated by department and shared with department heads.\n\nGitLab VP of Security [Johnathan Hunt](/company/team/#JohnathanHunt), engaged the culture committee to dive deeper into the Security department specific results from the Team Member Engagement Survey and help identify areas for improvement. After reviewing results, the committee outlined four focus areas where we could strengthen employee experience across the Security department based on survey results:\n\n* I believe there are good career opportunities at GitLab\n* I have access to the L&D I need to do my job well\n* GitLab is in a position to really succeed over the next three years\n* I have confidence in senior leaders and execs at GitLab\n\n**The culture committee established various channels for Security team members to share their feedback:**\n\n* Anonymous response to a Security department specific survey (delivered via Google forms)\n* Survey response provided to their manager in a 1:1 session where feedback was then summarized, anonymized, and provided to the committee\n* 1:1 feedback directly to a member of the culture committee over a coffee chat\n\n**About 62% of the Security department provided feedback, not including aggregated feedback that was provided to managers in 1:1 conversations. As part of the survey, we asked Security team members to:**\n\n* Prioritize and rank the four focus areas mentioned above\n* Provide recommendations for improvement within each focus area\n* Supply any additional feedback and recommendations they wanted to share\n\nOnce all feedback was gathered, the culture committee worked to consolidate and anonymize the data to ensure that specific team members could not be identified based on language used in their feedback. The next steps included sharing the qualitative survey data and summarized feedback with the entire team, and making recommendations for action, based on survey data, to senior leadership. Security leadership took the recommendations from the [top three focus areas and formalized an OKR](https://gitlab.com/groups/gitlab-com/gl-security/-/epics/109) for Q1.\n\nSo, what are the results so far?\n\n\u003Cdetails markdown=\"1\">\n\u003Csummary>\u003Cb>Priority 1 focus area: I believe there are good career opportunities at GitLab\u003C/b>\u003C/summary>\n\n* Implemented an [individual development plan](https://handbook.gitlab.com/handbook/security/individual-development-plan.html) so team members can continuously discuss career path and growth opportunities with their manager\n* Leadership exploration of additional career opportunities by mapping out additional role levels within the Security department\n\n\u003C/details>\n\n\u003Cdetails markdown=\"1\">\n\u003Csummary>\u003Cb>Priority 2: I have confidence in senior leaders and execs at GitLab \u003C/b>\u003C/summary>\n\n* Collaboration\n   * Established a [Security Department Team Day](https://gitlab.com/gitlab-com/gl-security/security-department-meta/-/issues/1133) to encourage collaboration and networking across the security organization\n   * Added a [Security OKR](https://handbook.gitlab.com/handbook/security/OKR.html) handbook page to encourage cross-functional OKRs\n* Diversity, Inclusion, and Belonging (DIB)\n   * Allyship training for Security department senior leadership team\n   * Planning for maturation of DIB specific metrics for the Security department\n* Transparency\n   * Updates to the [Security leadership job family](https://gitlab.com/gitlab-com/www-gitlab-com/-/merge_requests/78910) handbook page to further define responsibilities by role\n   * Include Security department activities within the [Engineering week-in-review](https://handbook.gitlab.com/handbook/engineering/#communication)\n\n\u003C/details>\n\n\u003Cdetails markdown=\"1\">\n\u003Csummary>\u003Cb>Priority 3: I have access to the L&D I need to do my job well\u003C/b>\u003C/summary>\n\n* Dedicated handbook page to centralize all [Learning and Development opportunities](https://handbook.gitlab.com/handbook/security/learning-and-development.html) for Security team members\n* Process to enable team members to prioritize and [dedicate eight working hours per month to L&D](https://handbook.gitlab.com/handbook/security/learning-and-development.html#dedicate-time-to-ld)\n\n\u003C/details>\n\n## What's next\n\nEach set of culture committee members are nominated to serve a six-month term. We, the first set of committee members, have established some basic processes and hit the ground running on a few initiatives that we hope has laid some groundwork for future committee members and impacts how we live our values within the Security department and throughout GitLab. We've started onboarding the next set of peer-nominated Security Committee members, which includes [Liz Coleman](/company/team/#lcoleman), [Devin Harris](/company/team/#dsharris), [Andrew Kelly](/company/team/#ankelly), [Philippe Lafoucrière](/company/team/#plafoucriere), [Marley Riser](/company/team/#marleyr), and [Juliet Wanjohi](/company/team/#jwanjohi).