IAPP

IAPP® Artificial Intelligence Governance Professional Study Guide

Master AI governance with our comprehensive IAPP AIGP certification training course.

(AIGP.AE1) / ISBN : 979-8-90059-153-7
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About This Course

This IAPP AIGP certification training course provides a rigorous, technical roadmap for professionals aiming to master AI governance. We bypass fluff, focusing on the 17 core chapters including the EU AI Act, NIST frameworks, and organizational readiness. You will utilize 320 practice quizzes and 104 flashcards to reinforce complex concepts like data lineage and model testing. While this guide is exhaustive, success requires disciplined application of these principles to real-world scenarios. We bridge the gap between theoretical policy and operational deployment, ensuring you are prepared for the 2026 exam objectives. This is the definitive resource for those serious about passing the AIGP exam on their first attempt.

Skills You’ll Get

  • AI Governance Strategy: Mastery of organizational readiness, cross-functional collaboration, and tailoring governance frameworks to specific business contexts.
  • Regulatory Compliance: Expert understanding of the EU AI Act, sectoral laws, intellectual property, and non-discrimination requirements in AI systems.
  • Risk Management Frameworks: Proficiency in applying NIST AI RMF, OECD principles, and ISO standards to identify and mitigate systemic AI risks.
  • Operational Lifecycle Management: Technical competence in documenting AI lifecycles, managing data provenance, and executing continuous monitoring post-deployment.
Download Course Outline

1

Introduction

  • Certification Requirements
  • The AIGP Exam
  • After the AIGP Exam
  • Study Guide Elements
  • AIGP Exam Objectives
2

AI and AI Governance

  • The Types of AI
  • Risks and Harms Posed by AI
  • Characteristics of AI Requiring Governance
  • Principles of Responsible AI
  • Summary
  • Exam Essentials
3

Organizational Readiness

  • Roles and Responsibilities for AI Governance Stakeholders
  • Cross-functional Collaboration in the AI Governance Program
  • Training and Awareness Program on AI Terminology, Strategy, and Governance
  • Tailoring AI Governance to Organizational Context
  • Developers, Deployers, and Users in AI Governance
  • Summary
  • Exam Essentials
4

Updating Policies for AI

  • Oversight in the Age of Autonomous Decision-making
  • Evaluate and Update Existing Data Privacy and Security Policies for AI
  • Policies to Manage Third-party Risk
  • Summary
  • Exam Essentials
5

Privacy and Data Protection Law

  • Notice, Choice, Consent, and Purpose Limitation in AI
  • Data Minimization and Privacy by Design in AI
  • Practical Implications and Governance
  • Data Controller Obligations in the AI Context
  • Understanding the Requirements That Apply to Sensitive or Special Categories of Data
  • Summary
  • Exam Essentials
6

Sectoral and Civil Laws

  • Intellectual Property Laws and AI
  • Non-discrimination Laws and AI
  • How Consumer Protection Laws Apply to AI
  • How Product Liability Laws Apply to AI
  • Summary
  • Exam Essentials
7

The EU AI Act

  • Risk Classification
  • Classification Requirements
  • Requirements for General-purpose AI
  • Enforcement and Penalties
  • Organizational Context
  • EU AI Act Implementation
  • Summary
  • Exam Essentials
8

AI Standards and Frameworks

  • OECD Principles
  • NIST AI Risk Management Framework (AI RMF)
  • NIST ARIA: Layered AI Model Evaluation
  • ISO Standards
  • Other AI-related Standards
  • Summary
  • Exam Essentials
9

 AI System Design

  • Documenting the Lifecycle
  • Business Context and Use Cases
  • Impact Assessments
  • AI System Design Considerations
  • Identifying and Managing Design Risks
  • Proprietary Model Considerations
  • Summary
  • Exam Essentials
10

Data Governance and Model Training

  • Data Governance
  • Data Lineage and Provenance
  • AI System Testing
  • Identifying and Managing Testing Risks
  • Summary
  • Exam Essentials
11

Deployment and Monitoring

  • Pre-release Readiness
  • Deployment Considerations
  • Continuous Monitoring
  • Summary
  • Exam Essentials
12

 AI Risk Management and Assurance

  • AI Risk Management and Forecasting
  • AI System Audits and Testing
  • Incident Management and Disclosures
  • Summary
  • Exam Essentials
13

Ongoing AI Operations

  • Managing Business Records
  • External Communications
  • AI System Retirement
  • Summary
  • Exam Essentials
A

Appendix: February 2026 Supplement to IAPP AIGP Study Guide

  • Executive Summary 
  • Introduction
  • Lesson 2: Organizational Readiness 
15

FlashCards

16

Test1 

17

Test2

1

AI and AI Governance

  • Assessing AI Risks and Responsible AI Principles
2

Organizational Readiness

  • Building an AI Governance Responsibility Matrix
  • Performing an AI Governance Gap Assessment
3

Updating Policies for AI

  • Assessing Third-Party AI and Vendor Risk
4

Sectoral and Civil Laws

  • Evaluating Legal and Civil Risks in AI Use Cases
5

The EU AI Act

  • Classifying AI Systems Under the EU AI Act
6

AI Standards and Frameworks

  • Applying the NIST AI Risk Management Framework
7

 AI Risk Management and Assurance

  • Investigating and Responding to an AI Incident
8

Ongoing AI Operations

  • Capstone: Governing an AI System Across Its Lifecycle
  • Retiring an AI System Responsibly

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As AI regulation intensifies, the AIGP credential validates your ability to manage legal and ethical risks, making you indispensable to organizations deploying AI.

This guide synthesizes the 17 chapters into actionable study paths, providing 320 practice questions and 104 flashcards to simulate actual exam conditions.

Focus on the intersection of policy and technical implementation. Use our practice tests to identify knowledge gaps in the EU AI Act and NIST frameworks.

Yes, our guide includes the February 2026 supplement, ensuring you are prepared for the latest organizational readiness and governance requirements.

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