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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteTreat every AI-generated or AI-modified pull request as untrusted code that still needs accountable human ownership. Before merge, require a named developer who understands and can maintain the change, run the repository’s normal build, test, static-analysis, dependency and secret checks, review AI-authored tests as carefully as production code, and increase scrutiny for security-sensitive or high-impact changes. A green pipeline or an automated AI review is evidence that checks passed—not proof that the change is correct.
What an enforceable standard must cover
A useful policy answers five questions for every pull request (PR): who owns the result, what automated evidence is required, how the tests themselves are reviewed, when specialist reviewers are needed, and what blocks a merge. Apply the same baseline whether an assistant wrote an entire module, suggested a few lines, generated tests, or modified an existing implementation.
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- Accountability: A named human developer is responsible for correctness, security, operational behavior and future maintenance.
- Verification: The ordinary build, functional tests and static-analysis checks run on the exact proposed revision.
- Independent test judgment: Test files receive human review; the reviewer checks whether they can actually fail when behavior is wrong.
- Risk escalation: Changes involving privilege, trust boundaries, sensitive data, dependencies, infrastructure or delivery systems receive deeper review.
- Enforcement: Repository permissions, code-owner routing and required CI status checks make the policy difficult to bypass.
Do not make the generating agent an approver. The person who accepts the merge must be able to explain the change, its assumptions and its failure modes without relying on the agent’s summary.
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Set the baseline for every AI-assisted PR
Write these requirements into the repository contribution policy and enforce them with branch protection or the equivalent controls in your source-control platform.
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| Requirement | What the PR must show | Merge rule |
|---|---|---|
| Named owner | A responsible developer is identified in the PR and accepts responsibility for operation and maintenance. | No owner, no merge. |
| Human approval | At least one qualified human has inspected the implementation, context and tests. | The generating agent, bot or automated review cannot satisfy this requirement. |
| Build or compile check | The project builds or compiles using the repository’s supported command and configuration. | Required check must pass on the merge commit. |
| Functional tests | Existing project tests run, with new or changed tests included in the same revision. | Required test status must pass; unexplained skips are a review finding. |
| Static analysis | The project’s normal lint, type, quality and security analyzers run without unreviewed critical findings. | Block on failures and on findings classified as merge-blocking by local policy. |
| Dependency and secret checks | Available software-composition, lockfile, license and secret-detection checks run as they do for non-AI changes. | Use the repository’s existing vulnerability and secret policy; do not create an AI-specific bypass. |
| Change explanation | The PR describes the requirement, design choice, externally visible behavior and known limitations. | Reject descriptions that merely paste an agent-generated summary without human verification. |
These are process controls, not a promise that every defect will be found. The policy should state that passing checks is necessary but not sufficient for approval.
Define what counts as AI-generated
Use a broad definition so the control cannot be avoided by describing generated work as “assisted.” Include code, configuration, infrastructure definitions, database migrations, documentation that changes operational behavior, test cases and generated dependency updates when an AI system produced or materially transformed them.
Require lightweight disclosure
Ask the author to mark the PR as AI-assisted and briefly record which parts were generated or substantially rewritten. Disclosure is for routing and review depth; it is not a substitute for understanding the code. A one-line declaration can identify generated implementation, generated tests, or both.
Keep ownership with the developer
The developer should confirm that they inspected the final diff rather than accepting an intermediate chat response. They remain responsible if the agent misunderstood a requirement, invented an API, copied an incompatible license, or introduced a vulnerability. Ownership also includes rollback, monitoring and follow-up work after merge.
Use a review sequence that starts with objective checks
Run automated checks before spending scarce specialist-review time on a diff. GitHub’s guidance recommends running automated tests and static analysis first; this ordering quickly removes changes that are mechanically invalid while leaving human reviewers to judge intent and risk.
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- Reproduce the supported environment. Use the repository’s documented runtime, compiler, dependency lockfile and test data. Record any environment-specific limitation in the PR.
- Build or compile. Catch type errors, generated-file mismatches, packaging failures and incompatible API calls.
- Run the full applicable test suite. Include unit, integration, end-to-end and migration tests that normally gate the affected component. Do not silently replace an expensive required suite with a smaller AI-generated subset.
- Run static analysis. Execute the project’s normal linters, type checks, code-quality rules and security analyzers. Review warnings that the tool classifies as actionable rather than treating a zero-warning result as proof of correctness.
- Run dependency and secret checks. Inspect new packages, version changes, lockfile edits, licenses, exposed credentials and unsafe configuration. Confirm that every new dependency is real, needed and compatible with the project.