\n\nSo, what should be prioritized and tackled first by this new committee? We know they will each come in with their own unique and valuable perspective and ideas on how to ensure our GitLab values are strengthened as we scale and represented in the work on the Security team and beyond. We look forward to continuing to contributing to this work on behalf of all of our team members and will keep you posted!\n\nHave some feedback on the initiatives we've worked on as part of our Security Culture Committee? Or suggestions based on what's worked within your organization? 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your pipeline for AI-discovered zero-days","AI is finding vulnerabilities faster than teams can patch. Learn how pipeline enforcement, automated triage, and AI remediation close the gap.",[718],"Omer Azaria","2026-04-20","Anthropic's [Mythos Preview model](https://red.anthropic.com/2026/mythos-preview/) recently identified thousands of zero-day vulnerabilities across every major operating system and web browser, including an OpenBSD bug that went undetected for 27 years. In testing, Mythos autonomously chained four vulnerabilities into a working browser exploit that escaped its sandbox. Anthropic is restricting access to Mythos, but the company’s head of offensive cyber research expects threats to have comparable tooling within six to twelve months.\n\nThe defender side of the equation hasn't kept pace. One third of exploited Common Vulnerabilities and Exposures (CVEs) in the first half of 2025 showed activity on or before disclosure day, before most teams even know there's something to patch. AI is compressing that window further, accelerating attackers and flooding teams with whitehat disclosures faster than they can triage. Defender tooling has improved, but most organizations can't operationalize it fast enough to close the gap between discovery and exploitation.\n\nWhen the window between disclosure and exploitation is measured in hours, the security team can't be the last line of defense. Security has to run where code enters the system: in the pipeline, on every merge request, enforced by policy. The fixes that can be automated should be. The ones that can't need to reach the right human faster than they do today.\n\n## Known vulnerabilities are already outpacing remediation\n\nThe bottleneck isn't detection, it's acting at scale on what teams already know. Sixty percent of breaches in the 2025 Verizon DBIR involved exploiting known vulnerabilities where a patch was already available. Teams couldn’t close them in time.\n\nThe backlog was untenable before Mythos. Developers spend [11 hours per month remediating vulnerabilities](https://about.gitlab.com/resources/developer-survey/) post-release instead of shipping new work. Over half of organizations have at least one open internet-facing vulnerability, and the median time to close half of those is 361 days. Exploitation takes hours, while remediation takes months.\n\nAI-assisted development is widening the gap, and stakeholders know it. By June 2025, AI-generated code was adding over 10,000 new security findings per month across Fortune 50 repositories, a 10x jump from six months earlier. Georgia Tech identified 34 [CVEs attributable to AI-generated code](https://research.gatech.edu/bad-vibes-ai-generated-code-vulnerable-researchers-warn) in March 2026, up from 6 in January, and that count reflects only the ones where AI authorship is clear. AI coding assistants hallucinate package names, reach for outdated patterns, and copy insecure examples from training data. More code, more dependencies, and more vulnerabilities per line are generated faster than security teams can review them.\n\nDefenders need to harness frontier AI models, too — not bolted onto the SDLC as external tooling, but running inside the same policies, approvals, and audit trail as the rest of the team. \n\n## Security at the speed of AI coding\n\nWhen a critical CVE drops, how quickly can your team confirm which projects are affected? How many tools does an alert cross before a developer can submit a fix?\n\nThe teams that benefit most from AI already have policies, enforcement, and controls embedded in their development workflows. AI amplifies that foundation. It doesn't replace it.\n\n**Enforcement at the point of change.