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Review AI-generated code as a proposed change, not a finished answer. Start with what it must do, inspect the full diff, trace data and permissions through the affected paths, challenge the tests, and run the security checks appropriate to the change. Passing tests and clean scans are useful evidence, but neither proves the code is correct or secure; a qualified human must understand and approve it.
1. Establish the intended behavior and the change’s risk
Before reading line by line, read the issue, acceptance criteria, relevant architecture and security requirements. Identify which components and assets the change can affect, then note any sensitive paths such as account access, payments, personal data, or deployment controls. OWASP’s Secure Code Review Cheat Sheet recommends setting this context and prioritizing the review rather than treating every changed line as equally risky.
For each changed file, ask why it changed and whether the change is necessary to meet the stated requirement. If the implementation’s behavior cannot be explained in terms of that requirement, pause and clarify before approving it.
2. Inspect the complete diff, including tests and configuration
Review the entire patch in the context of surrounding code. Look for unexpected files, scope expansion, removed safeguards, and changes to tests, build scripts, security settings, or project instructions. A change can introduce risk indirectly—for example, by altering a permission check elsewhere or weakening the test that used to enforce an invariant.
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When an AI agent can read repository files, issues, pull requests, logs, or tool responses, treat that context as potentially influential. OWASP’s Secure Coding with AI Cheat Sheet discusses prompt injection through these sources. Review persistent instruction files and unrelated edits; do not assume an agent stayed within the prompt simply because the final patch appears plausible.
3. Trace behavior and data through the application
Syntax and local plausibility do not establish that a change preserves the application’s rules. Follow the important execution paths from input to outcome, checking how values are validated, transformed, stored, and returned. Consider both the intended flow and ways it can fail or be abused.
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- Inputs: Identify user-controlled, external, and otherwise untrusted values. Check validation at the boundary and after transformations that could change their meaning.
- Access: Verify authentication and authorization where the protected operation or data is actually used. A UI restriction is not a substitute for a server-side permission check; examine tenant and object boundaries too.
- Business rules: Walk through important invariants, such as whether an operation can be repeated, whether state transitions are valid, and what happens when only part of a workflow succeeds.
- Failure and edge cases: Check retries, concurrent requests, empty or malformed values, limits, expired credentials, and partial failures. Confirm that errors do not expose secrets or sensitive implementation details.
OWASP’s secure-review guidance specifically highlights entry points, data flow, business logic, cryptography, error handling, and configuration. These are areas where application context matters and where a scanner may not know what the system is supposed to do.
4. Give security-sensitive changes a higher bar
Pay particular attention when a change touches authentication, authorization, cryptography, identity and access management (IAM) policies, CI/CD workflows, deployment manifests, sandboxing, or network policy. These changes can alter the system’s trust boundaries even when the code diff is small. OWASP’s AI Security and Privacy Guide (AISVS), version 1.0 recommends stronger review for such areas, including two-person review or security-team sign-off.
For the relevant code and configuration, examine whether the change:
- accepts unsafe input or creates an injection or deserialization risk;
- exposes secrets, stores them insecurely, or uses cryptography incorrectly;
- allows a user or service to cross an authorization or tenant boundary;
- changes deployment defaults, network reachability, or runtime privileges; or
- returns sensitive details in errors, logs, or responses.
Do not treat a clean scan as a substitute for answering these questions: tools can only flag patterns and conditions within their coverage.
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5. Verify every dependency the change introduces
Check that each new package exists and is the intended project, not merely a similarly named library. AI-generated suggestions can include nonexistent package names; OWASP warns that attackers may register such names. Verify package provenance and maintainers, review the selected version for known vulnerabilities, and follow your team’s normal pinning and update process. A plausible version number is not evidence that a dependency is safe or current.
6. Review tests as claims, not proof
Read the test diff as carefully as the implementation. Look for deleted tests, weaker assertions, mocks that bypass the behavior under review, and tests that simply reproduce the generated code’s assumptions. A green suite only shows that the tests passed; it does not show that the tests express the right requirement.
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Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.7. Use automated checks for the problems they can detect
Run the checks your project supports on the pull request, such as static application security testing (SAST), dynamic or interactive testing (DAST/IAST), secret scanning, infrastructure-as-code scanning, and software composition analysis (SCA). Use findings to direct investigation, and define a clear merge policy for critical issues. AISVS gives CVSS ≥ 9.0 as an example threshold for a critical finding and recommends blocking merge unless an authorized human approves a written exception; it is a policy example, not a universal severity rule.
| Review method | Useful for | What it cannot establish alone |
|---|---|---|
| Human review in application context | Requirements, business logic, data flow, and context-specific security boundaries | It can miss defects too; use tests and automated checks as additional signals. |
| SAST, DAST/IAST, secret, configuration, and dependency scans | Repeatable checks for the issue classes and code or systems within each tool’s coverage | A clean result does not prove correctness, cover every path, or settle business logic. |
| Automated tests | Whether specified scenarios pass under the assertions and setup used | They cannot validate requirements or scenarios they do not encode. |
| AI-assisted review | Additional suggestions about possible defects | It is advisory, not independent human approval or proof of safety. |
OWASP notes that business-logic and context-specific vulnerabilities require human judgment. Treat tool output as evidence to investigate, not a verdict about the whole change.
8. Make approval accountable
A qualified human reviewer must understand the change and take responsibility for approving it. AISVS calls for separation of duties: the reviewer should be a different identity from the person who prompted generation, and the AI agent does not count as a reviewer. Preserve attributable human approval, and escalate high-risk changes under your team’s review policy.
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OWASP puts the responsibility plainly: “AI tools do not accept responsibility for the code they generate. The developer who accepts and commits the code does.” GitHub similarly cautions in its Copilot responsible-use guidance that syntactically correct inline suggestions may not always be secure. That is vendor guidance about Copilot, not evidence that any particular tool or review process guarantees an outcome.
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