AI Policy Template for Engineering Teams
Built for software development and engineering teams
Engineering teams use AI to write code, review pull requests, and deploy models to production. Each of these activities carries governance risks that generic policies do not address: license contamination from AI-generated code, unreviewed security vulnerabilities, and undocumented training data. Engineering-specific AI policy puts guardrails where the code is written.
Policy Needs for Engineering Teams
- Code-generation AI acceptable-use rules covering Copilot, Cursor, and similar tools
- Open-source license compliance when AI generates code derived from licensed repositories
- Security review requirements for AI-generated code before production deployment
- Intellectual property assignment clauses for code produced with AI assistance
- AI model deployment governance including testing, staging, and rollback procedures
- Internal AI tool development guidelines covering training data, bias testing, and documentation
Key Clauses to Include
- 1Code AI Acceptable UseDefine which code-generation AI tools are approved for development use, what codebases they may access, and what review is required before AI-generated code is merged.
- 2License Contamination PreventionRequire automated license scanning of AI-generated code to detect potential open-source license contamination before the code enters the proprietary codebase.
- 3Security Review GateMandate security review of AI-generated code through automated SAST/DAST scanning and human code review before any production merge, with no exceptions for AI-generated patches.
- 4Model Deployment GovernanceEstablish a staged deployment pipeline for internally developed AI models with required testing, approval gates, monitoring, and documented rollback procedures.
- 5Training Data ProvenanceRequire documentation of training data sources, licensing, and bias characteristics for all internally developed AI models, maintained in a model card format.
What Generic Templates Miss
- Generic templates do not address code-generation AI tools and the unique license contamination risks they introduce to proprietary codebases
- Standard policies lack model deployment governance, treating AI models like regular software releases without the additional testing and monitoring they require
- Boilerplate frameworks ignore training data provenance documentation, which is essential for defending against IP infringement claims and bias challenges
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