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To add AI security scanning to a CI/CD pipeline, you add ordinary automated security checks as pipeline jobs: static analysis (SAST), infrastructure-as-code (IaC) checks, secret detection, dependency scanning, and container image scanning where you build images. Runtime tests such as DAST, API security testing, and fuzzing can follow once you have a deployed test target. The tools discussed here do not detect whether code was written by an AI. Code that an AI wrote or helped write should pass through the same gates as any other code, and that is the control to build.
What “AI security scanning” means in a pipeline
The phrase covers two different things, and only one of them is a scanning job you can configure.
- Standard application and supply-chain scanning is the established category. Scanners inspect source code, infrastructure definitions, secrets, third-party dependencies, and container images. GitLab’s documentation describes these as separate scanner types, each with its own analyzer or template.
- AI-assisted remediation is a vendor feature in some products. It suggests fixes for findings. Those suggestions are vendor claims. Verify them against your own code before accepting a fix, and do not treat them as a scanner’s detection capability.
The official documentation for GitLab, GitHub, and the OWASP DevSecOps Guideline does not describe a scanner that reliably identifies AI-generated code. Plan the pipeline around what each scanner inspects, not around who or what wrote the code.
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Scanner types differ in what they examine and whether they need a running application. Choose from the table based on the surfaces your repository actually contains.
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| Scan type | What it inspects | Needs a running application? | Typical pipeline point |
|---|---|---|---|
| SAST (static application security testing) | Source code, to find vulnerabilities before they reach production | No | Every pull request or merge request |
| IaC scanning | Infrastructure definitions such as Terraform or Kubernetes manifests, where your toolchain supports them | No | Every pull request or merge request |
| Secret detection | Committed credentials and tokens in the repository | No | Every pull request or merge request |
| Dependency scanning | Vulnerable third-party libraries listed in dependency manifests | No | Every pull request or merge request |
| Container image scanning | Vulnerabilities in images your build produces | No (scans the built image) | After the image build, only if the pipeline ships images |
| DAST (dynamic application security testing) | Behavior of a deployed application | Yes | After deployment to a test environment |
| API security testing | Behavior of deployed API endpoints | Yes | After deployment to a test environment |
| Fuzz testing | Runtime handling of malformed or unexpected inputs by a test target | Yes (a test target or harness) | Scheduled or after build, depending on runtime cost |
Repository-level scans give fast feedback and are the right starting point. Runtime checks need a test environment and safe test data, so add them after the repository checks are running reliably.
Set up the scans on your platform
The setup differs by platform. The steps below cover GitLab and GitHub as documented, followed by a general pattern for other CI systems.
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GitLab CI/CD
- Check your plan before you rely on a scanner. Standard analyzers are available across all GitLab tiers. Advanced SAST is part of the Ultimate tier, and the two differ in supported language coverage.
- Open
.gitlab-ci.ymlat the root of the project and include the SAST and IaC template:include: - template: Jobs/SAST-IaC.gitlab-ci.yml - Add the secret detection and dependency scanning templates from GitLab’s security scanning documentation in the same way, for the surfaces your project contains.
- Pin scanner versions according to your team’s update policy. GitLab’s analyzers are published as container images with version tags and are updated periodically. A pinned tag gives reproducible results until you choose to upgrade.
- Run a pipeline on a test branch and confirm that each scanner job completes and produces a security report.
GitHub Actions
- Run your static analyzer in a workflow job. GitHub documents analysis with the CodeQL CLI or another static analyzer that runs outside GitHub.
- Upload the analysis results to GitHub. The OWASP guideline’s structured formats, such as SARIF, are the usual carrier for this kind of upload.
- If you want notifications or automatic issues, configure code-scanning webhooks. GitHub supports them for integrations such as creating issues or sending alerts.
Other CI systems
The OWASP DevSecOps Guideline describes a common pattern for any CI system. Each scanner runs as a pipeline step and writes structured output, usually SARIF, JSON, or CycloneDX. A downstream step then sends that output to a vulnerability platform through an API or webhook, or stores it as an attestation. A policy engine can read the output and decide whether the build passes. Confirm that your chosen platform accepts the format each scanner emits, because the guideline describes general patterns rather than guaranteed compatibility.
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Get findings into the review workflow
Results only help if reviewers see them before code merges. In GitLab, enabled scanners run as separate jobs and produce security report artifacts. The platform validates and deduplicates those reports before users view or download them. Security scanning runs by default in branch pipelines, but merge request pipeline scanning requires explicit enablement. If you need findings in the merge request itself, enable that setting; otherwise the scans may only appear on the branch pipeline. IaC results can appear in merge requests and approval workflows on the Ultimate tier.
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- Security measures include Login Authentication with custom job menus, Encryption, Data Transmission Security, and more
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In GitHub, code-scanning alerts appear in the repository’s security tab after upload. Pair them with webhooks if reviewers need an issue or notification as well.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Roll out in stages
Turning on blocking gates before anyone has reviewed the findings causes most rollout failures. Use this order:
- Report only. Run every selected scan on pull or merge requests, but do not block merges. Collect findings for at least one normal development cycle.
- Triage with an owner. Assign one person or team to review new findings. Set a triage rule by severity, for example which findings must be fixed before merge and which can be tracked as backlog items. The thresholds depend on your team’s risk tolerance.
- Tune. Test any custom rules, exclusions, or scanner configuration on a test merge request before you merge it into the default branch. GitLab warns that untested custom configuration can produce unexpected results, including many false positives.
- Gate selectively. Enforce only the checks that have proven accurate in your codebase. Keep the rest in report mode.
- Add runtime tests. Introduce DAST, API, or fuzz testing once a stable test environment and test data exist.
Document each exception with a reason and an expiry date. Keep the scanner configuration in version control and review changes to it the same way you review application code.
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Troubleshooting common problems
- Too many false positives. Check whether the rule set matches your languages and frameworks. Tune rules or exclusions on a test merge request, not directly on the default branch.
- A language or file type is missing from results. Confirm that the analyzer supports it on your plan. Language coverage differs between standard analyzers and Advanced SAST.
- Scans run but results do not appear in the merge request. In GitLab, confirm that merge request pipeline scanning is enabled. Check that the job actually produced a report artifact.
- Scanners need secrets or access to a deployed environment. Confirm how your CI platform exposes secrets to pipelines triggered by external or untrusted pull requests. This guide does not cover each platform’s isolation settings, so check your platform’s documentation before granting runtime test jobs access to credentials.
Pipeline checklist
- Scanners are mapped to the code surfaces your repository contains.
- Scanner versions are pinned and updated on a defined schedule.
- Findings reach the review tool your team uses for merge or pull requests.
- Every finding category has an owner and a triage rule.
- Blocking gates are limited to checks that have been validated on your code.
- Runtime tests run only against a dedicated test environment.
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