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AIGP
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AIGP study guide
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Contents
Overview & AI Governance Foundations
1.1 AIGP exam facts & study strategy
1.2 What AI is: ML, deep learning & generative AI
1.3 Responsible AI principles
1.4 Stakeholders, roles & the AI lifecycle
Laws & Regulations Applying to AI
2.1 Existing law applied to AI: privacy, data protection & IP
2.2 Discrimination, consumer protection & liability
2.3 AI-specific laws: the EU AI Act & the US landscape
Standards & Frameworks
3.1 The OECD AI Principles
3.2 The NIST AI Risk Management Framework
3.3 ISO/IEC standards & assurance/audit
Governing AI Development
4.1 Use-case definition & impact/risk assessment
4.2 Design & build controls
4.3 Data governance: quality, bias & provenance
4.4 Training, testing & release readiness
Governing AI Deployment & Use
5.1 Deployment decisions & human oversight
5.2 Vendor & third-party risk review
5.3 Post-market monitoring & incident response
5.4 User controls, transparency & downstream-harm mitigation
Operationalizing AI Governance
6.1 Building an AI governance program & maturity models
6.2 Documentation, traceability & continuous improvement
