[{"data":1,"prerenderedAt":829},["ShallowReactive",2],{"/en-us/blog/gitlab-supports-banks-in-navigating-regulatory-challenges":3,"navigation-en-us":42,"banner-en-us":452,"footer-en-us":462,"blog-post-authors-en-us-George Kichukov|Allie Holland":700,"blog-related-posts-en-us-gitlab-supports-banks-in-navigating-regulatory-challenges":726,"blog-promotions-en-us":767,"next-steps-en-us":819},{"id":4,"title":5,"authorSlugs":6,"body":9,"categorySlug":10,"config":11,"content":15,"description":9,"extension":28,"isFeatured":13,"meta":29,"navigation":30,"path":31,"publishedDate":22,"seo":32,"stem":36,"tagSlugs":37,"__hash__":41},"blogPosts/en-us/blog/gitlab-supports-banks-in-navigating-regulatory-challenges.yml","Gitlab Supports Banks In Navigating Regulatory Challenges",[7,8],"george-kichukov","allie-holland",null,"security",{"slug":12,"featured":13,"template":14},"gitlab-supports-banks-in-navigating-regulatory-challenges",false,"BlogPost",{"title":16,"description":17,"authors":18,"heroImage":21,"date":22,"body":23,"category":10,"tags":24},"GitLab supports banks in navigating regulatory challenges","Learn the upcoming changes to key frameworks, how they impact organizations, and the DevSecOps platform features that can help address them.",[19,20],"George Kichukov","Allie Holland","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749664874/Blog/Hero%20Images/AdobeStock_880918603.jpg","2025-01-09","The risk of cyber attacks in the banking industry has reached unprecedented levels. Studies by the [International Monetary Fund](https://www.imf.org/-/media/Files/Publications/GFSR/2024/April/English/ch3.ashx) reveal that the financial sector is particularly vulnerable to cyber threats, with nearly one-fifth of reported incidents in the past two decades targeting this industry alone. As these threats continue to escalate, they drive the need for a regulatory response, prompting the banking and financial services industry to prepare for significant changes. GitLab enables financial institutions to proactively tackle these challenges, supporting banks on their regulatory journey while ensuring the operational resilience needed to protect the sensitive data pervasive throughout the banking ecosystem.\n\n## Understanding the upcoming regulatory changes\n\nAcknowledging that the regulatory landscape frequently changes, this article will concentrate on key frameworks in the EU poised to shape the future of banking and financial services. These frameworks not only address current industry challenges but also set the foundation for the development of a more secure and resilient financial ecosystem.\n\nHere are several regulations that are demanding the attention of the financial services industry. \n\n### [European Cyber Resilience Act (CRA)](https://digital-strategy.ec.europa.eu/en/policies/cyber-resilience-act)  \n\nImplemented as of **January 2024,** with a grace period extending for two years, the CRA establishes a comprehensive framework to enhance cybersecurity standards for digital products and services within the EU. This regulation seeks to mitigate the risks of vulnerabilities in software and hardware by ensuring that security is integrated throughout the entire product lifecycle, promoting a proactive “shift left” approach to security. By embedding security measures from the design phase onward, the CRA aims to safeguard the digital economy and bolster consumer trust in digital services.\n\n### [Digital Operational Resilience Act (DORA)](https://www.eiopa.europa.eu/digital-operational-resilience-act-dora_en)\n\nTaking effect on **January 17, 2025**, the Digital Operations Resilience Act aims to ensure that financial institutions can withstand, respond to, and recover from all types of information and communication technology related disruptions and threats. The goal is to unify and strengthen the resilience of the financial sector across Europe. \n\n### [European Data Act](https://digital-strategy.ec.europa.eu/en/policies/data-act)  \n\nAnticipated to become applicable on **September 12, 2025**, this regulation seeks to provide clearer rules regarding data use and sharing for AI and the internet of things, or IoT, enhancing data access and fostering innovation in various sectors, including finance.\n\n## Implications for banks and financial institutions \n\nAs financial institutions adapt to these evolving regulatory frameworks, the implications are significant and far-reaching. For instance, PYMNTS reports [59% of bankers see their legacy systems as a major business challenge](https://www.pymnts.com/digital-first-banking/2024/three-quarters-of-banks-face-digital-banking-infrastructure-issues/). These challenges present obstacles in the delivery of modern services, while hindering their ability to both detect and respond to modern cyber threats. According to the [2024 IBM Data Breach Report](https://www.ibm.com/downloads/cas/1KZ3XE9D), the average cost of a data breach in the financial services sector is a staggering $6.08 million, with breaches taking an average of 258 days to identify and contain. Unfortunately for banks, the most common type of data stolen or compromised was customer personally identifiable information, or PII. This highlights the urgent need for organizations to modernize their security practices and infrastructure.