AI Policy Frameworks Every Organization Needs
As AI adoption accelerates, organizations need governance frameworks that enable innovation while managing risk. Here's our approach.
The speed of AI adoption is outpacing most organizations' ability to govern it. Teams are deploying AI tools, employees are using ChatGPT for work tasks, and leadership is asking: "What's our AI strategy?"
The answer starts with policy.
Why AI Policy Matters Now
Without clear guidelines, organizations face real risks:
- Data leakage — Employees pasting sensitive data into public AI tools
- Compliance gaps — AI-generated content that doesn't meet regulatory requirements
- Liability questions — Who's responsible when AI makes a mistake?
- Inconsistent quality — Different teams using AI differently with varying results
Our Framework
At Subterra, we've developed a practical AI governance framework built around four pillars:
1. Acceptable Use
Define what AI tools are approved, what data can be shared with them, and what outputs need human review. This isn't about restricting innovation — it's about channeling it safely.
2. Data Classification
Not all data should be treated equally. Public marketing copy? Fine for AI assistance. Client financial records? That needs a different workflow with appropriate safeguards.
3. Quality Assurance
Establish review processes for AI-generated outputs. The level of review should match the stakes — a social media draft needs less oversight than a legal document.
4. Continuous Learning
AI capabilities change fast. Your policies should too. Build in quarterly reviews and create feedback channels so your team can flag issues and suggest improvements.
Getting Started
The biggest mistake organizations make is waiting for perfect policy before allowing any AI use. By then, your team is already using it — just without guardrails.
Start with the basics. Define your boundaries. Then iterate.
Need help building your AI policy framework? Let's talk.
