How do I review AI code for quality and security?
Start with a manual read-through
Before running any tools, read the AI-generated code carefully. Ask yourself if you understand every line and if the logic matches the intended behavior. AI often writes code that looks correct but has subtle flaws, like off-by-one errors or incorrect assumptions about data formats.
Pay special attention to how the code handles errors, user input, and external services. If you see something you don't understand, don't assume it's fine—look it up or ask a colleague. The goal is to catch issues that automated tools might miss.
Use automated tools for consistency
Static analysis tools, linters, and security scanners can catch common problems quickly. Run them on AI-generated code just as you would on human code. Dependency scanners are especially important because AI may suggest outdated packages with known vulnerabilities.
Many teams integrate these tools into their CI/CD pipeline so every pull request gets scanned automatically. That way, AI-generated code doesn't bypass your normal quality gates. Tools like SonarQube, Snyk, or GitHub's Dependabot can help, but they don't replace human judgment.
- Linters for style and syntax issues
- Static application security testing (SAST) tools
- Software composition analysis (SCA) for dependencies
- Secret scanners to catch hardcoded credentials
- Unit and integration test coverage reports
Test behavior, not just syntax
Write tests that exercise edge cases, error conditions, and unexpected inputs. AI code often works for the happy path but fails when given malformed data or when a service is unavailable. Tests help you verify that the code behaves correctly under stress.
For security-critical code, consider adding specific tests for authentication bypass, injection attacks, and authorization checks. If the AI generated a database query, test it with malicious input. If it generated an API endpoint, test it with missing or invalid tokens.
Common mistakes
- Trusting AI code because it passes a basic test without checking edge cases.
- Skipping dependency checks and assuming AI-suggested libraries are up to date.
- Reviewing only the code's functionality and ignoring security implications like data exposure or privilege escalation.
