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Prepare for the IAPP AIGP exam with a concise AI governance study guide: EU AI Act, NIST AI RMF, ISO 42001, and governing GenAI, RAG, and agentic AI.Key FeaturesPrepare for the IAPP AIGP exam with focused coverage of all four AIGP exam domainsApply the EU AI Act, NIST AI RMF, ISO/IEC 42001, impact assessments, audits, and lifecycle controlsGovern generative AI, RAG, agentic AI, and AI vendors through real-world governance scenariosBook DescriptionPrepare for the IAPP Artificial Intelligence Governance Professional (AIGP) exam and learn to govern AI systems in the real world. As organizations adopt generative AI, RAG, and agentic AI, AIGP candidates need to understand how governance decisions shape the entire AI lifecycle.This concise AIGP study guide covers all four AIGP exam domains. You'll start with AI fundamentals, OECD system classification, and responsible AI principles, then learn to identify AI harms, assess bias, privacy, and security risks, and run risk assessments using the NIST AI Risk Management Framework. You'll also work through impact assessments, testing, data governance, and model cards.As you progress, you'll govern AI in production through vendor risk management, drift monitoring, incident response, and audits, with dedicated guidance on deploying RAG safely and defending agentic AI against prompt injection. The book then covers the EU AI Act in depth, U.S. rules such as NYC Local Law 144 and Colorado's AI law, and ISO/IEC 42001 and 42005. Written for privacy, compliance, legal, risk, security, and product professionals, with no machine learning background required, this book prepares you to take the IAPP AIGP exam with confidence and apply AI governance at work.What you will learnExplain AI systems, model types, lifecycle stages, and AIGP governance terminologyIdentify AI harms, affected parties, bias, privacy risk, security issues, and misuseApply responsible AI principles through oversight, documentation, testing, and controlsBuild risk workflows using probability, severity, impact assessments, and risk tieringInterpret NIST AI RMF, OECD principles, ISO standards, and EU AI Act obligationsGovern AI vendors, deployment, audits, drift risks, and prepare for the AIGP examWho this book is forThis 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.
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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.