Review AI-generated code exactly as you would an unfamiliar contribution—with extra attention to what the model could not know about your system. Establish the intended behavior, inspect the complete diff, run deterministic tests and security checks, then manually trace important execution and data paths. A qualified human must understand, own, and approve the change; automated scanners and AI review comments can focus attention but cannot accept responsibility for a merge.
The safest workflow is risk-based. A small, isolated UI change may need normal peer review, while code handling authentication, authorization, cryptography, parsing, public endpoints, data stores, infrastructure, or CI/CD deserves deeper testing and independent security approval.
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What a good review has to establish
An AI-assisted patch is acceptable only when a reviewer can answer three questions:
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- Is it safe in its real environment? Inputs, permissions, secrets, dependencies, deployment settings, and failure paths must preserve the system’s security boundaries.
- Can the team maintain it? The code should fit local conventions, be understandable to future developers, and include tests and documentation that make later changes safe.
Human accountability is not optional. OWASP’s Secure Coding with AI Cheat Sheet says: “Every AI-assisted change should be reviewed, approved, and attributable to a developer who is responsible for its security and maintainability.” The person who requested generation should not automatically be the only reviewer for a sensitive change.
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Do not treat a model’s confidence, a green check mark, or a clean-looking diff as evidence that the code is correct. Generated code can be syntactically polished while misunderstanding a business rule, bypassing an authorization boundary, selecting an untrusted package, or adding a configuration change outside the visible feature.
1. Establish purpose and context before reading individual lines
Start with the change’s intended outcome, not with the code’s apparent elegance. Read the issue or design note, acceptance criteria, relevant architecture documentation, threat model, and existing security requirements. Identify what should change, what must not change, and which component owns each responsibility.
Build a change map
Write down the changed files and classify their effect before detailed review:
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- Trust boundaries where untrusted users, third-party services, browser clients, build agents, or deployment systems meet your code.
- High-risk functions for authentication, authorization, input parsing, deserialization, cryptography, file access, database access, network calls, or template rendering.
- Data stores, queues, caches, credentials, logs, and configuration touched by the patch.
- Infrastructure, container definitions, CI workflows, dependency manifests, AI instruction files, and hooks that may alter future generations or the patch’s blast radius.
Compare the diff with the intended scope. A feature that supposedly changes one endpoint but also alters a workflow permission, adds a package, disables a security check, or changes a deployment file needs clarification before approval. The OWASP Secure Code Review Cheat Sheet recommends preparing for review by understanding the application, its architecture, security requirements, and the areas affected by the change.
Check whether the patch is explainable
Ask the responsible developer to explain the control flow, data flow, dependencies, and failure behavior without relying on the model’s transcript. The owner should be able to state why each non-obvious line exists and how the code meets the acceptance criteria. If nobody can explain the behavior, stop the review and request a smaller patch, clearer design, or additional tests. “The model generated it” is not an explanation and is not a reason to waive normal engineering gates.
Inspect unfamiliar or newly introduced dependencies individually. Confirm the package name, version, license, provenance, transitive dependencies, and whether the selected library is actually needed. A plausible-looking import can be a typo-squatted package or an unnecessarily broad component with a large attack surface.
2. Inspect the complete diff, including files that do not look like application code
Review the entire pull request rather than only the main function. Generated changes often include tests, formatting edits, lockfiles, documentation, configuration, generated assets, and workflow modifications. Each can affect security or future maintenance.
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Look for hidden blast-radius changes
- CI/CD workflows: Check event triggers, job permissions, reusable workflows, action versions, artifact handling, and whether code from an untrusted pull request can run with privileged tokens.
- Infrastructure as code: Verify public exposure, network rules, identity and access management, encryption, logging, backup settings, and state-file handling.
- AI instruction files and hooks: Changes may influence later generations, automated commits, or tool execution. Review them as code, not as harmless documentation.
- Configuration: Check defaults for debug mode, CORS, cookie flags, TLS verification, host binding, rate limits, feature flags, and log destinations.
- Dependency manifests and lockfiles: Confirm that additions are intentional and that resolved versions match the reviewed package identity.
For every unrelated-looking change, ask whether it is necessary for the stated goal. Remove or split unrelated edits so reviewers can reason about one risk surface at a time.
