If you can’t explain what an AI-generated change does, don’t approve it yet. Review it like any other code: establish its purpose, trace its behavior, verify it independently, and bring in a qualified reviewer when the change or its risks exceed what you can confidently assess. AI authorship is not proof that code is unsafe, but neither an AI explanation nor a passing test suite makes it safe to merge.
How do I review AI-generated code I don’t understand?
Start with the change’s intended behavior, not with a line-by-line hunt for mistakes. Read the issue or specification, the pull request description, relevant repository documentation, and nearby implementation. Write down what the change must do and any constraints it must preserve. Then compare its design with the project’s architecture and conventions. GitHub’s review guidance recommends checking requirements, context, and assumptions; OWASP’s secure-review guidance likewise starts with architecture and business requirements (GitHub’s AI code review guidance; OWASP Secure Code Review Cheat Sheet).
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Make the patch small enough to reason about. Separate formatting or generated files from behavior changes, and ask for a smaller change if one diff combines unrelated work. Inspect each changed function or logical block alongside the callers and surrounding code it depends on. For each important path, be able to explain:
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- What inputs can be missing, malformed, unusually large, or controlled by an untrusted user?
- What data or state does it read or change?
- What does it return, log, send, or expose when something fails?
- Which assumptions must be true for it to work correctly, and what test would reveal if an assumption is false?
OWASP’s Top 10:2025 says, “You should be able to read and fully understand all code you submit, even if it is written by an AI or copied from an online forum” (OWASP Top 10:2025, A03: Software Supply Chain Failures). If you cannot explain a consequential part, pause approval. Ask the author or AI tool for an explanation of one small piece at a time, verify that explanation against the source and project, and request a simpler implementation if needed. An explanation is a way to investigate, not evidence that the code behaves as described.
How can I tell whether AI-written code is safe to merge?
There is no single check that establishes safety. A merge decision should combine understanding of the change, evidence that it meets the requirements, appropriate automated checks, and a risk assessment. Use the project’s normal review policy; do not equate a green checkmark with proof of correctness.
Run checks and compare them with the baseline
Run the project’s build and relevant tests, inspect new warnings, and use available static analysis, secret scanning, and dependency checks. Compare results with the baseline when possible so you can tell whether the change introduced a failure or warning. Check that tests assert the requested behavior, cover failure paths and edge cases, and do not merely reproduce the implementation’s assumptions.
Passing tests are useful evidence, but they cannot establish correctness if the tests encode the same mistaken assumptions as the code. OWASP cautions against relying on AI-generated test suites as security evidence or treating test pass rate alone as a measure of confidence. NISTIR 8397 describes complementary verification techniques including threat modeling, automated testing, static scanning, secret detection, black-box and structural tests, fuzzing, and web application scanners (NISTIR 8397; OWASP LLM Security Cheat Sheet).
Trace data, permissions, and external effects
For security-relevant changes, follow data from entry to use and output. Check validation, queries, shell commands, file paths, network destinations, and output encoding. Confirm authentication and authorization at the point where access is enforced, rather than assuming a UI or caller check is sufficient. Inspect secrets, error responses, external calls, cryptography, and dependency behavior.
Also inspect changes outside application logic. New packages and edits to package scripts, CI workflows, Dockerfiles, build files, or code that runs during install, test, build, or deploy can alter what executes and with what authority. OWASP’s AI coding guidance calls out these areas as security-sensitive and recommends manual review of data flow, business logic, and configuration because automated tools can miss contextual vulnerabilities (OWASP LLM Security Cheat Sheet; OWASP Secure Code Review Cheat Sheet).
Which review methods should I use?
Use each method for the evidence it can provide; none covers every risk. A manual walkthrough can test whether you understand the behavior and whether it fits business rules. Tests can check specified behavior and known edge cases. Static analysis and dependency scanning can flag classes of code or supply-chain problems. An AI explanation can help you navigate unfamiliar code, but it does not independently validate its own claims. A specialist can assess areas outside your expertise.
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These methods complement one another. Choose checks based on what changed, what could go wrong, and what your project requires. NIST recommends that organizations define when code review and analysis are used and record and triage findings; its verification guidance is not a measure of AI-generated code quality (NIST Secure Software Development Framework; NISTIR 8397).
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When should I ask for a rewrite or another reviewer?
Do not approve a change merely because it is difficult to understand or because an AI tool says it is safe. Ask for a simpler design or clearer names if opacity is unnecessary. Seek a qualified reviewer when the change touches security-critical or privileged areas, or when you cannot assess an important behavior or risk.
- Authentication, authorization, identity and access management (IAM), or cryptography
- CI/CD workflows, deployment manifests, build and install scripts
- Network access, sandbox policies, secrets, or other privileged operations
- Unfamiliar code whose failure could expose data, grant access, or disrupt a critical service
Keep an identifiable human owner accountable for correctness, security, and maintenance. OWASP explicitly recommends assigning an owner to every AI-generated code change and requiring human approval (OWASP LLM Security Cheat Sheet). Approve only when you understand the purpose and consequential behavior, the checks are appropriate and their results make sense, and any remaining risk is acceptable under your team’s policy.
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