Scan AI-generated code the same way you scan human code, but make sure the scans run automatically on every pull request. Use static analysis for code flaws, dependency checks for packages the assistant added, secret scanning before push, and tests for behavior. The source of the code changes the volume of review, not the kind of check.
Definitions
SAST. Static application security testing reads source code for patterns such as injection and unsafe deserialization.
SCA. Software composition analysis checks the third-party packages in your project against vulnerability data.
Secret scanning. Detection of credentials committed to a repository. Push protection blocks the push before the secret lands.
Dependency review. A check on what a change adds or updates in the dependency graph.
Which scan finds what
| Scan | Finds | AI-specific reason to run it |
|---|---|---|
| SAST | Injection, unsafe APIs, weak crypto, missing checks | Generated code can repeat common insecure patterns |
| SCA and dependency review | Vulnerable or unknown packages | Assistants add imports and install commands readily |
| Secret scanning | Keys and tokens in code | Sample code and pasted snippets often contain real or placeholder secrets |
| Tests and review | Wrong behavior, missing authorization | Code that looks plausible can still miss a check |
Steps
- Run scans in CI, required for merge. Make the status checks mandatory so the assistant's branch cannot merge past a failure.
- Add dependency review. Flag any new package. Ask whether it is real, maintained and needed. The related post on checking an AI-suggested package before installing covers this.
- Turn on push protection for secrets. GitHub documents that it blocks secrets from command line pushes, UI commits, file uploads and API requests, and logs bypasses in the audit log for repository level protection.
- Run SAST on the diff and on the whole repo on a schedule. The diff gives fast feedback, the full scan catches what slipped in earlier.
- Pin and lock dependencies. Install from the lockfile so a changed manifest does not silently fetch a new version.
- Test authorization explicitly. Write tests that a user cannot reach another user's data. Generated endpoints often handle the happy path and skip the check.
- Treat findings as input to verify. If you ask a model to review code, confirm each claim against the code before acting.
What coding agents already do
GitHub documents that its cloud coding agent runs CodeQL, checks new dependencies against the GitHub Advisory Database, scans for secrets and requests a second review. Admins can disable those checks. Even where an assistant runs checks itself, run your own in CI, because the assistant's checks are optional settings you do not control per pull request.
Try it yourself
Click through it at your own pace, or open it full screen. Look at how a finding links back to the line of code.
Quick check
Can a scanner tell which code was written by AI?
Not reliably, and it does not matter. Scan everything with the same rules.
Do I need a different tool for AI code?
No. The standard categories apply. What changes is volume, so automate and keep pull requests small.
Is a clean scan proof the code is safe?
No. Scanners miss logic and authorization flaws. Combine them with tests and review.
Where Safeguard fits
Safeguard provides SBOM and dependency scanning, reachability analysis to separate reachable findings from noise, and gates for pull requests. A free scan is at gold.safeguard.sh/scan.
Sources
Last reviewed: 19 October 2026.
