AIGP study guide
AIGP · Work through your exam, one topic at a time.
The Artificial Intelligence Governance Professional (AIGP) is a professional credential offered by the IAPP for people who need to understand and execute responsible AI governance. The IAPP frames it as a demonstration of competency in AI development, ethical AI deployment, and the use of best practice in AI management to ensure safety and trust - and notes that, now that AI has made its debut, professionals in all industries need this capability, not just technologists or lawyers. The AIGP study guide has 4 sections and about 1,394 words.
01Foundations of AI Systems, Their Use Cases and Impacts
The single most common source of missed points is confusing the nested categories. Artificial intelligence is the broad field of systems performing tasks associated with human cognition.
375 words, about 2 min02AI Laws, Regulation and Frameworks for Responsible Governance
Questions frequently test whether an instrument is hard law, a standard, or a framework. Binding: EU AI Act, GDPR, China's Interim Measures for Generative AI Services (2023), algorithm recommendation and deep synthesis rules, Colorado's AI Act (reasonable care and impact assessments for high-risk systems, with a delayed effective date), and sectoral US laws such as ECOA, FCRA, Fair Housing Act and HIPAA.
333 words, about 2 min03AI Life Cycle and AI Risk Management
A defensible life cycle runs: problem definition and business understanding → data sourcing and assessment → data preparation → model development and selection → validation and testing → deployment → monitoring and maintenance → decommissioning. Governance hooks attach to each stage: data provenance and lawful basis at sourcing; documentation and approval gates before deployment; drift detection, incident response and periodic revalidation after deployment; archival and model retirement at the end.
352 words, about 2 min04Emerging and Debated Issues in AI Governance
Many topics in this domain are contested rather than settled, and questions often reward recognising that a proposal is a proposal. Understand the main fault lines: capability versus existential risk framing; open-weight versus closed models (argued as both a safety risk and a transparency, competition and auditability benefit); compute governance and training-run thresholds; agentic systems that act autonomously across tools; and alignment and evaluation science, including benchmark contamination, red-teaming standards and the difficulty of measuring capability before deployment.
334 words, about 2 min