AIGP - AI Governance Professional
A self-contained, zero-background training mapped precisely to the AIGP Body of Knowledge v2.1. It preserves the exact AIGP roadmap - Domain to Competency (module) to Performance Indicator (chapter) - across 52 sequential chapters, each a short micro-lesson plus assets, with exam-aligned practice matching the BoK blueprint.
What it covers and how it works
The Certified AI Governance Professional (AIGP) validates the knowledge to develop, integrate, and deploy trustworthy AI systems in line with emerging laws and policy. This prep course maps precisely to the AIGP Body of Knowledge v2.1, covering the foundations of AI governance, how laws and frameworks apply to AI, and how to govern AI development, deployment, and use. 40 to 60 hours of self-paced learning, exam-aligned throughout.
It combines structured lessons with practical exercises and, where relevant, exam-aligned preparation. It is self-paced with lifetime access, and available as a mentor-led cohort or private corporate training.
12 modules · 52 chapters
A self-contained, zero-background training mapped precisely to the AIGP Body of Knowledge v2.1. It preserves the exact AIGP roadmap - Domain to Competency (module) to Performance Indicator (chapter) - across 52 sequential chapters, each a short micro-lesson plus assets, with exam-aligned practice matching the BoK blueprint.
01 Domain I.A - AI foundations & responsible-AI principles 4 chapters
Understand the foundations of AI governance.
- What is AI: types, definitions & simple analogies
- Mapping AI risks & harms (individual to societal)
- Unique AI characteristics that require governance
- Responsible AI principles: fairness, safety, privacy, transparency, accountability
02 Domain I.B - Roles, responsibilities & governance teams 5 chapters
Assign accountability across the AI governance function.
- Defining roles & responsibilities for AI governance stakeholders
- Building cross-functional AI governance teams
- AI training & awareness program for all stakeholders
- Tailoring governance to size, maturity & industry
- Developers, providers, deployers, users - responsibilities compared
03 Domain I.C - Policies across the AI lifecycle 3 chapters
Govern the AI lifecycle through policy.
- Policies for oversight across the AI lifecycle
- Policy gap analysis: updating privacy, security & related policies for AI
- Third-party risk policies: procurement, contracts & supply chain controls
04 Domain II.A - Privacy & data-protection law applied to AI 4 chapters
Apply privacy law to AI systems.
- Transparency, choice, lawful basis & purpose limitation in AI
- Data minimization & privacy-by-design for AI (DPIAs)
- Controller obligations: DSRs, cross-border transfers, breach reporting
- Handling sensitive/special categories of data (e.g., biometrics)
05 Domain II.B - Other laws that shape AI 4 chapters
Navigate IP, nondiscrimination, consumer and liability law.
- Intellectual property: training data & model output risks
- Nondiscrimination law implications (employment, credit, housing)
- Consumer protection law & deceptive AI practices
- Product liability basics for AI systems
06 Domain II.C - The EU AI Act & risk-based regulation 5 chapters
Apply the EU AI Act's risk-based obligations.
- AI risk classification frameworks (prohibited to minimal risk)
- Risk management, technical documentation & impact assessments
- Human oversight, transparency & quality management requirements
- Distinct obligations for general-purpose AI models (GPMs)
- Enforcement, penalties & role-based differences (provider vs deployer)
07 Domain II.D - Frameworks & standards for trustworthy AI 3 chapters
Operationalize the major AI frameworks and standards.
- OECD principles for trustworthy AI: practical translation
- NIST AI RMF: core functions, categories & lifecycle mapping
- Core ISO AI standards overview (ISO 22989, 42001, 42005)
08 Domain III.A - Governing AI design & build 5 chapters
Govern the design and build phase.
- Define business context & use case for the AI system
- Conduct and review AI impact assessments
- Ethics-by-design: requirements, architecture & human oversight
- Identify & mitigate design/build risks (probability/severity matrix)
- Documentation & traceability for design & build
09 Domain III.B - Data governance for training & testing 5 chapters
Govern the data behind model development.
- Data governance for training/testing: lawful rights & fit-for-purpose
- Data lineage & provenance practices and documentation
- Training & testing plans: performance, bias, security, interpretability
- Issue identification & mitigation during training/testing
- Documenting training & testing results for validation & audit
10 Domain III.C - Release, monitoring & incidents 6 chapters
Govern release, monitoring, and incident response.
- Release readiness: model card, conformity & production checklist
- Continuous monitoring strategy & schedule for maintenance/retraining
- Periodic assessment: audits, red-teaming & threat modeling
- Incident management: identification, documentation & remediation
- Cross-functional collaboration to diagnose AI incidents
- Public disclosures, technical documentation & post-market monitoring
11 Domain IV.A - Governing AI deployment context 3 chapters
Assess the deployment context.
- Deployment context assessment: objectives, data & workforce readiness
- AI model types: classic vs generative; proprietary vs open
- Deployment options: cloud, on-prem, edge; fine-tuning, RAG, agents
12 Domain IV.B & IV.C - Deployment risk & operational controls 5 chapters
Govern deployment risk and run operational controls.
- Deployment impact assessment for the selected AI system
- Vendor & licensing agreement risk identification & review
- Risks & obligations unique to proprietary, in-house models
- Operational controls: policies, data governance & risk management
- Monitoring, post-market assurance, deactivation controls & external communications
Explore the track
How you learn
- Self-paced with lifetime access
- Mentor-led cohorts
- Private corporate training
- Certificate and digital badge
AIGP - AI Governance Professional - answered
What is the AIGP - AI Governance Professional program?
A self-contained, zero-background prep course mapped precisely to the IAPP AIGP Body of Knowledge v2.1 (effective 2 Feb 2026) - four domains, twelve modules, 52 sequential chapters.
How is it delivered?
It combines structured lessons with practical exercises and, where relevant, exam-aligned preparation. It is self-paced with lifetime access, and available as a mentor-led cohort or private corporate training.
Is it aligned to the official body of knowledge?
Yes. The curriculum is mapped to the current official body of knowledge for this credential. This is independent training and is not affiliated with or endorsed by the certifying body; it does not itself confer certification.
Who is it for?
Privacy, governance, compliance, and data professionals - and the teams around them - preparing for certification or building applied capability in this area.
Do I need prior experience?
Each program is structured so motivated learners can follow the full arc; certification tracks assume the background the relevant exam expects.
Is there a downloadable brochure?
Yes. A PDF brochure for this track is available from the download button on this page.
Can my organization run this privately?
Yes. It can be delivered as a private corporate cohort tailored to your context. Use the enquire button to scope it.
How do I enrol or enquire?
Use the enquire button to request details or a corporate cohort, and a specialist will respond.
AIGP - AI Governance Professional for you or your team
Enquire about enrolment, or scope a private corporate cohort.