[{"data":1,"prerenderedAt":817},["ShallowReactive",2],{"/en-us/blog/zero-trust-at-gitlab-where-do-we-go-from-here":3,"navigation-en-us":38,"banner-en-us":448,"footer-en-us":458,"blog-post-authors-en-us-Mark Loveless":700,"blog-related-posts-en-us-zero-trust-at-gitlab-where-do-we-go-from-here":714,"blog-promotions-en-us":755,"next-steps-en-us":807},{"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":33,"tagSlugs":34,"__hash__":37},"blogPosts/en-us/blog/zero-trust-at-gitlab-where-do-we-go-from-here.yml","Zero Trust At Gitlab Where Do We Go From Here",[7],"mark-loveless",null,"security",{"slug":11,"featured":12,"template":13},"zero-trust-at-gitlab-where-do-we-go-from-here",false,"BlogPost",{"title":15,"description":16,"authors":17,"heroImage":19,"date":20,"body":21,"category":9,"tags":22},"Zero Trust at GitLab: Where do we go from here?","We take a look back at how far we've come in our ZTN implementation, and at the progress we still need to make.",[18],"Mark Loveless","https://res.cloudinary.com/about-gitlab-com/image/upload/v1749679704/Blog/Hero%20Images/puria-berenji-Dyi1K2atCRw-unsplash.jpg","2019-10-15","\n\n*Zero Trust is the practice of shifting access control from the network perimeter to the assets, individuals, and the respective endpoints. For GitLab, Zero Trust means that all users and devices trying to access an endpoint or asset within our GitLab environment will need to authenticate and be authorized. This is part 6 of 6 in our series.*\n* Part one: [The evolution of Zero Trust](/blog/evolution-of-zero-trust/)\n* Part two: [Zero Trust at GitLab: Problems, goals, and coming challenges](/blog/zero-trust-at-gitlab-problems-goals-challenges/)\n* Part three: [Zero Trust at GitLab: The data classification and infrastructure challenge](/blog/zero-trust-at-gitlab-the-data-classification-and-infrastructure-challenge/)\n* Part four: [Zero Trust at GitLab: Mitigating challenges with data zones and authentication scoring](/blog/zero-trust-at-gitlab-data-zones-and-authentication-scoring/)\n* Part five: [Zero Trust at GitLab: Implementation challenges](/blog/zero-trust-at-gitlab-implementation-challenges/)\n\nWe've talked pretty openly about forming our ZTN approach and the challenges we expect along the way – as well as the challenges we've already met. If there is an area of ZTN that we've not addressed, or if you're interested in diving deeper into the topic, we invite you to join us October 29, 3-4 pm ET for our [Zero Trust Reddit AMA](https://www.reddit.com/r/netsec/comments/d71p1d/were_a_100_remote_cloudnative_company_and_were/) where you can Ask Us Anything!\n\n## Where we are\nI guess it makes sense to talk about where we are at with this whole ZTN thing. In addition to establishing policies for team members (based upon job descriptions and placement in the org chart), we have classified our data and mapped out our environment so we know where all of the parts are. But there are a few items we want to explain with a bit of detail.\n\n### Getting SaaS\n\nUsing [Okta](https://www.okta.com), we have managed to get (as of this writing) 70 of our [SaaS](https://en.wikipedia.org/wiki/Software_as_a_service) apps under some semblance of control. This “control” has varied heavily – some SaaS apps cleanly and seamlessly integrated with Okta, and some were working kinda-sorta-good-enough to call them integrated. The majority of SaaS integrations work fine as they used [SAML](https://en.wikipedia.org/wiki/Security_Assertion_Markup_Language) and easily integrate in minutes. We can provision and deprovision accounts with simple assignments. Departments like People Ops can do provisioning within minutes instead of days. For some of the integrations, we can force the user to go through Okta, and in a few cases where we have sensitive data, we have extra security steps. For example, to access [BambooHR](https://www.bamboohr.com) users have to go through Okta first (and using Multi-Factor Authentication aka MFA) instead of direct access, and they have to perform yet one more MFA-style step of authentication just for BambooHR.\n\nAre there problems with this? Sure. Not everything integrates as well as [Greenhouse](https://www.greenhouse.io) or BambooHR, because each SaaS has implemented their own APIs and done their own SAML setup. Some don’t offer consistent interfaces to integrate with, which means that our team members can bypass Okta and go straight into the SaaS app in some cases, and in others they are forced to use Okta. This workflow inconsistency is sometimes frustrating for team members. We’re constantly [updating our team member instructions](https://handbook.gitlab.com/handbook/security/corporate/end-user-services/okta/okta-enduser-faq/) on Okta usage and try to communicate it to all team members as best we can, but we are impacting some users’ workflows. For example, if you sign in via Okta, you need to keep that tab open in your browser, otherwise your Okta session will end and you’ll find yourself repeatedly “MFAing” until you’re blue in the face. Many people are not used to working that way, and not having all SaaS apps working exactly the same doesn’t help. But overall, the time savings and security are great gains for ZTN and we are quite happy with the implementation.