AIGP EXAM PREP SUITE (2025 EDITION)

Modules Overview:

1. Module 1: Understanding AI and Its Need for Governance
   Provides foundational knowledge of AI, including accepted definitions, types, and the various risks and harms it may pose to individuals, organizations, and society. Examines the unique characteristics of AI that necessitate a structured governance approach and introduces the core principles of responsible AI (e.g., fairness, safety and reliability, transparency, accountability).
2. Module 2: Establishing and Communicating Organizational Expectations for AI Governance
   Focuses on defining and assigning governance roles and responsibilities for AI stakeholders. Emphasizes cross-functional collaboration, training, and awareness initiatives. Explains how governance approaches vary by company size, industry, products, and risk tolerance, and highlights differences among AI developers, deployers, and users.
3. Module 3: Establishing Policies and Procedures Throughout the AI Lifecycle
   Covers the creation and implementation of comprehensive AI governance policies, including oversight, accountability, ethics by design, data privacy, security, and third-party risk management. Explains how to integrate these policies at every stage of the AI lifecycle, from initial use case assessment through deployment and beyond.
4. Module 4: Understanding How Existing Data Privacy Laws Apply to AI
   Explores how fundamental data privacy requirements—such as notice, choice, consent, purpose limitation, data minimization, and privacy by design—apply to AI systems. Discusses specific obligations of data controllers, including privacy impact assessments, third-party processor requirements, cross-border data transfers, and incident management, along with regulations covering special categories of data.
5. 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 prohibitions and limitations on data usage, requirements for fair and equitable AI outcomes, and potential liabilities arising from AI design or operational defects.
6. Module 6: Understanding the Main Elements of the EU AI Act
   Introduces the EU AI Act’s risk classification framework (prohibited, high-risk, limited-risk, minimal-risk) and its corresponding obligations. Explains key requirements for high-risk and other AI categories, distinct provisions for general-purpose AI models, and the enforcement mechanisms for non-compliance. Highlights how responsibilities differ depending on whether an organization is a provider, deployer, importer, or distributor.
7. Module 7: Understanding the Main Industry Standards and Tools That Apply to AI
   Discusses prominent global AI frameworks and guidelines, such as the OECD AI principles, the U.S. Executive Order on AI, the NIST AI Risk Management Framework, and the ISO AI standards (e.g., ISO 22989 and ISO 42001). Emphasizes how these standards and tools foster trustworthy AI through methodologies, metrics, and best practices.
8. Module 8: Governing the Designing and Building of the AI Model
   Focuses on defining the AI model’s business context, conducting impact assessments, identifying applicable laws, and applying ethical and governance considerations to model architecture and data analysis. Details how to manage internal and external risks and the importance of thorough documentation to establish compliance and accountability.
9. Module 9: Governing the Collection and Use of Data in Training and Testing the AI Model
   Covers data governance requirements and lawful rights for data collection and usage. Explains how to establish data lineage and provenance, plan and execute model training and testing, and manage risks that arise during these processes. Emphasizes documenting each step to validate outcomes and maintain compliance.
10. Module 10: Governing the Release, Monitoring, and Maintenance of the AI Model
    Addresses preparing the AI model for release into production, including creating model documentation and meeting conformity obligations. Examines continuous monitoring and maintenance procedures, periodic assessments, and incident management. Highlights cross-functional collaboration to identify root causes of AI incidents and the importance of public disclosures for transparency.
11. Module 11: Evaluating Key Factors and Risks Relevant to the Decision to Deploy the AI Model
    Guides organizations on assessing business objectives, performance requirements, data availability, ethical considerations, and workforce readiness before AI deployment. Distinguishes among various AI model types (e.g., classic vs. generative, proprietary vs. open source) and deployment options (e.g., cloud, on-premise, or edge).
12. Module 12: Performing Key Activities to Assess the AI Model
    Describes the processes for conducting or reviewing impact assessments on selected AI models, identifying and interpreting relevant legal obligations, and reviewing contract terms and risks in vendor or open-source agreements. Highlights unique considerations for organizations deploying their own proprietary AI solutions.
13. Module 13: Governing the Deployment and Use of the AI Model
    Explains how to apply governance policies, procedures, and ethical considerations to the live operation of AI models. Emphasizes continuous monitoring, periodic performance and safety evaluations, and thorough documentation of incidents. Includes strategies for mitigating secondary or unintended uses, plans for external communication, and procedures to deactivate or localize AI models as necessary.

Who This Course Is For:

• Certification Seekers: Individuals aiming to become Certified AI Governance Professionals.
• Career Advancers: Those looking to future-proof their careers by developing AI governance expertise.
• Technology Professionals, Data Scientists, Legal Officers, Policy Makers, Project Managers, Academics, and Enthusiasts across various fields involved in AI development, ethics, and governance.