[{"data":1,"prerenderedAt":814},["ShallowReactive",2],{"/en-us/blog/gitlabs-newest-continuous-compliance-features-bolster-software":3,"navigation-en-us":38,"banner-en-us":448,"footer-en-us":458,"blog-post-authors-en-us-Cindy Blake":698,"blog-related-posts-en-us-gitlabs-newest-continuous-compliance-features-bolster-software":712,"blog-promotions-en-us":752,"next-steps-en-us":804},{"id":4,"title":5,"authorSlugs":6,"body":8,"categorySlug":9,"config":10,"content":14,"description":8,"extension":25,"isFeatured":12,"meta":26,"navigation":27,"path":28,"publishedDate":20,"seo":29,"stem":34,"tagSlugs":35,"__hash__":37},"blogPosts/en-us/blog/gitlabs-newest-continuous-compliance-features-bolster-software.yml","Gitlabs Newest Continuous Compliance Features Bolster Software",[7],"cindy-blake",null,"security",{"slug":11,"featured":12,"template":13},"gitlabs-newest-continuous-compliance-features-bolster-software",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"GitLab’s newest continuous compliance features bolster software supply chain security","Business leaders and DevOps teams can continuously mitigate the risk of cloud-native environments and use guard rails to automate software compliance.",[18],"Cindy Blake","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749667466/Blog/Hero%20Images/GitLab-Sec.png","2022-02-09","\n_This blog post contains information related to upcoming products, features, and functionality. It is important to note that the information presented is for informational purposes only._\n\n_Please do not rely on this information for purchasing or planning purposes._\n\n_As with all projects, the items mentioned in the blog post and linked pages are subject to change or delay. The development, release, and timing of products, features, or functionality remain at the sole discretion of GitLab, Inc._\n\nCompliance and risk management have become the responsibility of everyone in an organization, and DevOps is no exception. To ensure the greatest level of security with the least exposure, business leaders must be able to trust that when they adopt or create compliance frameworks and policies, the associated rules will be able to be automatically deployed and enforced throughout the software development lifecycle. GitLab’s newest functionality and our near-term roadmap will help companies shift compliance left just as they have done for security, and also simplify governance and risk management across the entire software lifecycle.\n\n## Software supply chain risks\n\nHigh-profile attacks on software supply chains, and the resulting demand for tighter controls in software development and deployment by the U.S. government and customers worldwide, have put compliance and risk management front and center. Companies are not only struggling to protect their traditional architecture, but cloud-native transformation has introduced new attack surfaces that require [DevSecOps](/topics/devsecops/) teams to secure more than just the code. Containers, orchestrators, microservices, and the cloud environment as a whole make the job of identifying and mitigating vulnerabilities and risks even more challenging.\n\nTraditional application security is [no longer enough](/blog/are-you-ready-for-the-newest-era-of-devsecops/) in the era of DevOps automation and growth of cloud-native applications. In addition to testing and monitoring the new attack surfaces, complicated toolchains full of disparate products make it difficult to gain the visibility necessary to meet compliance demands and manage risk.\n\nAt GitLab, we remain focused on innovating an end-to-end DevOps Platform that organizations can leverage to simplify all aspects of security, compliance, governance, and risk management – no matter if you are developing software in a traditional environment, a cloud-native workspace, or a hybrid of the two.\n\nSecurity and compliance remain key focuses for our product investment. Let’s take a quick look at recent innovations along with what’s coming in the near-term within the three themes of:\n\n- Enabling secure cloud-native development\n- Security governance\n- Leveraging the DevOps Platform for better security and compliance\n\nAll of the information from these additional scans is available within existing workflows so DevSecOps teams can get the actionable insight they need to quickly find and fix issues from within the continuous integration (CI) pipeline. Here is how it looks for the developer:\n\n![WIP: Feature branch](https://about.gitlab.com/images/blogimages/cindyfeaturebranch.png){: .shadow}\n\nAt the same time, security pros get early insight into risks as vulnerabilities are merged into feature branches (pre-production). The [vulnerability report](https://docs.gitlab.com/ee/user/application_security/vulnerability_report/) helps review and triage of vulnerabilities not resolved by the developer. This information is available at the project and group levels.\n\n![Vulnerability report](https://about.gitlab.com/images/blogimages/cindyvulnerabilityreport.png){: .shadow}\n\nThese capabilities are part of the existing GitLab Ultimate tier – no integrations or added costs required.\n\n## Enabling secure cloud-native development\n\nHere’s **what’s new** in GitLab to help DevSecOps secure cloud-native development:\n\n**Infrastructure as code scanning** – Many DevSecOps teams have started to implement [IaC](https://docs.gitlab.com/user/application_security/iac_scanning/) as part of their software development lifecycle, so GitLab has introduced robust scanning tools that can analyze the IaC configuration files (i.e., YAML, Kubernetes, CloudFormation, Terraform) to identify common security issues of these new attack surfaces.