Agentic AI for Cybersecurity: Building Autonomous Defenders and Adversaries

Agentic AI for Cybersecurity: Building Autonomous Defenders and Adversaries

Unlock the power of nextgeneration 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, handson 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, multiagent systems, and how frameworks like Model Context Protocol (MCP) and agenttoagent (A2A) are creating an emerging Internet of Agents.

Through stepbystep projects and realworld 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 firstclass concern, not an afterthought.

Designed for both technical and nontechnical readers, this book bridges the gap between AI theory and realworld impact.

Key Features

• Handson 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)
• Securityfirst approach with dedicated coverage of adversarial threats and governance
• PlainEnglish 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.

Table of 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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