What AI-assisted review can examine
Treat these as review leads, not confirmed defects. A reviewer must check the actual code, call chain and test evidence.
- Missing null, timeout, retry and exception handling in new branches.
- Mismatches in interface fields, types and error codes.
- Duplicate code, unused values or obvious complexity growth.
- Missing negative, boundary and failure-path tests.
- Drift between comments, interface documentation and implementation.
- Changes to sensitive directories, dependencies or configuration.
Set review depth by change risk
- Low risk: copy, comments, boilerplate and non-critical mappings. AI can screen for consistency, followed by owner confirmation and basic checks.
- Medium risk: ordinary business logic, interface adaptation, data processing and dependency updates. Require module-owner review plus unit and integration tests.
- High risk: authentication, permissions, payment, secrets, privacy, concurrency, production configuration, safety or device-critical paths. AI provides leads only; require specialist review, target-environment validation and explicit approval.
- Do not classify risk by line count. A small authorization change may matter more than hundreds of lines of styling; dependencies, data scope and failure impact also count.
An executable AI-assisted review workflow
- Keep each change focused and separate unrelated formatting, dependency and feature work.
- Provide the goal, diff, interface constraints, prohibited actions, environment, existing tests and risk tier without exposing the whole repository by default.
- Run formatting, compilation, type checks, static analysis, dependency scanning and automated tests before generative review.
- Require each finding to identify location, trigger, impact and a verification method; generic advice is not a defect record.
- Have an accountable reviewer accept or reject findings with evidence. AI output is never a merge approval.
- Record the change ID, tool version, review scope, results and final approver for later reconstruction.
Prevent more comments from making review slower
- Limit comments to the current change and do not propose unrelated refactoring.
- Prioritize security, data corruption, privilege escalation and availability risks.
- Move style preferences to formatters and static rules.
- Mark unsupported concerns as requiring verification rather than blocking immediately.
- Track accepted, rejected and repeated findings. Narrow or remove rules when valid defect discovery and review time do not improve.
How to evaluate the review process
Compare time to first useful feedback, human review waiting, accepted and rejected AI findings, defects later found in testing or production, and completion of required approvals for high-risk changes.
The number of generated comments is not a quality metric. Review must also keep code, tests, interface documentation and change records consistent.
Can AI code review replace human review?
No. It can provide an initial screen and a second perspective, but it cannot assume business accountability, security approval or the final merge decision. High-risk changes require reviewers with the relevant context and authority.
Should the entire enterprise repository be shared with the tool?
Not by default. Confirm deployment, retention, training use, access control, third-party processing and audit capability first, then provide only the minimum context needed.
How does AI review differ from static analysis?
Static analysis handles stable and deterministic rules. AI can relate natural-language constraints, change intent and multiple files to suggest leads. Use them together; do not replace repeatable checks with generative judgment.
Which code requires stricter human review?
Authentication, permissions, payment, secrets, personal data, cryptography, concurrency, production configuration, device control and any path with material failure impact require stronger human review and validation.
Sources and scope
- NIST Secure Software Development Framework: https://csrc.nist.gov/pubs/sp/800/218/final
- NIST AI Risk Management Framework: https://www.nist.gov/itl/ai-risk-management-framework
- This general engineering control method does not replace enterprise security, privacy, intellectual-property or supplier-admission review.