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Swearingen K: The Capability Debt

The Capability Debt

Why AI is Exposing What Organizations Never Built

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Large Language Models (LLMs) are rapidly transforming how enterprises operate, powering customer support, internal assistants, automated workflows, search, analytics, and decision-making systems. But as organizations adopt AI at scale, they are also introducing a new and expanding attack surface.¿Jailbreaking LLMs¿explores how attackers manipulate AI systems through prompt injection, jailbreaks, adversarial inputs, data poisoning, context manipulation, retrieval attacks, and unsafe tool usage to bypass safeguards, leak sensitive data, and influence AI behavior in unexpected ways. 
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This book provides a practical guide to understanding, testing, and defending enterprise AI systems in the real world. Through real attack scenarios, security frameworks, red-teaming methodologies, governance strategies, and defensive architecture patterns, readers will learn how to build secure, resilient, and enterprise-ready LLM deployments. Covering everything from RAG security and agentic systems to incident response, AI governance, runtime monitoring, and future attack trends, this book connects AI innovation with modern cybersecurity practices. 
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What you will learn 
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Understand how LLM jailbreaks, prompt injection, and adversarial attacks work 




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Protecting the Future of Enterprise Security

Jailbreaking LLMs

Wiley Finance

The handbook to unleash the power of AI in Finance with guidance to pragmatically managing AI Risk Are you ready to harness the transformative potential of artificial intelligence and machine learning in finance while effectively managing the associated risks? "AI and Risk Management in Finance: A Practitioner's Handbook" by Sri Krishnamurthy, CFA, is your indispensable guide. Tailored for financial professionals, this comprehensive handbook delivers practical insights, case studies, and frameworks to navigate the complex landscape of AI and ML in the financial sector. This pragmatic handbook, developed primarily for professionals involved in designing, developing, and deploying AI models in the financial industry explores cutting-edge technologies, regulatory landscapes, and pragmatic frameworks. From generative AI to robust risk management strategies, this handbook equips you with the tools you need to validate models, ensure compliance, and operationalize AI risk management effectively. Leveraging lessons from QuantUniversity's AI and Machine Learning Risk Management program offered in 18 countries, this book focuses on enabling practitioners apply the state-of-the-art AI and machine learning techniques factoring unique risks specific to the financial services domain. Whether you're a seasoned professional or a newcomer, this book offers invaluable insights for harnessing the power of AI responsibly in the financial industry. Highlights of the Book: <ul> <li>Machine Learning and AI in Financial Services: Dive into the fundamentals of ML and AI, exploring various applications within the financial industry through real-world case studies.</li> <li>Generative AI and LLMs in Finance: Understand the emerging landscape, key players, and advanced concepts like prompt engineering and fine-tuning with practical examples.</li> <li>Risk Management for AI/ML Applications: Learn about current and emerging regulatory landscapes, AI risk management frameworks, and legislative efforts shaping the field.</li> <li>Data Governance Issues: Address critical aspects of data quality, privacy, security, and governance, with actionable frameworks and illustrative case studies.</li> <li>Model and System Governance: Gain insights into validating machine learning models, quantifying model risk, and ensuring robust system governance with MLOps and model monitoring.</li> <li>Key Risk Assessment Facets: Focus on documentation, explainability, fairness, security, and reliability with detailed case studies.</li> <li>Verification and Validation: Distinguish between verification and validation and learn frameworks for effective AI application validation.</li> <li>Algorithmic Auditing: Conduct comprehensive algorithmic audits with detailed processes and real-world examples.</li> <li>Operationalizing AI Risk Management: Discover ten essential strategies for implementing AI risk management practices effectively in your organization.</li> </ul> Prepare for the future of finance with expert advice and actionable strategies. Don't miss out on this comprehensive guide-your roadmap to navigating AI and risk management in the financial sector.

A Practitioner's Guide to Machine Learning, Generative AI, Agentic Systems, and Responsible Deployment

AI and Risk Management in Finance

The Governance Gap examines the widening divide between rapid AI adoption and executive control. As organizations race to deploy generative AI, agentic systems, automation, and AI-enabled workflows, many are relying on outputs before they have clearly defined ownership, review, escalation, documentation, vendor controls, and accountability.

The book argues that the real risk does not begin when AI produces an answer. It begins when an institution chooses to rely on that answer.

Through practical governance framing, operational examples, and executive level analysis, The Governance Gap gives leaders a clear way to understand where AI risk enters the organization, how invisible rework forms, and why governance must be built before reliance becomes business action.

The Governance Gap

Shared Intelligence

Coming of Age

Lecture Notes in Operations Research

Proceedings of 2026 INFORMS Conference on Service Science, Hangzhou, China, May 30 - June 1, 2026

Weaving the World with Digitalization and AI

Perspectives in Law, Business and Innovation

Artificial intelligence, large language models, chatbots, extended reality, and other emerging technologies are becoming central to modern healthcare, industry, and society. They offer major benefits, from faster diagnoses and personalized medicine to immersive collaboration and new ways of protecting information. At the same time, they create new risks for privacy, cybersecurity, fairness, and accountability, all of which are important for maintaining public trust in technology.
 
