AI Code Review Policy for Copilot Teams

The first time an auditor asks "what's your process for reviewing AI-generated code?" and you don't have an answer, you realize the gap. Most teams have informal practices — "we review it like any other PR" — but no written policy. That worked when AI assistants generated occasional snippets. It breaks down when 40% of the diff is AI-generated.

A written policy does two things: it gives your team a consistent standard to enforce, and it gives your auditor something to read. This post walks through the three gates a policy needs, shows the tooling and config for each, and ends with a one-page template you can adapt today.

Quick Navigation

• Why "Review It Like Any Other PR" Fails
• The Three Gates a Policy Should Define
• Gate 1 — Pre-Commit
• Gate 2 — CI Enforcement
• Gate 3 — Human Review Threshold
• The Audit Trail
• A One-Page Policy Template

Why "Review It Like Any Other PR" Fails

AI-generated code fails in categories that human-written code rarely does: hallucinated package imports that install at pip-install time and fail at runtime, hardcoded credentials in example blocks that reviewers miss because the pattern looks intentional, and O(N²) performance anti-patterns that are invisible on small datasets. Standard PR review norms weren't designed to catch these.

Stack Overflow's Developer Survey has tracked a widening gap between AI tool adoption and developer trust in that output. Developers are using AI coding tools faster than teams are building governance for them. The informal review norm — "another pair of eyes on the diff" — assumes the reviewer knows what failure modes to look for. With AI-generated code, the failure modes shifted and the review norms didn't.

Three things change when a significant share of your code comes from an AI assistant. Reviewers lose calibration: when every function looks plausible, the reviewer's "this feels wrong" detector degrades. The blast radius of a bad pattern grows: AI assistants repeat their own errors consistently, so one bad pattern tends to appear across many files. And the paper trail disappears: "the model generated this" isn't an explanation a security team can evaluate.

A policy replaces the calibration loss with explicit gates.

The Three Gates a Policy Should Define

A code review policy for an AI-augmented team needs three defined gates: a pre-commit check that runs locally before a developer pushes, a CI gate that must pass before a pull request can merge, and a human review threshold that specifies when a human is required rather than optional. Without all three, the policy has gaps.

A pre-commit gate catches the worst problems before they enter the repository. A CI gate makes passing the scan a merge prerequisite, not a suggestion. A human review threshold handles the cases that a pattern scanner doesn't have context to evaluate — logic errors, architectural decisions, and anything touching authentication or money. Each gate handles a different failure class. Removing any one of them leaves a category uncovered.

Gate 1 — Pre-Commit

BrassCoders runs a local, offline scan before each commit succeeds, catching critical findings — hardcoded secrets, insecure subprocess calls, hallucinated imports — before they enter the repository. The OSS core makes zero outbound network calls, so the check works on every developer machine without an account. It adds roughly 10 to 30 seconds to the commit flow.

Wire it up with pre-commit:

# .pre-commit-config.yaml
repos:
  - repo: local
    hooks:
      - id: brasscoders-scan
        name: BrassCoders offline scan
        language: system
        entry: brasscoders --offline scan
        pass_filenames: false
        stages: [commit]

Install BrassCoders from PyPI (pip install brasscoders) and activate the hooks with pre-commit install. The hook runs brasscoders --offline scan on every commit. Critical findings exit with code 1 and block the commit. Non-critical findings write to .brass/ for review.

Policy language to include:

Pre-commit gate: All commits to shared branches must pass a BrassCoders offline scan.
Critical findings block the commit. Non-critical findings are logged to .brass/ for review.

Gate 2 — CI Enforcement

BrassCoders exits with code 1 on any CRITICAL finding, which fails the GitHub Actions step — and paired with a branch protection rule requiring that check to pass before merge, the gate is enforceable rather than advisory. The .brass/detailed_analysis.yaml artifact uploads with each run and serves as the audit trail.

GitHub Actions configuration:

# .github/workflows/brass-scan.yml
name: BrassCoders Scan

on:
  pull_request:
    branches: [main, develop]

jobs:
  brass-scan:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Set up Python
        uses: actions/setup-python@v5
        with:
          python-version: "3.12"

      - name: Install BrassCoders
        run: pip install brasscoders

      - name: Run BrassCoders scan
        run: brasscoders --offline scan

      - name: Upload scan artifact
        if: always()
        uses: actions/upload-artifact@v4
        with:
          name: brass-analysis
          path: .brass/
          retention-days: 90

After the workflow runs once, add a branch protection rule in GitHub: Settings > Branches > Branch protection rules > Require status checks to pass before merging, then add BrassCoders Scan / brass-scan as a required check. Reference: GitHub's branch protection documentation.

Policy language to include:

CI gate: All pull requests must pass the BrassCoders CI scan before merge.
Branch protection rules require this check. Override requires an explicit exception logged in the PR description.

