Your developers need to understand what you expect when it comes to AI-assisted coding. Hoping for the best will end in frustration, so let’s prevent that from happening! Here’s a template for a mid-sized company.
🎯 Core Principle
You are always responsible for the code you submit. "AI wrote it" is never an excuse.
📋 Overview
This policy balances innovation with governance, enabling teams to leverage AI tools effectively while maintaining enterprise-grade quality, security, and compliance standards. We encourage AI adoption within established guardrails.
What You SHOULD Do
Development Practices
Use approved AI tools for productivity gains (see approved tools list)
Follow our AI workflow standards documented in the engineering wiki
Test AI-generated code thoroughly using our standard testing protocols
Review and understand every line of code before submission
Use AI for documentation generation, following our documentation standards
Leverage AI for code reviews as a supplement to human review, not replacement
Documentation & Communication
Document AI assistance in commit messages using standardized tags: [AI-ASSISTED]
Update architecture decisions when AI suggests significant pattern changes
Share AI wins in monthly engineering retrospectives
Report AI tool issues through proper channels (see escalation section)
Quality Assurance
Run full test suites regardless of code authorship
Perform security scans on all AI-assisted code changes
Follow code review checklist including AI-specific review points
Validate AI-generated tests actually test the intended functionality
Monitor code metrics for AI-assisted vs. traditional development
Security & Compliance
Use only company-approved AI tools that meet our security standards
Never share sensitive data with external AI services (PII, API keys, proprietary algorithms)
Follow data classification guidelines when using AI tools
Report security concerns immediately to the security team
What You SHOULD NOT Do
Never Submit Code That:
You cannot explain or debug - understanding is mandatory
Hasn't passed our quality gates (tests, security scans, peer review)
Was generated by unapproved tools - stick to the approved list
Contains hardcoded secrets or credentials - even if AI suggested them
Violates our architecture patterns without proper approval
Never:
Use "AI wrote it" as an excuse for bugs or poor design choices
Skip code review processes - AI code requires the same scrutiny
Submit code you're unqualified to review - stay within your expertise area
Use AI for security-critical code without security team review
Share customer data with AI tools, even for debugging
Avoid:
Using AI as a substitute for understanding the problem domain
Generating large code changes without incremental review
Relying solely on AI for architectural decisions
Bypassing established approval processes for significant changes
🔧 Approved Tools & Governance
Pre-Approved Tools (No Additional Approval Needed)
GitHub Copilot Enterprise - Company license with policy enforcement
Internal AI assistant - Company-hosted solution
VS Code AI extensions - Specific approved list in IT portal
Requires Manager Approval
External AI services (Claude, ChatGPT Pro) - for specific use cases
New AI tools - Must go through evaluation process
Custom AI integrations - Requires architecture review
Prohibited Tools
Consumer AI services with proprietary code
Unvetted AI tools not on approved list
Personal AI accounts for work purposes
📊 Monitoring & Metrics
Required Tracking
AI tool usage (automatically tracked via approved tools)
Code quality metrics for AI-assisted development
Security incident rates involving AI-generated code
Productivity improvements from AI adoption
Reporting Requirements
Monthly usage reports to engineering leadership
Quarterly ROI assessment of AI tool investments
Annual security review of AI tool compliance
🚨 Incident Response
If AI-Assisted Code Causes Issues:
Follow standard incident response - same process as any production issue
Take full ownership - AI assistance doesn't change responsibility
Conduct blameless post-mortem - focus on process improvement
Update AI usage guidelines if patterns emerge
Escalation Paths
Technical issues: Team Lead → Engineering Manager → VP Engineering
Security concerns: Immediate notification to security@company.com
Policy violations: Engineering Manager → HR → Legal (if needed)
Tool requests: Submit through IT service portal
🎓 Training & Certification
Mandatory Training
AI Tools Certification - Required before using approved tools
Security awareness - Updated annually to include AI-specific risks
Code review training - Includes AI-assisted code review techniques
Role-Specific Requirements
Senior Engineers: Complete advanced AI workshop
Team Leads: AI adoption coaching certification
Architects: AI architectural impact assessment training
📝 Code Review Process
Pre-Review Requirements
Tag AI assistance in PR description with specific tools used
Self-review checklist completed, including AI-specific items
Automated checks passed (tests, security scans, quality gates)
Reviewer Responsibilities
Verify code understanding - author can explain AI-generated logic
Check for AI-specific risks - over-engineering, security gaps, maintainability
Validate test coverage - especially for AI-generated test suites
Ensure compliance with coding standards and architecture guidelines
AI-Assisted Code Review Checklist
Code author understands all generated logic
No hardcoded secrets or credentials
Follows company coding standards
Appropriate error handling implemented
Security implications considered
Performance impact assessed
Documentation updated appropriately
🔄 Policy Governance
Review Process
Bi-annual policy review by Engineering Leadership Team
Quarterly metrics review to assess effectiveness
Ad-hoc updates for new tools or security requirements
Change Management
Policy changes require Engineering Leadership approval
Tool additions follow IT security evaluation process
Training updates coordinated with HR and Learning & Development
❓ FAQ
Q: Can I use AI for customer-facing features?
A: Yes, but follow all quality gates including security review and thorough testing.
Q: What if AI generates code I don't fully understand?
A: Don't submit it. Either learn the concepts or ask for help from senior team members.
Q: Can I use personal AI accounts for work?
A: No, use only company-approved tools to ensure compliance and security.
Q: How do I request a new AI tool?
A: Submit request through IT portal with business justification and security assessment.
Q: What if I accidentally share sensitive data with an AI tool?
A: Immediately report to security team following data breach procedures.
AI tools are powerful productivity enhancers when used responsibly. This policy ensures we gain the benefits while maintaining our standards for quality, security, and professional accountability.