- Inspect the diff and generated artifacts. Look for unrelated edits, disabled checks, broad formatting churn, altered build scripts, generated files that should not be committed and changes outside the stated requirement.
- Perform human intent review. Verify that the change solves the requested problem, fits the architecture and preserves behavior that the automated checks do not model.
Pin required checks to the protected branch so an author cannot merge by rerunning only a convenient subset. If a check is unavailable, document the reason and require the same authorized exception process used for other pull requests.
Review AI-authored tests as production code
Tests generated by the same agent as the implementation are not independent evidence. OWASP’s Secure Coding with AI Cheat Sheet states that “A passing test suite generated by the same agent that produced the code provides no independent assurance.” The reviewer must evaluate whether each test expresses the requirement or merely confirms the generated implementation.
Check for lost or weakened coverage
- Compare the test diff for deleted cases, reduced assertion counts, broader matchers and changed expected values.
- Look for tests that now skip, return early or swallow exceptions.
- Check whether a formerly meaningful integration test was replaced with a mock of the component under test.
- Confirm that assertions inspect outcomes, permissions, persisted state or emitted events—not just that a function returned without throwing.
Challenge implementation-coupled tests
A brittle test can pass because it repeats the implementation’s algorithm. For example, a test that calculates an expected value by calling the same helper or accepting the exact internal sequence offers little protection against a shared mistake. Prefer fixtures and expected results derived from the requirement, a trusted independent implementation, or reviewed examples.
Add failure and boundary cases
For meaningful risks, require independent invalid-input, authorization, boundary, concurrency, timeout, retry and partial-failure cases as appropriate to the component. Ask what should happen when input is empty, oversized, malformed, duplicated, out of order or outside the documented range. For security controls, include both an allowed case and an attempted bypass.
Question every new mock
Mocks are useful when an external system is slow, costly or nondeterministic, but they can hide integration defects. The author should explain why each new mock is needed, what contract it represents and where a real integration or contract test covers the connection. A mock that simply returns the expected answer from the implementation under test is not meaningful verification.
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Raise review depth according to risk
There is no universal reviewer count or test-coverage percentage for AI-generated changes. Set local thresholds by comparing security impact, data sensitivity, trust-boundary or privilege changes, production blast radius, reversibility, dependency or infrastructure impact and confidence that tests cover both expected and failure behavior.
| Change class | Typical signals | Required evidence | Approval approach |
|---|---|---|---|
| Routine | Localized behavior, low-sensitivity data, no privilege or trust-boundary change, easy rollback. | Baseline build, tests, static analysis, dependency and secret checks; human review of implementation and tests. | Normal qualified human approval under branch policy. |
| Elevated | New dependency, data-handling change, shared library, migration, production configuration or wider blast radius. | Baseline evidence plus explicit compatibility, rollback and failure-path analysis; independent tests for important boundaries. | Route to the relevant code owner and add reviewer expertise required by local policy. |
| Critical | Authentication, authorization, cryptography, input parsing, deserialization, file upload, security configuration, CI/CD, infrastructure or a new trust boundary. | All baseline checks, focused negative tests, security analysis and a documented threat or abuse-case review where applicable. | Use qualified security or platform reviewers and consider two-person approval according to organizational risk policy. |
Classify the highest-risk path touched by the PR, not the average risk of its files. A small change to an authorization middleware can be more consequential than a large documentation update.
Route sensitive code to qualified reviewers
Maintain a code-owner map for security-sensitive directories and require those owners to approve matching changes. Include authentication and authorization modules, cryptographic code and key handling, parsers and deserializers, file-upload paths, security configuration, deployment pipelines, infrastructure definitions and any code that crosses a new trust boundary.
Ownership should be based on demonstrated expertise, not merely team membership. If the regular owner authored the change, route it to another qualified person. For high-impact changes, involve the security team or use two-person approval when that is the organization’s established control. Record the reason when an owner is unavailable; do not replace a required specialist with an unqualified rubber stamp.
OWASP’s DevSecOps Guideline summarizes the expectation plainly: “Generated code needs at least the same scrutiny as code from an unknown contributor.” Apply that rule even when the author has a strong history, because generated output can contain plausible but incorrect assumptions that are hard to spot in a quick review.
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Make merge gates and exceptions explicit
Block predictable failures
Protect the target branch with required status checks for build, applicable tests and static analysis. Block merges on critical security findings, leaked secrets, failed required checks and unresolved code-owner approvals. Define “critical” in the organization’s existing vulnerability policy rather than inventing a separate AI scale. OWASP AISVS uses CVSS 9.0 or higher as an example of critical severity, or an organization’s equivalent threshold; treat that as a reference for policy design, not a universal mandate.
