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An AI-generated code review checklist should treat the output as untrusted code, not as pre-reviewed code. Require a qualified human owner and approver, verify behavior and architecture, run the same security and quality gates used for human-written changes, inspect dependencies and secrets, test failure and abuse cases, and record which developer, reviewer, AI tool, and model were involved.
Start with ownership, approval, and separation of duties
The first checklist item is a person, not a scanner: assign a qualified engineer who is accountable for correctness, security, and maintainability. The AI agent does not count as the human reviewer. OWASP’s AI security verification guidance also recommends that, when separation of duties is required, the reviewer should not be the same identity that prompted the generation.
Require explicit human approval
Do not merge an AI-assisted change merely because it compiles or because an AI assistant says it is safe. The responsible developer should identify the change, explain its intended behavior, and obtain approval from a qualified reviewer before merge. For security-sensitive work, use a second reviewer or security-team sign-off.
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Apply the same baseline to every source of code
AI-generated code does not receive a lighter review than code written manually. Use the project’s normal engineering and security standards for both. Authentication, authorization, cryptography, payment handling, personal-data processing, CI/CD, infrastructure, and security libraries deserve explicit security approval even when the generated diff is small.
Confirm what the change is supposed to do
Before reading individual lines, compare the diff with the issue, acceptance criteria, API contract, data model, and design decision that authorized it. AI output can satisfy the example in a prompt while violating an unstated requirement elsewhere in the system.
Check the complete behavior surface
- Trace the normal success path from input to output.
- Trace boundary values, empty and oversized inputs, malformed data, and unsupported options.
- Trace failures, retries, timeouts, cancellation, partial completion, and service unavailability.
- Trace concurrent requests, duplicate submissions, race conditions, and transaction boundaries.
- Check every authorization branch, tenant boundary, and state transition, not only the path shown in a test fixture.
Verify platform and framework assumptions
AI models can have stale or incomplete knowledge of framework behavior. Microsoft’s guidance specifically warns that generated APIs should be checked against current authoritative documentation. Confirm method names, parameter semantics, defaults, deprecations, version compatibility, thread-safety guarantees, and error behavior for the exact versions used by the project.
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Look for plausible but wrong behavior
Pay particular attention to silent fallbacks, swallowed exceptions, incorrect default values, implicit type conversions, skipped validation, and code that returns a successful-looking result after an operation failed. Ask a reviewer who understands the domain to explain why each important branch is correct; “the model generated it” is not an explanation.
Run a focused security review
Use the following security checks as a minimum. A passing unit-test suite does not establish security: OWASP recommends adversarial testing and independent analysis rather than relying on “all tests pass” as the confidence measure.
Secrets and sensitive data
- Search the diff and nearby configuration for hardcoded passwords, API keys, tokens, private keys, connection strings, and certificates.
- Check that secrets come from the approved secret store or runtime mechanism, are not exposed in source control, and are not printed in logs, exceptions, telemetry, or test snapshots.
- Review generated examples and fixtures; realistic-looking credentials can be copied into production by mistake.
Input handling and injection
- Validate input at the trust boundary and use the correct canonicalization before validation.
- Check SQL, shell, template, expression, LDAP, XML, HTML, and NoSQL construction for injection opportunities.
- Review deserialization settings, type restrictions, archive extraction, path traversal defenses, and file-upload handling.
- For outbound requests, examine server-side request forgery protections, destination allowlists, redirects, DNS behavior, and response-size limits.
Identity, authorization, and tenant isolation
- Verify authentication on every protected route and reject missing, expired, malformed, or incorrectly scoped credentials.
- Check object-level authorization, role and permission checks, administrative paths, and indirect object references.
- Confirm that one tenant, account, or user cannot read or alter another tenant’s data through alternate endpoints, exports, caches, background jobs, or error paths.
- Ensure privilege is not accidentally retained across retries, queued work, impersonation, or service-to-service calls.
Cryptography, transport, and error behavior
- Reject custom cryptography and weak randomness unless a qualified security engineer has approved the design.
- Check key storage, rotation, algorithm choices, nonce or IV handling, password hashing, certificate validation, and HTTPS configuration.
- Ensure exception messages, stack traces, internal paths, SQL fragments, and debug flags cannot reach end users or untrusted logs.