3. Run deterministic checks before relying on human judgment
Run the project’s normal build, test, lint, type-checking, static-analysis, dependency, and secret-scanning commands. In a pull request, OWASP recommends layering static application security testing (SAST), software composition analysis (SCA), and secret scanning; the OWASP AISVS 1.0 AI-for-code-generation guidance also lists interactive and dynamic testing, infrastructure-as-code scanning, and related validation for AI-generated changes.
| Check | What it can reveal | What it cannot prove |
|---|---|---|
| Build, compiler, and type checker | Syntax errors, incompatible APIs, type mismatches, missing symbols, and some unreachable paths. | Correct business behavior, complete authorization, safe defaults, or secure deployment. |
| Unit and integration tests | Regressions and expected behavior exercised by the test cases, including interactions between components. | Unrepresented edge cases, malicious inputs, race conditions, and requirements that were never encoded. |
| SAST and linters | Known insecure patterns, suspicious data flows, style violations, and some reliability defects. | Whether a user is allowed to perform an action or whether a workflow implements the right business rule. |
| SCA and lockfile review | Known vulnerable dependencies, license concerns, unexpected transitive packages, and version drift. | Whether a legitimate package is used safely or whether an unlisted runtime service is trustworthy. |
| Secret scanning | Credentials and tokens that match detector patterns in source, history, or artifacts. | Secrets encoded in unusual ways, leaked through logs, or exposed by an overly permissive identity. |
| DAST, IAST, and API tests | Runtime behavior, deployed configuration, and some exploitable input or authorization paths. | Full source coverage, every race, every tenant boundary, or untested business logic. |
| Infrastructure-as-code scanning | Public exposure, weak IAM, unencrypted resources, risky network rules, and insecure defaults. | Whether the deployed environment matches reality or whether application code uses the resource safely. |
Use the repository’s documented commands and preserve their output with the review record. Typical projects might use commands such as npm test, pytest, mvn test, or go test ./..., but the project’s own scripts are authoritative. A failed check needs a fix or an explicitly documented, approved reason; do not hide failures by weakening the check.
A clean scan is the beginning of review, not the end. OWASP’s DevSecOps Secure Code Review guidance notes that scanners rarely detect broken access control and business-logic flaws. Treat automated output as evidence to investigate, not as a verdict.
4. Test behavior, boundaries, and failure paths
Compare the implementation with the intended behavior line by line at the level of states and decisions. A generated patch often handles the happy path while omitting what happens when a value is missing, repeated, stale, malformed, too large, concurrent, or partially processed.
Use a boundary checklist
- Test empty, null, maximum-length, negative, unusually large, and wrong-type values where the language or protocol permits them.
- Test duplicate requests, retries, timeouts, cancellations, partial writes, and out-of-order events.
- Test expired sessions, revoked credentials, deleted records, disabled accounts, and objects belonging to another tenant.
- Test downstream failures such as unavailable databases, slow APIs, malformed third-party responses, and exhausted queues.
- Check that errors fail closed, return an appropriate status, avoid leaking secrets or internal details, and leave state consistent.
- Verify that tests assert meaningful outcomes rather than merely that a function does not throw.
For input validation, authorization logic, and deserialization safety, add differential fuzz testing or property-based tests when the risk justifies it. AISVS 1.0 specifically recommends these techniques for security-critical behavior. A useful property might state that changing an object identifier must never grant access to another user’s object, or that any input rejected by the parser cannot reach a privileged operation.
Check state and concurrency
Trace read-modify-write sequences, cache invalidation, idempotency keys, transaction boundaries, locks, and retries. Ask what happens if two requests arrive at once or if a worker restarts after performing only half the operation. Generated code frequently copies a synchronous example into an asynchronous system without preserving cancellation, ordering, or atomicity.
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5. Trace security-sensitive data flows manually
For each untrusted input, follow the complete path: entry point, parsing, normalization, validation, authentication, authorization, use, storage or transmission, and output. Record the control that is supposed to protect every transition and verify that it occurs at the correct boundary.
Input and output handling
- Reject invalid data with allowlists and explicit limits where feasible; do not rely on client-side validation.
- Use parameterized database queries or the framework’s safe query API instead of concatenating strings.
