AI Policy Framework
Establish clear boundaries and guidelines for AI usage within your organization with this comprehensive policy framework.
Core Principles
Every AI policy should be built on these foundational principles:
- Transparency - Be clear about when and how AI is used
- Accountability - Humans remain responsible for AI-assisted outputs
- Security - Protect sensitive data from exposure
- Quality - Maintain standards through human review
Key Policy Areas
Data Protection
Rule: No proprietary code in public LLMs
Never input the following into public AI tools:
- Source code
- API keys or credentials
- Customer data
- Financial information
- Strategic plans
- Legal documents
Human Oversight
Rule: Human review required for all AI output
Every AI-generated output must be:
- Reviewed by a qualified human
- Validated for accuracy
- Checked for bias or errors
- Approved before publication/use
Policy Template
Copy this template to create your organization's AI usage policy:
Building an AI agent? This policy covers organization-wide AI usage. Agents that take action on real systems have additional requirements — see Agent Governance & HITL for HITL approval patterns, operating rules, evaluation, and observability.
Implementation Checklist
When rolling out your AI policy:
- Executive sponsorship secured
- Legal review completed
- IT security assessment done
- Training materials developed
- Communication plan created
- Monitoring tools in place
- Incident response defined
- Review schedule established
Quick Reference: Do's and Don'ts
Do
- Use AI as an assistant, not a replacement
- Verify all AI outputs before use
- Report suspicious AI behavior
- Stay updated on policy changes
- Use approved tools only
Don't
- Share confidential data with AI
- Publish AI output without review
- Bypass security controls
- Assume AI is always correct
- Use personal AI accounts for work
Need help implementing or feeling stuck? Contact us today to establish a consulting relationship.