Secure the AI Systems That Act on Your Behalf
Agentic AI systems introduce an entirely new class of security risks that traditional Application Security (AppSec) tools were not built to handle. Agentic AI Security Engineering is the definitive, hands-on engineering reference guide for securing production agentic infrastructure end to end. Written specifically for security engineers, AppSec practitioners, platform engineers, and ML developers, this book bridges the critical gap between advanced AI capabilities and enterprise-grade cybersecurity operations.
What You Will Master
Harden MCP Servers: Implement secure authentication, authorization scopes, and strict rate limiting on Model Context Protocol layers.
Defeat Tool-Call Injection: Identify, mitigate, and block prompt injections masked as tool execution results.
Secure Orchestration Pipelines: Build robust trust boundaries for LangGraph, CrewAI, and AutoGen multi-agent frameworks.
Isolate Runtime Environments: Sandbox autonomous code execution securely using gVisor, Firecracker, and Linux namespace isolation.
Prevent Memory Poisoning: Protect agentic memory systems from persistent context manipulation and adversarial inputs.
Implement Secrets Management: Establish tight credential hygiene for AI agents interacting with external APIs.
Enterprise-Grade Defense for Autonomous Agents
As agents evolve from conversational chatbots to autonomous systems that write code, call internal APIs, and manage credentials, the threat landscape shifts. This book provides practical, production-ready architecture patterns, sandboxing blueprints, and incident response playbooks adapted for the era of agentic workflows. Protect your organization's digital assets—secure your AI agents before they deploy.
