Artificial intelligence is no longer a future concern-it is an operational reality shaping decisions, workflows, and risks across every modern enterprise. Yet while AI systems have accelerated innovation, they have also outpaced the governance structures designed to manage them. AI Governance and IT Risk Management is the definitive guide for leaders who must now navigate this widening gap with clarity, discipline, and confidence.
Across industries, organizations are deploying AI into mission¿critical environments-healthcare triage, financial underwriting, hiring pipelines, customer service, logistics, and public services. But as adoption accelerates, so do the consequences of inadequate oversight. Bias, privacy violations, model drift, regulatory exposure, vendor misalignment, and opaque decision¿making are no longer theoretical risks. They are real failures happening inside real organizations, often without warning and without the documentation needed to explain what went wrong. This book equips managers, directors, and executives with the frameworks required to prevent those failures before they occur.
Drawing from modern governance practices, regulatory trends, and practical enterprise experience, this book provides a complete, end¿töend blueprint for governing AI responsibly. It begins by grounding readers in the societal, ethical, and organizational forces that make AI governance urgent today. It then introduces the core governance structures-roles, committees, escalation paths, decision rights, and auditability-that allow organizations to manage AI with the same rigor applied to financial controls or cybersecurity. Readers learn how to integrate AI oversight into existing IT governance frameworks such as COBIT, ITIL, and NIST, ensuring that AI does not become a parallel system operating outside established risk disciplines.
The book goes further by offering detailed, actionable guidance on implementing governance in real environments. It covers risk identification, control selection, policy drafting, training, continuous monitoring, and overcoming the cultural and operational barriers that often derail governance programs. Specialized chapters address the AI development lifecycle, data governance and privacy, third¿party risk, generative AI and large language model controls, and the emerging regulatory landscape-including the EU AI Act, U.S. algorithmic accountability laws, and the White House AI Bill of Rights.
Unlike high¿level frameworks that leave managers wondering how to begin, this book provides concrete tools: model card templates, risk registers, incident postmortem structures, governance cadence schedules, and a 90¿day implementation roadmap. Each chapter concludes with a manager's checklist designed to be used immediately in audits, vendor reviews, and internal governance assessments.
AI Governance and IT Risk Management is written for the leaders who must bridge the gap between technical complexity and organizational accountability. Whether you oversee IT, compliance, data, security, operations, or AI¿enabled business functions, this book gives you the clarity to ask the right questions, the structure to build a sustainable governance program, and the confidence to lead your organization through an era where intelligent systems shape outcomes at scale.
In a world where AI is moving faster than policy, faster than regulation, and faster than many organizations can adapt, governance is no longer optional-it is the foundation of trust, safety, and responsible innovation. This book shows you how to build that foundation with precision, integrity, and long¿term resilience.
