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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
Who This Book Is For
Why Now?
AI adoption is accelerating faster than security frameworks can adapt. While organizations are still asking, "What don't we know?," attackers are already acting. This book equips you with the frameworks, technical strategies, questions to ask, and operational playbooks to build secure, trustworthy AI systems, grounded in Zero Trust principles from day one.
Securing Generative AI, LLMs, and ML Using Zero Trust Architecture is your definitive guide for protecting the future of AI intelligently, holistically, and securely.
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