** As exploitation windows compress, every line of code entering a repository needs to pass through a defined set of controls. Not a separate review, in a different tool, by a different team. Organizations need the ability to enforce security policies across every group and project, with the merge request as the enforcement point. Policies defined once, applied everywhere, with exceptions reviewed, approved, and logged.\n\n**Simple issues caught before the merge request, not during.** Hardcoded secrets, known-vulnerable imports, and deprecated API calls can be flagged in the IDE before a developer pushes a commit. Catching them at authoring time means fewer findings blocking the MR, so review cycles go to the findings that require cross-component context: reachability, exploitability, and architectural risk.\n\n**Triage automated by default, not by exception.** Embedding security into every merge request creates a volume problem. More scans, more findings, more noise reaching developers who aren’t trained to distinguish a reachable critical from a theoretical one. AI must handle false positive detection, reachability, exploitability context, and severity assessment before a developer sees the finding, so the findings they see actually warrant their time.\n\n**Remediation governed like any other change.** AI-based remediation compresses the timeline for closing vulnerabilities, but every generated fix must move through the same governance as a human-authored change: policies enforce scans, the right reviewers approve, and evidence is recorded. GitLab’s automated remediation capability proposes each fix in a merge request with a confidence score. The MR records which policy applied, which scans ran, what they found, and who approved. Human code and AI-generated code move through the same process, with the same audit trail.\n\n## What a ready pipeline looks like\n\nHere's how these pieces work together when a high-severity vulnerability is discovered and the clock is running.\n\nA proof-of-concept exploit for a vulnerability in a popular open-source package appears on a security mailing list. There’s no CVE, no National Vulnerability Database (NVD) entry, and no scanner signature yet. The security team finds out the usual way: someone shares it in Slack.\n\nA security engineer asks the security agent if the package is in use, which projects have affected versions, and whether any vulnerable call paths are reachable in production. The agent checks the dependency graph for every project, matches the affected versions and entry points from the disclosure, and returns a ranked list of exposed projects with details about reachability. There’s no need to search through repositories by hand or wait for a scanner update. The question, \"Are we exposed?\" is answered in minutes.\n\nThe engineer starts a remediation campaign for every exposed project. The remediation agent suggests fixes: version updates where a patched release is available, and targeted call-path patches where it is not. Scan execution policies are already in place for projects tagged SOC 2. The engineer hardens the rules to block merges on any merge request that introduces or keeps the affected dependency, and an approval policy now requires security sign-off on every fix. The agent's first proposed patch fails the pipeline when an integration test catches a regression. The agent revises the patch based on the test failure, and the second attempt passes. Developers review the changes, security signs off under the stricter policy, and merges proceed across the campaign.\n\nAt the next audit review, the security team presents a report showing how policies were enforced and risks were reduced during the campaign. It includes scan results, policies applied, approvers, and merge timestamps for every MR in every affected project. The evidence was automatically generated in flight, not assembled after the fact.\n\n## Close the gaps now\n\nMythos exists today, and comparable models will be in attacker hands within a year. Every month between now and then is a chance to strengthen your software supply chain.\n\nAsk these questions about your pipeline:\n\n* How do you enforce that security scans run on every merge request, not just the projects where teams configured them?\n\n* If a compromised package entered your dependency tree today, would your pipeline catch it before build?\n\n* When a scanner flags a critical finding, how many tool boundaries does it cross before a developer starts the fix?\n\n* If an AI agent proposed a code fix for a vulnerability, what process would that fix go through before reaching production, and is that process auditable?\n\n* When auditors ask for evidence that a specific policy was enforced on a specific change, how long does it take to produce?