Gate 3 — Human Review Threshold

Automated gates catch rule-based patterns. Human review catches logic errors, architectural concerns, and context a scanner doesn't have. A policy should define when human review is required — not optional.

The minimum threshold: any PR touching authentication, payment handling, or data persistence requires human approval from a senior engineer, regardless of automated scan results. The scan passed doesn't mean the logic is sound.

Change type	Automated scan sufficient?	Senior review required?
Internal tooling, no user data	Yes	No
API endpoint changes	Yes	Yes
Auth / session handling	Yes	Yes (mandatory)
Payment / billing code	Yes	Yes (mandatory)
Data migration	Yes	Yes + DBA sign-off

The right column answers the question independently of what the scan found. A CRITICAL finding in auth code requires both the scan to pass (after the finding is resolved) and a senior approval. A clean scan on auth code still requires a senior approval. These aren't redundant: the scan catches pattern violations; the human catches the things a pattern scanner has no visibility into.

Set this threshold in your GitHub branch protection rules by requiring specific code owners to review PRs that touch specified path patterns. A CODEOWNERS file maps directory paths to required reviewers:

# CODEOWNERS
/src/auth/           @your-org/senior-engineers
/src/payments/       @your-org/senior-engineers
/src/migrations/     @your-org/senior-engineers @your-org/dba-team

The Audit Trail

The .brass/detailed_analysis.yaml file generated by each CI run contains every finding with its file path, line number, severity, scanner source, and evidence string — and uploaded as a GitHub Actions artifact, it gives an auditor a reproducible, timestamped record of what was scanned, what was found, and what passed the gate.

BrassCoders runs 12 scanners — Bandit, Pylint, Pyre/Pysa (taint analysis), Semgrep, ast-grep, detect-secrets from Yelp, plus six custom detectors for secrets, privacy/PII, AI-generated patterns, performance, content moderation, and JavaScript/TypeScript. The detailed_analysis.yaml reflects the union of their findings, with each finding tagged to its originating scanner. An auditor can trace any finding back to the specific pattern that fired it.

The artifact retention in the Actions YAML above is set to 90 days. Match this to your organization's minimum retention requirement. Most SOC 2 programs ask for 90 days of scan history; some require a year. Adjust the retention-days value accordingly. The same commit hash plus the same BrassCoders version always produces the same output, so the artifact is reproducible as well as timestamped.

One more thing worth noting: the BrassCoders OSS core is Apache 2.0 licensed. The CI scan runs entirely offline. No data leaves the machine during a CI scan. If your auditor asks what data leaves the environment, the answer for the OSS core is: nothing.

A One-Page Policy Template

Copy and adapt this for your organization. Replace bracketed placeholders with your specifics.

# AI-Augmented Code Review Policy

**Version:** 1.0 | **Effective:** [DATE] | **Owner:** [TEAM LEAD / VP ENG]

## Scope

This policy applies to all code commits where AI coding tools — including
GitHub Copilot, Cursor, Claude Code, and similar — were used to generate
or substantially modify code.

## Pre-Commit Gate

All commits to shared branches must pass a BrassCoders offline scan
(`brasscoders --offline scan`). Critical findings block the commit.
Non-critical findings are logged to .brass/ for developer review before
the next commit.

## CI Gate

All pull requests must pass the CI BrassCoders scan before merge.
Branch protection rules require this check.

Exception: hotfixes during an active incident may bypass with an explicit
PR comment documenting the exception and the reason. A follow-up PR
resolving the bypassed findings must be opened within [X] business days.

## Human Review Requirements

- Routine changes with no user data: 1 peer review
- API endpoint changes: 1 peer review + 1 human reviewer
- Authentication, session handling, payment code, data persistence:
  1 senior engineer approval (mandatory, regardless of scan result)
- Data migrations: senior engineer approval + DBA sign-off

## Audit Trail

Each CI scan uploads .brass/detailed_analysis.yaml as a GitHub Actions
build artifact. Retention: 90 days minimum (adjust to meet your
compliance requirement).

## Exceptions

Any bypass of a required gate must be documented in the pull request
description with the exception reason and the name of the approving
engineer.

## Review Cycle

This policy is reviewed [quarterly / annually] or following any
security incident attributable to AI-generated code.

The template covers the three gates, defines explicit human review triggers, and specifies the audit artifact and its retention. It doesn't try to enumerate every AI tool or every failure mode — that's the scanner's job. The policy defines what must pass, when a human must sign off, and what gets saved for the auditor.

Start by wiring up the CI gate. It's the highest-leverage change: it makes the scan a merge prerequisite rather than a suggestion, and it generates the audit artifact automatically on every PR. The pre-commit hook and the human review threshold add depth — but the CI gate is the one that closes the loop.

BrassCoders OSS core is on PyPI: pip install brasscoders. The source is at github.com/CopperSunDev/brasscoders. Apache 2.0, no account required for the offline scan.