Securing AI Using Zero Trust Principles | Cisco Press
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Securing AI Using Zero Trust Principles
By Cindy Green-Ortiz, Zig Zsiga, Saskia Laura Schröer
Published May 21, 2026<br>by Cisco Press.<br>Part of the Networking Technology: Security series.
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Description
Sample Content
Updates
Copyright 2026
Pages: 704
Edition: 1st
eBook
ISBN-10: 0-13-836332-3
ISBN-13: 978-0-13-836332-1
Securing AI Using Zero Trust Principles<br>Strategic Guidance for Defending AI Systems in a Rapidly Evolving Threat Landscape<br>Artificial intelligence is reshaping industries, driving innovation in critical sectors such as healthcare, finance, energy, and government. Yet, as organizations integrate AI into business operations, they inherit new risks, many of which conventional security models fail to address. Adversaries are weaponizing AI to automate reconnaissance, bypass defenses, and exploit vulnerable systems. The solution is not more trust, but less.<br>Zero Trust offers a foundational paradigm shift: no identity, device, system, or interaction is inherently trusted. Security must be continuously enforced, context-aware, and resilient by design. This book demonstrates how Zero Trust, when strategically applied to AI environments, enables organizations to secure data pipelines, mitigate emergent threats, and maintain control over evolving digital ecosystems.<br>Key insights include<br>AI Through a Security Lens: Demystifies machine learning, generative AI, and large language models with a focus on operational and business impact.<br>Zero Trust Foundations: Provides a historical and architectural overview of Zero Trust, including Ciscos Five Zero Trust Categories.<br>Security by Design for AI: Offers guidance on protecting AI development workflows, from data ingestion and model training to inference and deployment.<br>Threat Mitigation Strategies: Addresses adversarial AI, data poisoning, shadow AI, and insider misuse through identity enforcement, segmentation, and telemetry.<br>Strategic Execution: Maps Zero Trust principles to regulatory frameworks including NIST AI RMF, EU AI Act, DORA, and ISO 27001, and provides actionable templates for running successful Zero Trust Segmentation Workshops.<br>Who Should Read This Book:<br>CISOs and security architects building AI-resilient architectures<br>AI and data leaders embedding AI into enterprise infrastructure<br>Risk, compliance, and governance professionals navigating regulatory change<br>Technical teams seeking secure-by-design methodologies for AI initiatives<br>Why This Matters Now:<br>AI systems are expanding faster than most organizations can govern them. The risks, ranging from operational disruption to model corruption, require proactive, architectural defenses. This book bridges the gap between AI innovation and trusted enterprise security.<br>Securing AI Using Zero Trust Principles delivers the strategic playbook for building resilient, trustworthy, and standards-aligned AI systems that can withstand the threats of today and tomorrow.
Table of Contents
Part I: Defining Responsible AI and the Evolving AI Landscape<br>Chapter 1 Overview 1<br>Foundations of Zero Trust in AI Security 3<br>The Origins and Evolution of Zero Trust 3<br>Zero Trust Principles in AI Security 4<br>Key Frameworks and Regulations 5<br>The Intersections of AI and Security 8<br>Zero Trust as a Paradigm Shift in Securing AI 11<br>Ways to Build AI-Ready Data Centers and Cloud Architecture 12<br>Network Design Basics with AI in Mind 13<br>Key Components Required for an AI-Ready Environment 14<br>AI Data Center Deployment Options 17<br>Summary 21<br>Key Terms 22<br>End-of-Chapter Questions and Answers 22<br>Chapter 2 Responsible AI and Integrated Awareness 29<br>Definition and Principles of Responsible AI 29<br>Ethical AI Development 30<br>The Landscape...