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Martin D: Bears & Mosquitoes

Bears & Mosquitoes

Building Trusted AI from the Inside Out - A Field Guide to AI That Earns Its Keep

Non-human identities now outnumber people, and someone must sign for what the machines do.
Defensible Identity Architecture is a professional reference by Professor Kai London. It covers non-human identities, agentic AI, the regulatory crosswalk, machine accountability, decision rights architecture, the identity control plane, SPIFFE and workload identity, ITDR and cyber-insurance posture.
Written for boards, executives, CISOs, engineers, risk and compliance leaders and practitioners who need clear, defensible guidance they can put to work, the book combines explanation with practical frameworks, checklists and decision aids.
Key topics include: non-human identities, agentic AI, the regulatory crosswalk, machine accountability, decision rights architecture, the identity control plane, SPIFFE, workload identity, ITDR, cyber-insurance posture.

Defensible Identity Architecture

Build a resilient, high-performing organization by integrating agentic AI into strategy, operations, and decision-making. Building an Agentic Organization is a strategic guide for senior technology, AI, and business leaders responsible for delivering value from agentic AI while managing its risks. As organizations move toward human and AI collaboration at scale, leaders must balance innovation with governance, adoption, and long-term economic impact.Written by experienced AI executive Geoffrey Schaefer, this book provides a comprehensive framework for designing an agentic organization by examining strategy, governance, and economics. It explores how to define the purpose of agentic AI, align it with business objectives, and create the structures needed to support safe, scalable adoption. Addressing both opportunity and risk, it equips leaders to navigate workforce concerns, reputational exposure, and the evolving role of AI agents in enterprise environments.You'll learn how to: - Define a clear strategy for integrating agentic AI - Establish governance frameworks to manage risk and accountability - Drive workforce adoption and address resistance to AI collaboration - Assess economic value and impact of agentic AI systems - Manage reputational and operational risks from AI agent behaviorFeaturing real-world examples from Leidos, JPMorgan Chase, SY Partners, and OMA, this book provides a practical roadmap for embedding agentic AI into the fabric of your organization while protecting value, trust, and long-term performance.Themes include: agentic AI, AI strategy, AI governance, AI transformation, enterprise AI, risk management

The Strategy, Governance, and Economics of Agentic AI

Building an Agentic Organization

Wie Teamleads die Arbeit mit KI produktiv und menschenzentriert gestalten

Mensch-KI-Teams führen

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

This book equips AI developers, machine learning engineers, and software architects with the knowledge and tools needed to design, build, and deploy scalable autonomous agents. Beginning with foundational concepts in LLMs and agent architectures, the book transitions into hands-on design patterns and real-world implementation guidance. Readers will follow practical Python examples using cutting-edge frameworks like LangChain and AutoGen, with full reproducible code available on GitHub.
Emphasizing engineering essentials scalability, interoperability, observability, and ethics the book dives into multi-agent coordination, including emerging protocols such as MCP and A2A. It wraps up with compelling case studies from customer service bots to multi-agent analytics systems demonstrating how to build secure, stable, and trust-aligned AI agents. This guide bridges theory with practice for the modern AI practitioner.
You Will:
<ul>
<li>Discover reusable patterns such as reactors, planners, and tool-using agents that power effective autonomous systems.</li>
<li>Learn to build agents using Python with popular frameworks like LangChain and AutoGen, complete with reproducible GitHub code.</li>
<li>Dive into cutting-edge coordination mechanisms (e.g., MCP, A2A) that enable agents to collaborate and assign tasks dynamically.</li>
</ul>
&nbsp;
This book is for : Software Architects &amp; System Designers
&nbsp;

