Artificial Intelligence Governance Professional (AIGP) Certification Guide

Prepare for the AIGP exam with a practical guide to AI governance, responsible AI, risk management, EU AI Act compliance, lifecycle controls, audits, trusted deployment, vendor oversight, and certification-ready concepts

Key Features

• Prepare for the AIGP exam with focused coverage of AI governance, risk, and responsible AI
• Apply EU AI Act, NIST AI RMF, ISO standards, audits, impact assessments, and lifecycle controls
• Use checklists and scenarios to govern generative AI, RAG, vendors, and model deployment

Book Description

AI governance is now a priority for organizations adopting machine learning, generative AI, RAG, and agentic AI systems. Professionals preparing for the Artificial Intelligence Governance Professional (AIGP) exam need more than definitions; they need a way to connect certification domains with governance work.

Artificial Intelligence Governance Professional turns AIGP preparation into a structured book journey for readers across privacy, compliance, legal, risk, security, product, policy, data, and technology roles. You will begin with AI fundamentals, model types, system classification, the AI lifecycle, responsible AI principles, and governance roles. The book then explains how to identify AI harms, assess bias, privacy, security, operational, and business risks, and apply controls such as documentation, human oversight, testing, monitoring, impact assessments, incident response, and audit evidence.

As you progress, you will build exam-ready and workplace-ready knowledge of risk tiering, engagement, policy ownership, lifecycle checkpoints, validation, vendor due diligence, disclosures, and drift monitoring. You will also explore the EU AI Act, global AI laws, OECD principles, NIST AI RMF, and ISO AI standards. By the end, you will be prepared for the AIGP exam and confident in applying AI governance frameworks at work.

What you will learn

• Explain AI systems, model types, lifecycle stages, and AIGP governance terminology
• Identify AI harms, affected parties, bias, privacy risk, security issues, and misuse
• Apply responsible AI principles through oversight, documentation, testing, and controls
• Build risk workflows using probability, severity, impact assessments, and risk tiering
• Interpret NIST AI RMF, OECD principles, ISO standards, and EU AI Act obligations
• Govern AI vendors, deployment, audits, drift risks, and prepare for the AIGP exam

Who this book is for

This book is for professionals preparing for the Artificial Intelligence Governance Professional (AIGP) certification and for those responsible for AI governance in practice. It is also useful for data scientists, AI engineers, business leaders, policy teams, consultants, and public sector professionals who must evaluate, approve, deploy, or monitor AI systems responsibly. No prior AI governance experience is required, although a basic understanding of AI, business operations, privacy, compliance, or technology projects will help readers apply the frameworks faster.

Table of Contents

1. Foundations of Artificial Intelligence and AI Governance
2. AI Impacts on People and Responsible AI Principles
3. Responsible AI Governance and Risk Management
4. Governing AI Development
5. Governing AI Deployment
6. The EU AI Act
7. Other Laws and Standards Related to AI

Pavan Kumar is a technology author, AI governance practitioner, and enterprise IT specialist with expertise in artificial intelligence, AI governance, Microsoft 365, Azure Cloud, cybersecurity, endpoint management, automation, and enterprise infrastructure. He has extensive experience designing and managing modern workplace and cloud environments, with a focus on governance, risk management, compliance, and digital transformation. Pavan actively applies AIGP principles and contributes to professional education through technical training and certification-focused content. His work focuses on helping organizations adopt AI responsibly while aligning innovation with governance, security, and business objectives.

Avnish Singh is an AI practitioner, technology strategist, and management professional specializing in generative AI, agentic AI systems, AI framework implementation, and AI-driven business transformation. He is passionate about evaluating emerging AI technologies and translating them into practical solutions for organizations. His interests span AI governance, intelligent automation, Agile delivery, project management, workforce transformation, and HR-focused AI initiatives. Avnish brings a hands-on, experimentation-driven perspective to AI adoption, helping bridge the gap between rapidly evolving technologies and real-world enterprise implementation.