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Review AI-generated code exactly as you would any other proposed change—but do not let the model, an AI review comment, or a green test run stand in for accountable human judgment. Before merge or deployment, a qualified engineer who understands the diff should verify the requirements, inspect security and operational behavior, examine tests and dependencies, run risk-appropriate automated checks, resolve findings, and explicitly accept ownership. Where practical, the reviewer should be different from the person who prompted the code generator.
That workflow follows recommendations from OWASP AISVS Appendix C, the OWASP Secure Coding with AI Cheat Sheet, and NIST’s secure-development guidance. These are frameworks and recommendations to tailor to your system, risk, and organizational policy—not a universal approval count or mandatory test list.
What a safe review must establish
An AI coding tool proposes text and changes; it does not establish that the resulting behavior is correct, secure, maintainable, licensed appropriately, or suitable for your production environment. The release gate is the human approval attached to the actual diff. A reviewer must be able to explain what changed, why it changed, what assumptions it makes, and how it behaves when inputs, dependencies, services, or permissions do not match the happy path.
OWASP AISVS states, “Verify that AI-generated code always goes through code review by a qualified human engineer.” Its control also recommends separating the reviewer from the identity that asked the AI to generate the code where feasible. The OWASP cheat sheet similarly says to “Require explicit developer approval before merging any AI-generated code.” Treat those statements as security guidance; your company, contract, or regulator may impose stricter rules.
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The approval should cover every artifact in the change, not only the lines that visibly came from a model:
- Application code and generated files.
- Unit, integration, end-to-end, and test-fixture changes.
- Dependency manifests, lockfiles, build scripts, and package sources.
- Configuration, infrastructure-as-code, deployment workflows, and permissions.
- Documentation or policy files that alter operational or security behavior.
A reviewer can use AI assistance to summarize a diff or suggest cases, but the reviewer remains responsible for checking the claims against the source, requirements, and runtime context.
Set roles, provenance, and an approval gate
Assign a human owner
Every AI-assisted change needs one named human owner who accepts responsibility for correctness, security, and maintainability. The owner may be the author, but the identity must be explicit in the pull request, change record, or equivalent system. Approval is not implied by a model-generated explanation, an automated status check, or a merge button pressed by a service account.
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Use a second qualified engineer for review when staffing and policy allow it, especially for security-sensitive code. Independence helps expose assumptions shared by the person who wrote the prompt and the model that answered it. If the same person must generate and review a small change, document that exception and apply any additional checks your policy requires.
Record how the change was produced
Identify the AI-assisted portions if they are known, the tool or model used when your process requires that information, and the prompt or task context needed to reproduce important decisions. Preserve an audit trail appropriate to the repository and data classification. OWASP AISVS recommends a written workflow covering approved AI tools, permitted data classifications, and security gates throughout the lifecycle; its guidance is a control recommendation, not a substitute for your own policy.
Define the merge gate before reviewing
Write down which checks are required for the repository and which conditions trigger escalation. A practical gate includes a human approval, passing required CI checks, triaged security findings, and confirmation that no unreviewed generated artifact is included. Do not let a model silently create or deploy production changes outside those controls. NIST’s DevSecOps reference model describes AI outputs moving through established peer review, security validation, automated testing, and approval workflows rather than deploying independently; see the NIST NCCoE Notional Reference Model for DevSecOps.
Step 1: Establish the exact scope of the diff
Start with the version-controlled change, not the model’s narrative. Confirm the base revision, files changed, generated artifacts, and intended release. A useful scope record answers:
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- Which ticket, design, incident, or acceptance criteria does this change implement?
- Which services, libraries, data stores, queues, jobs, and user roles can it affect?
- Which lines were generated, substantially rewritten, or copied from an external suggestion?
- Does the change add code, tests, dependencies, configuration, infrastructure, or workflow permissions?
- Does it alter a public API, data format, migration, log schema, or backward-compatibility promise?
Look for generated files that are easy to overlook: lockfiles, documentation used as configuration, CI definitions, container files, database migrations, and policy manifests. Compare the final diff with the task description; an apparently small prompt can produce unrelated cleanup or broad refactoring. Remove unrelated edits before review so the reviewer can reason about one coherent change.
