Introduction
Artificial intelligence already takes part in customer service, finance, hiring and operations decisions. When a model decides or recommends, the company answers for the outcome, even if a third party built the model. That is why an AI governance framework is no longer optional.
AI governance is the set of policies, roles, controls and processes that lets you use this technology with judgment: knowing which systems exist, what data they use, who approves them and how they are watched once in production. Without that framework, AI grows through isolated initiatives and risks surface only after an incident.
A moving regulatory environment adds to this. Europe, the United States and several Latin American countries are advancing laws, voluntary frameworks and standards that affect any company operating or selling in those markets. Preparing before it is mandatory costs less than fixing things afterwards.
This whitepaper presents a seven-pillar governance framework, a sample risk register, a summary of the regulatory landscape and a 30-60-90 day roadmap to get started.
What you'll learn
- How to structure AI governance in seven actionable pillars.
- Which risks to include in an AI risk register and how to prioritize them.
- What the EU AI Act, the NIST AI RMF and ISO/IEC 42001 require or recommend.
- How to gauge your organization's maturity with a checklist.
- What to do in the first 30, 60 and 90 days.
