Artificial intelligence and advanced analytics are now embedded in core financial decision-making. With that reliance comes heightened regulatory scrutiny, operational risk, and accountability at the executive level.Model Risk for Financial Leaders is a practical guide for finance executives, risk leaders, and audit stakeholders responsible for governing analytical and AI-driven models across regulated financial environments. The book translates technical model risk management conc...

Generative AI is rapidly entering clinical and administrative workflows, introducing new risks to patient safety, data privacy, and regulatory compliance. Securing Generative AI in Healthcare provides a practical governance and security guide for healthcare organizations adopting AI responsibly.This book addresses the unique risk profile of healthcare AI, where errors, bias, or data leakage can directly affect patient outcomes. It explains how generative models differ from traditio...

As artificial intelligence becomes a material business risk and strategic enabler, boards are increasingly expected to provide informed oversight. Board-Ready AI Governance Templates equip directors and senior executives with practical, ready-to-use materials to govern AI responsibly and confidently.This book focuses on what boards actually need: clear papers, decision frameworks, and accountability structures that translate complex AI topics into governance-relevant information. I...

As organizations increasingly adopt AI, employees need structured guidance to use these tools safely and responsibly. Building Internal AI Certifications provides a practical framework to design, implement, and manage internal training and micro-credential programs.Written for HR professionals, L&D teams, and organizational leaders, this book explains how to define learning objectives, assess competency, and recognize achievement with badges or certifications. It emphasizes respons...

AI ethics often fails not because principles are wrong, but because they are not operationalized. Operationalizing AI Ethics shows how organizations translate high-level ethical commitments into concrete product decisions, controls, and release criteria.Written for product managers and delivery leaders, this book bridges the gap between abstract ethics frameworks and day-to-day product development. It explains how ethical risks emerge during design, data selection, model developmen...

Most organizations adopting AI do not need multi-year transformation programs—they need clarity, prioritization, and momentum. 90-Day AI Readiness Sprint provides a focused, tactical playbook for small teams that want to adopt AI safely and credibly without excessive cost or bureaucracy.This book is structured as a practical sprint, breaking AI readiness into clear phases that can be executed over ninety days. It emphasizes decision-making, governance basics, and stakeholder alignm...

All AI models have an end of life. Failing to retire them safely can introduce security, compliance, and operational risks.Safe Model Retirement is a practical guide for model owners and technical leaders responsible for deciding when and how to retire, replace, or sunset machine learning models. The book focuses on controlled offboarding rather than abrupt shutdowns, ensuring continuity, accountability, and regulatory alignment.This guide addresses the often-overlooked fin...

Unapproved AI tools are already operating inside most organizations—often without security review, data controls, or executive awareness.Governing Shadow AI is a practical guide for IT leaders and executives responsible for identifying and managing unsanctioned AI use across the business. The book focuses on detection, containment, and governance rather than blanket prohibition, recognizing that shadow AI often emerges from productivity pressure rather than malicious intent.

High-quality data labeling is foundational to trustworthy artificial intelligence. Labeling for Trust provides practical, operational guidance for building, managing, and governing data labeling programs that reduce bias, improve model performance, and withstand audit and regulatory scrutiny.This book focuses on the real-world challenges data teams face when working with internal annotators, external vendors, and offshore labeling suppliers. It explains how labeling decisions affec...

As organizations deploy AI at scale, security leaders face a fragmented and rapidly evolving vendor landscape. Choosing the wrong platform—or deploying the right one incorrectly—can create blind spots rather than reduce risk.AI Security Platforms Explained is a vendor-neutral guide for CISOs and security leaders responsible for evaluating, procuring, and operating AI security solutions. The book focuses on capabilities, integration points, and operational outcomes rather than marke...

Small businesses increasingly face pressure to adopt AI to stay competitive, yet resources and expertise are often limited. AI for Small Businesses provides practical guidance on safely integrating AI into operations while capturing economic value.This book helps owners and managers understand where AI can add measurable benefit, how to evaluate tools, and implement simple but effective safeguards. It emphasizes low-complexity controls that protect data, maintain trust, and reduce ...

Boards are increasingly accountable for decisions involving artificial intelligence, yet most directors are not technical experts. AI Briefings for Non-Technical Boards provides clear, practical explanations that enable effective oversight without requiring technical depth.This book is designed as a set of concise primers that translate AI concepts into governance-relevant language. It explains what AI systems can and cannot do, where risks typically emerge, and how boards should i...