Über uns

À propos de nous

Open Access

Karriere

Carrière

Aboverträge Kündigen

Résiliation d’abonnement

Vertrag widerrufen

Résiliation de contrat 

Datenschutz

Protection des données

Impressum

Mentions légales

Barrierefreiheit

Accessibilité

Zahlung und Lieferung

Paiement et livraison

Libra

Was ist Libra?

C'est quoi Libra ?

Kostenlos testen

Tester gratuitement

LEXIA

LEXIA Arbeitsrecht

Was ist LEXIA?

C'est quoi LEXIA ?

LEXIA Familienrecht

Zeitschriften

Revues

recht

Pflegerecht

ZBJV

forumpoenale

ZStrR

Anwaltsrevue

Revue de l'avocat

RSAS

FamPra.ch

ASYL

Life Science Recht

BlAR

Sachbuch

Livres spécialisés

LOKI

Semaphor

Fokusthemen

Focus

Thema

Recht studieren

Arbeitsrecht

Droit du travail

Studium

Études

Familienrecht

Droit de la famille

Sozialversicherungsrecht

Droit des assurances sociales

Strafrecht

Droit pénal

Aktienrecht

Droit des sociétés anonymes

Persönlichkeitsrecht

Droit de la personnalité

Publikationen

Publications

Literatur

Digitales Angebot

Bücher

Livres

Buchreihen

Collections

Onlinemodule LEXIA

Modules en ligne LEXIA

recht.ch

Studienliteratur

Littérature pour les études

Swisslex

Dienstleistungen

Services

Dienstleistungen für

Corporate Books

Publishing Lösungen

Aktuelles

Geschäftskunden

Professionnels

Corporate Publishing

Content solutions

Buchhandlungen

Librairies

Veranstaltungen

Événements

Presse

Dozierende

Enseignants

Studierende

Étudiants

Inserierende

Annonceurs

Newsletter

Blog

Autorinnen/Autoren

Auteurs

Unsere Schreibenden

Hilfe

Unsere Autorinnen und Autoren

Liste

Autorenlounge

Espace des auteurs

Bei uns publizieren

Publier chez nous

Open Access Policy

Verlag

Éditions

Themen

Mehr wissen

Mitarbeitende

Collaborateurs

Geschichte

Histoire

Unser Magazin

Notre magazine

Vision und Mission

Vision et mission

Partner

Partenaires

Unternehmenskultur

Culture d'entreprise

Kontakt

Contact

Shop

Schweizer Recht

Ausländisches Recht

Über unseren Shop

Neuerscheinungen

Nouveautés

Sortiment & Suche

Catalogue & Recherche

Vorankündigungen

À paraître

Nach Rechtsgebiet

Par domaine juridique

Kommentare

Commentaires

Gesamtkatalog

Catalogue complet

Fachkatalog Recht

Catalogue de droit

Adendorff D: Decision Before Consequence

Decision Before Consequence

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

Routledge Revivals

First published in 1989, Shaping the Corporate Future describes how executives in some of Europe's leading companies see the future for their organizations. It also sets out some of the steps they are taking in an attempt to ensure a successful future. The companies that feature in this book range from large well-known internationals like ICI, Electrolux and Shell UK to medium size firms such as Jaguar, BMW and JCB.The book discusses important themes like coping with unpredictability; 1992 and the global market; stimulating freedom and responsibility; towards a new idea of management; creating the international manager; and rethinking the market. This is an important reference work for students of business management, executive training programs and for corporate professionals.

