Review an AI-generated pull request as you would any other change: verify its intent, inspect the full diff and surrounding code, test the required behavior independently, and get accountable human approval. AI authorship does not make code inherently unsafe, but tests produced alongside an implementation are not independent proof that it is correct.
1. Establish what the change is meant to do
Start with the issue, acceptance criteria, or user-visible behavior—not the implementation’s explanation. Write down the required outcome and important constraints so you can compare the patch against them.
- Identify what changed and what should remain unchanged.
- Check whether the patch is appropriately scoped and whether interfaces, data contracts, and compatibility promises are preserved.
- Decide whether error behavior is intentional, including what users or callers see when an operation fails.
- For changes with design-level security implications, consider threat modeling rather than limiting review to individual lines. NIST includes threat modeling among software verification techniques in its Guidelines on Minimum Standards for Developer Verification of Software.
2. Inspect the complete diff and its context
Read every changed line, including tests and configuration. Then inspect the surrounding code, callers, and relevant interfaces; a locally plausible edit can still break an assumption elsewhere.
Trace the important paths
- Follow data from input through validation and authorization, into state changes or persistence, and on to output.
- Check failure paths, boundary conditions, and concurrency or lifecycle assumptions when they apply.
- Review dependency and package changes, including what is added, removed, or pinned.
- Pay particular attention to generated changes in build scripts, package lifecycle scripts, CI workflows, Docker or build files, and deployment infrastructure. These may execute in trusted contexts with elevated privileges. OWASP discusses this risk in its Secure Coding with AI Cheat Sheet.
3. Verify behavior independently
Use the requirement to decide what evidence is needed. Run focused tests for the changed behavior, then the relevant broader suite. Add checks for plausible misuse and failure cases instead of asking only the same model that wrote the implementation to test it.
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Choose verification methods for the risk
NIST’s verification guidance presents a range of techniques, not a single mandatory checklist. Depending on the system and change, useful methods include:
- Automated tests, including black-box tests of externally visible behavior and structural tests of code paths.
- Static code scanning, secret detection, and built-in platform protections.
- Fuzzing or web application scanners where they fit the application and risk.
- Historical tests based on previously found defects.
- Review of included libraries, packages, and services.
- Threat modeling for design-level security issues.
Also run relevant type checks, linters, dependency checks, and application-specific scans when available and appropriate. No single check establishes correctness; choose complementary methods that address the change’s likely failure modes.
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Include negative and boundary cases
Test cases should express the intended behavior, including what must not happen. Depending on the feature, consider invalid inputs, expired credentials, malformed payloads, permission failures, and concurrent operations. For authentication, authorization, input validation, and cryptographic behavior, OWASP recommends independent adversarial tests and manually authored tests rather than relying only on generated cases.
4. Audit the test changes as carefully as the implementation
A passing suite is useful only when its tests represent the required behavior and cover relevant failure modes. Tests generated with the implementation may inherit the same mistaken assumptions.
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- Inspect deleted and edited tests; ask why each was changed or removed.
- Compare assertions before and after for weakened expectations or broader acceptance.
- Look for new mocks that bypass the real dependency or behavior the change is supposed to exercise.
- Check that assertions express the requirement, not merely the output produced by the generated implementation.
OWASP states: “A passing test suite generated by the same agent that produced the code provides no independent assurance.” Read that as a reason to seek independent evidence, not as a reason to disregard all generated tests.
5. Treat AI review as an additional signal
A code-review assistant may identify issues, but its comments need human evaluation and it should not replace an accountable reviewer. Check which files your configured tool reviews and excludes before relying on it as part of a merge gate.
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For example, GitHub’s Copilot code review documentation lists dependency-management files such as package.json and Gemfile.lock, as well as log and SVG files, among excluded categories. Coverage and availability depend on plan and organization settings, and product details can change.
GitHub also documents repository-wide and path-specific review instructions in its guide to using Copilot code review. That documentation describes configurable Copilot approvals as a feature that has been in public preview. Check your current organization settings and policies before treating such a feature as a required approval.
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6. Make a traceable merge decision
Before merging, confirm that expected tests and checks completed, findings were resolved or explicitly accepted under team policy, and an appropriate human reviewer approved the change. Escalate review and testing for high-impact or security-critical changes according to your team’s risk policy. Record material assumptions and any accepted residual risk so the decision is understandable later.
There is no universal approval count or severity threshold that fits every repository. The appropriate gate depends on the change, the system’s risk, and the team’s documented policy.
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