\n\nIf the answers expose gaps, address them now. [Talk to a GitLab solutions architect](https://about.gitlab.com/sales/) about the role of security governance in your development lifecycle.",[722,9,531],"AI/ML","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772195014/ooezwusxjl1f7ijfmbvj.png",{"featured":26,"template":13,"slug":725},"prepare-your-pipeline-for-ai-discovered-zero-days",{"content":727,"config":739},{"title":728,"description":729,"authors":730,"heroImage":732,"date":733,"category":9,"tags":734,"body":738},"Manage vulnerability noise at scale with auto-dismiss policies","Learn how to cut through scanner noise and focus on the vulnerabilities that matter most with GitLab security, including use cases and templates.",[731],"Grant Hickman","https://res.cloudinary.com/about-gitlab-com/image/upload/v1774375772/kpaaaiqhokevxxeoxvu0.png","2026-03-25",[9,735,563,736,737],"tutorial","features","product","Security scanners are essential, but not every finding requires action. Test code, vendored dependencies, generated files, and known false positives create noise that buries the vulnerabilities that actually matter. Security teams waste hours manually dismissing the same irrelevant findings across projects and pipelines. They experience slower triage, alert fatigue, and developer friction that undermines adoption of security scanning itself.\n\nGitLab's auto-dismiss vulnerability policies let you codify your triage decisions once and apply them automatically on every default-branch pipeline. Define criteria based on file path, directory, or vulnerability identifier (CVE, CWE), choose a dismissal reason, and let GitLab handle the rest.\n\n## Why auto-dismiss?\nAuto-dismiss vulnerability policies enable security teams to:\n- **Eliminate triage noise**: Automatically dismiss findings in test code, vendored dependencies, and generated files.\n- **Enforce decisions at scale**: Apply policies centrally to dismiss known false positives across your entire organization.\n- **Maintain audit transparency**: Every auto-dismissed finding includes a documented reason and links back to the policy that triggered it.\n- **Preserve the record**: Unlike scanner exclusions, dismissed vulnerabilities remain in your report, so you can revisit decisions if conditions change.\n\n## How auto-dismiss policies work\n\n1. **Define your policy** in a vulnerability management policy YAML file. Specify match criteria (file path, directory, or identifier) and a dismissal reason.\n\n2. **Merge and activate.** Create the policy via **Secure > Policies > New  policy > Vulnerability management policy**. Merge the MR to enable it.\n3. **Run your pipeline.** On every default-branch pipeline, matching vulnerabilities are automatically set to \"Dismissed\" with the specified reason. Up to 1,000 vulnerabilities are processed per run.\n4. **Measure the impact.** Filter your vulnerability report by status \"Dismissed\" to see exactly what was cleaned up and validate that the right findings are being handled.\n\n## Use cases with ready-to-use configurations\n\nEach example below includes a policy configuration you can copy, customize, and apply immediately.\n\n### 1. Dismiss test code vulnerabilities\n\nSAST and dependency scanners flag hardcoded credentials, insecure fixtures, and dev-only dependencies in test directories. These are not production risks.\n\n```yaml\nvulnerability_management_policy:\n  - name: \"Dismiss test code vulnerabilities\"\n    description: \"Auto-dismiss findings in test directories\"\n    enabled: true\n    rules:\n      - type: detected\n        criteria:\n          - type: file_path\n            value: \"test/**/*\"\n      - type: detected\n        criteria:\n          - type: file_path\n            value: \"tests/**/*\"\n      - type: detected\n        criteria:\n          - type: file_path\n            value: \"spec/**/*\"\n      - type: detected\n        criteria:\n          - type: directory\n            value: \"__tests__/*\"\n    actions:\n      - type: auto_dismiss\n        dismissal_reason: used_in_tests\n\n```\n\n### 2. Dismiss vendored and third-party code\n\nVulnerabilities in `vendor/`, `third_party/`, or checked-in `node_modules` are managed upstream and not actionable for your team.