Agentic AI for Cybersecurity: Foundations, Frameworks, and Field Practice (en Inglés) - Amit Singh

Agentic AI is no longer theoretical—but most cybersecurity teams are still struggling to move from concept to production. Agentic AI for Cybersecurity fills the critical gap between vendor hype and academic abstraction, delivering a practical, engineering-first guide that security architects can apply immediately. This is the book for professionals who need to build real systems—not just talk about them.

At its core, this book argues that agentic AI is not a toolset but a new operating model for the Security Operations Center. That shift changes everything: architecture, governance, and risk. Unlike other titles, it refuses to separate capability from security. Offensive and defensive use cases are treated together, reflecting the real dual-use nature of agentic systems and equipping readers to design—and defend—against both.

Timeliness is what makes this book essential. It is the first practitioner-focused guide to unify three converging forces shaping modern security: MCP-based agentic systems, their emerging and largely undocumented attack surface, and the parallel migration to post-quantum cryptography. Each is complex on its own—together, they define the next generation of security architecture. This book addresses them as a single, integrated challenge.

Built for real-world use, every chapter follows a ‘what / how / why’ structure and culminates in runnable, production-grade architectures. Readers don’t just learn concepts—they deploy them, using reference implementations drawn from actual environments and supported by a living GitHub repository. From MCP servers and adversary emulation to AI-driven detection and quantum-resistant infrastructure, this is hands-on guidance grounded in reality.

For practitioners who need clarity, credibility, and actionable design patterns, Agentic AI for Cybersecurity stands apart. It is unapologetically practical, rigorously honest about risks, and singular in its integration of architecture, security, and emerging AI paradigms—making it a must-have resource for building the next generation of secure systems.

What You Will Learn:

Who this Book is for:

The target reader is a working network or security practitioner — a SOC analyst, network engineer, security architect, security engineer, or incident responder — who is being asked, by their employer or by the field, to integrate agentic AI into their operational practice and who needs guidance that is more concrete than vendor whitepapers and more applicable than academic papers.

The book does not assume prior experience with agentic AI specifically, but it does assume the reader is comfortable with at least one of the three domains the book unifies: enterprise network operations, security operations, or applied machine learning. Readers from adjacent communities — DevSecOps engineers, cloud security architects, AI/ML platform engineers responsible for AI safety — will also find Part II directly applicable to their work.