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Treat AI-generated code as an untrusted draft, not as an implementation you can approve because the tests pass. A human reviewer must first confirm that it solves the requested problem, then verify behavior with deterministic checks, inspect the complete diff, challenge the tests, examine dependencies and build boundaries, and perform a risk-based security review. Only a qualified human should approve the change for production.
The practical standard is the same one you would apply to code written by a colleague, with extra attention to plausible-looking errors, hallucinated packages, weakened tests, and changes to scripts or infrastructure that can execute with your credentials.
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Why does AI-generated code need a human reviewer?
Code-generation systems optimize for a likely-looking answer, not for your product’s intent, threat model, operational constraints, or long-term maintenance cost. They can produce code that compiles and passes a narrow test while violating an acceptance criterion, mishandling an edge case, exposing data, or adding an unnecessary dependency.
Human review supplies context that automated checks do not. The reviewer decides whether the requirement was understood, whether the architecture remains coherent, whether a simpler design would be safer, and whether the evidence is strong enough for release. The UK Home Office Engineering Guidance (2025) requires AI-assisted outputs to be reviewed and approved by a human before production, with traceability and testing expectations equal to those for human-written code.
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What should I establish before running tests?
Start with the requested behavior, not with the generated implementation. Read the issue, acceptance criteria, architecture notes, and the complete pull-request diff. Identify what is intentionally in scope and what must remain unchanged.
- Write down the expected behavior. Include successful flows, validation rules, error responses, authorization decisions, data-retention requirements, and performance or compatibility constraints.
- Map the change to the system. Locate the entry points, services, data stores, queues, and external integrations it touches. Confirm that the chosen layer follows local conventions.
- Check the entire diff. Generated changes can hide edits in configuration, lockfiles, tests, scripts, documentation, or generated artifacts. Review additions, deletions, and permission changes, not just the main source file.
- Define a review boundary. Decide which files, services, environments, and data are affected. A clear boundary makes later security findings and test results easier to interpret.
GitHub’s review guidance places context and intent after initial functional checks, but establishing that context up front prevents a green build from creating false confidence.
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Run repeatable checks before making a qualitative approval decision. Record the command, commit, environment, and result so another reviewer can reproduce the evidence.
| Check | What it can establish | What it cannot establish |
|---|---|---|
| Compile or build | Syntax, type, generated-artifact, and packaging problems are exposed. | That the feature matches the requirement or is secure. |
| Unit tests | Small components behave for the cases represented by the tests. | Correct integration, realistic dependencies, or missing cases. |
| Integration tests | Components communicate correctly with configured services and data stores. | All production configurations or hostile inputs. |
| End-to-end and regression tests | User-visible flows and previously supported behavior remain intact. | Unexercised paths, rare races, or undocumented requirements. |
| Lint and static analysis | Known style, type, data-flow, and defect patterns can be detected consistently. | Business intent or every exploitable design flaw. |
| Dependency and secret scans | Known vulnerable packages, suspicious secrets, and policy violations may be found. | Whether an unlisted or novel flaw is exploitable in your deployment. |
Use the project’s normal build, test, lint, and static-analysis commands rather than inventing a one-off invocation. GitHub specifically identifies CodeQL, Dependabot, and code-quality checks as useful parts of this process. NIST’s 2021 guidance broadens the set of techniques worth considering:
- Threat modeling and review of built-in protections.
- Automated tests, including black-box, structural, and historical tests.
- Static code scanning and heuristic secret checks.
- Fuzzing and web-application scanners.
- Verification of included libraries and services.
Choose the checks that fit the change, but document why a technique is not applicable. For example, a parser handling untrusted input merits fuzzing; a pure presentation change may not.
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How do I review the diff for human-level quality?
After deterministic checks, read the code as a maintainer who must debug it at 2 a.m. Look for a design that is understandable, observable, and easy to change.
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Confirm the implementation fits the requirement
- Trace each acceptance criterion to code and to at least one meaningful test.
- Check defaults, permissions, retries, timeouts, and failure responses rather than only the happy path.
- Look for accidental behavior changes in shared utilities, schemas, migrations, and public interfaces.
Check readability and maintainability
- Prefer names that describe domain meaning over generated abbreviations.
- Remove duplicated branches, unreachable code, speculative abstractions, and comments that merely narrate syntax.