 AI-by-Design: Integrating Privacy, Security, and Ethical Governance in Health, Industry, and Society starts from a simple idea: good governance should be built into technology from the beginning, rather than added later when problems arise. Privacy, security, and ethical safeguards should therefore be part of the design and development of AI systems from their earliest stages. The book explores how privacy-by-design, security-by-design, and ethics-by-design can be brought together into a single approach for developing AI and other technologies that people can trust.
 
 This is particularly important because the rules governing AI are changing quickly. AI governance is not a fixed set of requirements that organizations can follow once and then forget. Laws, technologies, political priorities, and public expectations continue to change. The AI-by-Design approach helps address this challenge because it does not depend on any single law or regulatory framework. Instead, key principles such as data minimization, transparency, security, human oversight, explainability and accountability are built directly into the technology and the way it is developed and used. In this sense, design choices are also governance choices. They shape how data is collected and used, how cybersecurity risks are managed, how individual rights are protected, and how the benefits and risks of new technologies are shared across society.
 
 The book brings together contributions from areas such as healthcare, industry, and society. In healthcare, the authors examine AI-supported diagnostics, mental health chatbots, mobile health applications, and other technologies that are changing how care is provided while raising concerns about transparency, bias, privacy, and cybersecurity. In industry, the book looks at the growing use of AI, data infrastructures, and extended reality and considers how legal requirements can be put into practice through technical standards and concrete safeguards. At the broader societal level, the contributors examine AI reasoning, explainability, accountability, and the need for governance approaches that can respond to rapid technological change.
 
 Throughout the volume, the contributors connect legal and ethical principles with practical solutions. The book shows how privacy, security, fairness, transparency, and accountability can move beyond general principles and become part of the technical, organizational, and legal choices made throughout the design and use of AI systems.

Integrating Privacy, Security, and Ethical Governance in Health, Industry, and Society

AI-by-Design

Stop choosing between profit and principles. Every leader deploying AI feels the same pressure: move fast and accept avoidable risk, or slow down and lose ground. The Golden Algorithm shows a better path, one where principled leadership becomes the advantage that lets you scale.We are entering the Verification Economy, an era where customers, regulators, and partners care less about what your AI can do and more about what you can prove. Trust is no longer claimed. It is demonstrated.Built around the GOLDEN framework, this book helps leaders build AI systems that perform without eroding trust. Through real-world scenarios, practical playbooks, scorecards, and Monday 9 a.m. actions teams can run immediately, Dr. Ryan Baltrip shows how to identify dangerous proxies, build meaningful constraints, strengthen accountability, and turn ethical AI into a durable business advantage.Ideal for leaders, managers, and teams navigating AI strategy, governance, and decision making in the real world.

How to Build Ethical, Profitable AI

The Golden Algorithm

AI and quantum computing are two disruptive forces about to push financial services and the banking industry over the edge, demanding an unprecedented transformation. They are not fleeting trends but fundamental disruptors that will reshape the core of banking and finance. Authored by a senior Swiss banking technology executive, this practical guide helps board members and senior leaders turn disruption into a disciplined strategic agenda. It offers a rare insider perspective and a roadmap for navigating these seismic shifts. Bridging the gap between technological innovation and practical application, this book equips leaders with the tools to address the challenges and opportunities posed by AI and quantum computing. It features a thorough review of the latest business opportunities and threats associated with these technologies, alongside foresight-based strategic decision frameworks to guide prioritisation. Real-world case studies and an online companion with 125 AI use cases tailored for banks provide actionable insights. It also introduces an original AI Adoption Maturity Model and a Hybrid Enterprise Reference Architecture to ensure safe integration of internal systems with external frontier models. Finally, it provides two detailed multi-year roadmaps, offering a clear path to harness AI's potential and to prepare for a post-quantum future through crypto agility.Rather than offering abstract predictions or generic solutions, this book presents financial-services leaders with the frameworks, choices, and implementation path they need to act with confidence at a moment when waiting is no longer an option.

Evolutions at the Bleeding Edge

How AI and Quantum Computing Will Make or Break the Bank

Studies in International Trade and Investment Law

This open access book examines the evolving role of international economic law in addressing two interrelated challenges of the 21st century: enabling digital development and bridging the digital divide. The rapid digitalisation of the economy has generated significant challenges, including a deepening global digital and data divide and a persistent lack of inclusive digital development. Confronting these challenges head on, this pioneering book brings together experts across different legal subfields, geographies, and disciplines to examine the role and relevance of international economic law in bridging the global digital divide and enabling digital development, particularly from the perspective of the Global South. The contributions are anchored in a distinctive analytical framework that traces future directions through which international economic law interacts with digital development, demonstrating how it may function as a constraint, fade into irrelevance, or eventually act as an enabler. The chapters focus on a broad range of areas, including international trade, investment, tax, development, competition, finance and related regulatory and policy domains; they consider multilateral, regional, and plurilateral frameworks; and draw on a plurality of perspectives from Africa, Asia, the Middle East, and Latin America. The authors employ a wide range of methodological approaches to address issues such as digital sovereignty and digital industrial policy, cross-border data flows and digital global value chains, competition in data markets, AI governance, financial inclusion, and the sustainability-digitalisation nexus. This book speaks to scholars, policymakers, and practitioners in international economic law, development studies, and technology law who seek theoretical and pragmatic pathways for reorienting international economic law as a tool for equitable, sustainable, and meaningful digital development. The ebook editions of this book are available open access under a CC BY-NC-ND 4.0 licence on bloomsburycollections.com.

