AI Governance and IT Risk Management: A Manager's Guide — Overview of Process and Needs for Governing an Artificial Intelligence Environment

Artificial intelligence has undergone a remarkable evolution, propelling us into an era in which machines are not just learning but making informed decisions—selecting which ads to display, deciding if a loan should be granted, flagging security threats, and prioritizing patient care. With that power comes an urgent management challenge: how do you govern systems that learn, adapt, and make decisions at a scale and speed no human team can match?

This book is the manager's guide to answering that question.

AI governance is not only about compliance. It is about maintaining ethical standards, protecting against financial, legal, and reputational risks, and supporting the responsible development of technology. AI models can drift, affecting output quality and reliability. Governance must be continuous, systematic, and embedded in how your organization operates.

You will learn how to:

• Understand AI as a social paradigm—why governance must address not just technical performance but the human and societal impact of intelligent systems • Build AI governance across three layers: the Environmental Layer (society, regulation), the Organizational Layer (policy, roles, processes), and the AI System Layer (technical controls on models and data) • Navigate the legal framework including the EU AI Act, U.S. executive orders, sector-specific regulations, GDPR, and emerging international AI standards • Implement governance and risk management using structured methodologies—identifying, assessing, and mitigating AI risks before they become incidents • Improve IT governance by integrating AI oversight into existing ITSM, ITIL, and enterprise risk management frameworks • Build an IT policy framework that addresses AI-specific concerns—acceptable use, data handling, model documentation, access control, and incident response • Manage the AI development lifecycle from data collection and model training through deployment, monitoring, and retirement—with governance checkpoints at every stage • Leverage Microsoft Purview and Priva as practical enterprise tools for AI oversight, data classification, sensitivity labeling, compliance monitoring, and privacy management • Apply the White House AI Bill of Rights—its five principles (safe and effective systems, algorithmic discrimination protections, data privacy, notice and explanation, human alternatives) and what they mean for your AI deployments

Each chapter builds on the last, progressing from the broad social context of AI through legal requirements, organizational governance structures, policy implementation, and hands-on technical oversight tools.

Whether you are a CIO standing up your first AI governance program, an IT manager integrating AI into existing risk frameworks, or a compliance officer trying to make sense of the regulatory landscape, this book provides the foundation you need.

Illustrated throughout with governance frameworks and process diagrams by Kyasia Morgan.

© 2024 PublishDrive (E-bog): 9781972752265

Udgivelsesdato

E-bog: 1. januar 2024