About this book:

This book serves as a comprehensive operational manual for modern cybersecurity practitioners, detailing the integration of artificial intelligence and automation into Blue Team operations. It moves beyond theoretical concepts to provide structured playbooks for the entire incident lifecycle, from data engineering and threat modeling using the MITRE ATT&CK framework to automated detection, containment, and recovery. By advocating for "detection-as-code" and "playbook-as-code," the text emphasizes a shift from manual, reactive firefighting to a proactive, scalable defense architecture that leverages Large Language Models (LLMs) and Machine Learning (ML) to augment human judgment.

The technical core of the book explores the orchestration of diverse telemetry sources—including endpoint (EDR), network (NDR), cloud, and identity signals—into a centralized, AI-enabled Security Operations Center (SOC). Specific chapters provide actionable strategies for high-signal detection, behavioral analytics (UEBA), and SIEM tuning to reduce alert fatigue. The playbook approach is applied to high-stakes scenarios such as Ransomware, Business Email Compromise (BEC), and Insider Threats, demonstrating how Security Orchestration, Automation, and Response (SOAR) platforms can execute adaptive containment and recovery at machine speed.

Beyond technical implementation, the book stresses the importance of continuous validation through Red, Blue, and Purple teaming. It introduces adversary emulation as a tool for stress-testing automated defenses and refining detection logic. Performance is measured through rigorous metrics and Service Level Agreements (SLAs), focusing on reducing Mean Time to Detect (MTTD) and Mean Time to Respond (MTTR). The final chapters address the critical necessity of Resilience Engineering, ensuring that systems are built to withstand and rapidly recover from inevitable breaches.

The book concludes by addressing the governance, risk, and ethical implications of deploying AI in security. It provides a framework for managing algorithmic bias, ensuring transparency through Explainable AI (XAI), and maintaining human-in-the-loop oversight for high-impact automated actions. Ultimately, the work presents a vision of a modern SOC where human expertise and artificial intelligence work in a symbiotic relationship to defend complex, hybrid environments with unprecedented speed, precision, and accountability.

What You'll Find Inside:

• Adopt an automated defense mindset: automate repetitive tasks, base decisions on data, track measurable outcomes, iterate continuously, and foster collaboration across security functions.
• Design an AI-enabled SOC architecture with robust data pipelines, AI-driven intelligence (UEBA, LLMs, anomaly detection), SOAR orchestration, and integrated threat intelligence for end-to-end visibility and response.
• Create high-signal detections using domain-specific languages (DSLs) and large language models (LLMs) to increase precision, reduce noise, and enrich alerts with context for faster triage.
• Automate threat hunting and response through hypothesis‑driven query packs, SOAR playbooks, and serverless routines to enable rapid containment, evidence collection, and recovery at scale.
• Implement governance, risk, and ethical controls for AI in defense: ensure transparency, human‑in‑the‑loop approvals, bias mitigation, and compliance with regulations to maintain trustworthy automation.

Who's It For:

This book is intended for SOC analysts, incident responders, detection engineers, threat hunters, and security leaders who need practical, repeatable procedures to detect, contain, and recover from cyber incidents using AI and automation. It serves both small teams looking to start with minimal viable automation and large enterprises aiming to scale to high‑assurance, measurable workflows. Readers should have basic scripting proficiency, access to telemetry sources, and a commitment to measuring what matters.