AI Governance 101 for Financial Institutions

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.

Core Framework

A practical AI governance framework for regulated organizations typically addresses four domains:

1. Inventory & Classification — What AI tools are in use, who approved them, and what data do they touch?
2. Policy & Acceptable Use — What are the rules for using AI, and are they written down and communicated?
3. Vendor Risk Management — How are third-party AI providers vetted, contracted, and monitored?
4. 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.