- Ensure logging and metrics reveal useful identifiers without exposing credentials or personal data.
- Ask whether a small rewrite would be clearer and safer than preserving a complicated generated approach.
Inspect error handling and edge cases
Generated code often handles the expected type and silently mishandles absent, duplicated, oversized, expired, or malformed values. Review transaction boundaries, cleanup, cancellation, concurrency, partial failures, and idempotency. Verify that errors neither leak sensitive details to users nor disappear from operational logs.
How can I tell whether AI-generated tests are trustworthy?
Tests produced by the same agent that wrote the code are useful as a starting point, but they are not independent assurance. OWASP warns that an agent can make CI green by deleting failing tests, weakening assertions, mocking the unit under test, or asserting buggy behavior.
Look for tests that prove behavior, not implementation
- Reject tests that only verify a private call sequence or reproduce the generated algorithm line by line.
- Check that assertions inspect meaningful outcomes, state transitions, authorization decisions, and error types or codes.
- Search the diff for deleted, skipped, disabled, or downgraded tests and explain every change.
- Ensure mocks stand in for external boundaries, not for the very function or validation rule under examination.
Add adversarial and negative cases
At minimum, exercise invalid inputs, malformed payloads, expired tokens, missing permissions, boundary values, duplicate requests, concurrent updates, and abuse cases. Include oversized data and unexpected encodings where parsers or file handling are involved. A test that passes only because the fixture is perfectly formed is not evidence that production input is safe.
Seek independent test design
Have a person or a separate test strategy derive cases from the requirement and threat model rather than from the generated implementation. Compare expected behavior with prior releases when compatibility matters; NIST calls these historical tests. The independent reviewer should be able to explain what would fail if the implementation were subtly wrong.
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How do I inspect dependencies and the software supply chain?
Verify every new package, action, image, and service before it enters production. An AI suggestion is not evidence that a package exists, is maintained, has a compatible license, or is the intended project.
- Confirm the exact package name, version, registry, publisher, and checksum where your ecosystem supports it.
- Check maintenance activity, supported runtime versions, known vulnerabilities, transitive dependencies, and license compatibility.
- Compare the dependency with an approved internal or standard-library alternative. Remove it if the feature does not justify the added attack and maintenance surface.
- Review lockfile changes and install scripts. Watch for hallucinated packages and “slopsquatting,” where an attacker publishes a package using a plausible name that a generator invented.
- Pin or constrain versions according to your update policy and record why the dependency is needed.
Do not allow a package into production solely because an AI tool recommended it. Dependency alerts and automated scanning support the decision; they do not replace provenance and maintainer review.
Which changes require a deeper security review?
Use risk to determine review depth. Ask a security specialist or the owner of the affected control when a change touches a high-impact boundary.
| Change area | Questions to answer |
|---|---|
| Authentication and authorization | Can an unauthenticated, lower-privilege, or cross-tenant user reach the operation? Are tokens validated for issuer, audience, expiry, and intended scope? |
| Cryptography and secrets | Are approved algorithms, key storage, rotation, and failure behavior used? Are secrets absent from source, logs, and test fixtures? |
| Input parsing and deserialization | Are malformed, oversized, recursive, and hostile inputs bounded and rejected safely? |
| File upload and public endpoints | Are type, size, path, storage, rate, and content controls enforced before processing? |
| New integrations, data stores, CORS, or network exposure | What data crosses the boundary, which identities are trusted, and what happens during timeout or partial failure? |
| CI workflows, Dockerfiles, infrastructure, and release scripts | What commands, downloads, third-party actions, permissions, and deployment targets can this change execute? |
| Lockfile or generated artifact changes | Did the resolved code change even when application files appear unchanged? |
OWASP’s DevSecOps guidance captures the division of labor: automated scanners find known patterns, while human review finds flaws that require context. Threat modeling should therefore cover both the feature and the delivery path used to build and deploy it.
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How do I verify build and agent boundaries?
Treat package scripts, CI configuration, Dockerfiles, infrastructure manifests, and deployment files as executable production code. A generated edit that looks like “configuration” can download code, run shell commands, alter permissions, or send data externally.
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- Review every new network request, download URL, shell invocation, post-install hook, and third-party CI action.
- Check least-privilege permissions for runners, containers, cloud roles, and service accounts.
- Keep coding agents away from production credentials, signing keys, customer data, and unrestricted network access.