\n\n### SSH access\nAs I write this, we are getting ready to start the [Okta ASA](https://www.okta.com/products/advanced-server-access) rollout to Staging to give it a good test. Like SaaS, we expect a few hiccups here and there – especially since this is a new product for Okta, [released earlier this year](https://www.okta.com/blog/2019/04/advanced-server-access-and-infrastructure-identity/). And talk about workflow changes – if you thought browser-based application users were picky, command line SSH users are a bizarre bunch indeed. Command line junkies practically have their own religion around workflow and we’re introducing a change to that workflow. Yes, it is a minor change, but it already concerns me. Truthfully, because I am one of those oddball Linux users who lives on the command line and I tend to get fairly picky after a couple decades of being able to adjust and customize every aspect of my experience.\n\n### Camo proxy\nThis will seem like a weird one, but mitigating a security issue actually helped us out from a ZTN perspective. There was a security issue reported via our [HackerOne program](https://handbook.gitlab.com/handbook/security/security-engineering/application-security/runbooks/hackerone-process.html) that allowed for malicious users to gather IP addresses from unsuspecting victims via embedded image files. The solution was to use Camo proxy to resolve the [issue](https://gitlab.com/gitlab-org/gitlab-foss/issues/55115). The Camo proxy was widely deployed to ensure all possible links were protected and had the side benefit of ensuring communications going through the proxy were encrypted. Encrypting communications was one of the items we wanted as a part of ZTN and, as it turned out, we’d already done it.\n\n### A sound foundation\nThere are two things we want from our servers and containers and databases. First, we want them buttoned down tight and properly secured. All of these systems have robust controls, and we can perform all kinds of monitoring, but we have to do it at scale. Tightening security controls is especially important if you are using some of the Zero Trust-ish solutions out there to regulate access to these systems. We’re talking about automation of access provisioning, so we want to make sure that minimal access levels required for data stored on systems *remains* minimal access. This means no escalation of privileges due to configuration mistakes or security vulnerabilities. We also want to make sure that all services being offered up by these systems are as secure as possible against compromise, either locally or remotely.\n\nSecond, we want complete visibility into our infrastructure. If something goes awry with a vulnerability being disclosed that potentially impacts our systems or a security incident happens, we want to be able to quickly assess the state of the environment, ensure patches are installed, receive alerts based upon custom triggers to help monitor everything, and so on.\n\nWe are using [Tenable](https://www.tenable.com/products/tenable-io) (mainly for assessments) and Uptycs (mainly for monitoring and alerting) in our environment to help with this visibility. Both certainly handle the basics just fine, in fact Tenable has been quite up to the task. We are facing a few challenges with [Uptycs](https://www.uptycs.com) as we’d like to do more than what the product currently offers. This may not sound like traditional ZTN territory, but it is. It does no good to offer up state-of-the-art authentication and authorization to resources that are poorly maintained and monitored. Like everything else in our company, we face issues with scale – our infrastructure needs to grow and managing the security of that infrastructure must also scale well. Right now we can manage the security of our environment just fine. In fact, it is quite strong, but a lot of it relies on manual intervention which has scaling issues. We have a lot of hash marks in the “win” column with Tenable, but as we scale and expand we’re challenged by Uptycs. In the spirit of openness, we’ll keep you posted on how this progresses.\n\n### The log ride\nTo get a grip on all of this activity, we need to be able to grab all the logs, toss them into one place, and make sense out of them. Our goal is two-fold: we need to understand how our system is being used so we can fine-tune it and we need to be able to detect anomalous events that could signify potential breaches. All of our systems put out logs, and we’ve designed systems to monitor those logs. It is nice to automate alerts so as odd events occur, we’re immediately notified, and in some cases, issues are automatically opened for further triage. We’ve started down this path with deployment of several technologies, related to the [Logging Working Group](https://handbook.gitlab.com/handbook/company/structure/working-groups/logging/). We’re in the initial first steps, and we expect that logs generated from the various ZTN implementations will help improve the logging efforts, perhaps even propel it along quicker as we work out the kinks.