\n\n**More flexible container scanning** – While we already had container scanning available in GitLab, we have switched to [Trivy open-source container vulnerability scanner technology](https://docs.gitlab.com/releases/#container-scanning-integration-with-trivy) for pre-production environments. Trivy covers more languages and has better results than previous scanners. We also are beta-testing container scanning for production environments and [cluster image scanning](https://docs.gitlab.com/ee/user/clusters/agent/vulnerabilities.html).\n\n**API security** – APIs represent a tremendous attack surface when not properly secured. We are using the state-of-the-art fuzzing technology [acquired from Peach Tech and Fuzzit](/press/releases/2020-06-11-gitlab-acquires-peach-tech-and-fuzzit-to-expand-devsecops-offering.html) to test APIs. In addition, our [dynamic application security testing for APIs](https://docs.gitlab.com/ee/user/application_security/dast_api/) (DAST) is in beta.\n\nResults from all of the scanners (IaC, containers and APIs) are incorporated into GitLab’s CI pipeline alongside other scan results enabling correction before configuration errors manifest in production.\n\nHere’s **what’s next** that will help DevSecOps secure cloud-native development:\n\n**Production container scanning** – We plan to make production container scanning generally available to scan containers for vulnerabilities after they’ve [already been deployed](https://docs.gitlab.com/user/application_security/container_scanning/). This will help surface vulnerabilities from new exploits not tested for during development.\n\n**DAST API scanner** – We will be making our [DAST API scanner](https://docs.gitlab.com/user/application_security/dast/) generally available to enable broader coverage, better quality, and easier configuration. This will help you apply even greater defense-in-depth.\n\n**API Discovery** – DevSecOps teams will be able to leverage access to code to automatically [discover and test the APIs](https://gitlab.com/gitlab-org/gitlab/-/issues/38384)  being used throughout the organization’s software supply chain. Understanding the attack surface is important to protecting it.\n\n## Security governance\n\nHere’s **what’s new** to help organizations establish and manage security and compliance guardrails that allow developers to run fast while also managing risk:\n\n**Continuous compliance** – Organizations can shift compliance left, similar to security, to identify and mitigate violations early on to avoid delays at go-live. Compliant workflow automation enables a DevOps admin to assign a compliance framework to a project and enforce scans and other common controls across all project pipelines. Developers may not easily sidestep required controls.\n\n**Policy Engine** – GitLab automates a comprehensive set of security and compliance scans within the CI pipeline. Automating what happens when exceptions are encountered has been fairly simplistic. Now, GitLab provides users with a [policy editor](https://docs.gitlab.com/ee/user/application_security/policies/#policy-editor) that provides more fine-grained rules that can determine what approvals are required helping you manage your own unique appetite for risk.\n\nThe policy engine is part of a larger direction for [Security Orchestration](https://docs.gitlab.com/user/application_security/policies/) that includes continued iteration on Security Alert Management, Security Policy Management, and Security Approvals.\n\nHere’s **what’s next** that will help organizations establish and manage security governance:\n\n**Compliance checks in MRs** – GitLab is further automating continuous [compliance checks into the developer’s daily workflow](https://docs.gitlab.com/ee/user/compliance/compliance_report/index.html#approval-status-and-separation-of-duties) in a similar way as security scans. This will help compliance essentially shift left so developers can find and fix compliance violations early and stay on schedule.\n\n**Governance at the group level** – We are working to bring the controls found at the project level up to the group level so that policies may be more easily applied across a broad set of projects. This project is tied to the completion of workspaces.\n\n## The benefits of a single DevOps Platform\n\nHere’s **what’s new** that enables you to leverage the benefits of a single DevOps Platform in GitLab’s Ultimate version:\n\n**Unified vulnerability management and reporting** – We’ve consolidated security findings into a [single dashboard](https://docs.gitlab.com/ee/user/application_security/vulnerability_report/) that aggregates information from GitLab and other sources, including third-party scanners, our [security partners](/partners/technology-partners/#security), and more. You can [pull in vulnerability data from other systems](/blog/three-things-you-might-not-know-about-gitlab-security/), manual pen testing, bug bounty programs, or even from security tools that don’t run in GitLab pipeline jobs. Vulnerability management in GitLab Ultimate helps you manage all of your [software vulnerability information](https://docs.gitlab.com/ee/user/application_security/vulnerabilities/) in one place to efficiently triage and remediate findings.\n\n**Proprietary SAST scanner** – We have [replaced some of our language-specific open-source scanners (OSS)](https://docs.gitlab.com/ee/user/application_security/sast/#supported-languages-and-frameworks) with [Semgrep](https://r2c.dev/blog/2021/introducing-semgrep-for-gitlab/), a proprietary scanner, to improve coverage, accuracy, and speed. Semgrep's flexible rule syntax is ideal for streamlining the [GitLab Custom Rulesets](https://docs.gitlab.com/ee/user/application_security/sast/#customize-rulesets) feature for extending and modifying detection rules. It also allows GitLab customers access to Semgrep's community rules.