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Review AI-generated code as an untrusted proposed change: establish what it is supposed to do, run the same build, test, and security gates used for human code, inspect the diff and every new dependency, and add tests designed independently of the generated implementation. A qualified human must approve the change before it is merged or reaches production. AI assistance does not lower the bar for correctness or security; it makes a disciplined review process more important because plausible-looking code and passing tests can still encode the wrong assumptions.
What does a safe review of AI-generated code require?
A safe review checks both whether the change meets its requirements and whether it introduces unacceptable risk. The reviewer should be able to explain the intended behavior, understand the changed code and dependencies, see evidence from relevant checks, and make a human decision about whether the remaining risk is acceptable.
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Do not treat a generated patch as trusted because it compiles, because its authoring tool says it is complete, or because its own tests pass. The UK Home Office engineering guidance says AI-assisted output must receive human review and approval before production, and that AI-assisted code must meet the same security expectations as human-written code. The accepting team remains accountable for the result.
Keep the process proportional to the change, but not weaker than the project’s normal standard. A small documentation-only edit and a change to authentication logic do not need the same depth of specialist review; both still need a clear scope, an appropriate review, and the checks required by the repository.
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How should I define the change before reviewing it?
Write down the behavior the change must provide, the boundaries it must respect, and what evidence will demonstrate success. This gives reviewers a basis for assessing the result instead of judging whether the code merely looks reasonable.
Record the expected behavior and boundaries
- Requirement: State the user-visible or system behavior in observable terms, including relevant success and failure outcomes.
- Interfaces: Identify affected APIs, data formats, command-line interfaces, database schemas, or other contracts that must remain compatible.
- Security and data: Note authorization expectations, sensitive data involved, and any restrictions on reading, storing, transmitting, or logging it.
- Allowed scope: List the areas the change is expected to touch. Investigate edits outside that scope rather than assuming they are harmless.
- Project fit: Check that the proposed solution follows the repository’s architecture and conventions instead of introducing a parallel pattern without a reason.
This written scope is also useful when a generated patch is broad: it helps distinguish necessary supporting edits from incidental changes that should be removed or reviewed separately.
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Start with repeatable build and test checks, then run the security and quality gates relevant to the project. These checks can quickly expose breakage, but they do not replace understanding the diff or independently evaluating the requirement.
- Build or compile the change. Use the project’s established build process and confirm it completes successfully in the intended environment.
- Run the existing tests. Include the unit, integration, end-to-end, and regression suites that apply to the changed behavior. A green result is evidence about those tests, not proof that every requirement is met.
- Run static and dependency checks. Use the project’s established static application security testing (SAST), software composition analysis (SCA), dependency analysis, and code-quality checks. GitHub’s guidance names CodeQL, Dependabot, and code-quality checks among relevant tools; OWASP recommends applying the same SAST, SCA, and secret-scanning gates to AI-generated code as to other code.
- Scan for secrets. Check for credentials, tokens, private keys, and other sensitive material introduced in code, configuration, or test fixtures.
- Run applicable infrastructure and dynamic checks. If the change affects infrastructure configuration or behavior that can only be evaluated at runtime, include the relevant infrastructure-as-code or dynamic checks used by the project.
- Review the results, not only the exit status. Investigate failures and new findings. Record any accepted exceptions and the person authorized to accept them rather than silently bypassing a gate.
Automate these deterministic checks for every relevant change where the repository’s workflow supports it. OWASP’s guidance emphasizes that AI-generated code should face the same security gates as other code; generation is not a reason to skip a scan or waive a finding.
How do I inspect an AI-generated diff for security and maintainability?
Read the diff as if it came from an unfamiliar contributor, tracing how data and authority move through the changed code. Focus on whether the implementation respects the requirement and the project’s trust boundaries, not on whether the code is polished or confidently explained.
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Trace inputs, permissions, and outputs
- Authorization: Verify that access is checked at the right boundary and for the correct resource or action. Look for paths where a new endpoint, background task, or alternate code path bypasses an existing check.