This book equips AI developers, machine learning engineers, and software architects with the knowledge and tools needed to design, build, and deploy scalable autonomous agents. Beginning with foundational concepts in LLMs and agent architectures, the book transitions into hands-on design patterns and real-world implementation guidance. Readers will follow practical Python examples using cutting-edge frameworks like LangChain and AutoGen, with full reproducible code available on GitHub.Emphasizing engineering essentials scalability, interoperability, observability, and ethics the book dives into multi-agent coordination, including emerging protocols such as MCP and A2A. It wraps up with compelling case studies from customer service bots to multi-agent analytics systems demonstrating how to build secure, stable, and trust-aligned AI agents. This guide bridges theory with practice for the modern AI practitioner.You Will:<ul><li>Discover reusable patterns such as reactors, planners, and tool-using agents that power effective autonomous systems.</li><li>Learn to build agents using Python with popular frameworks like LangChain and AutoGen, complete with reproducible GitHub code.</li><li>Dive into cutting-edge coordination mechanisms (e.g., MCP, A2A) that enable agents to collaborate and assign tasks dynamically.</li></ul>&nbsp;This book is for : Software Architects &amp; System Designers&nbsp;

Architecting Scalable, Ethical, and Trustworthy Systems for Enterprise

Agentic AI Systems in Practice

Design AI systems that teach your workforce instead of replacing it The Resilient Enterprise: Designing Artificial Intelligence that Teaches the Workforce draws on cognitive psychology and UX principles to show business leaders how to deploy AI that strengthens employee competence rather than eroding it. Victor S. Yocco, PhD, a Staff UX Researcher at ServiceNow with over a decade directing product design research at EY, delivers specific pedagogical design patterns that leaders can implement immediately to counter skill atrophy caused by AI overreliance. The World Economic Forum identifies skill atrophy as a top concern for Chief People Officers due to AI overreliance. This book directly addresses that risk by providing actionable frameworks for designing AI-powered tools and agents that preserve structural knowledge across junior and senior staff. It connects cognitive psychology research to practical interface design, equipping leaders with methods to build organizations where humans and machines both perform at their highest capability. Readers will also find: <ul> <li>Pedagogical design patterns for AI interfaces that reinforce rather than replace employee reasoning and decision-making skills</li> <li>Strategies for preventing cognitive skill atrophy among senior staff whose expertise is being offloaded to AI-powered automation</li> <li>Approaches to ensure junior employees develop the structural knowledge required for their roles alongside AI tools</li> <li>UX principles grounded in cognitive psychology that guide how AI agents should interact with human workers</li> <li>Frameworks for building organizational resilience so teams can function effectively both with and without AI assistance</li> </ul> Written for Chief People Officers, Chief Information Officers, Vice Presidents of Product Management, Directors of Learning and Development, and Senior Managers of Operations, this book provides the pedagogical design patterns and cognitive frameworks needed to deploy AI that preserves and strengthens workforce expertise rather than undermining it.

Designing Artificial Intelligence that Teaches the Workforce

The Resilient Enterprise

Turn AI investment into scalable business impact by enabling your workforce to build the solutions they actually need. From AI Users to Solution Builders is a strategic guide for senior AI, technology, and transformation leaders who want to move beyond underperforming, centralized AI initiatives, and unlock real enterprise value. As organizations struggle to translate AI into measurable results, this book shows how shifting from top-down deployment to workforce-led solution building creates faster adoption, stronger ROI, and sustained innovation. Written by experienced AI leader Wei Manfredi, this book combines strategic insight with practical execution. It draws clear parallels between failed digital transformation efforts and current AI adoption challenges, while demonstrating how natural language interfaces are reshaping access to AI. By enabling non-technical professionals to design their own tools, organizations can embed AI directly into workflows, decision-making, and daily operations. You'll learn how to: - Shift from centralized AI models to workforce-driven innovation - Enable employees to build AI solutions for real workflows - Design scalable infrastructure for AI solution development - Apply change management to overcome resistance and drive adoption &gt;Featuring real-world examples from organizations including Lululemon, Google, Oracle, and JPMorgan Chase, this book provides a proven framework for embedding AI into everyday collaboration, accelerating innovation, and future-proofing your organization's competitive advantage. Themes include: AI strategy, AI adoption, workforce transformation, AI governance, enterprise AI, change management

