Review AI-Generated Code for Security Before You Merge
AI-generated code needs the same security review as code written by a person—and sometimes more explanation, because the author may not know why a particular library or permission was chosen. A quick, useful review traces each input to its use, checks who is allowed to access the result, and runs the project’s existing security tools before a change is merged.
This is a defensive review checklist for code you own or are authorized to maintain. It does not replace a security professional’s review for a high-risk system.
Start with the intended behavior
Before reading the patch, write down what the feature is meant to do, who can use it, what data it touches, and what should happen when information is missing or invalid. Without a clear requirement, a reviewer cannot tell whether an AI suggestion has quietly broadened access or skipped an important check.
Ask the assistant to explain the change and list assumptions, but treat that explanation as a hypothesis. Compare it with the actual diff, neighboring code, and project documentation.
Six security checks for an AI-generated change
- Inputs and injection. Trace user-controlled values into SQL, shell commands, HTML, templates, and file paths. Use the framework’s parameterized query or encoding mechanisms rather than constructing executable strings from untrusted input. Test empty, malformed, oversized, and unexpected values.
- Authentication and authorization. Check that the server verifies identity and permission for the specific record or action. Hiding a button in the interface is not authorization. Test a user who should not be able to access another user’s record.
- Secrets and sensitive output. Search the diff and logs for keys, passwords, tokens, personal data, and internal URLs. Keep credentials in approved secret storage and avoid echoing secrets into error messages or telemetry.
- Dependencies and downloads. Inspect every new package, version, license, and installation script. Confirm it is the intended project and maintained source. A familiar package name can still point to an unintended dependency.
- Errors and logging. Confirm that failures do not expose database details or confidential values. Logs should support investigation without collecting more personal data than the workflow needs.
- Tests and boundaries. Add tests for denied access, invalid input, missing records, and failure paths. A passing happy-path test does not show that the boundary is secure.
Use tools as a second set of eyes
Run the repository’s configured static analysis, dependency checks, and secret scanning where available. GitHub’s code-scanning documentation explains that scanning can surface potential vulnerabilities and coding errors as alerts, using CodeQL or compatible third-party tools. A scanner can miss issues or flag code that is safe in context; review and triage the findings instead of treating a clean result as proof that the change is secure.
Keep generated changes in a branch, inspect the exact diff, run tests in a non-production environment, and ask a maintainer to review security-sensitive code. Do not let an AI agent deploy changes or access production credentials as part of a code review.
A short example: a customer lookup
Suppose a generated endpoint accepts a customer ID and returns an order summary. Check that the database query treats the ID as data rather than executable SQL, that the signed-in user is authorized to view that customer’s orders, and that an invalid or missing ID produces a safe response. Then test both an allowed and a denied account. The key question is not whether the endpoint works for one example; it is whether it enforces the intended boundary for every caller.
When to escalate
Escalate to the security or application owner if the change handles authentication, payment data, file uploads, encryption, permissions, or sensitive records; introduces an unfamiliar dependency; or changes how a service trusts input from another system. Pause the merge if you cannot explain the access model or reproduce the test results.
For analytics code, Faqroon’s AI-generated DAX review guide shows how to check logic against known cases. For a business-wide response to an actual incident, see the small-business cyber incident plan. A team using AI in a workflow can also record recurring issues in its AI error log.
Bottom line
Do not ask whether an AI-written patch “looks secure.” Trace inputs, access checks, secrets, dependencies, error handling, and tests. Use automated scanning as a supplement, keep changes reviewable, and require qualified human review where the impact is significant.