Enterprise AI Governance in Practice: Create risk assessments, policies, oversight workflows, audits, and monitoring for responsible AI

Create an enterprise AI governance framework that turns responsible AI principles into risk tiers, policies, oversight, audits, and monitoring aligned with the EU AI Act, NIST AI RMF, and ISO 42001 for trustworthy deployment.

Key Features

• Build a complete AI governance framework with risk tiers, policies, roles, and approval workflows
• Apply the EU AI Act, NIST AI RMF, and ISO 42001 to assess risk and strengthen compliance
• Create model cards, fairness tests, audit plans, monitoring controls, and a 30-60-90 roadmap

Book DescriptionAI adoption is moving faster than many organizations can define accountability, control risk, or demonstrate responsible use. Enterprise AI Governance Framework in Practice shows how to convert principles and standards into an operating model for trustworthy AI. You will begin by distinguishing AI governance from ethics and compliance, mapping stakeholders, inventorying systems, and aligning priorities with the EU AI Act, NIST AI RMF, and ISO 42001. The book then shows you how to build a risk taxonomy, score likelihood and severity, conduct Algorithmic Impact Assessments, and tier AI use cases. You will draft acceptable-use and model-lifecycle policies, establish a governance committee, and create risk-based approval workflows. Next, you will govern data quality, lineage, consent, and representativeness; test fairness using demographic parity and equalized odds; and document systems with Model Cards and Datasheets for Datasets. You will apply SHAP, LIME, and counterfactual explanations, design transparency notices and human-oversight mechanisms, monitor drift and performance, audit systems, assess vendors, and plan incident response. A capstone unifies these controls through a maturity assessment and 30-60-90-day roadmap, preparing you to operationalize scalable governance for conventional, foundation, and agentic AI. What you will learn

• Differentiate AI governance, AI ethics, and AI compliance
• Audit AI use and map legal, product, engineering, and leadership roles
• Build risk taxonomies, score severity, and complete AIAs
• Classify AI systems by risk and define deployment approvals
• Draft acceptable use, lifecycle, and governance committee policies
• Assess fairness and document models with Model Cards and Datasheets
• Design transparency, human oversight, appeals, and incident workflows
• Plan monitoring, audits, vendor reviews, and a 30-60-90-day roadmap

Who this book is for

This book is for AI governance leads, risk and compliance professionals, legal and audit teams, product managers, data scientists, AI/ML engineers, technology leaders, consultants, and founders responsible for deploying AI safely. It also suits professionals moving into responsible AI or governance roles. No prior AI or machine learning experience is required; familiarity with business, product, data, or software workflows is helpful. Readers will gain practical frameworks and artifacts to establish oversight, assess risk, document controls, and scale governance across an organization.