Towards Digital Development

Reorienting International Economic Law for the Digital Economy

Build an AI security operating model that manages risk, strengthens governance and enables AI to scale securely. AI Risk Management is a practical guide for AI risk owners, GRC managers, AI governance managers and technology risk professionals responsible for managing the security risks of AI.Written by security expert Rafah Knight, this book shows how to make AI security operational, measurable and embedded into existing business processes. It provides practical guidance on establishing ownership and accountability for AI risk, designing effective governance structures, implementing policies and controls and managing third-party AI vendor exposure.Drawing on real-world examples from financial services, retail, the public sector and healthcare, learn how to: - Build an effective AI security operating model - Establish clear ownership and accountability for AI risk - Design AI governance policies, controls and assurance processes - Measure AI security and operational risks effectively - Manage third-party AI vendor exposure at scaleAI Security gives you the practical structures and controls to operationalize AI assurance, strengthen AI governance and support secure AI adoption at scale.Themes include: AI security, AI risk management, AI governance, GRC, AI assurance, third-party AI risk

How to Scale AI Securely

AI Risk Management

The evolution of AI isn’t just about predictive models and automation; it’s about the emergence of "agentic" capabilities—autonomous and specialized AI systems that operate with a sense of independence by acting on a user’s behalf. This book explores through dialogue between the authors, the transformative role of AI in modern organizations, beginning with its evolution from automation to augmentation and the paradigm shift in human-machine collaboration.

The book starts with Microsoft's AI ecosystem, showcasing tools like Copilot, Power Platform, Dynamics 365, and Azure AI that enable AI agentic solutions. It further emphasizes responsible AI implementation, covering ethical guidelines, risk mitigation, and security measures with a specific focus on agentic systems. Finally, it provides a roadmap for businesses and professionals to adapt to an agentic workforce, measuring AI’s impact while preparing for future breakthroughs. 

After reading this book, you will be able to leverage agentic systems at scale and also address the implications of using agentic AI responsibly.

What You Will Learn:

The foundations of "agency" in AI

Practical strategies for implementing agentic AI solutions using Microsoft’s ecosystem

Best practices in Responsible AI, ethical considerations, and risk mitigation for autonomous agents

Actionable next steps for embracing agentic AI in day-to-day work and broader AI transformation efforts




Who This Book Is For:

AI engineers, Business Analysts, and enterprise architects curious about specialized AI strategies

Leveraging Microsoft AI and Autonomous Agents to Transform Work and Business

The Agentic AI Revolution

Artificial intelligence is transforming how organizations operate, innovate, and compete, but it also introduces new challenges in governance, risk, security, and accountability. This book provides leaders with proven frameworks for adopting AI responsibly while maintaining control over its risks. Written by Alec Crawford, a Harvard-trained AI expert and CEO of a leading AI risk firm, it equips readers with actionable frameworks for governance, risk management, compliance, and cybersecurity tailored to the realities of AI adoption.Inside, you’ll find tools like The AI Journey, a roadmap for safe implementation; AI GRCC, an integrated framework for Governance, Risk, Compliance, and Cybersecurity; and The AI Team, a model for defining roles, responsibilities, and oversight. Real-world examples and cautionary case studies demonstrate how governance failures occur and what organizations can do to prevent them.Whether you’re a business executive, risk professional, compliance leader, or technology manager, this book helps you balance innovation with oversight, navigate an evolving regulatory landscape, and build trustworthy AI-enabled organizations. Accessible yet authoritative, it’s essential reading for anyone shaping strategy in the AI era.Alec Crawford has been at the forefront of artificial intelligence and risk management for nearly four decades. His journey began in 1987 at Harvard College, where he designed and built neural networks long before AI entered the mainstream.He went on to hold senior positions at Goldman Sachs and Morgan Stanley before becoming Partner and Chief Investment Risk Officer at Lord Abbett, where he oversaw global investment risk and spearheaded initiatives applying AI to investment management.Today, Alec is the Founder and CEO of Verapath, a pioneering company that helps financial institutions adopt AI securely and responsibly, and the inventor of patented systems for AI Governance, Risk, Compliance, and Cybersecurity (AI GRCC).An independent corporate director, sought-after speaker, and host of the top-ranked AI podcast AI Trust but Verify, Alec combines deep technical expertise and senior financial leadership with a practical perspective on building trust in AI.

A Framework for Innovating Safely and Building Trust

Leading AI Risk Management

The Capability Debt

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