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Full Description
Gain a practical framework for securing generative AI and agentic systems before they outpace your organization's governance and controls. As GenAI and agentic systems move from pilots into core business processes, security and governance leaders face systems that are probabilistic, adaptive, and capable of acting independently - systems that traditional, deterministic security models were never built to govern.
Drawing on real-world scenarios across financial services, healthcare, insurance, retail, and logistics, this book gives you a practical playbook for enterprise AI security: a three-domain risk model: data, identity, and autonomy, for translating AI threats into risk your business already understands; a five-category threat framework for reasoning about how AI systems actually fail; and a six-phase AI security lifecycle that embeds governance from initial strategy and use-case definition through deployment, operation, and decommissioning. Rather than treating AI security as a narrow, model-centric problem, you'll learn to frame it as an enterprise decision-making challenge spanning architecture, identity, compliance, and organizational accountability.
Most AI security guidance either targets data scientists tuning individual models or repeats generic governance principles disconnected from architecture. This book is different: written by a practitioner for practitioners, it translates practical experience from regulated and high-stakes environments into frameworks you can apply directly, whether you're establishing your first AI governance program or hardening an agentic system already in production.
You Will Learn
Understand why AI security is fundamentally different, including how AI changes the trust boundaries, system behaviour, decision-making, and traditional security assumptions.
Identify and assess AI-specific threats across input manipulation, data exposure,behavioral integrity, tool and action abuse, and autonomy & governance
Assess enterprise AI risk across data, identity, and autonomy, including dynamic data access, non-human identities, delegated authority, and autonoumous decision-making
Design security across the AI lifecycle, from strategy and use-case definition through data and model prepration, architecture, deployment, operations, change, and decommissioning.
Secure GenAI and agentic systems in practice using behavioural controls, identity and access controls, retrieval security, tool governance, human oversight, monitoring, and accountability mechanisms.
This Book Is For
This book is intended for senior security leaders, CISO's, decision-makers, responsible for enterprise risk and AI-enabled systems; security and enterprise architects designing or reviewing AI solutions; senior practitioners operationalizing AI security and governace;professionals involved in AI policy, compliance, and risk management; and advanced students and early-career professionals preapring for roles in cybersecurity or AI governance.