\n\n```yaml\nvulnerability_management_policy:\n  - name: \"Dismiss vendored dependency findings\"\n    description: \"Findings in vendored code are managed upstream\"\n    enabled: true\n    rules:\n      - type: detected\n        criteria:\n          - type: directory\n            value: \"vendor/*\"\n      - type: detected\n        criteria:\n          - type: directory\n            value: \"third_party/*\"\n      - type: detected\n        criteria:\n          - type: directory\n            value: \"vendored/*\"\n    actions:\n      - type: auto_dismiss\n        dismissal_reason: not_applicable\n\n```\n\n### 3. Dismiss known false positive CVEs\n\nCertain CVEs are repeatedly flagged but don't apply to your usage context. Teams dismiss these manually every time they appear. Replace the example CVEs below with your own.\n\n```yaml\nvulnerability_management_policy:\n  - name: \"Dismiss known false positive CVEs\"\n    description: \"CVEs confirmed as false positives for our environment\"\n    enabled: true\n    rules:\n      - type: detected\n        criteria:\n          - type: identifier\n            value: \"CVE-2023-44487\"\n      - type: detected\n        criteria:\n          - type: identifier\n            value: \"CVE-2024-29041\"\n      - type: detected\n        criteria:\n          - type: identifier\n            value: \"CVE-2023-26136\"\n    actions:\n      - type: auto_dismiss\n        dismissal_reason: false_positive\n\n```\n\n### 4. Dismiss generated and auto-created code\n\nProtobuf, gRPC, OpenAPI generators, and ORM scaffolding tools produce files with flagged patterns that cannot be patched by your team.\n\n```yaml\nvulnerability_management_policy:\n  - name: \"Dismiss generated code findings\"\n    description: \"Generated files are not authored by us\"\n    enabled: true\n    rules:\n      - type: detected\n        criteria:\n          - type: directory\n            value: \"generated/*\"\n      - type: detected\n        criteria:\n          - type: file_path\n            value: \"**/*.pb.go\"\n      - type: detected\n        criteria:\n          - type: file_path\n            value: \"**/*.generated.*\"\n    actions:\n      - type: auto_dismiss\n        dismissal_reason: not_applicable\n\n```\n\n### 5. Dismiss infrastructure-mitigated vulnerabilities\n\nVulnerability classes like XSS (CWE-79) or SQL injection (CWE-89) that are already addressed by WAF rules or runtime protection. Only use this when mitigating controls are verified and consistently enforced.\n\n```yaml\nvulnerability_management_policy:\n  - name: \"Dismiss CWEs mitigated by WAF\"\n    description: \"XSS and SQLi mitigated by WAF rules\"\n    enabled: true\n    rules:\n      - type: detected\n        criteria:\n          - type: identifier\n            value: \"CWE-79\"\n      - type: detected\n        criteria:\n          - type: identifier\n            value: \"CWE-89\"\n    actions:\n      - type: auto_dismiss\n        dismissal_reason: mitigating_control\n\n```\n\n### 6. Dismiss CVE families across your organization\n\nA wave of related CVEs for a widely-used library your team has assessed? Apply at the group level to dismiss them across dozens of projects. The wildcard pattern (e.g., `CVE-2021-44*`) matches all CVEs with that prefix.\n\n```yaml\nvulnerability_management_policy:\n  - name: \"Accept risk for log4j CVE family\"\n    description: \"Log4j CVEs mitigated by version pinning and WAF\"\n    enabled: true\n    rules:\n      - type: detected\n        criteria:\n          - type: identifier\n            value: \"CVE-2021-44*\"\n    actions:\n      - type: auto_dismiss\n        dismissal_reason: acceptable_risk\n\n```\n\n## Quick reference\n\n| Parameter | Details |\n|-----------|---------|\n| **Criteria types** | `file_path` (glob patterns, e.g., `test/**/*`), `directory` (e.g., `vendor/*`), `identifier` (CVE/CWE with wildcards, e.g., `CVE-2023-*`) |\n| **Dismissal reasons** | `acceptable_risk`, `false_positive`, `mitigating_control`, `used_in_tests`, `not_applicable` |\n| **Criteria logic** | Multiple criteria within a rule = AND (must match all). Multiple rules within a policy = OR (match any). |\n| **Limits** | 3 criteria per rule, 5 rules per policy, 5 policies per security policy project. Vulnerabilty management policy actions process 1000 vulnerabilities per pipeline run in the target project, until all matching vulnerabilities are processed. |\n| **Affected statuses** | Needs triage, Confirmed |\n| **Scope** | Project-level or group-level (group-level applies across all projects) |\n\n## Getting started\nHere's how to get started with auto-dismiss policies:\n\n1. **Identify the noise.** Open your vulnerability report and sort by \"Needs triage.\" Look for patterns: test files, vendored code, the same CVE across projects.\n\n2. **Pick a scenario.** Start with whichever use case above accounts for the most findings.\n\n3. **Record your baseline.** Note the number of \"Needs triage\" vulnerabilities before creating a policy.\n\n4. **Create and enable.