\n\nHere are four ways to address this challenge.\n\n1. **Increase investment in technology:** Banks will need to significantly increase their investments in technology and infrastructure. This involves evaluating current systems and processes to ensure they align with the stringent requirements of CRA, DORA, the European Data Act, and other regulations.  \n\n2. **Heighten risk management practices:** A cultural shift will be necessary within organizations, as teams will need to prioritize risk management and resilience strategies. DORA, in particular, emphasizes not just compliance but the ability to anticipate and recover from disruptions.  \n\n3. **Enhance data governance:** Many of these new regulations will require banks to prepare for new approaches to data sharing and governance. Banks will have to rethink how data is collected, stored, and analyzed, with a strong focus on transparency, accountability, and collaboration across departments.  \n\n4. **Strengthen cybersecurity:** As cyber threats evolve, the importance of robust cybersecurity measures cannot be overstated. The CRA mandates that financial institutions implement comprehensive security protocols, requiring banks to prioritize cybersecurity investments at every phase of the software development lifecycle. \n\n## How GitLab can help   \nWith years of experience working with some of the [largest financial organizations in the world](https://about.gitlab.com/customers/all/?industry=financial-services), GitLab stands ready to support banks and other financial institutions in their compliance efforts. Our integrated suite of features empowers development teams to streamline their workflows, allowing them to concentrate on software development rather than becoming bogged down by the manual tracking and monitoring of evolving compliance regulations. \n\n**[GitLab Dedicated](https://about.gitlab.com/dedicated/)**, our fully isolated, single-tenant SaaS solution, is designed to meet the complex compliance and data residency requirements of highly regulated industries. Hosted and managed by GitLab, in your chosen cloud region, GitLab Dedicated ensures that sensitive data remains secure and compliant with local regulations. GitLab can help banks navigate these challenges effectively with:\n\n1. [Comprehensive application security and compliance features](https://about.gitlab.com/stages-devops-lifecycle/secure/)\n\n-  __Security scanning built into developer workflows:__ Many financial institutions still rely on disparate tools for security checks, which can lead to gaps in coverage and oversight. GitLab offers built-in security scanning tools that automatically identify vulnerabilities and provide remediation guidance throughout the application lifecycle. By embedding security checks into [CI/CD pipelines](https://about.gitlab.com/topics/ci-cd/cicd-pipeline/), banks can detect and resolve issues early in the development process, where they are less costly and less risky to fix, ensuring that they adhere to necessary security protocols. GitLab offers the following [security scanner types](https://docs.gitlab.com/ee/user/application_security/secure_your_application.html):\n\n      1. [Static Application Security Testing (SAST)](https://docs.gitlab.com/ee/user/application_security/sast/index.html) \n\n      2. [Dynamic Application Security Testing (DAST)](https://docs.gitlab.com/ee/user/application_security/dast/index.html)  \n      3. [Secret Detection](https://docs.gitlab.com/ee/user/application_security/secret_detection/index.html)\n\n      4. [Infrastructure as Code (IaC) Scanning](https://docs.gitlab.com/ee/user/application_security/iac_scanning/index.html)\n\n      5. [Dependency (+ License) Scanning](https://docs.gitlab.com/ee/user/application_security/dependency_scanning/index.html)  \n      6. [Coverage-guided Fuzz Testing](https://docs.gitlab.com/ee/user/application_security/coverage_fuzzing/index.html)  \n\n      7. [Web API Fuzz Testing](https://docs.gitlab.com/ee/user/application_security/api_fuzzing/)  \n\n      8. [Container Scanning](https://docs.gitlab.com/ee/user/application_security/container_scanning/index.html)  \n\n      9. [API Security Scanning](https://docs.gitlab.com/ee/user/application_security/api_security/index.html)\n\n- __Compliance and enforceable policies:__ Our platform enables [separation of duties](https://about.gitlab.com/blog/ensuring-compliance/), by allowing security and compliance teams to manage security policies independently, allowing developers to focus purely on development. This approach supports the [principle of least privilege](https://about.gitlab.com/blog/the-ultimate-guide-to-least-privilege-access-with-gitlab/), where developers access only what they need. For multinational banks or financial institutions who operate globally, GitLab’s policies and compliance dashboards assist in meeting strict geographical and regulatory requirements. These tools help maintain consistent adherence to compliance regulations, giving organizations clear visibility into their security posture across regions, industries, and regulations.