- Encode output for its actual context—HTML, JavaScript, URL, shell, SQL, log, or structured data—and avoid mixing contexts.
- Handle archive paths, filenames, redirects, and URLs carefully to prevent traversal, open redirects, or server-side request forgery.
- Do not deserialize untrusted objects with mechanisms that can instantiate arbitrary classes or execute callbacks.
Authentication and authorization
Verify authentication before privileged work and authorization against the resource being accessed, not merely against a route or UI state. Check ownership, tenant, role, scope, and operation separately where the application requires it. Look for direct-object-reference bugs, missing checks on secondary endpoints, confused deputy behavior, and authorization that is performed only in the client.
Secrets and cryptography
Search source, configuration, logs, tests, and generated files for hard-coded credentials, tokens, private keys, and connection strings. Confirm that secret values come from the approved secret-management path and are not printed during errors. Reject custom cryptography and deprecated algorithms unless a security expert has documented the requirement; use the platform’s maintained primitives with correct key handling, nonce rules, certificate validation, and rotation behavior.
Errors, logs, and privacy
Ensure exceptions do not reveal stack traces, credentials, session identifiers, personal data, SQL statements, or internal network details to untrusted callers. Logs should support investigation without becoming a second data-leak channel. Check redaction, access controls, retention, correlation identifiers, and whether attacker-controlled text can forge log entries.
Dependencies and provenance
Confirm the identity and source of every new package, action, container image, model-generated snippet, and remote script. Pin versions or immutable references where the project’s policy requires it, review update mechanisms, and verify that build jobs cannot silently download and execute an unexpected artifact.
6. Give infrastructure and pipeline changes their own security review
A code change can be functionally correct while its deployment makes the system unsafe. Inspect whether services are exposed to the public internet, which identities can read or write resources, whether data is encrypted in transit and at rest, and whether audit logging is enabled. Check network segmentation, firewall rules, storage policies, backup access, and the permissions granted to runtime and build identities.
For CI/CD, examine trigger conditions and token scope. Untrusted pull-request code must not receive production credentials or broad repository write access merely because a workflow needs to run tests. Review action and image pinning, artifact uploads, cache sharing, script interpolation, and secret availability in forked or external contributions. Run infrastructure-as-code and secret scans alongside application checks, then manually compare the declared configuration with the organization’s deployment policy.
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7. Review maintainability as a first-class requirement
Correct and secure code still creates long-term risk if the next developer cannot understand or safely modify it. Compare the patch with established architectural boundaries and familiar patterns in the repository.
Architecture and complexity
- Does each responsibility live in the layer that owns it, or did the generated code place authorization, persistence, and presentation logic together?
- Does it introduce a new abstraction where an existing one would suffice?
- Does it duplicate validation, error mapping, serialization, or business rules that can drift apart?
- Are control flow, side effects, and state transitions easy to follow?
- Are timeouts, retries, resource limits, and cancellation explicit?
Names, tests, and documentation
Names should describe domain intent rather than implementation trivia. Comments should explain constraints and decisions, not repeat obvious syntax or claim behavior the code does not enforce. Tests should demonstrate the contract, include failure and authorization cases, and remain readable after the model’s generated fixtures are removed. Update interface documentation, migration notes, operational runbooks, and configuration examples when the change alters how another team uses the system.
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8. Scale review depth to risk
Use the changed behavior and trust boundaries—not the number of generated lines—to set the approval threshold.
| Risk level | Typical examples | Minimum review response |
|---|---|---|
| Routine | Isolated presentation change with no new data, permissions, dependencies, or deployment effects. | Normal owner review, build, tests, linting, and diff inspection. |
| Elevated | New external integration, data-store query, file handling, background job, dependency, or public endpoint. | Expanded edge-case and data-flow testing, SAST/SCA/secret checks, and a second engineer familiar with the component. |
| Critical | Authentication, authorization, cryptography, deserialization, tenant isolation, IAM, network exposure, deployment, or CI/CD privilege. | Qualified independent human review, targeted or property-based security tests, infrastructure checks, and the organization’s required security-team or two-person sign-off. |
OWASP AISVS 1.0 says AI-generated code should always receive review by a qualified human engineer and calls for stronger review controls for security-critical code. Record the approving developer and, when useful for your process, the AI tool and model version. Attribution makes later incident analysis and maintenance more reliable; it does not make the model responsible for the decision.
