Why This Matters
AI adoption in regulated industries is accelerating faster than governance frameworks can keep pace. The gap between deployment speed and governance maturity is where regulatory exposure — and real organizational risk — lives. This guide is built to help you close that gap quickly and practically.
Key insight: Regulators are not waiting for AI governance to mature. Examination teams are actively asking about AI use today — including tools your employees may be using without formal approval.
Core Framework
A practical AI governance framework for regulated organizations typically addresses four domains:
- Inventory & Classification — What AI tools are in use, who approved them, and what data do they touch?
- Policy & Acceptable Use — What are the rules for using AI, and are they written down and communicated?
- Vendor Risk Management — How are third-party AI providers vetted, contracted, and monitored?
- Monitoring & Accountability — Who owns AI risk, how is it reported, and how are incidents handled?
Common Gaps We Find
- No formal AI use inventory — shadow AI is widespread
- Existing acceptable-use policies don't mention AI tools
- Vendor contracts don't address AI data training or retention
- No designated AI risk owner or governance committee
- Board and executive briefings haven't included AI risk
Where to Start
If you're starting from scratch, begin with an inventory. You can't govern what you don't know exists. A two-week sprint to understand what AI tools are in use — approved and unapproved — gives you the foundation for everything else.
If you already have some governance in place, a structured gap assessment against NIST AI RMF or your industry's specific regulatory guidance will tell you exactly where to focus next.
Want a structured assessment?
Our AI Risk Scorecard delivers a 48-hour evaluation of your governance posture — starting at 50.
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