\n\n## The Budget Issue\nA big ZTN question we get involves budget. After all, one company’s solution may involve a couple of small purchases and a large effort of tweaking and reconfiguring existing technology that is already deployed. Another company might have to make some major investments in new products just to get started. In other words, how do you budget for a solution when you don’t know exactly what that solution will look like?\n\nThis is probably one of those things a lot of organizations do not discuss, at least in any detail outside of “it’s expensive”. The idea of ZTN as a concept is an easy sell to most organizations because the benefits are so great. At the lofty bullet-point level on vendor slides, they often seem completely undeniable. But when you break down a concept into digestible and deployable components, you are often into interesting budget territory. Getting a department to buy into the concept is much easier than getting a department to alter their budget and purchase the XYZ product, deploy it, maintain it, and oh yeah please give the security department all of the logs. Of course this is a slight exaggeration to convey a point, but it is more often on the mark than not. We simply couldn’t fully budget for most of this because we didn’t know what we were going to be deploying until we found a particular solution.\n\nIn this case we have to be able to show an [ROI](https://en.wikipedia.org/wiki/Return_on_investment), which means we need to help a department understand the benefits and actually show an improvement to that department’s bottom line. For example, Okta has allowed us to change some onboarding and offboarding processes from days into minutes – and it's a massive timesaver. The push for Okta ASA is because our Infrastructure department saw the gains realized from our Okta rollout, and asked for something similar. Regardless of which department’s budget this could go against, it has to be sold to someone internally. Showing an ROI that clearly states we could financially benefit in one or more areas is really the only way to go about it. Showing the benefits is critical when you are searching for solutions to problems with no idea which solution will work.\n\n## Advice\nSince a lot of people ask for advice on ZTN in general, I’d like to share some impressions from our experience. Here are some major things that really have helped us.\n\n### Break down your needs into simple components\nYou do this by defining the problem end-to-end. For us, we could break it down into user identification and authentication, device identification and authorization, data classification, and policy enforcement. Each part was further broken down into smaller pieces – which includes a lot of what we covered in previous blog posts. This deconstruction helped us understand all of the areas we needed to work with.\n\n### Look at areas of winning\nIf a deployed technology is already solving part of the problem, can it be expanded? If it can’t, why not? Where are the gaps? List those gaps and use them to identify possible solutions during the review. We covered this topic in detail in a previous blog post, [ZTN implementation challenges ](/blog/zero-trust-at-gitlab-implementation-challenges/).\n\n### Ignore the vendor “spin”\nThere are vendors that sell solutions where they claim to be solving ZTN. In my ancient past, I worked for a company that sold (among other things) system administration tools. One day our boss handed us a list of compliance guidelines for three different standards. We were to go through each bullet item for each standard, point out the system administrative tools and the various system checks in our products that lined up with each bullet item, and write them down. This process took a few days, and by the end of the week each compliance standard had a list of checks. The product team grouped these checks together, and just like that we were a compliance company. Now the product line was actually quite good and robust which made this fairly easily, but the pivot of the company to being compliance-focused took longer for that marketing team to print up flyers than it did for the tech part. Yes, we were incomplete – we weren’t asked to write additional checks, we were asked to just use existing checks. But we literally were ready in less than a week with something we could call compliance.\n\nMy point here is that I often get the feeling that ZTN vendors do the same thing. They looked over their existing product line, figured out what they could even remotely claim as being a part of a “Zero Trust” solution, and overnight became a ZTN solutions provider. Of course, if your own organization’s world view on what ZTN is lines up with a particular vendor, great! Buy it. But, for GitLab, we had to break down what we wanted the various components of our technology and data to do and align them with our own ideas of ZTN, refine our model, and then go find vendors that did extremely specific things. For example, we’ve approached Okta with the breakdowns we are trying to solve – and they have products that solve them. For the most part we’ve ignored the whole “ZTN packaged solutions” approach and went after the core of what their products do, and we’re solving our problems as a result.