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Full Description
Agentic AI for Cybersecurity: Building Autonomous Defenders and Adversaries
Unlock the power of next‑generation AI agents to transform cybersecurity, business operations, and productivity.
Agentic AI has transformed cybersecurity and other domains with autonomous, intelligent software agents that can plan, reason, and take action. Instead of simply answering questions, these agents work like digital analysts using tools and adapting to new information, while collaborating with humans and other agents to get real work done.
Agentic AI for Cybersecurity is a practical, hands‑on guide to building, deploying, and securing these systems. Written in plain English, the book explains how modern agentic AI frameworks (such as LangChain, LangGraph, CrewAI, and LlamaIndex) enable autonomous, multi‑agent systems, and how frameworks like Model Context Protocol (MCP) and agent‑to‑agent (A2A) are creating an emerging Internet of Agents.
Through step‑by‑step projects and real‑world case studies, readers learn how to apply agentic AI to cybersecurity defense, adversarial testing, business automation, and productivity workflows. The book also addresses critical risks, including adversarial threats, governance, and compliance. The goal is to make security a first‑class concern, not an afterthought.
Designed for both technical and non‑technical readers, this book bridges the gap between AI theory and real‑world impact.
Key Features
Hands‑on projects with practical examples for building and deploying AI agents
Covers leading agentic AI frameworks and protocols (LangChain, CrewAI, LangGraph, MCP, A2A, n8n, OpenAI Agent Builder, and more)
Security‑first approach with dedicated coverage of adversarial threats and governance
Plain‑English explanations with diagrams, exercises, and GitHub resources
Who This Book Is For
Cybersecurity professionals, AI engineers, developers, architects, business and technology leaders, or anyone looking to understand and apply agentic AI responsibly and effectively.
Contents
Chapter 1, Foundations of to Agentic AI Systems
Chapter 2, Retrieval-Augmented Generation (RAG), Agentic RAG, and Model Context Protocol (MCP)
Chapter 3, Protocols for the Internet of Agents
Chapter 4, Orchestration with LangChain, LangGraph, and LlamaIndex
Chapter 5, Exploring CrewAI, n8n, Apache Airflow, OpenAI Agent Builder, and other Agentic Frameworks
Chapter 6, The Architect's Cockpit: AI-Powered IDEs and Coding Agents
Chapter 7, Building an Autonomous SOC Analyst
Chapter 8, Agentic Penetration Testing, Red Teaming, and Bug Bounties
Chapter 9, AI Agents for Business Leaders and Project Managers
Chapter 10, Securing Agentic AI Systems
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