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Check the repository and data boundaries
Confirm that source code, credentials, personal data, proprietary prompts, and production data were handled according to your approved-tool and data-classification rules. A reviewer should not assume that an authorized developer’s use of a model made every input permissible. If provenance is unknown, ask the author to explain the source or replace the artifact with code that can be inspected and maintained under your policy.
Step 2: Understand behavior against requirements
Read the ticket and design first, then trace the implementation from entry points to side effects. The question is not whether the code looks plausible; it is whether it fulfills the requirement under expected and adversarial conditions.
Trace inputs and trust boundaries
Identify every input source—HTTP requests, messages, files, environment variables, database rows, user-provided templates, and third-party responses. Follow each value across parsing, validation, authorization, storage, logging, and outbound calls. Mark where data crosses a trust boundary and verify that validation occurs before use, not only at a later layer that may be bypassed.
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Check success, failure, timeout, retry, cancellation, and partial-completion paths. Look for duplicated side effects, non-idempotent retries, stale state, race conditions, unsafe caching, and transactions that do not cover all related updates. Verify error handling does not expose secrets or silently convert a failure into success. For concurrent code, reason about ordering and shared state rather than relying on a test that happened to pass once.
Verify authorization and least privilege
For every operation, identify the authenticated principal, the resource owner, and the permission decision. Ensure authorization is enforced at the server-side operation that changes or discloses data, not merely in a user interface or route guard. Review newly requested roles, tokens, service accounts, firewall rules, and cloud permissions for the narrowest scope that meets the requirement.
Check compatibility and maintainability
Compare interfaces, serialization formats, database schemas, feature flags, and supported runtime versions with the systems that consume them. Generated code may use a language feature, library API, or configuration key unavailable in your supported environment. Ask whether another engineer can debug and safely modify the code: names should reflect domain concepts, invariants should be visible, and comments should explain decisions rather than restate syntax.
NIST’s SP 800-218A places code review and analysis in the context of secure coding standards and security requirements. Use your organization’s standards as the acceptance criteria, recording and triaging issues rather than treating a review as an informal read-through.
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Raise the review threshold when a change affects a control that protects identity, data, or execution. OWASP guidance specifically highlights security-critical files and behaviors such as input validation, authorization, and deserialization.
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Input validation and output handling
Verify type, length, encoding, range, and allowed-value checks at the trust boundary. Test malformed, oversized, duplicate, and unexpected inputs. For output rendered in a browser, shell, query, template, log, or serialized format, check context-appropriate escaping and canonicalization. Do not assume a generic “sanitize” helper addresses every output context.
Authentication, authorization, and session logic
Check login, token verification, session expiry, password-reset, impersonation, and recovery paths for bypasses. Confirm that authorization failures are consistent and do not reveal whether a protected object exists. Review default roles, newly added endpoints, background jobs, and administrative paths, including those not exposed through the main user interface.
Deserialization, cryptography, and secrets
Identify deserialization of untrusted data and verify that the format, classes, and allowed types are constrained. Confirm cryptographic algorithms, key handling, randomness, rotation, and failure behavior meet your approved standard; do not accept a generated algorithm choice simply because the API compiles. Search code, configuration, logs, and test fixtures for hard-coded credentials, tokens, private keys, or sensitive data.
High-impact changes need extra sign-off
Set an elevated threshold in policy—for example, a security specialist or second qualified reviewer—for authentication, authorization, cryptography, deserialization, payment, safety, privacy, or production-access changes. OWASP AISVS and its Secure Coding with AI guidance recommend enhanced review and security testing for critical behaviors, while leaving organizations to define their own thresholds and exception process.
Step 4: Inspect dependencies, configuration, and supply-chain changes
Generated code can invent a package name, select an obsolete release, or add a real package that is inappropriate for your environment. Treat every new dependency as an explicit design decision.
Validate each dependency
- Confirm the package exists in the intended registry and that the name and publisher are correct.
- Check the selected version, license, transitive dependencies, maintenance signals, and known vulnerabilities using your approved software-composition process.
- Read the small amount of integration code that grants the dependency access to files, network services, credentials, or user data.
- Ensure lockfiles resolve to the reviewed source and have not changed unrelated packages.