Control exceptions
An exception should identify the failed check or finding, business reason, compensating control, expiration date and person authorized to approve it. The authorized human must understand the residual risk and assign follow-up work. Never allow the generating agent to approve its own exception, and do not turn repeated exceptions into a permanent bypass without revising the policy.
Preserve an audit trail
Keep the PR’s owner, approvals, check results, exception record and final commit together. This lets the organization determine whether a failure came from the implementation, a missing test, a misconfigured gate or an inadequate risk classification.
Give reviewers a concrete checklist
Place a short checklist in the PR template. Reviewers can mark each item or explain why it does not apply.
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- Can the responsible developer explain the design, dependencies and operational behavior?
- Does the implementation fit existing architecture, conventions and error-handling patterns?
- Did the change alter access control, input validation, data exposure or trust boundaries?
- Are every new dependency and configuration value real, necessary and compatible?
- Were tests deleted, skipped, weakened or replaced with mocks that bypass real behavior?
- Do tests include independent expected results and relevant invalid, boundary and negative cases?
- Did build, project tests, static analysis, dependency checks and secret checks run on this revision?
- Are required code owners and any security or platform specialists involved?
- Are rollback, migration and monitoring implications understood?
The checklist should prompt investigation, not become a ritual. A checked box without evidence is a reason to ask a follow-up question.
Configure repository controls that enforce the policy
- Protect the target branches. Require pull requests, prevent direct pushes, require the status checks that represent the baseline, and require the approvals specified for each risk class.
- Define code ownership. Map sensitive paths to qualified owners and make those approvals mandatory for matching changes. Review the map when teams or system boundaries change.
- Standardize the PR template. Include AI-assistance disclosure, responsible owner, affected risk areas, test commands and results, dependency or secret-check status, and rollback notes.
- Make CI reproducible. Use pinned tool and dependency versions where the project supports them, fail clearly when required checks cannot run, and publish logs that reviewers can inspect.
- Separate advisory from required checks. An experimental AI reviewer may comment, but only explicitly designated checks should gate merging. Do not imply that an automated review replaces human approval.
- Review the policy periodically. Examine escaped defects, false-positive rates, recurring exceptions and changes in architecture. Adjust local thresholds using observed risk, while keeping human accountability and independent verification intact.
Recognize red flags before approval
- The PR description says the agent “verified” the change, but no human owner is named.
- Tests pass only after a test was deleted, an assertion was weakened or a check was disabled.
- A new mock returns the exact value needed to make the implementation pass, with no contract or integration coverage.
- The diff adds a dependency whose package name, publisher, license or purpose the author cannot verify.
- Security-sensitive code changed without a code-owner approval or without negative and boundary tests.
- Build or security checks are marked optional because the agent could not run them.
- The implementation is much broader than the stated requirement, especially in configuration, permissions or CI files.
- The owner cannot explain why an algorithm, permission, retry rule or data transformation is correct.
When a red flag appears, stop the merge, narrow the change or request additional evidence. Do not compensate for uncertainty by adding an automated approval bot.
Understand what the major guidance sources do—and do not—establish
OWASP AISVS Appendix C, the OWASP Secure Coding with AI Cheat Sheet and the OWASP DevSecOps Guideline support treating generated code as code from an unknown contributor, scrutinizing generated tests and applying risk-based security review. AISVS is a living repository artifact, so verify its current version and wording before embedding quotations or thresholds in a formal policy.
GitHub’s “Review AI-generated code” documentation describes suggested workflow practices, including running automated tests and static analysis before human review. It is vendor guidance, not a universal industry standard; adapt the workflow to your build system and risk model.
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NIST’s Secure Software Development Practices for Generative AI and Dual-Use Foundation Models: An SSDF Community Profile was published on July 26, 2024. It provides broader secure-development context rather than a prescriptive pull-request checklist. Use it to align the surrounding development lifecycle, while keeping the repository policy specific about owners, checks, approvals and exceptions.
Adopt the standard without creating a second process
Start by applying the baseline to every AI-assisted PR through the same branch protection, CI and ownership controls used for other code. Then add risk-based routing and stronger evidence only where the affected system justifies it. This avoids a false choice between speed and safety: routine changes keep a predictable path, while changes that can compromise identities, data or delivery systems receive the expertise and independent testing they require.
The decisive test is human understanding. If the accountable developer and required reviewers cannot describe what the code does, why its tests are trustworthy and how the system fails safely, the PR is not ready to merge—regardless of how clean the generated diff looks or how many automated checks are green.
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