- Input handling: Follow untrusted input through parsing, validation, query construction, and output. Consider malformed, unexpected, or oversized input, and look for injection risks.
- Data exposure: Check what the change returns, persists, transmits, and logs. Ensure sensitive information does not leak through errors, debugging output, or newly added telemetry.
- Secrets and cryptography: Inspect how credentials are read and handled, and whether cryptographic choices fit the project’s established standards. Escalate uncertainty rather than accepting a plausible-looking implementation.
- Failure behavior: Examine timeouts, errors, retries, and partial failures. Check that failures do not accidentally grant access, corrupt state, or leave a process in an unsafe condition.
- Concurrency: Consider whether parallel requests, background jobs, or shared state can create race conditions or inconsistent results.
- Permissions: Look for changes that quietly broaden roles, filesystem access, network access, or deployment privileges.
Assess architecture and ownership
Generated abstractions may work in isolation yet be difficult to maintain in the repository. Ask whether an existing component already solves the problem, whether the new logic belongs at this layer, and whether a future maintainer can understand and change it. Remove unnecessary abstractions or incidental edits when they increase review surface without serving the stated requirement.
How should I verify dependencies suggested by an AI tool?
Verify every added package before allowing it into the project. A generated dependency name may be incorrect or may point to a package that is unnecessary, vulnerable, poorly maintained, or incompatible with project policy.
- Confirm that the package exists in a trusted registry and that the project is resolving the intended package, not a similarly named one.
- Inspect maintainer and release history for signs that the dependency is maintained well enough for the project’s needs.
- Check known vulnerability information and the license against project requirements.
- Assess whether the dependency is architecturally appropriate and whether existing project code can meet the requirement without it.
- Pin versions where project policy requires it, and ensure the lockfile or equivalent dependency record reflects the intended resolution.
OWASP specifically warns that AI tools can suggest hallucinated or malicious dependencies and recommends dependency checks. A successful installation alone does not establish that a package is safe or suitable.
How can I tell whether AI-written tests actually test the requirement?
Do not rely only on tests generated alongside the implementation. They may reproduce the same assumptions or mistakes, so design additional cases from the requirement and expected failure modes rather than from the code’s current behavior.
Build tests from behavior and boundaries
For each stated requirement, identify at least one observable assertion that would fail if that requirement were broken. Then consider cases that challenge the implementation’s assumptions:
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- Boundary cases: Inputs at, just below, or just above meaningful limits, and empty or missing values where they are possible.
- Malformed input: Invalid formats and unexpected types, with an assertion about safe, useful failure behavior.
- Authorization: Requests from users or processes without the required permission, as well as access to resources outside the authorized scope.
- Concurrency: Overlapping operations or repeated requests where shared state could be affected.
- Recovery: Timeouts, dependency failures, interrupted work, and retry or rollback behavior relevant to the change.
- Abuse cases: Attempts to make a security control fail or to use an input in an unintended way.
Review assertions for substance: a test that merely confirms a function ran or returned some value may not prove the required behavior. OWASP SAMM advises meaningful assertions and misuse testing that tries to make controls fail. Its guidance also says AI-based verification may complement deterministic checks, not replace them.
NIST’s 2025 GenAI (Pilot) Code Challenge Evaluation Plan evaluates AI-generated unit tests for elementary Python code. It is an evaluation plan, not a universal measure of defect rates or evidence that tests generated for a different language, project, or requirement are adequate.
Which changes need extra review or security sign-off?
Increase scrutiny when a change can affect identity, access, sensitive data, external input, or production systems. Authentication, authorization, cryptography, parsers, external interfaces, CI/CD workflows, build scripts, deployment files, and secret-handling code warrant a qualified reviewer; project policy may also require a second reviewer or security-team sign-off.
OWASP’s Application Security Verification Standard for LLM-based applications (AISVS) calls for elevated review of security-critical files and automated testing on every AI-generated pull request. Treat this as a reason to make high-risk paths visible in your workflow, not as a substitute for your organization’s own approval policy.
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How do I keep an AI coding agent from creating avoidable risk?
