The AIGP, in four domains.

What AI is, where its risks come from, the principles every framework shares, and how an organization sets up people and policy to govern it.

1. 1.1 What AI is, and why it needs governing
2. 1.2 The AI lifecycle
3. 1.3 AI risks and harms
4. 1.4 Responsible and trustworthy AI principles
5. 1.5 Building an AI governance program

How privacy, consumer, anti-discrimination and IP law already reach AI; the EU AI Act in depth; NIST AI RMF and ISO standards; and the global landscape.

1. 2.1 Privacy and data protection law applied to AI
2. 2.2 Consumer, anti-discrimination, liability and IP law
3. 2.3 EU AI Act I: scope, roles and risk tiers
4. 2.4 EU AI Act II: obligations, GPAI, dates and penalties
5. 2.5 NIST AI RMF, ISO standards and other frameworks
6. 2.6 The global AI law landscape

The controls and records that belong at every stage when you build an AI system: design, data, training, testing, release and monitoring, plus generative AI specifics.

1. 3.1 Plan and design: scoping the system
2. 3.2 Data governance and bias
3. 3.3 Building and training with privacy in mind
4. 3.4 Testing, explainability and security
5. 3.5 Release, monitoring and change control
6. 3.6 Governing generative AI

Governing AI you buy or deploy: assessing the use case, choosing human oversight, vendor due diligence and contracts, monitoring, incidents and retirement.

1. 4.1 Assessing the use case and human oversight
2. 4.2 Procurement, third-party risk and contracts
3. 4.3 Operating, incidents and decommissioning