\n\n- __[Software supply chain security](https://about.gitlab.com/solutions/supply-chain/):__ GitLab ensures the security of the entire build, development, and deployment environment through a comprehensive approach to software supply chain security. Our [software composition analysis (SCA)](https://about.gitlab.com/blog/reduce-supply-chain-risk-with-smarter-vulnerability-prioritization/) provides deep insights into component versions, licenses, and known vulnerabilities in dependencies which can be proactively remediated to reduce enterprise risk. This comprehensive approach also includes [software bill of materials (SBOM)](https://docs.gitlab.com/ee/user/application_security/dependency_list/) generation, ensuring transparency and compliance with industry standards. Finally, as highlighted above, GitLab provides controls to enforce the principle of least privilege to mitigate threats that compromise the software development environment itself.  \n\n2. [Robust risk management tools](https://docs.gitlab.com/ee/user/application_security/)\n\n- __Issue tracking and management:__ Within a bank, ineffective risk management can lead to overlooked vulnerabilities and inefficient mitigation strategies. GitLab’s issue tracking capabilities allow security vulnerabilities to appear alongside feature requests in the backlog, creating full visibility across teams. Sensitive issues can also be marked as confidential, so that only those who have sufficient permissions can access. This combination of transparency and controlled access supports a culture of collaboration and accountability, as development, security, and operations teams work together seamlessly on risk management. This cultural shift is crucial; rather than merely purchasing tools or one-off solutions, organizations must embed collaboration into their workflows to ensure security becomes a key part of the development process. \n\n- __[Automated testing](https://about.gitlab.com/blog/how-to-choose-the-right-security-scanning-approach/):__ A common challenge in the financial services industry is centered around the fact that homegrown solutions that were once robust processes become slow and cumbersome over time, leading to reduced agility. So much so that [Forbes](https://www.aba.com/-/media/documents/industry-insights/2023-thoughtmachine-banking-at-a-crossroads-the-threat-of-legacy-infrastructure.pdf?rev=6ce18fa56f0547e5a8c8433b50aef931) found that 60% of banking leaders consider legacy infrastructure to be the major factor keeping them from unlocking incremental growth. To compensate, the industry has shifted toward giving developers more freedom, but often at the cost of maintaining high security standards.\n\u003Cbr>\u003C/br>\n  GitLab solves this challenge by [automating testing within CI/CD pipelines](https://about.gitlab.com/topics/devops/devops-test-automation/), enabling financial institutions to maintain both speed and security. Developers can configure pipelines to fit their workflows, while security and compliance teams retain control over policies, ensuring adherence to critical security measures. By automating testing processes, GitLab helps banks remain resilient and functional, reducing the likelihood of disruptions.\n\n3. [Enhanced data governance](https://about.gitlab.com/stages-devops-lifecycle/govern/)\n\n- __Data management and compliance:__ GitLab’s data management features enable organizations to securely handle sensitive information. With embedded [audit logs](https://docs.gitlab.com/ee/user/compliance/audit_events.html), banks can track data access and changes, ensuring transparency and accountability in their data practices. These logs can show actions such as who changed the permission level of a particular user for a project, and when.\n\n- __[Collaboration tools](https://about.gitlab.com/topics/gitops/gitops-gitlab-collaboration/):__ GitLab promotes collaboration among cross-departmental teams, facilitating communication between IT, compliance, and business units. This integrated approach is essential for effective data governance, allowing banks to align their data practices with organizational goals.\n\n4. [Efficient incident reporting and response](https://docs.gitlab.com/ee/operations/incident_management/)\n\n- __[Centralized incident management](https://handbook.gitlab.com/handbook/engineering/infrastructure/incident-management/):__ GitLab provides centralized project management capabilities for logging and tracking significant incidents. This allows teams to respond quickly and effectively, ensuring that incidents are managed in a timely manner.\n\n- __[Incident response guides](https://handbook.gitlab.com/handbook/security/security-operations/sirt/sec-incident-response/):__ With GitLab, organizations can develop and maintain incident response plans within the platform. By simulating potential incidents and testing response protocols, banks can ensure preparedness and resilience in the face of unexpected challenges.