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. 

Hitender Saxenais an Associate Director and Lead Technologist with over two decades of experience in cybersecurity architecture, cloud security, and emerging technology security and risk management. He currently serves as Global AI Security Lead and Emerging Technology Security & Innovation Leader, where his work focuses on defining security approaches for AI-enabled platforms and enterprise technology environments at scale.

His professional experience spans cloud infrastructure security, enterprise cybersecurity architecture, AI governance, and secure digital transformation across industries, including financial services, insurance, manufacturing, and transportation. He has contributed to industry analyst engagements with IDC on cloud security in the era of artificial intelligence and with Forrester on AI security, enterprise cyber defence, and emerging technology governance. He has represented cybersecurity perspectives on AI adoption and security governance at the India AI Summit 2026. His expertise also extends to forward-deployed engineering (FDE), supported by completion of a dedicated Forward-Deployed Engineer program.

He holds industry leading certifications including TOGAF Enterprise Architecture, Microsoft Azure Solutions Architect Expert, AWS Certified Solutions Architect, Microsoft Azure Security Engineer , AI Engineer Associate, the Certificate of Cloud Security Knowledge (CCSK) from the Cloud Security Alliance, OpenAI Cyber Practioner, OpenAI Consultative Solutions Practioner, VCP, CCA and an Internet of Things Fundamentals credential from Stanford University. He holds an MBA in Information Technology.