** Navigate to **Secure > Policies > New policy > Vulnerability management policy**. Paste the configuration from the use case above, then merge the MR.\n\n5. **Validate results.** After the next default-branch pipeline, filter by status \"Dismissed\" to confirm the right findings were handled.\n\nFor full configuration details, see the [vulnerability management policy documentation](https://docs.gitlab.com/user/application_security/policies/vulnerability_management_policy/#auto-dismiss-policies).\n\n> Ready to take control of vulnerability noise? [Start a free GitLab Ultimate trial](https://about.gitlab.com/free-trial/) and configure your first auto-dismiss policy today.\n",{"slug":740,"featured":26,"template":13},"auto-dismiss-vulnerability-management-policy",{"content":742,"config":751},{"title":743,"description":744,"authors":745,"heroImage":747,"date":748,"body":749,"category":9,"tags":750},"GitLab 18.10 brings AI-native triage and remediation ","Learn about GitLab Duo Agent Platform capabilities that cut noise, surface real vulnerabilities, and turn findings into proposed fixes.",[746],"Alisa Ho","https://res.cloudinary.com/about-gitlab-com/image/upload/v1773843921/rm35fx4gylrsu9alf2fx.png","2026-03-19","GitLab 18.10 introduces new AI-powered security capabilities focused on improving the quality and speed of vulnerability management. Together, these features can help reduce the time developers spend investigating false positives and bring automated remediation directly into their workflow, so they can fix vulnerabilities without needing to be security experts.\n\nHere is what’s new:\n\n* [**Static Application Security Testing (SAST) false positive detection**](https://docs.gitlab.com/user/application_security/vulnerabilities/false_positive_detection/) **is now generally available.** This flow uses an LLM for agentic reasoning to determine the likelihood that a vulnerability is a false positive or not, so security and development teams can focus on remediating critical vulnerabilities first.  \n* [**Agentic SAST vulnerability resolution**](https://docs.gitlab.com/user/application_security/vulnerabilities/agentic_vulnerability_resolution/) **is now in beta.** Agentic SAST vulnerability resolution automatically creates a merge request with a proposed fix for verified SAST vulnerabilities, which can shorten time to remediation and reduce the need for deep security expertise.  \n* [**Secret false positive detection**](https://docs.gitlab.com/user/application_security/vulnerabilities/secret_false_positive_detection/) **is now in beta.** This flow brings the same AI-powered noise reduction to secret detection, flagging dummy and test secrets to save review effort.\n\nThese flows are available to GitLab Ultimate customers using GitLab Duo Agent Platform. \n\n## Cut triage time with SAST false positive detection\n\nTraditional SAST scanners flag every suspicious code pattern they find, regardless of whether code paths are reachable or frameworks already handle the risk. Without runtime context, they cannot distinguish a real vulnerability from safe code that just looks dangerous.\n\nThis means developers could spend hours investigating findings that turn out to be false positives. Over time, that can erode confidence in the report and slow down the teams responsible for fixing real risks.\n\nAfter each SAST scan, GitLab Duo Agent Platform automatically analyzes new critical and high severity findings and attaches:\n\n* A confidence score indicating how likely the finding is to be a false positive  \n* An AI-generated explanation describing the reasoning  \n* A visual badge that makes “Likely false positive” versus “Likely real” easy to scan in the UI\n\nThese findings appear in the [Vulnerability Report](https://docs.gitlab.com/user/application_security/vulnerability_report/), as shown below. You can filter the report to focus on findings marked as “Not false positive” so teams can spend their time addressing real vulnerabilities instead of sifting through noise.\n\n![Vulnerability report](https://res.cloudinary.com/about-gitlab-com/image/upload/v1773844787/i0eod01p7gawflllkgsr.png)\n\n\nGitLab Duo Agent Platform's assessment is a recommendation. You stay in control of every false positive to determine if it is valid, and you can audit the agent's reasoning at any time to build confidence in the model. \n\n\n## Turn vulnerabilities into automated fixes\n\nKnowing that a vulnerability is real is only half the work.  Remediation still requires understanding the code path, writing a safe patch, and making sure nothing else breaks.