9. Compare review methods by coverage, not marketing claims
Teams often combine ordinary peer review, deterministic scanners, model-generated review comments, and specialized security testing. Compare each method against the actual risk rather than asking which tool is “best.”
| Comparison axis | Questions to ask |
|---|---|
| Coverage | Does it inspect functional behavior, security boundaries, maintainability, dependencies, secrets, infrastructure, or only one of these? |
| Determinism | Will the same input produce a reproducible result, and can a finding be tied to a rule or test? |
| Context | Can it see architecture, historical decisions, runtime configuration, identity policy, and neighboring services? |
| Verification | Can a reviewer reproduce the finding, inspect the evidence, and test the proposed remediation? |
| Repository support | Does it understand the languages, frameworks, generated files, monorepo layout, and deployment system you actually use? |
| CI integration | Can it run at the right pull-request stage without exposing secrets or granting untrusted code excessive permissions? |
| Human ownership | Is a qualified developer still accountable for the decision and able to explain why the result is acceptable? |
GitHub’s documentation describes deterministic CodeQL quality queries alongside LLM-powered findings; that combination can broaden attention, but it is not evidence that every defect will be found. Keep manual review for authorization, business rules, architecture, and deployment context that automated analysis cannot infer reliably.
10. A repeatable approval procedure
- Read the requirement and architecture. State the intended behavior, non-goals, affected components, data classifications, and security controls.
- Map the diff. List changed application files, tests, dependencies, configuration, workflows, infrastructure, instruction files, and hooks.
- Confirm ownership. Identify a developer who understands the patch and can explain every consequential decision.
- Run the normal gates. Build, type-check, lint, unit tests, integration tests, SAST, SCA, secret scanning, and relevant dynamic or infrastructure scans.
- Review test quality. Add or request tests for boundaries, failure paths, authorization, concurrency, retries, and state consistency.
- Trace sensitive flows. Follow untrusted input through validation, authorization, use, storage, and output; inspect secrets, cryptography, deserialization, errors, and dependency provenance.
- Inspect deployment effects. Check IAM, public exposure, encryption, logging, workflow triggers, token scope, pinned actions or images, and secret handling.
- Assess maintainability. Verify architecture fit, simplicity, names, duplication, documentation, operational behavior, and future testability.
- Resolve findings. Require evidence for fixes, rerun affected checks, and document accepted residual risk with an owner and expiry where applicable.
- Apply the right approval threshold. Obtain independent qualified review or security sign-off for critical paths before merge.
Common failure modes and how to recover
“All tests passed, so it is safe.”
Tests cover only their assertions. Add manual authorization and data-flow tracing, negative cases, dependency review, and deployment checks. If the requirement is not represented in a test, write one or record why another control covers it.
“The scanner found nothing.”
Verify that the scan covered the changed languages, generated files, dependencies, infrastructure, and reachable deployment configuration. Then perform manual review for broken access control, business logic, unsafe defaults, and trust-boundary mistakes that scanners commonly miss.
“The model’s explanation sounds reasonable.”
Reconstruct the behavior from the code and tests. Ask the owner to explain assumptions, failure paths, and permissions. Replace unsupported assumptions with explicit validation, tests, documentation, or a narrower design.
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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minute“The patch is too large to understand.”
Pause approval. Split unrelated refactors, generated formatting, dependency upgrades, and feature work into separate changes. A smaller, reviewable patch reduces the chance that a security-sensitive line is lost in noise.
“A security fix broke compatibility.”
Do not revert a protection blindly. Reproduce the compatibility issue, identify the contract that changed, and design a safe migration—such as versioned input handling, staged enforcement, or a documented client update—while preserving the security boundary.
“A finding is probably a false positive.”
Show the data-flow or configuration evidence that makes it unreachable or safe, add a regression test or suppression rationale where appropriate, and assign an owner to revisit the decision if the surrounding code changes.
The decision rule
Merge AI-generated code only when a human owner can explain it, the complete diff matches the intended change, deterministic checks pass, consequential paths have been tested and traced, maintainability is acceptable, and the approval level matches the risk. AI can accelerate implementation and point reviewers toward suspicious areas; it cannot replace human understanding, security judgment, or accountability.
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