\n\nHere’s **what’s next** that will enable organizations to leverage the benefits of a single DevOps Platform in GitLab’s Ultimate version:\n\n**Software supply chain security** – Organizations will be able to secure the full software supply chain with one application while improving confidence in its integrity and security. GitLab has put together a framework describing the various aspects that are required to accomplish this based on feedback from customers, inspiration from common standards (such as SLSA), as well as thought leadership from industry analysts. We would love your thoughts and contributions to these epics. Check out our [Software Supply Chain Security direction page](https://docs.gitlab.com/user/application_security/).\n\n**Inline security training** – Developers will have just-in-time access to popular third-party security training as they encounter vulnerabilities. For instance, if a vulnerability is detected, a module will pop up that the developer can click on to learn more, including what the vulnerability is and how to fix it. This optimizes security training with an immediate need. More details coming soon.\n\n**Intelligent code security** – Leveraging a previous acquisition, GitLab plans to help organizations automatically detect and remediate insecure coding practices using machine learning. This will help our customers further reduce risk and technical debt.\n\nGitLab is uniquely transparent. By making our product roadmaps public, we encourage contribution and iteration. We invite you to contribute your ideas by checking out our [upcoming releases](/releases/whats-new/) and commenting on [upcoming releases](/releases/whats-new/).\n",[23,9,24],"DevOps","features","yml",{},true,"/en-us/blog/gitlabs-newest-continuous-compliance-features-bolster-software",{"title":30,"description":16,"ogTitle":30,"ogDescription":16,"noIndex":12,"ogImage":19,"ogUrl":31,"ogSiteName":32,"ogType":33,"canonicalUrls":31},"GitLab strengthens supply chain with compliance features","https://about.gitlab.com/blog/gitlabs-newest-continuous-compliance-features-bolster-software","https://about.gitlab.com","article","en-us/blog/gitlabs-newest-continuous-compliance-features-bolster-software",[36,9,24],"devops","uXZKi3ukJMsIwmuqI-WWG-D6lnIpm12BFJLpSaqNhB8",{"data":39},{"logo":40,"freeTrial":45,"sales":50,"login":55,"items":60,"search":368,"minimal":399,"duo":418,"switchNav":427,"pricingDeployment":438},{"config":41},{"href":42,"dataGaName":43,"dataGaLocation":44},"/","gitlab 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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.",[722,9,533],"AI/ML","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772195014/ooezwusxjl1f7ijfmbvj.png",{"featured":27,"template":13,"slug":725},"prepare-your-pipeline-for-ai-discovered-zero-days",{"content":727,"config":738},{"title":728,"description":729,"authors":730,"heroImage":732,"date":733,"category":9,"tags":734,"body":737},"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",[9,735,563,24,736],"tutorial","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":739,"featured":27,"template":13},"auto-dismiss-vulnerability-management-policy",{"content":741,"config":750},{"title":742,"description":743,"authors":744,"heroImage":746,"date":747,"body":748,"category":9,"tags":749},"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.",[745],"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_).",[736,9,24],{"featured":12,"template":13,"slug":751},"gitlab-18-10-brings-ai-native-triage-and-remediation",{"promotions":753},[754,768,779,790],{"id":755,"categories":756,"header":758,"text":759,"button":760,"image":765},"ai-modernization",[757],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":761,"config":762},"Get your AI maturity score",{"href":763,"dataGaName":764,"dataGaLocation":242},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":766},{"src":767},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":769,"categories":770,"header":771,"text":759,"button":772,"image":776},"devops-modernization",[736,566],"Are you just managing tools or shipping innovation?",{"text":773,"config":774},"Get your DevOps maturity score",{"href":775,"dataGaName":764,"dataGaLocation":242},"/assessments/devops-modernization-assessment/",{"config":777},{"src":778},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":780,"categories":781,"header":782,"text":759,"button":783,"image":787},"security-modernization",[9],"Are you trading speed for security?",{"text":784,"config":785},"Get your security maturity score",{"href":786,"dataGaName":764,"dataGaLocation":242},"/assessments/security-modernization-assessment/",{"config":788},{"src":789},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":791,"paths":792,"header":795,"text":796,"button":797,"image":802},"github-azure-migration",[793,794],"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":798,"config":799},"See how GitLab compares to GitHub",{"href":800,"dataGaName":801,"dataGaLocation":242},"/compare/gitlab-vs-github/github-azure-migration/","github azure migration",{"config":803},{"src":778},{"header":805,"blurb":806,"button":807,"secondaryButton":812},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":808,"config":809},"Get your free trial",{"href":810,"dataGaName":49,"dataGaLocation":811},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":504,"config":813},{"href":53,"dataGaName":54,"dataGaLocation":811},1777302637429]