Leading Executives Share their Vision and Strategies

Shaping the Corporate Future

Security, Audit and Leadership Series

Organizational Debt and AI Failure: Why Your AI Project Fails challenges the assumption that unsuccessful artificial intelligence initiatives are primarily caused by weak models, insufficient computing power, or poor technology choices. Instead, Scott Andersen argues that AI often fails because it is introduced into organizations carrying years of unresolved structural debt.The book presents a practical framework for identifying and managing five interconnected forms of organizational debt: technical, data, personnel, security, and embedded debt. Technical debt creates fragile architectures and brittle integrations. Data debt undermines meaning, lineage, context, and trust. Personnel debt appears through skill gaps, unclear responsibilities, burnout, and dependence on a few critical individuals. Security debt weakens identity, governance, assurance, and accountability. Embedded debt connects them through undocumented assumptions, informal processes, workarounds, and hidden dependencies.AI does not necessarily create these weaknesses-it exposes and accelerates them. Processes that remained stable through human judgment and manual intervention can collapse when automated systems begin operating continuously and at machine speed. What appeared to be resilience may have depended on people quietly compensating for organizational problems.Drawing on decades of enterprise architecture and technology experience, Andersen explains why promising pilots become trapped in "pilot purgatory," why local improvements can increase systemwide risk, and why failures frequently emerge at the seams between technologies, teams, data, and decision authorities. He introduces practical concepts and measures-including Total AI Debt Exposure and Total AI Operational Pressure-to help leaders make risk visible and actionable.This book is written for executives, architects, technology leaders, security professionals, data teams, program managers, and organizations preparing to scale AI. Its central message is direct: successful AI transformation does not begin with adoption. It begins with organizational truth. By identifying, measuring, and governing the debt already present, organizations can move beyond experimentation and build AI systems that achieve delivery, adoption, and sustained operational value.

Why your AI projects fail

Organizational Debt

Nearly every financial institution is racing to put AI into production. Most programs stall, and the gap is rarely technical.

A CEO asks her executive team whether they are falling behind on AI, and the room erupts. The technology chief lists vendors. The risk officer wants to know who approved the model running in consumer lending. The head of people reports that three high performers just resigned, citing fear of being replaced. Every person at that table is experiencing AI as a threat to something they care about, and nobody has given them a shared way to think about what comes next.

Human in the Lead argues that the hardest problems in financial services AI are not algorithmic. They are human: employees who do not trust the systems, customers who feel processed instead of served, and oversight that performs compliance without delivering it. In an industry where a flawed model does not recommend a bad film but denies a mortgage, freezes an account, or rejects an insurance claim, moving fast and breaking things is not a strategy.

Drawing on fifteen years of technology transformation inside banks, wealth managers, and insurers, Smit Jani sets out a practical operating model for AI adoption that regulators respect, employees embrace, and customers trust.

Inside this book:
Three models of human involvement, human-in-the-loop, human-in-command, and human-in-the-lead, with a decision framework for matching the right model to each use case.
Governance foundations that make AI systems audit-ready and aligned with regulatory expectations without strangling innovation.
A ninety-day roadmap that takes a pilot from leadership alignment through controlled launch to a scaling decision.
Role redesign playbooks for relationship managers, contact center teams, operations staff, and risk and compliance professionals.
A balanced scorecard that measures trust, employee adoption, and customer outcomes alongside efficiency, because what you measure shapes what your AI becomes.
Case vignettes from banking, wealth management, and insurance, including the operations program that nearly failed and the fraud system that balanced detection with fairness.

A complete implementation toolkit of templates, checklists, and maturity models, plus a governance readiness framework for the coming wave of agentic AI.

Written for chief executives, board members, risk and compliance leaders, and the transformation teams who answer to them. Each chapter stands alone, making the book equally useful read cover to cover or kept on the desk as a working reference.

Smit Jani has spent fifteen years leading technology transformation at financial institutions across North America, from global banks to wealth managers. He is a management consultant at eClerx, leading an AI transformation and automation squad at a global investment bank. The frameworks in this book were built and tested inside live regulated environments, not theorized from outside them.

The institutions that win with AI will not be the ones that automate the most. They will be the ones that keep humans in the lead.

The Executive Playbook for Safe, People-First AI in Financial Services

Human In The Lead

How to Secure, Control, and Optimize Artificial Intelligence Initiatives

AI Governance Playbook

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

AI Firing Freeze: Why fully-staffed organizations thrive in an AI-driven economy challenges one of the biggest assumptions in modern business; that artificial intelligence capabilities should automatically lead to workforce reduction.

As organizations race to implement AI chatbots, AI agents and agentic AI solutions, many are planning layoffs and hiring freezes in pursuit of short-term cost savings. But what if that strategy is fundamentally flawed?

Drawing on proven patterns of technological transformation, innovation, market expansion and real-world operational risks, AI Firing Freeze argues that the organizations most likely to thrive in an AI-driven economy will not be those that aggressively reduce headcount, but those that retain, repurpose and upskill their teams to work alongside AI for exponential results.

AI Firing Freeze shows you how to combine the strengths of AI with the human insight, critical thinking, experience, curation and governance your organization needs for success.

