playbook
AI code review policy for small engineering teams
A practical policy template for small engineering teams using AI to write code, review pull requests, and enforce secure review gates.
Bottom line
AI can help draft code, explain diffs, suggest tests, and flag review issues, but it should not become the reviewer of record for production code. A small team still needs human ownership, branch protection, required checks, security review for sensitive areas, and a clear rule for when AI-generated changes must be narrowed or rejected.
Use this operating rule:
AI may assist code review, but a qualified human approves the pull request, owns the risk decision, and confirms the evidence before merge.Before adopting AI review broadly, run the AI Tool Risk Checker and record the approved tool, repository scope, and review owner in the Small Team AI Security Checklist.
Policy template
Copy and adapt this one-page policy.
AI code review policy
Purpose:
AI tools may help developers draft code, summarize pull requests, suggest tests, and identify review questions. AI does not replace human code ownership.
Allowed:
- Ask AI to explain a diff.
- Ask AI to suggest tests or edge cases.
- Ask AI to identify risky files, missing validation, or unclear behavior.
- Use AI review comments as reviewer input.
Not allowed:
- Merge a pull request solely because AI approved or suggested it.
- Use AI to bypass required reviews, branch protection, CI, secret scans, or deployment checks.
- Paste secrets, customer data, production logs, private keys, or regulated data into an AI prompt.
- Accept large AI-generated changes that reviewers cannot understand.
- Let AI-authored code reach production without a human reviewer and test evidence.
Required for merge:
- Named human author or owner.
- Human reviewer for the affected code path.
- Passing CI and security checks.
- Test evidence or documented manual verification.
- Extra review for auth, billing, customer data, permissions, migrations, infrastructure, CI, deployment, and security-sensitive files.
Review cadence:
Review this policy every 30 days during the AI coding pilot, then quarterly.Pull request risk matrix
| PR type | AI use allowed | Required review gate |
|---|---|---|
| Docs, comments, tests, small refactors | AI may draft and summarize changes. | One human reviewer or normal team process. |
| Product code in low-risk paths | AI may suggest code and tests. | Human reviewer confirms behavior, tests, and rollback. |
| Authentication, authorization, sessions, secrets | AI may explain or suggest tests, but changes need careful human review. | Owner review plus targeted security checks. |
| Billing, payments, data deletion, privacy controls | AI may draft only under close supervision. | Senior review, test evidence, and rollback plan. |
| Database migrations and infrastructure | AI may help prepare a plan or checklist. | Manual approval before execution and production deployment. |
| CI, release, branch protection, secret scanning | AI may explain existing config. | Senior review before any change that can weaken controls. |
Review checklist
Use this checklist for every AI-assisted PR.
- PR description says whether AI drafted, reviewed, or summarized the change.
- A human understands the diff without relying on the AI summary.
- The change is small enough to review in one sitting.
- Tests cover the behavior that changed.
- CI and secret scanning passed.
- No secrets,
.envvalues, customer data, or production logs were used in prompts. - New dependencies are justified and lockfile changes are reviewed.
- Auth, billing, data, infrastructure, CI, and deployment files have an appropriate reviewer.
- Rollback path is clear for production-facing changes.
- AI suggestions that were rejected are not hidden in follow-up commits.
Branch protection baseline
Small teams using AI code generation should not rely on personal discipline alone. At minimum:
| Control | Suggested setting |
|---|---|
| Pull request reviews | Require at least one human approval before merge. |
| Stale approvals | Dismiss or re-check approvals after new commits to sensitive PRs. |
| Required checks | Require build, tests, linting, and secret scan where available. |
| Code owners | Use owners for auth, billing, data, infrastructure, and deployment paths. |
| Direct pushes | Restrict direct pushes to protected branches. |
| Force pushes | Disable on protected branches. |
| Admin bypass | Keep bypass narrow and documented. |
AI reviewer rules
AI review comments are useful input, not final authority.
| AI reviewer output | Team action |
|---|---|
| Finds a likely bug | Ask the human author to respond with code, test, or explanation. |
| Suggests a style change | Accept only if it matches project standards. |
| Flags a security concern | Route to an owner; do not dismiss because the AI might be wrong. |
| Says “looks good” | Treat as no approval. Human review is still required. |
| Produces many noisy comments | Narrow the prompt, reduce PR size, or disable AI review for that path. |
| Misses an obvious issue | Record the gap and avoid relying on AI review for that class of change. |
Evidence checked
- GitHub’s protected branch documentation covers required reviews, code owner review, stale approval handling, required status checks, and branch protection behavior.
- GitHub’s Copilot code review documentation explains how Copilot review can be requested and notes that new pushes after a Copilot review are not automatically re-reviewed.
- NIST’s Secure Software Development Framework SP 800-218 provides a secure development baseline for reducing software vulnerability risk across the SDLC.
- OWASP’s Code Review Guide and Secure Code Review Cheat Sheet support keeping manual secure code review in the development process.
- Cybergiz’s AI coding production incident risk playbook and AI agent terminal approval playbook define extra controls for agentic changes and terminal commands.
Two-week rollout
| Day | Action | Exit condition |
|---|---|---|
| 1 | Pick one repository and one AI review tool. | Repository owner and pilot scope are written down. |
| 2 | Turn on branch protection and required checks if missing. | PRs cannot bypass human review. |
| 3-5 | Use AI only for diff explanation, test ideas, and review prompts. | Human reviewers still make final decisions. |
| 6-8 | Review AI comments for usefulness and noise. | Keep, narrow, or disable AI review by path. |
| 9-10 | Test one sensitive PR path with extra review. | Auth, data, or deployment changes require owners. |
| 11-14 | Decide whether to expand. | Decision includes owner, risk notes, and next review date. |
FAQ
Can AI approve pull requests?
No. AI can provide review input, but the accountable approval should come from a human reviewer who understands the affected code path.
Should every AI-generated PR disclose AI use?
Yes for a small team. Disclosure does not need to be dramatic; a short PR note such as “AI-assisted draft, human-reviewed” is enough to help reviewers calibrate risk.
What if Copilot or another AI reviewer misses a bug?
Treat that as expected. AI review is an assistant layer, not a quality guarantee. Keep required tests, branch protection, human review, and release rollback controls.
Are AI code review tools safe for security review?
They can help surface questions, but security-sensitive changes still need a human reviewer and targeted tests. Do not outsource authentication, authorization, secrets, or data-handling review to AI alone.
What is the simplest rule for a five-person team?
Keep PRs small, require one human approval, run tests and secret scans, and never merge an AI-authored change that the reviewer cannot explain. Record this in the Small Team AI Security Checklist.