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...

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...

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...

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 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...

Generative AI systems introduce new attack surfaces that traditional security testing does not cover. Red teaming is essential to uncover how large language models fail under adversarial conditions.Red Teaming Generative AI is a hands-on guide for security testers and engineers tasked with identifying weaknesses in LLM-based systems. The book focuses on practical testing techniques rather than theory, providing repeatable exercises that mirror real-world attack scenarios.It...

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 ...

As organizations increasingly procure AI-enabled products and services, contractual gaps have become a primary source of legal, operational, and financial risk. Standard technology agreements often fail to address the unique characteristics of AI systems, leaving buyers exposed.Contractual Controls for AI is a practical guide for procurement professionals, legal teams, and risk leaders responsible for negotiating and managing AI-related contracts. The book focuses on how to transla...

Synthetic data is increasingly used to accelerate AI development, protect sensitive information, and overcome data scarcity. However, poorly governed synthetic data can introduce hidden bias, privacy leakage, and regulatory risk that undermine its intended benefits.Governance for Synthetic Data is a technical yet practical guide for security, data, and AI leaders responsible for approving, deploying, and overseeing synthetic data initiatives. The book explains when synthetic data i...

Machine learning systems are now business-critical infrastructure, yet many organizations deploy models without the operational security discipline applied to traditional software. Secure MLOps Fundamentals provides a practical, end-to-end security framework for building, deploying, and operating machine learning systems safely at scale.This book translates security principles into concrete MLOps controls that engineering and platform teams can apply immediately, without academic t...

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...

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...