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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

AI in Practice

Useful & Usable

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

The rise of AI agents is transforming how companies create value and compete.Companies have long operated on an assumption of scarcity: Talent is limited, expertise is expensive, and good judgment is difficult to scale. But these constraints are disappearing.Digital labor can interpret goals, apply reason, coordinate tasks, make decisions, and act with autonomy. As the cost of human knowledge and expertise falls, a new economic reality is taking shape: Intelligence is abundant.Deloitte's Nitin Mittal and futurist Mike Walsh reveal what happens when judgment is no longer the scarce resource it once was. Drawing on inside access to leaders driving AI transformations at some of the world's most powerful companies--NVIDIA, BMW, Moderna, Sanofi, and others--they show how abundant intelligence is reshaping work, decision-making, operating models, and competitive advantage and what leaders must do to capitalize on the shift. Central to the book is the Intelligence Signature, a new framework that shows leaders how to combine human and machine intelligence to create a distinctive advantage.We are at the beginning of a revolution in human-machine intelligence. Powerful AI tools and digital labor are rewriting the rules of business. The companies that master this technology will operate with growing confidence, creativity, and capacity, leaving a widening gap between themselves and everyone else.

How Digital Labor Will Rewrite the Rules of Business

Abundant Intelligence

Most AI governance is built around the wrong object.

Enterprises watch the model. They should be governing the crossing-the moment-generated language asks to become institutional reality.

The Bounding Gap exposes the substitutions that pass for AI safety: observation for control, behavior modification for enforcement, explanation for proof, and human review after the decisive movement has already begun. It relocates governance from the model itself to the boundary where generated possibility becomes institutional consequence.

Through a series of scientific inversions, the book develops a new architecture of authority. Gödel shows why a system cannot certify itself. Turing is inverted to ask not whether machines can imitate human judgment but whether their outputs possess standing to enter institutional life. Wilson's correlation geometry explains why modern AI cannot be governed through the old imagination of local logic and linear control. Hooke and Payne-Gaposchkin reveal why observation without the correct interpretive structure remains blind.

These arguments converge on one claim:

Authority cannot live inside the generative field. It must be external, deterministic, and enforceable before consequence becomes real.

The book also names the danger created by speed. Synthesis entropy and governance dilation describe what happens when machine-generated states multiply faster than institutions can establish admissibility. A rationale can now be produced in milliseconds, while responsibility, evidence, and authority remain unresolved.

For enterprise architects, Chief Compliance Officers, General Counsels, Chief Risk Officers, AI founders, and regulators, The Bounding Gap offers more than a critique of current governance. It identifies the missing boundary and shows why observation, monitoring, alignment, and review cannot substitute for executable refusal.

The future of institutional AI will belong to those who can govern the crossing before machine-generated possibility becomes consequence.

Why Institutions Must Govern Before AI Becomes Consequence

THE BOUNDING GAP

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

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

Implement trusted AI systems that deliver measurable business value while managing risk, compliance and long-term performance. Decision-Quality AI by Eva-Marie Muller-Stuler is a practical guide for data, product and technology leaders responsible for turning AI ambition into real-world outcomes. Covering the complete AI lifecycle from strategy and team design to deployment and governance, it provides the frameworks and technical foundations needed to develop AI products that are effective, responsible and aligned with business goals. Designed for leaders implementing AI at scale, this book helps you move beyond experimentation and hype to build AI solutions that solve genuine business challenges while meeting regulatory and operational requirements. You will learn how to: - Define AI use cases that address real business needs - Build teams, processes and governance for AI delivery - Develop, test and deploy AI with confidence - Manage risk, compliance, security and transparency - Monitor performance throughout the AI lifecycle Decision-Quality AI combines responsible AI principles with the practical disciplines required for successful implementation. It explores areas such as MLOps, ModelOps, AIOps, DevSecOps and cybersecurity, giving you a complete framework for delivering AI that creates lasting impact. Whether you are launching your first AI initiative or scaling enterprise-wide adoption, this book provides the tools, processes, and leadership guidance you need to build trusted AI systems that deliver results. Themes include: AI development, AI governance, AI security, MLOps, ModelOps, risk management, DevSecOps, data architecture, AI strategy

From AI Ambition to Operational Success

Decision-Quality AI

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.

From Theory to Practice

Artificial Intelligence in Finance

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

Language Operations: The Operating System for Global Communication - A Workbook for Executives

The LangOps Playbook

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

Bears & Mosquitoes

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