Limit what the tool can access and do, especially while it is editing or executing code. A review process cannot undo harm from an agent that already had access to production secrets, signing keys, or unrestricted irreversible actions.
- Run the agent in a sandbox or disposable environment when practical.
- Apply least privilege, and use short-lived credentials with only the scope needed for the task.
- Allowlist tools and resources rather than granting broad access by default.
- Require approval prompts for irreversible actions, such as destructive operations or changes with deployment consequences.
- Keep production secrets and signing keys out of the agent’s environment.
- Review repository instruction files and tool descriptions as untrusted inputs; do not assume they are safe merely because they appear in the project.
These safeguards reduce the consequences of mistakes or malicious instructions. They complement code review: containment limits what the agent can affect, while review determines whether its proposed changes are acceptable.
What should a review record before a change is merged?
Leave an auditable record connecting the AI-assisted change to its intent, checks, exceptions, and human decision. That makes it possible to understand what was accepted and who accepted responsibility.
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- Record what was generated and what a person amended, at a level useful to the project’s traceability requirements.
- Attach or link the applicable build, test, and scan results within the project’s normal review workflow.
- Document exceptions, unresolved limitations, or checks that could not be run, together with the rationale and approval required by policy.
- Identify the human reviewer or approver who accepted the change.
UK Home Office engineering guidance calls for traceability and states that teams retain full accountability for AI-assisted outputs. A traceable approval is not a formality: it makes the decision and its evidence discoverable after the original authoring session is over.
How do I decide whether an AI-assisted change is ready to merge?
Use the same release standard as for human-written code, and make the decision against the evidence rather than the origin of the patch. A change is not ready merely because it passes a subset of checks or because its generated tests are green.
| Review dimension | Evidence to look for |
|---|---|
| Coverage | Applicable build, unit, integration, end-to-end, regression, SAST, SCA, secret, infrastructure, and dynamic checks. |
| Independence | Requirement-focused tests and human review that do not rely solely on the generated implementation or its generated tests. |
| Risk sensitivity | Additional qualified review and any required second or security-team approval for security-critical changes. |
| Traceability | AI attribution, review approvals, check results, and documented exceptions. |
| Agent containment | Sandboxing where practical, least-privilege access, appropriately scoped credentials, and approval gates for irreversible actions. |
| Maintainability | Verified dependency quality, architectural fit, readable code, and a clear path for long-term ownership. |
If a required check fails, an important requirement lacks an independent test, a dependency cannot be verified, or a high-risk change lacks the required expertise or sign-off, do not merge until the gap is addressed or formally handled under project policy. If the change is ready, the human approver should be able to explain both what the evidence establishes and what residual uncertainty remains.
Why is there no universal defect rate for AI-generated code?
A single percentage would not tell a team whether a particular patch is safe. Results depend on the tool, language, repository, task, and review process; the available named NIST work is a 2025 pilot evaluation plan for tests on elementary Python code, not a cross-tool study establishing a general application-code defect rate. Evaluate the change and the workflow you actually use instead of relying on a broad statistic.
Frequently Asked Questions
Can I use another AI tool to review code generated by an AI coding tool?
It can provide an additional perspective, such as suggesting overlooked edge cases, but treat its findings as leads for verification rather than approval. Verify any claim against the code, requirements, and deterministic checks before acting on it.
What should I do if an AI-generated change has no meaningful existing test coverage?
Ask for or create requirement-based tests before accepting the change, and make any remaining coverage limitation explicit in the review record. Whether a change can proceed without a particular test depends on project policy and the risk of the behavior being changed.
Should I keep prompts or full agent transcripts for every code change?
The traceability guidance calls for recording what was generated and amended, attribution, evidence, exceptions, and the human approver; it does not establish one universal transcript-retention rule. Follow your organization’s data-retention and privacy policies when deciding whether to store prompts or conversations.
Does a prototype or internal-only tool need the same review process?
The level of review can be proportionate to exposure and impact, but internal use does not by itself remove security, privacy, or reliability risks. Apply the project’s required gates and account for the data, permissions, and systems the prototype can reach.
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