\n\n## Conclusion\nWe’re getting there. We have a lot of wins, and a number of interesting challenges. Every once in a while we will post a new blog to keep you current on our security saga with Zero Trust, and hopefully you can learn from our examples – including our challenges – and help make your systems, data, and users as secure as possible. We hope you’ll follow along and, if you’ve got a ZTN viewpoint to share, we invite you to comment below.\n\n*Special shout-out to the entire security team for their input on this blog series.*\n\nPhoto by [Puria Berenji](https://unsplash.com/@ipuriagram?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText) on [Unsplash](https://unsplash.com/?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText).\n\n\n",[23,9,24],"inside GitLab","zero 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statement",{"items":690},[691,694,697],{"text":692,"config":693},"Terms",{"href":518,"dataGaName":519,"dataGaLocation":466},{"text":695,"config":696},"Cookies",{"dataGaName":528,"dataGaLocation":466,"id":529,"isOneTrustButton":27},{"text":698,"config":699},"Privacy",{"href":523,"dataGaName":524,"dataGaLocation":466},[701],{"id":702,"title":18,"body":8,"config":703,"content":705,"description":8,"extension":25,"meta":709,"navigation":27,"path":710,"seo":711,"stem":712,"__hash__":713},"blogAuthors/en-us/blog/authors/mark-loveless.yml",{"template":704},"BlogAuthor",{"name":18,"config":706},{"headshot":707,"ctfId":708},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1749664093/Blog/Author%20Headshots/mloveless-headshot.png","mloveless",{},"/en-us/blog/authors/mark-loveless",{},"en-us/blog/authors/mark-loveless","gYgzUdwKCnypExwE5MZ2RPPrl97ftdqEfuAoem8PmXM",[715,728,743],{"content":716,"config":726},{"title":717,"description":718,"authors":719,"date":721,"body":722,"category":9,"tags":723,"heroImage":725},"Prepare 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.",[720],"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.",[724,9,533],"AI/ML","https://res.cloudinary.com/about-gitlab-com/image/upload/v1772195014/ooezwusxjl1f7ijfmbvj.png",{"featured":27,"template":13,"slug":727},"prepare-your-pipeline-for-ai-discovered-zero-days",{"content":729,"config":741},{"title":730,"description":731,"authors":732,"heroImage":734,"date":735,"category":9,"tags":736,"body":740},"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.",[733],"Grant Hickman","https://res.cloudinary.com/about-gitlab-com/image/upload/v1774375772/kpaaaiqhokevxxeoxvu0.png","2026-03-25",[9,737,565,738,739],"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":742,"featured":27,"template":13},"auto-dismiss-vulnerability-management-policy",{"content":744,"config":753},{"title":745,"description":746,"authors":747,"heroImage":749,"date":750,"body":751,"category":9,"tags":752},"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.",[748],"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_).",[739,9,738],{"featured":12,"template":13,"slug":754},"gitlab-18-10-brings-ai-native-triage-and-remediation",{"promotions":756},[757,771,782,793],{"id":758,"categories":759,"header":761,"text":762,"button":763,"image":768},"ai-modernization",[760],"ai-ml","Is AI achieving its promise at scale?","Quiz will take 5 minutes or less",{"text":764,"config":765},"Get your AI maturity score",{"href":766,"dataGaName":767,"dataGaLocation":242},"/assessments/ai-modernization-assessment/","modernization assessment",{"config":769},{"src":770},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/qix0m7kwnd8x2fh1zq49.png",{"id":772,"categories":773,"header":774,"text":762,"button":775,"image":779},"devops-modernization",[739,568],"Are you just managing tools or shipping innovation?",{"text":776,"config":777},"Get your DevOps maturity score",{"href":778,"dataGaName":767,"dataGaLocation":242},"/assessments/devops-modernization-assessment/",{"config":780},{"src":781},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138785/eg818fmakweyuznttgid.png",{"id":783,"categories":784,"header":785,"text":762,"button":786,"image":790},"security-modernization",[9],"Are you trading speed for security?",{"text":787,"config":788},"Get your security maturity score",{"href":789,"dataGaName":767,"dataGaLocation":242},"/assessments/security-modernization-assessment/",{"config":791},{"src":792},"https://res.cloudinary.com/about-gitlab-com/image/upload/v1772138786/p4pbqd9nnjejg5ds6mdk.png",{"id":794,"paths":795,"header":798,"text":799,"button":800,"image":805},"github-azure-migration",[796,797],"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":801,"config":802},"See how GitLab compares to GitHub",{"href":803,"dataGaName":804,"dataGaLocation":242},"/compare/gitlab-vs-github/github-azure-migration/","github azure migration",{"config":806},{"src":781},{"header":808,"blurb":809,"button":810,"secondaryButton":815},"Start building faster today","See what your team can do with the intelligent orchestration platform for DevSecOps.\n",{"text":811,"config":812},"Get your free trial",{"href":813,"dataGaName":49,"dataGaLocation":814},"https://gitlab.com/-/trial_registrations/new?glm_content=default-saas-trial&glm_source=about.gitlab.com/","feature",{"text":504,"config":816},{"href":53,"dataGaName":54,"dataGaLocation":814},1777302627222]