\n\n5. [Documentation and audit readiness](https://docs.gitlab.com/ee/administration/compliance.html)\n\n- __Continuous compliance documentation:__ Traditionally, banks have been locked into rigid 12-month audit cycles, preparing documentation to meet stringent regulations like the Bank Secrecy Act (BSA), Automated Clearing House (ACH) rules, and Anti-Money Laundering (AML) requirements. However, as the pace and complexity of threats grow, the financial industry is shifting from reactive, periodic audits to a proactive, [continuous compliance model](https://about.gitlab.com/blog/meet-regulatory-standards-with-gitlab/). With GitLab, teams know exactly where they stand at any given moment, leveraging real-time compliance data to their advantage. This continuous insight empowers teams to address issues as they arise, rather than waiting for an audit, creating a more agile and resilient compliance posture.\n\n- **Customizable reporting:** With GitLab’s customizable reporting features, organizations can generate detailed reports that showcase compliance violations based on severity levels, violation types, and merge request titles. These reports provide valuable insights for both internal stakeholders and external parties, ensuring transparency and accountability.\n\n## Connect with GitLab today\n\nAs banks and financial institutions embrace these regulatory changes, GitLab not only provides the technology necessary to ensure compliance, but also fosters a culture of continuous improvement. This proactive approach allows financial institutions to release software with confidence, knowing they have the systems in place to mitigate risks and respond quickly to incidents.\n\nGitLab’s commitment to supporting the financial sector through these transitions ensures that organizations are not only compliant but also resilient and prepared for the challenges ahead. Together, we can build a safer and more secure financial future. \n\n> **[Reach out](https://about.gitlab.com/solutions/finance/) to learn more about how we can help meet your regulatory challenges.**\n\n## Read more\n\n- [What the Digital Operational Resilience Act means for banks](https://about.gitlab.com/blog/what-the-digital-operational-resilience-act-means-for-banks/)\n- [Meet regulatory standards with GitLab security and compliance](https://about.gitlab.com/blog/meet-regulatory-standards-with-gitlab/)\n- [How to ensure separation of duties and enforce compliance with GitLab](https://about.gitlab.com/blog/ensuring-compliance/)",[25,26,27,10],"financial services","DevSecOps","DevSecOps 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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.",[732],"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.",[736,10,27],"AI/ML","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772195014/ooezwusxjl1f7ijfmbvj.png",{"featured":30,"template":14,"slug":739},"prepare-your-pipeline-for-ai-discovered-zero-days",{"content":741,"config":753},{"title":742,"description":743,"authors":744,"heroImage":746,"date":747,"category":10,"tags":748,"body":752},"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.",[745],"Grant Hickman","https://res.cloudinary.com/about-gitlab-com/image/upload/v1774375772/kpaaaiqhokevxxeoxvu0.png","2026-03-25",[10,749,26,750,751],"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":754,"featured":30,"template":14},"auto-dismiss-vulnerability-management-policy",{"content":756,"config":765},{"title":757,"description":758,"authors":759,"heroImage":761,"date":762,"body":763,"category":10,"tags":764},"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.",[760],"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_).",[751,10,750],{"featured":13,"template":14,"slug":766},"gitlab-18-10-brings-ai-native-triage-and-remediation",{"promotions":768},[769,783,794,805],{"id":770,"categories":771,"header":773,"text":774,"button":775,"image":780},"ai-modernization",[772],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":776,"config":777},"Get your AI maturity score",{"href":778,"dataGaName":779,"dataGaLocation":246},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":781},{"src":782},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":784,"categories":785,"header":786,"text":774,"button":787,"image":791},"devops-modernization",[751,39],"Are you just managing tools or shipping innovation?",{"text":788,"config":789},"Get your DevOps maturity score",{"href":790,"dataGaName":779,"dataGaLocation":246},"/assessments/devops-modernization-assessment/",{"config":792},{"src":793},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":795,"categories":796,"header":797,"text":774,"button":798,"image":802},"security-modernization",[10],"Are you trading speed for security?",{"text":799,"config":800},"Get your security maturity score",{"href":801,"dataGaName":779,"dataGaLocation":246},"/assessments/security-modernization-assessment/",{"config":803},{"src":804},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":806,"paths":807,"header":810,"text":811,"button":812,"image":817},"github-azure-migration",[808,809],"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":813,"config":814},"See how GitLab compares to GitHub",{"href":815,"dataGaName":816,"dataGaLocation":246},"/compare/gitlab-vs-github/github-azure-migration/","github azure migration",{"config":818},{"src":793},{"header":820,"blurb":821,"button":822,"secondaryButton":827},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":823,"config":824},"Get your free trial",{"href":825,"dataGaName":53,"dataGaLocation":826},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":508,"config":828},{"href":57,"dataGaName":58,"dataGaLocation":826},1777302610259]