Prepare for the AIGP examination with structured training on AI regulation, governance frameworks, model accountability, responsible deployment and scenario-based exam strategy.
The AIGP Exam Preparation — AI Governance Professional (IAPP) course provides structured preparation for learners seeking to strengthen their understanding of artificial intelligence governance, responsible AI practices and organisational risk management. It is designed to help candidates organise their study, connect major governance concepts and develop the analytical judgement required when approaching AI governance scenarios.
The course begins with the AIGP exam domains and modern AI foundations, including predictive, generative, foundation and agentic AI. Learners will examine the AI lifecycle from design through deployment and eventual retirement while reviewing responsible AI principles and oversight responsibilities.
The curriculum then explores global AI law and regulatory readiness, including the EU AI Act, GDPR, automated decision-making, profiling, discrimination, copyright, biometrics and AI liability. Learners will also review recognised governance and risk frameworks such as the NIST AI RMF, the NIST Generative AI Risk Profile and ISO IEC 42001.
Further modules cover use-case approval, AI data governance, intellectual property controls, model testing, validation, red teaming, documentation and compliance evidence. The final module examines deployment controls, human oversight, third-party AI, vendor governance, monitoring, incidents, retirement and scenario-based exam readiness.
By combining technical foundations, legal awareness, governance frameworks and applied risk scenarios, the course can support preparation for the AIGP examination and broader professional development in responsible AI governance.
5 sections
•
23 lectures
Understand AIGP Exam Domains
Differentiate Main AI System Types
Map the AI Lifecycle and Controls
Grasp Responsible AI Principles
Understand EU AI Act Risk Structures
Apply GDPR and DPIA Concepts
Identify Profiling and Automated Decision Risks
Compare Global Regulatory Approaches
Recognize Key Legal Risks in AI (Liability, Discrimination, Copyright, Biometrics)
Understand NIST AI RMF Core Functions
Identify Modern Risks in Generative AI
Describe ISO IEC 42001 Management Systems
Integrate Frameworks into Enterprise Risk Models
Conduct Use-Case Risk Assessments
Establish Approval Gates for AI Projects
Implement Data Governance and IP Controls
Perform Model Testing, Validation, and Red Teaming
Create Documentation for Compliance and Accountability
Implement Deployment Controls and Human Oversight
Manage Third-Party AI and Vendor Risks
Establish Robust Monitoring and Incident Management
Determine Retirement Triggers for AI Systems
Refine Scenario-Based Exam Reasoning
Clear, well-paced training. The explanation of governance frameworks, accountability and AI risk was especially useful, and I can see myself using it when structuring my AIGP revision. I would have valued a few more timed practice questions.
Very informative and surprisingly easy to work through. I have already shared a couple of the key points about AI governance with my team. A few more timed practice questions would be a useful addition.
This course is suitable for AIGP exam candidates, AI governance professionals, privacy specialists, compliance teams, risk professionals and others involved in responsible AI oversight.
The course introduces modern AI foundations before progressing into regulation, governance frameworks, model accountability and assurance. Some previous awareness of privacy, compliance, risk or technology may be helpful.
Yes. The curriculum covers EU AI Act risk, general-purpose AI, transparency, AI literacy, GDPR, DPIAs, profiling and automated decision-making.
Yes. Learners review the NIST AI RMF, the NIST Generative AI Risk Profile, ISO IEC 42001 and enterprise risk, assurance and audit models.
No. This is an independent exam preparation course and does not award the official AIGP certification or guarantee an examination pass. Candidates must complete the relevant certification process separately and satisfy the certification provider’s requirements.
