Most startups have more AI in production than leadership realizes — engineers calling LLM APIs directly, teams using consumer AI tools with company data, and no consistent policy tying it together. AI governance doesn't need to be heavyweight, but it does need to exist before it becomes an incident.
Step 1: Discover shadow AI first
You can't govern what you don't know exists. Before writing any policy, inventory actual AI usage: which teams use ChatGPT, Claude, Copilot, or other tools with company or customer data; which engineering systems call LLM APIs directly; and which SaaS vendors you use have quietly added AI features that process your data.
Step 2: Classify your AI use cases by risk
| Risk tier | Example | Governance needed |
|---|---|---|
| Low | Internal brainstorming, non-sensitive drafting | General acceptable use policy |
| Medium | Customer support drafting, internal code review assistance | Data handling guidelines, no customer PII in prompts |
| High | Customer-facing AI features, automated decision-making | Formal review process, testing, human oversight requirements |
Step 3: Write a policy people will actually follow
A 40-page AI policy nobody reads is worse than no policy — it creates false compliance confidence. Keep it to the essentials: what data can and can't go into AI tools, which tools are approved, who to ask before adopting a new one, and what happens if AI-generated content ships to customers.
Step 4: Build in review points for AI-powered features
- Security review before any customer-facing AI feature ships
- Bias and accuracy testing appropriate to the use case's impact
- Clear disclosure to customers when they're interacting with AI
- Human escalation path for high-stakes automated decisions
Step 5: Revisit quarterly
AI tooling and organizational usage change fast. A governance framework that was right six months ago may already be missing new tools or use cases — build in a quarterly review rather than treating this as a one-time project.
Bottom line
Start with visibility, not restriction. Most AI governance failures come from not knowing what's being used, not from having weak rules for what you do know about.