AIGP EXAM PREP SUITE 2026

Modules Overview

Module 1: Understanding AI and Its Need for Governance

Introduces foundational AI concepts, accepted definitions, and common risks and harms associated with AI use. Explains why AI requires governance and introduces core principles such as fairness, safety, transparency, and accountability.

Module 2: Establishing and Communicating Organizational Expectations for AI Governance

Focuses on defining governance roles and responsibilities for AI stakeholders. Emphasises cross-functional collaboration, training, and awareness. Explains how governance approaches differ depending on organisational size, industry, and risk tolerance.

Module 3: Establishing Policies and Procedures Throughout the AI Lifecycle

Covers the development and implementation of AI governance policies, including oversight, accountability, ethics, privacy, security, and third-party risk management. Explains how these policies apply across the full AI lifecycle.

Module 4: Understanding How Existing Data Privacy Laws Apply to AI

Explores how data protection requirements such as transparency, lawful basis, purpose limitation, data minimisation, and privacy by design apply to AI systems. Addresses impact assessments, processor relationships, cross-border transfers, and incident management.

Module 5: Understanding How Other Types of Existing Laws Apply to AI

Examines how intellectual property, non-discrimination, consumer protection, and product liability laws affect AI. Addresses limitations on data use, requirements for equitable outcomes, and liability risks linked to AI systems.

Module 6: Understanding the Main Elements of the EU AI Act

Explains the EU AI Act’s risk-based framework and corresponding obligations. Covers requirements for high-risk AI systems, general-purpose AI, and enforcement mechanisms. Highlights differences in responsibilities across lifecycle roles.

Module 7: Understanding the Main Industry Standards and Tools That Apply to AI

Discusses global AI governance frameworks and standards, including OECD principles, NIST AI Risk Management Framework, and ISO AI standards. Emphasises how standards support trustworthy and measurable governance practices.

Module 8: Governing the Designing and Building of the AI Model

Focuses on defining business context, conducting impact assessments, identifying applicable legal requirements, and integrating governance considerations into model design and data analysis.

Module 9: Governing the Collection and Use of Data in Training and Testing the AI Model

Covers lawful data collection and usage, data provenance, training and testing processes, and documentation requirements to support accountability and compliance.

Module 10: Governing the Release, Monitoring, and Maintenance of the AI Model

Addresses preparation for deployment, documentation, conformity obligations, continuous monitoring, incident handling, and cross-functional collaboration during live operation.

Module 11: Evaluating Key Factors and Risks Relevant to the Decision to Deploy the AI Model

Guides assessment of business objectives, performance needs, ethical considerations, and deployment options. Differentiates between model types and deployment environments.

Module 12: Performing Key Activities to Assess the AI Model

Describes activities for conducting or reviewing impact assessments, interpreting legal obligations, and managing contractual risks related to vendors and open-source components.

Module 13: Governing the Deployment and Use of the AI Model

Explains how governance policies apply during live use. Covers monitoring, evaluation, documentation of incidents, mitigation of unintended use, external communication, and deactivation strategies.

Who This Course Is For

• Certification seekers preparing for the AIGP exam
• Professionals aiming to strengthen AI governance expertise
• Technology, data, legal, compliance, policy, and project professionals involved in AI systems
• Academics and practitioners seeking structured understanding of AI governance