Do not install a suggested package merely to make a generated example compile. If an internal library already provides the needed capability, prefer the maintained, approved component and record the reason.
Review configuration and infrastructure
Inspect environment variables, default values, network routes, storage policies, container settings, deployment manifests, and CI workflow permissions. Look for debug modes, public exposure, wildcard identities, disabled verification, overly broad roles, unencrypted storage, and secrets written to logs or artifacts. Infrastructure changes deserve the same human review as application code because an unsafe setting can negate otherwise correct logic.
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Run infrastructure and policy checks where your platform provides them, then inspect findings manually. A scanner can identify a broad permission or public bucket; only a reviewer who understands the intended architecture can decide whether it is necessary and how to constrain it.
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Step 5: Review tests as carefully as production code
A passing suite is evidence about the cases it actually exercises, not proof of correctness. OWASP warns that coding agents can delete tests, weaken assertions, replace real calls with unrepresentative mocks, or generate tests that merely confirm the behavior they invented.
Audit the test diff
- Check for deleted, renamed, skipped, quarantined, or narrowed tests.
- Read assertions to ensure they verify the requirement, security property, and externally observable result—not an incidental implementation detail.
- Confirm mocks and fixtures preserve relevant authentication, authorization, serialization, timing, and failure behavior.
- Look for altered test configuration, coverage exclusions, snapshot updates, and disabled linters.
Add cases the generator is unlikely to choose
Write or request independent tests for invalid input, boundary values, empty and duplicate data, missing permissions, expired credentials, dependency failure, timeout, retry, partial update, concurrent access, and recovery after interruption. Include negative authorization tests that prove one user cannot access another user’s resource. For parsers and deserializers, use malformed and unexpected structures; for rate limits and quotas, test the exact limit and one step beyond it.
Keep tests independent from generated assumptions
Have a human derive at least some cases directly from the requirement, threat model, or incident history rather than asking the same agent to generate both implementation and verification. When a test fails, determine whether the implementation, the test expectation, the fixture, or the environment is wrong. Never weaken an assertion solely to obtain a green build.
Step 6: Run layered automated and manual checks
Select checks according to the affected technology and risk. NIST identifies complementary methods including code-review tools, static and dynamic analysis, software-composition analysis, and penetration testing. Its minimum software-verification guidance provides additional context for vendor and developer testing; it does not prescribe one universal suite.
| Check | What it can reveal | What the reviewer still must decide |
|---|---|---|
| Unit and integration tests | Regressions in specified behavior and component interactions | Whether important requirements, failure paths, and boundaries are represented |
| Static analysis and linting | Language errors, data-flow warnings, insecure patterns, and quality issues | Whether findings are exploitable, false positives, or evidence of a broader design flaw |
| Dynamic or end-to-end testing | Runtime behavior, configuration problems, and integration failures | Whether environments, identities, data, and scenarios reflect production risk |
| Software-composition analysis | Dependency versions, licenses, and known vulnerability records | Whether a component is appropriate, maintained, and granted excessive access |
| Secret scanning | Credentials and key-like material in source or history | Whether suspected values are real, where exposure occurred, and how to rotate them |
| Infrastructure and policy checks | Unsafe permissions, exposed services, and configuration drift | Whether the intended architecture justifies the setting and how to reduce scope |
| Penetration or adversarial testing | Exploitability of critical paths and trust-boundary failures | Whether coverage matches the changed attack surface and residual risk is acceptable |
Run the repository’s normal CI and security gates, preserve their results, and investigate failures instead of rerunning until they disappear. Tool output is additional evidence; it cannot replace understanding the code. Conversely, a clean scan can miss a business-logic flaw, an unsafe assumption, or a requirement that was never encoded in a rule.
Step 7: Triage findings and decide whether to escalate
Classify every review, test, and scanner finding as fixed, accepted under an approved exception, deferred with an owner and deadline, or still blocking. Record the reasoning and the evidence supporting the decision. “The model said it was safe” is not a disposition.
Escalate when impact or uncertainty is high
- The change handles credentials, personal data, financial transactions, safety functions, or production administration.
- A trust boundary, authorization decision, cryptographic operation, parser, or deserializer changed.
