Use AI Code Assistants Safely

Adopt AI pair-programmers without compromising code quality, security, or IP.

Principles

• Human-in-the-loop: Treat AI as an assistant, not an authority.
• Review everything: Always diff, lint, and test AI changes.
• Context discipline: Share only what’s required.
• Document decisions: Record trade-offs in PRs.

Safe Workflow

1. Scaffold: Use AI to draft boilerplate, tests, and docs.
2. Isolate: Create a branch; commit small, reviewable diffs.
3. Verify: Run linters, formatters, and unit tests locally/CI.
4. Refine: Ask AI to improve names, split functions, and add comments.
5. Review: Perform normal peer review before merge.

Security & IP

• Avoid pasting secrets or customer data into prompts.
• Use enterprise options (SSO, data controls) where available.
• Prefer on-device or self-hosted models for sensitive code.
• Attribute third-party code and check licenses.

Quality & Tests

• Require tests for AI-generated logic; include edge cases.
• Enable static analysis (ESLint, TypeScript, Sonar) in CI.
• Benchmark performance-sensitive paths; avoid hidden complexity.

Team Policy Checklist

• Approved tools and versions.
• Allowed repositories/projects and data handling rules.
• Prompt hygiene and output review requirements.
• Incident process for potential IP or security exposure.

Recommended Tools

These integrate with common IDEs and support team controls. Choose based on IDE fit, privacy posture, and budget.

FAQ

Will AI leak my code? Use enterprise settings and read vendor data policies; avoid sharing secrets in prompts.

How to measure ROI? Track PR cycle time, defect rates, and time-on-task before/after adoption.