- Use isolated, reproducible environments for generated builds and inspect artifacts before promotion.
- Require an explicit reviewer for changes that can publish releases, modify infrastructure, or sign binaries.
How should I reproduce and triage findings?
AI-assisted testing can produce both false positives and hallucinated vulnerabilities. The reviewer, not the tool, defines scope, reproduces the finding, judges exploitability and impact, and decides what to report.
- Capture the exact commit, input, environment, tool version, and command that produced the finding.
- Reduce the report to a minimal reproducible case and verify that it occurs in the affected configuration.
- Trace the data flow and required privileges. A theoretical pattern without a reachable path may need a different disposition than an exploitable production route.
- Classify severity using your organization’s standard, assign an owner, and set a remediation deadline.
- Re-run the original check after the fix and add a regression test that would fail if the issue returns.
How do I document accountability for an AI-assisted change?
Mark the use of AI in the commit or pull request according to your team policy, retain review evidence, and identify the qualified human who approved production release. Keep the prompt or generation context when it is needed to explain a non-obvious decision, but never store credentials or sensitive data in that record.
The approval record should identify the scope reviewed, checks run, exceptions accepted, security findings, dependency decisions, and test evidence. Traceability makes later incident investigation possible and prevents an unowned generated change from becoming permanent infrastructure.
What criteria should I use when comparing review approaches?
Whether you are evaluating a checklist, an AI review bot, or a conventional peer review, compare the same dimensions rather than counting comments.
| Criterion | What strong evidence looks like |
|---|---|
| Functional correctness | Acceptance criteria mapped to passing positive, negative, and regression tests. |
| Requirement and architectural fit | Review notes explain why the design belongs in the chosen layer and respects local conventions. |
| Test independence and adversarial coverage | Cases were derived separately from the implementation and include abuse and boundary behavior. |
| Security and threat-model coverage | Trust boundaries, privileges, data exposure, and high-risk controls were examined. |
| Dependency provenance | Packages and actions are verified, maintained, licensed, and intentionally selected. |
| Maintainability | Code is readable, observable, and simpler to refactor than the alternatives. |
| Reproducibility | Commands, environments, inputs, and tool versions are recorded. |
| Reviewer accountability | A named, qualified human accepts the residual risk before release. |
| CI/CD integration | Checks run consistently without granting agents unnecessary production authority. |
What should be on my pull-request checklist?
- The issue, acceptance criteria, architecture notes, and complete diff were read.
- The build, unit, integration, end-to-end, regression, lint, static-analysis, dependency, and secret checks appropriate to the change were run.
- Deleted tests, weakened assertions, skipped cases, and mocks of the unit under test were investigated.
- Negative, adversarial, boundary, concurrency, and malformed-input cases were added where relevant.
- Dependencies, lockfiles, licenses, provenance, and install behavior were verified.
- Authentication, authorization, parsing, deserialization, uploads, integrations, network exposure, CI, containers, infrastructure, and release paths were assessed according to risk.
- Findings were reproduced, triaged for exploitability and impact, fixed or explicitly accepted, and regression-tested.
- AI assistance, review evidence, and the named human production approver were recorded.
Approval is justified only when the evidence supports the requested behavior, the implementation is maintainable, the tests are independent enough to be meaningful, and the remaining risk is understood by an accountable human.
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Frequently Asked Questions
What is a historical test in software verification?
A historical test compares the new version with behavior from an earlier release or known-good revision. It is useful when compatibility, serialization formats, migrations, or public APIs must remain stable, and it can reveal regressions that newly generated examples never cover.
How should I handle an AI scanner finding that I cannot reproduce?
Preserve the exact input, commit, environment, and tool version, then reduce the report to a minimal case. If the path is unreachable or the required privileges are absent, document that reasoning and disposition rather than silently deleting the alert; revisit it when the surrounding design changes.
Can a separate AI reviewer provide independent assurance?
Separation of tools can reduce correlated mistakes, but independence depends on the test oracle and review context, not merely on using a second model. A qualified human still has to validate scope, reproduce important findings, and accept residual risk.
Why review lockfiles when application code did not change?
A lockfile can resolve a different package version, transitive dependency, install hook, or platform-specific artifact without changing the visible application logic. That altered supply-chain behavior may affect security and reproducibility, so it belongs in the same review boundary.
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