This book gives you clear guidance for maximizing the value - and minimizing the risk - of your AI solutions. It shows you how to leverage high-powered hybrid human and AI teams to run your operations; how to identify the AI initiatives that deliver the greatest ROI; how to inventory, monitor and manage all of your AI initiatives; how to secure your investments with AI governance; and how to ensure that every AI solution has the right level of supervision and human intervention to protect your organization.

Inside, readers will discover:Why human vision, creativity, judgement, quality control and expertise are critical differentiators in an AI-driven economy
The hidden operational, financial, reputational and liability exposures of overreliance on AI with insufficient human involvement
What is causing the "AI boomerang effect" and how to avoid that trap
A step-by-step process for building your AI-empowered organization
Role-specific guidance on leveraging your current teams 
Tools for realistic resource planning with hybrid human and AI teams
How to establish an AI governance framework that helps your organization scale safely and sustainably.





AI Firing Freeze inspires your organization to see beyond the initial allure of using AI capabilities for quick overhead reductions. It shows you how to combine AI and human strengths to superpower your teams, unlock innovation, expand your markets, increase resilience and create sustainable long-term growth.

Before you downsize for the promise of AI, you need to read this book.

Why fully-staffed organizations thrive in an AI-driven economy

AI Firing Freeze

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

Artificial intelligence has crossed the liability Rubicon. It is no longer a backend IT experiment governed by soft ethics, voluntary frameworks, and vague policy statements; it is a material financial asset driving enterprise revenue and unprecedented legal exposure. As we transition into Industry 5.0, algorithms are making autonomous, consequential decisions at machine speed. Yet, corporate boards and risk committees are attempting to govern these gigahertz systems using static compliance checklists designed for a slower, simpler era. The result is a looming crisis of algorithmic discrimination, regulatory friction, and catastrophic reputational decay. When an opaque, black-box model hallucinates financial advice or leaks proprietary trade secrets, the ensuing failure cascade can destroy billions in market capitalization overnight.AI Governance and Risk Management provides the definitive, boardroom-ready blueprint for surviving the fragmented global regulatory dragnet. This book dismantles the illusion of traditional "Human-in-the-Loop" oversight, proving that true algorithmic safety requires hard, architectural constraints. Drawing on decades of technology operations and academic leadership, the text translates classical financial auditing principles into executable machine logic. The author brings unparalleled practical experience-from supporting six Silicon Valley startups in client-facing roles to develop computer vision use cases, to leading over 30 continuous improvement projects for Cisco Systems. Having authored foundational texts on Segregation of Duties (SOD) and Hierarchical SOD two decades ago, and backed by 15 years of teaching financial risk management, this expertise culminates in the AI Integrity Framework (AIIF). This Hamiltonian approach empowers organizations to embed continuous improvement cycles and strict SOD directly into their deployment pipelines.Readers will discover how to engineer automated "Build Breaks," architect deterministic kill switches, and secure the unstructured data supply chain against copyright contamination and intellectual property theft. By shifting from reactive public relations to proactive "Governance as Code," Chief Risk Officers, legal counsel, and audit committees can mathematically prove fiduciary control to regulators across overlapping jurisdictions, from California to the European Union.Authored by a veteran industry practitioner, creator of five previous Wiley &amp; Sons texts, and current developer of the Industry 5.0 and AI curriculum at Santa Clara University, this release is not a theoretical ethics manual. It is an uncompromising operational survival guide. It provides the exact structural tools necessary to transform artificial intelligence from an unquantified off-balance-sheet liability into a fully auditable, legally defensible, and highly profitable enterprise asset.

The AI Integrity Framework (AIIF) for Enterprise Control and Regulatory Compliance

Auditable AI Governance and Risk Management

When the Decision Matters

<div>
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. 
</div>
<div>
 
</div>
<div>
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. 
</div>
<div>
What you will learn 
</div>
<div>
<ul>
<li>
Understand how LLM jailbreaks, prompt injection, and adversarial attacks work 




</li></ul></div>
<div>
<ul>
<li class="OutlineElement Ltr SCXW159986485 BCX0" role="listitem" aria-setsize="-1" data-leveltext="¿" data-font="Symbol" data-listid="1" data-list-defn-props="{" 335552541":1,"335559685":720,"335559991":360,"469769226":"symbol","4697<="" p="">
</li></ul></div>

Protecting the Future of Enterprise Security

Jailbreaking LLMs

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

Decision Before Consequence

Autres titres sur ce thème