- Check rate limits, lockout behavior, replay protections, and resource limits where the change handles authentication or expensive operations.
Microsoft’s pre-shipping examples include no hardcoded secrets or credentials, input validation, allowed-directory checks, least-necessary capabilities, dependency vulnerability scanning, HTTPS, and preventing internal paths or stack traces from reaching users. Treat those as concrete review prompts rather than optional polish.
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Use tests and automated gates as evidence
Run and extend the project test suite
- Run unit tests for changed functions and their collaborators.
- Run integration tests against real protocol, database, queue, and identity boundaries used by the feature.
- Run end-to-end and regression tests for user-visible workflows and previously fixed defects.
- Add tests for new behavior, rejection paths, permission failures, timeouts, retries, malformed input, and concurrency where relevant.
- Record failures and environmental assumptions instead of rerunning until the result happens to be green.
Put automated analysis on the pull request
For a pull request containing AI-generated code, run the same automated gates as any other change and add coverage where the risk warrants it:
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- Static application security testing (SAST).
- Interactive or dynamic testing (IAST or DAST) where the application and environment support it.
- Secret scanning.
- Infrastructure-as-code scanning for deployment and cloud changes.
- Software-composition analysis for direct and transitive dependencies.
- Code-quality and data-flow analysis.
Define the merge policy in advance. Block a merge for critical findings according to the organization’s severity policy, and document the owner, rationale, expiry or follow-up, and approval for any accepted exception.
Use adversarial and generative testing for high-risk logic
For security-critical input validation, authorization, parsers, and deserialization, add property-based tests or differential fuzzing. Generate unexpected encodings, boundary lengths, duplicate fields, conflicting claims, malformed nested objects, and sequences of operations rather than testing only hand-picked examples. Compare implementations or versions where a trusted reference exists, and investigate every discrepancy.
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Review maintainability and architecture
AI can produce code that is locally neat but architecturally misplaced. Review the change at repository scale, not only line by line.
Fit with local conventions
- Check naming, formatting, error conventions, logging, configuration patterns, and test style against neighboring code.
- Remove duplication and generated boilerplate that adds no behavior.
- Check cohesion, coupling, complexity, and whether a smaller change would solve the requirement.
- Confirm that abstractions are used consistently rather than introducing a second pattern for the same concern.
Check operational consequences
- Review database migrations, backward compatibility, rollback and roll-forward plans, and data repair needs.
- Check observability: useful metrics, structured logs, traces, alerts, and correlation identifiers without leaking sensitive values.
- Assess latency, memory, CPU, network calls, connection use, queue growth, and behavior under load.
- Verify feature flags, deployment ordering, cache invalidation, and failure recovery.
A future maintainer should be able to explain the code from the repository and its documentation without reconstructing the original prompt. If understanding depends on hidden conversational context, capture the design rationale in a normal engineering record or simplify the implementation.
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Inspect dependencies and the software supply chain
Make an inventory of everything the change introduces or updates: packages, transitive packages, CI actions, model SDKs, generated assets, container images, infrastructure modules, and external services.
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Verify each new component
- Confirm provenance and that the package or action is the intended one, not a similarly named substitute.
- Check license compatibility with the project and distribution model.
- Pin versions or immutable references according to project policy; avoid mutable tags and unreviewed floating branches.
- Check vulnerability status, maintenance signals, and whether the dependency is actually necessary.
- Review install scripts, post-install hooks, generated code, and runtime permissions.
Review CI/CD and infrastructure changes
Inspect workflow permissions, cloud roles, secret exposure, artifact handling, deployment targets, network access, and approval gates. A generated pipeline can be functionally correct while granting write access to every repository or running untrusted pull-request code with production credentials. Require least-necessary capabilities and verify that deployment steps cannot be redirected by mutable references or untrusted input.
Record AI provenance and an audit trail
Record that AI assistance was used, the responsible developer, the reviewer and approver, the tool, and the model version when available. OWASP recommends an audit trail showing which developer approved which AI-generated change and which AI tool or model was used.
Preserve enough context to investigate later
- Link the change to its issue, design decision, or task.
- Preserve the prompt or task context when organizational policy requires reproducibility or incident investigation.
- Identify generated portions when they are mixed with manually written code.