Scaling AI Across Your Organization

From AI Users to Solution Builders

AI in Practice

Useful & Usable

Risk, Governance and Society

From Data Analytics to Global Risk Governance

Evolution of Anomaly Detection

Turn Intelligence into Your Competitive Advantage

Enterprise 5.0

Computers, Privacy and Data Protection

Who decides how data and AI are governed, and whose rights prevail?This open access book brings together contributions from the CPDP conference in Brussels, Europe's leading forum on privacy and data protection. Ten years after the GDPR, digital governance stands at a crossroads. The EU has layered the DSA, DMA, Data Act and AI Act onto its data protection regime, while calls for simplification grow louder and governments from Brazil and Mexico to Canada and Southeast Asia pursue their own regulatory visions. The book examines algorithmic oversight and automated decision-making in the UK, AI and directors' duties in Canadian corporate law, data governance for connected vehicles in the EU, GDPR-inspired access rights across Japan and Southeast Asia, the Brussels Effect in Brazil and Mexico, location-data provenance and digital trust infrastructures, and the inequalities produced by biometric and health data infrastructures in Europe and India.The chapters show that governance failures rarely stem from a shortage of rules. They arise when regulatory visions, technical architectures and institutions pull in different directions. Read together, they make the case for treating disagreement not as an obstacle but as a democratic resource.With its blend of doctrinal analysis, critical theory and socio-technical insight, this book is for academics, policymakers, practitioners, DPOs and students working on data protection, AI regulation and digital society.The ebook editions of this book are available open access under a CC BY-NC-ND 4.0 licence on bloomsburycollections.com.

Competing Visions, Shared Futures

Data Protection, Privacy and Artificial Intelligence, Volume 19

This textbook examines how artificial intelligence, machine learning, generative AI, and data-driven decision systems are reshaping financial services. Moving from core AI concepts to applied financial use cases, it covers credit risk assessment, fraud detection, Monte Carlo methods, stock and cryptocurrency valuation, knowledge graphs, AI-enabled IoT, large language models, Retrieval-Augmented Generation (RAG), AI agents, and no-code AI platforms. It also looks at infrastructure choices for enterprise AI, including cloud and on-premise deployment, before turning to sustainable finance, greenwashing detection, and AI governance in regulated environments. Throughout, the book emphasizes decision quality, transparency, accountability, model risk management, and practical implementation. It will be of interest to upper-level undergraduate and postgraduate students in finance, banking, FinTech, risk management, and business analytics, as well as practitioners seeking a structured introduction to AI applications and governance in finance.
 Migdat Hodžić
 is Principal and Chief Technology Officer at ARTI Analytics and a professor at the International University of Sarajevo and Džemal Bijedić University of Mostar. He holds a PhD from Santa Clara University and works across artificial intelligence, machine learning, optimization, and quantitative finance. He has led applied AI research and product development in Europe and the United States and is an inventor on multiple US patents.
 Tarik Hubana
 is Chief Operating Officer at ARTI Analytics and Assistant Professor at Džemal Bijedić University of Mostar. He holds a PhD in technical sciences from TU Graz and works on applied AI, machine learning, predictive modelling, and enterprise AI systems. His academic and industry experience spans analytics, digital transformation, and AI deployment in data-intensive sectors. He leads applied AI research and product development in Europe and the United States and is an inventor on multiple US patents.
 Admir Mešković
 is Assistant Professor of Finance at the International University of Sarajevo and Manager of the Center for Islamic Finance, Innovation and Sustainability. He holds a PhD in Finance from the University of Sarajevo and an MBA from Durham University Business School. His research focuses on Islamic finance, FinTech, risk management, and sustainable finance.

This textbook examines how artificial intelligence, machine learning, generative AI, and data-driven decision systems are reshaping financial services. Moving from core AI concepts to applied financial use cases, it covers credit risk assessment, fraud detection, Monte Carlo methods, stock and cryptocurrency valuation, knowledge graphs, AI-enabled IoT, large language models, Retrieval-Augmented Generation (RAG), AI agents, and no-code AI platforms. It also looks at infrastructure choices for enterprise AI, including cloud and on-premise deployment, before turning to sustainable finance, greenwashing detection, and AI governance in regulated environments. Throughout, the book emphasizes decision quality, transparency, accountability, model risk management, and practical implementation. It will be of interest to upper-level undergraduate and postgraduate students in finance, banking, FinTech, risk management, and business analytics, as well as practitioners seeking a structured introduction to AI applications and governance in finance.

From Theory to Practice

Artificial Intelligence in Finance

Bears & Mosquitoes

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