Overview

Hook Question: When your AI agents make decisions in milliseconds, what prevents an attacker from turning them into autonomous threats? Autonomous AI systems now control critical operations across manufacturing, finance, healthcare, and beyond. While they deliver unprecedented efficiency, they also introduce vulnerabilities that traditional security models cannot address. Agentic AI Security: Protecting Autonomous Systems provides the first comprehensive blueprint for defending software entities that learn, adapt, and act without continuous human oversight. This book examines the expanding attack surface created by interconnected AI agents and the specific threats targeting them. Through analysis of prompt injection techniques, adversarial machine learning exploits, and insider threat vectors, you'll understand why conventional defenses fail against agents designed for independent operation. What you'll find inside: - The five architectural patterns that create hidden security gaps in autonomous systems - Methods for implementing real-time monitoring when AI behavior evolves post-deployment - Frameworks for maintaining security controls without compromising agent performance - Analysis of recent AI security incidents and the prevention strategies they reveal - Compliance approaches for emerging AI governance and data protection regulations The challenges-unpredictable behavior, limited visibility, and continuously evolving threats-demand specific solutions. This book delivers practical implementations of zero-trust principles for AI ecosystems, strategies for balancing autonomy with oversight, and techniques for building resilient agents that detect their own compromises. Secure your competitive advantage by protecting the systems that drive modern enterprise innovation. The future belongs to organizations that deploy AI agents with confidence, not uncertainty.

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