- Record material edits made after generation and the tests or scans run before approval.
Do not use “the AI wrote it” as a reason to waive ownership. Attribution makes responsibility clearer; it does not transfer responsibility to a tool.
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Not every generated line needs the same process, but the threshold should rise with potential impact. OWASP’s AI security verification guidance calls for an elevated threshold, such as two-person review or security-team sign-off, for security-critical files.
| Change type | Minimum review emphasis | Escalation to require |
|---|---|---|
| Documentation, comments, or isolated refactoring with no behavior change | Human review, build and regression checks, consistency with local conventions | Additional review if generated text changes security or operational instructions |
| Routine application behavior | Acceptance criteria, edge cases, tests, dependency and secret scans | Domain owner when data integrity or external contracts are affected |
| Authentication, authorization, personal data, or cryptography | Adversarial tests, abuse cases, current platform references, explicit security review | Two-person review or security-team approval |
| Payment, CI/CD, infrastructure, or security-sensitive libraries | Least privilege, rollback, supply-chain review, environment and deployment testing | Two-person review, security or platform-team sign-off, and documented exception handling |
A practical review procedure for an AI-generated pull request
- Identify ownership. Confirm the responsible developer, qualified reviewer, required approvers, and whether separation of duties applies.
- Read the intent first. Compare the issue, acceptance criteria, API contract, and design with the complete diff.
- Map trust boundaries. Mark user input, external services, databases, queues, files, identities, secrets, and privilege changes.
- Trace behavior. Walk success, boundary, failure, retry, timeout, concurrency, and unauthorized paths.
- Check current references. Verify framework and platform APIs, defaults, deprecations, and security recommendations against authoritative documentation.
- Perform the security pass. Search for secrets, injection, unsafe deserialization, SSRF, path traversal, broken authorization, weak cryptography, data leakage, and unsafe errors.
- Inspect supply-chain changes. Review every package, action, SDK, asset, image, permission, version pin, license, and vulnerability result.
- Run evidence-producing checks. Execute unit, integration, end-to-end, regression, SAST, secret, dependency, IaC, and applicable dynamic or interactive scans.
- Attack the risky parts. Add property-based or differential fuzz tests and explicit abuse cases for security-critical logic.
- Review operations and maintainability. Check migrations, rollback, observability, performance, resource use, architecture, and whether a maintainer can understand the result without the prompt.
- Resolve findings. Fix defects, rerun affected checks, and document any approved exception with an owner and follow-up.
- Record and approve. Capture AI provenance, test and scan evidence, reviewer decisions, and final human approval before merge.
How review models compare
| Approach | Human accountability and depth | Security and evidence | Best use and limitation |
|---|---|---|---|
| Manual-only review | Strong local-context understanding when reviewers have enough time; depends heavily on individual consistency | May miss repeatable scanner findings or supply-chain issues if gates are not automated | Useful for small, familiar changes; weak as a sole control for high-volume or high-risk work |
| AI-assisted review | Can quickly explain diffs, suggest cases, and identify patterns, but does not provide accountable approval or guaranteed architectural understanding | Helpful as a supplemental signal; must not replace SAST, SCA, secret scanning, tests, or adversarial analysis | Useful for triage and review acceleration; unsafe when treated as an authority on its own output |
| Hybrid gate | Combines qualified human ownership and approval with automated analysis and, where needed, independent security review | Provides repeatable scans, test evidence, provenance, and risk-based escalation while retaining human judgment | Strong default for AI-generated changes, with stricter controls for security-critical files |
Can an AI review its own code?
An AI system can perform a second-pass critique, generate test cases, summarize a diff, or flag common patterns. Those tasks can improve review coverage and reduce mechanical work. They do not satisfy the human-review requirement, separation of duties, or accountability controls. The generated critique is another untrusted suggestion and should be checked against tests, scanners, documentation, and a qualified engineer’s judgment.
What a complete checklist should leave behind
At merge time, the record should show a clear requirement, named owner and approver, risk classification, behavior and architecture review, security findings and dispositions, test and scan results, dependency and permission decisions, operational considerations, AI tool and model details when available, and any approved exception. That evidence makes the decision reproducible and gives incident responders something more useful than a statement that an assistant produced the code.
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