\n\nIf the vulnerability is identified as likely not be a false positive by the SAST false positive detection flow, the Agentic SAST vulnerability resolution flow automatically:\n\n1. Reads the vulnerable code and surrounding context from your repository  \n2. Generates high-quality proposed fixes  \n3. Validates fixes through automated testing   \n4. Opens a merge request with a proposed fix that includes:  \n   * Concrete code changes  \n   * A confidence score  \n   * An explanation of what changed and why\n\nIn this demo, you’ll see how GitLab can automatically take a SAST vulnerability all the way from detection to a ready-to-review merge request. Watch how the agent reads the code, generates and validates a fix, and opens an MR with clear, explainable changes so developers can remediate faster without being security experts.\n\n\u003Ciframe src=\"https://player.vimeo.com/video/1174573325?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 18.10 AI SAST False Positive Auto Remediation\">\u003C/iframe>\u003Cscript src=\"https://player.vimeo.com/api/player.js\">\u003C/script>\n\nAs with any AI-generated suggestion, you should review the proposed merge request carefully before merging.\n\n## Surface real secrets\n\nSecret detection is only useful if teams trust the results. When reports are full of test credentials, placeholder values, and example tokens, developers may waste time reviewing noise instead of fixing real exposures. That can slow remediation and decrease confidence in the scan.\n\nSecret false positive detection helps teams focus on the secrets that matter so they can reduce risk faster. When it runs on the default branch, it will automatically:\n\n1. Analyze each finding to spot likely test credentials, example values, and dummy secrets  \n2. Assign a confidence score for whether the finding is a real risk or a likely false positive  \n3. Generate an explanation for why the secret is being treated as real or noise  \n4. Add a badge in the Vulnerability Report so developers can see the status at a glance\n\nDevelopers can also trigger this analysis manually from the Vulnerability Report by selecting **“Check for false positive”** on any secret detection finding, helping them clear out findings that do not pose risk and focus on real secrets sooner.\n\n## Try AI-powered security today\n\nGitLab 18.10 introduces capabilities that cover the full vulnerability workflow, from cutting false positive noise in SAST and secret detection to automatically generating merge requests with proposed fixes.\n\nTo see how AI-powered security can help cut review time and turn findings into ready-to-merge fixes, [start a free trial of GitLab Duo Agent Platform today](https://about.gitlab.com/gitlab-duo-agent-platform/?utm_medium=blog&utm_source=blog&utm_campaign=eg_global_x_x_security_en_).",[737,9,736],{"featured":12,"template":13,"slug":752},"gitlab-18-10-brings-ai-native-triage-and-remediation",{"promotions":754},[755,769,780,791],{"id":756,"categories":757,"header":759,"text":760,"button":761,"image":766},"ai-modernization",[758],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":762,"config":763},"Get your AI maturity score",{"href":764,"dataGaName":765,"dataGaLocation":240},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":767},{"src":768},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":770,"categories":771,"header":772,"text":760,"button":773,"image":777},"devops-modernization",[737,566],"Are you just managing tools or shipping innovation?",{"text":774,"config":775},"Get your DevOps maturity score",{"href":776,"dataGaName":765,"dataGaLocation":240},"/assessments/devops-modernization-assessment/",{"config":778},{"src":779},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":781,"categories":782,"header":783,"text":760,"button":784,"image":788},"security-modernization",[9],"Are you trading speed for security?",{"text":785,"config":786},"Get your security maturity score",{"href":787,"dataGaName":765,"dataGaLocation":240},"/assessments/security-modernization-assessment/",{"config":789},{"src":790},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":792,"paths":793,"header":796,"text":797,"button":798,"image":803},"github-azure-migration",[794,795],"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. Find out what it means for you.",{"text":799,"config":800},"See how GitLab compares to GitHub",{"href":801,"dataGaName":802,"dataGaLocation":240},"/compare/gitlab-vs-github/github-azure-migration/","github azure migration",{"config":804},{"src":779},{"header":806,"blurb":807,"button":808,"secondaryButton":813},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":809,"config":810},"Get your free trial",{"href":811,"dataGaName":47,"dataGaLocation":812},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":502,"config":814},{"href":51,"dataGaName":52,"dataGaLocation":812},1777302608958]