The Tool Desk
Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Review AI-generated code as you would any consequential change: verify it against the requirement, test its behavior independently, trace security-sensitive data flows, and decide whether another developer can safely maintain it. A clean-looking diff or passing test suite is not proof that the code is correct. No single checklist catches every defect, so scale review effort to the change’s impact, threat model, and your organization’s requirements.
1. Establish what the change is supposed to do
Start with the issue, acceptance criteria, design notes, and surrounding implementation—not with the generated code’s explanation. Identify the changed files, expected behavior, affected components, and relevant trust boundaries. GitHub’s [code review guidance](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/reviewing-changes-in-pull-requests/about-code-review) recommends checking the change against requirements and architecture; OWASP’s [Code Review Guide](https://owasp.org/www-project-code-review-guide/) likewise emphasizes preparing by understanding the diff and the security controls it may affect.
- Read the full diff and inspect nearby code where the change plugs in.
- Check whether the change is limited to the intended scope or also alters configuration, dependencies, permissions, or deployment behavior.
- Write down observable outcomes the implementation must satisfy, including relevant failure and boundary cases.
This gives you a standard independent of the code itself. Otherwise, it is easy to mistake an implementation that is internally consistent for one that meets the actual requirement.
2. Verify behavior with evidence, not appearance
Build or compile where appropriate, run the existing test suite, and inspect any new or modified tests. Add cases for invalid input, boundary conditions, error paths, and concurrency when those apply. Tests should check the requirement’s intended outcomes, not merely confirm that the generated implementation behaves as it was written.
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A green test run is useful evidence, but it can be misleading if tests were deleted, weakened, replaced with overly broad mocks, or written around a mistaken assumption. OWASP’s AI-specific guidance warns that generated tests can be fabricated or removed; its [Secure Coding with AI Cheat Sheet](https://cheatsheetseries.owasp.org/cheatsheets/Secure_Coding_with_AI_Cheat_Sheet.html) recommends human review of AI-assisted changes.
- Look for tests of both expected inputs and rejected or malformed inputs.
- Check that assertions would fail if the requirement were violated.
- Inspect changes to test fixtures, mocks, and test configuration as carefully as production code.
- Use static analysis, secret scanning, dependency checks, or fuzzing where relevant, then investigate findings in context.
Automated checks help surface recurring classes of problems, but they do not reliably establish business-logic correctness or account for every application-specific assumption. NIST’s [Secure Software Development Framework: Recommendations for Mitigating the Risk of Software Vulnerabilities (SP 800-218A)](https://csrc.nist.gov/pubs/sp/800/218/a/final), the July 2024 final community profile, places review and analysis within organization-defined secure development practices; it is not a guarantee that a particular checklist will catch every flaw.
3. Trace security-sensitive behavior
Review security by following data and authority through the change. For each untrusted input, trace where it comes from and whether it reaches storage, a database query, a shell command, a template, or a network request. Check that validation, authorization, and error handling fit the surrounding application rather than relying on names or comments to imply safety.
- Identity and access: verify authentication, authorization checks, and access-control boundaries, including who can invoke a new path.
- Data handling: check treatment of sensitive data, secrets, logs, and errors; confirm secrets are not embedded in code or exposed through diagnostics.
- Input-to-operation flows: inspect parsers and deserialization, database queries, shell execution, template construction, and network requests for unsafe handling of untrusted values.
- Security controls: examine cryptography, configuration, and business logic in context; a syntactically valid check may still enforce the wrong policy.
- Dependencies: review added packages, versions, provenance, and why each is needed.
OWASP notes that manual review can expose context-dependent issues automated tools miss. Its [Code Review Guide](https://owasp.org/www-project-code-review-guide/) provides security-oriented review topics; use those as prompts for the actual architecture and threat model, not as a claim that every listed risk applies to every change.
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Code that runs during installation, builds, tests, or deployment can have substantial privileges. Give changes to package scripts, container files, CI/CD workflows, deployment configuration, downloaded resources, network access, and shell execution explicit attention. OWASP’s [Secure Coding with AI Cheat Sheet](https://cheatsheetseries.owasp.org/cheatsheets/Secure_Coding_with_AI_Cheat_Sheet.html) calls for human review of AI changes to CI/CD pipelines, Dockerfiles, and package scripts, and recommends pinning third-party GitHub Actions to commit SHAs rather than mutable tags.
Confirm what the changed automation can access and execute, whether that access is necessary, and whether referenced actions or resources are pinned and trustworthy. A small workflow or configuration diff may affect the whole build or release path, so its risk is not measured by line count.
5. Decide whether the code is maintainable and proportionate
Correct behavior is not enough if future developers cannot understand or safely change the implementation. Compare it with local conventions and judge whether its names, abstractions, and error handling make the behavior clear. Non-obvious decisions may need explanation, while unnecessary layers or complexity can make a small feature harder to debug.
- Can a maintainer trace the main behavior without reconstructing hidden assumptions?
- Are names and interfaces consistent with nearby code?
- Is the solution proportionate to the problem, or would a simpler approach be clearer?
- Would debugging a failure or changing the behavior later be straightforward?
GitHub’s [code review guidance](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/reviewing-changes-in-pull-requests/about-code-review) includes readability and maintainability among review considerations and cautions against accepting code that is hard to follow or would take longer to refactor than rewrite. Apply that judgment to the project’s needs, not to stylistic preference alone.
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6. Escalate review effort where impact is high
Review depth should follow the change’s potential impact, exposure, and threat model. Give heightened scrutiny to authentication and authorization, sensitive data, cryptography, parsers and deserialization, database queries, shell or template construction, network requests, dependency changes, infrastructure-as-code, CI/CD workflows, and security configuration. These are priorities drawn from OWASP’s secure-review topics and AI-specific cautions; the risk of any item depends on how the application uses it.
Escalate to the appropriate security, infrastructure, or domain owner when a reviewer cannot establish the safety of a sensitive path, when a change expands privileges or external exposure, or when a finding conflicts with organizational standards. NIST SP 800-218A recommends combining review and analysis under organization-defined standards and recording and triaging findings.
7. Record findings and make ownership explicit
Document defects and required remediation in the review, request changes when requirements or security controls are not met, and ensure a named developer understands and owns the change before merge. OWASP’s [Secure Coding with AI Cheat Sheet](https://cheatsheetseries.owasp.org/cheatsheets/Secure_Coding_with_AI_Cheat_Sheet.html) states: “Every AI-assisted change should be reviewed, approved, and attributable to a developer who is responsible for its security and maintainability.”
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