AI Governance and IT Risk Management - Overview of Process and Needs for Governing an Artificial Intelligence Environment #970173

AI Governance and IT Risk Management is a practical, manager‑focused guide that turns the abstract urgency around artificial intelligence into a concrete program of controls, accountability, and operational discipline. Designed for IT leaders, business managers, compliance officers, and anyone responsible for AI‑enabled processes, the book explains why governance is no longer optional and shows how to build systems that prevent harm, satisfy regulators, and sustain innovation.
The book opens with a vivid real‑world scenario: “A regional hospital network had deployed an AI‑assisted triage tool across its emergency departments.” That example frames the central problem: capable technologies often outpace the organizational structures meant to oversee them. From there the text maps the evolving AI landscape—rules‑based automation, supervised machine learning, and generative large language models—highlighting distinct failure modes and governance needs for each.
Readers get a clear taxonomy of primary risks—bias and fairness, privacy and data protection, operational safety, and governance/accountability—and practical frameworks to address them. The book demystifies legal and regulatory expectations, translates technical controls into managerial checklists, and integrates AI governance with established IT risk frameworks like COBIT and ITIL. It also provides ready‑to‑use artifacts: model card templates, risk registers, incident postmortem forms, and a 90‑day implementation roadmap.
A recurring theme is managerial ownership: “Most organizations didn’t decide to become ‘AI organizations.’ It crept up on them.” The book insists that managers must act as integrators—assembling technical, legal, and compliance inputs into enforceable programs and ensuring that governance is practiced, not just documented.
Case studies and operational playbooks show how to govern generative AI, manage third‑party risk, and embed human‑in‑the‑loop controls where they matter most. Each chapter ends with a manager’s checklist to support rapid assessment and remediation.
Concise, actionable, and grounded in real organizational constraints, this book equips leaders to move beyond checkbox compliance toward a resilient governance posture that enables safe, accountable, and value‑driven AI deployment.