- The dependency or generated artifact has unclear provenance or unusual permissions.
- Tests were deleted, weakened, replaced with mocks, or cannot exercise the affected path.
- Static, dynamic, dependency, secret, or infrastructure analysis reports an unresolved high-severity issue.
- The reviewer cannot explain an important branch, side effect, or failure mode from the source.
Escalation may mean a security review, architecture review, second independent approval, threat modeling, targeted adversarial testing, or a smaller staged release. Set the response in policy so schedule pressure does not decide inconsistently.
Step 8: Record approval and release evidence
Before merge, verify that the final diff—not an earlier revision—has the required approvals and passing checks. Record the human owner, reviewers, security sign-off when required, tool or model provenance required by policy, test and scan results, and dispositions for exceptions. If the change was amended after approval, send it back through the appropriate review rather than assuming the original approval still applies.
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The approver should be able to answer four questions from the record:
- What requirement and production behavior does this change implement?
- Which security, dependency, configuration, and test risks were examined?
- Which findings remain, who accepted them, and under what expiration or mitigation?
- Who is accountable for correcting a defect discovered after release?
After deployment, monitor the changed paths with the same operational controls used for other releases: logs, alerts, error rates, access events, and rollback procedures. Monitoring does not lower the pre-release standard; it provides a way to detect assumptions that testing could not cover.
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- Scope: The final diff matches the requirement; unrelated generated edits are removed.
- Ownership: A named human accepts responsibility; independent review is used where feasible or required.
- Behavior: Data flow, trust boundaries, state changes, errors, retries, concurrency, compatibility, and maintainability were traced.
- Security: Validation, output handling, authentication, authorization, deserialization, cryptography, secrets, and least privilege were checked.
- Dependencies: New packages, versions, sources, licenses, transitive components, and lockfile changes are understood.
- Configuration: Permissions, network exposure, deployment settings, infrastructure, and workflow access are intentional and constrained.
- Tests: Existing tests were not weakened; new tests cover negative, boundary, failure, authorization, and concurrency cases that matter.
- Automation: Required tests, static and dynamic analysis, composition checks, secret scans, and infrastructure checks ran and were triaged.
- Escalation: Sensitive or high-impact changes received the additional specialist or second-reviewer sign-off required by policy.
- Evidence: Findings, exceptions, approvals, provenance, and final check results are recorded against the exact revision to be merged.
Common review failures and their corrections
“The code is obvious, so it is safe.”
Readable syntax does not prove correct authorization, error handling, data flow, or compatibility. Trace the behavior to requirements and trust boundaries, then test the cases the happy path hides.
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An AI critique can suggest questions, but it is not the qualified human approval required by OWASP guidance. Use it as a supplementary signal and have a human validate every material conclusion.
“All tests are green.”
Green tests can coexist with deleted cases, weak assertions, unrealistic mocks, or missing requirements. Review the test diff and add independently derived negative and boundary cases.
“The scanner found nothing.”
Scanners cover the patterns and databases they know. They may not understand business rules, deployment intent, novel vulnerabilities, or a package that is technically valid but inappropriate. Investigate coverage and inspect the design manually.
“The dependency name came from a code example.”
Verify existence, publisher, version, source, maintenance, and permissions before installation. Replace suspicious or unnecessary dependencies with approved components or an implementation your team can maintain.
“We will fix the review record later.”
Unrecorded decisions become unowned risk. Require the approval, findings, exceptions, and exact revision to be captured before merge, and repeat review when the diff changes materially.
How to tailor the workflow to risk
Not every change needs a penetration test or a security specialist, but every change needs an accountable human who understands it. A documentation-only edit may need ordinary peer review; a parser exposed to untrusted input may require focused fuzzing and security sign-off; a change to identity or production permissions may require independent approval, adversarial testing, and a staged rollout. Define these levels in advance using impact, exposure, data sensitivity, and change complexity.
NIST’s secure-development practices are intentionally adaptable, and OWASP’s AI-specific controls are recommendations rather than a single mandated process. Tailor the controls to your architecture while preserving the non-negotiables: qualified human understanding, review against secure coding requirements, independent scrutiny of tests, layered automated evidence, explicit escalation, and a recorded owner who approves the release.
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