Author Description

Securing Generative AI, LLMs, and ML Using Zero Trust Architecture The first comprehensive guide to securing AI systems using Zero Trust architecture. Artificial intelligence is no longer emerging; it’s embedded in almost every organizational interaction. From predictive analytics in healthcare or military operations, to generative models driving innovation in financial or governmental services, AI is powering critical decisions and business outcomes across every sector. But with transformative potential comes unprecedented risk. Threat actors are weaponizing AI to automate phishing, poison training data, manipulate outputs, and breach digital infrastructure at scale. Zero Trust secures the AI path forward. Unlike legacy “trust but verify” models, Zero Trust never assumes trust. Every identity, system, and interaction must be continuously verified. This architectural shift is essential to securing the entire AI lifecycle, from training data and models to endpoints, APIs, and decision-making pipelines. This book shows you how to secure AI, responsibly and resiliently. What You’ll Learn Demystify the AI Ecosystem: Understand what generative AI, LLMs, embedded AI, Agentic AI, and machine learning really are and how they’re reshaping enterprise architecture. Recognize AI-specific Threats: Explore how adversaries are targeting AI models, training data, and inference pipelines and why conventional defenses fall short, including the shift to quantum technology. Embed Zero Trust in AI workflows: Apply microsegmentation, adaptive authentication, continuous verification, and telemetry, along with post quantum cryptographic algorithms, to protect AI across hybrid and cloud environments. Secure the AI Supply Chain: Harden training data, third-party integrations, model outputs, and API ecosystems to prevent compromise and misuse. Operationalize Strategy: Bridge the gap between executive