THE AUTONOMOUS SOC
How Artificial Intelligence Is Reinventing Cyber DefenseBy Silver Nsaka
Length3h 51m
About this audiobook
In an era where cyberattacks launch in milliseconds and breach organizations in seconds, the traditional Security Operations Center is reaching the end of its operational viability. THE AUTONOMOUS SOC is the definitive guide to the AI-driven transformation that every security organization must undertake to survive the next decade of cyber warfare. Written by Silver Nsaka — cybersecurity strategist, AI architect, and founder of Kingdom AI Solutions — this book delivers a complete blueprint from the failing SOC of today to the autonomous, AI-driven cyber defense platform of tomorrow. WHAT YOU WILL LEARN: How machine learning, behavioral analytics, and graph-based detection replace static rules with systems that think and adapt. How LLMs and agentic AI systems transform analysts into strategic orchestrators of autonomous defense. How to architect a complete AI-SOC platform on AWS, Azure, and Google Cloud.
Audiobook details
GenreTechnology
Length3 hrs 51 mins
Narrated byListen with 1,000+ voices
FormateBook with Audio
Publish dateMar 27, 2026
LanguageEnglish
Table of contents
1Introduction
48ML Techniques for Security: Selection Guide
2Foreword
49Chapter 12: The AI-SOC Reference Architecture
3Preface
50The Complete AI-SOC Architecture
4Table of Contents
51Layer-by-Layer Design Principles
5Chapter 1: The Broken SOC — Why Traditional Security Operations Are Failing
52Chapter 13: Security Data Engineering — Building the Intelligence Foundation
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6The Alert Tsunami
53The Security Data Lake Architecture
7Mean Time to Detect and Mean Time to Respond: The Metrics of Failure
54Telemetry at Scale: Engineering for Billions of Events
8The Tool Proliferation Problem
55Chapter 14: The AI-Powered SIEM — From Log Aggregation to Intelligent Detection: Reinventing the SIEM for AI-First Operations
9The Talent Crisis
56Chapter 15: SOAR 2.0 — AI-Orchestrated Security Automation
10Architecture Diagrams: The Traditional SOC vs. The AI-SOC
57The Evolution of Security Orchestration
11The Case for Radical Transformation
58Automated Incident Triage at Scale
12Traditional SOC vs. AI-SOC: Key Metrics
59Chapter 16: Detection Engineering with AI: AI-Generated Detection Rules
13Chapter 2: The Threat Landscape Has Evolved — Your Defenses Have Not
60Chapter 17: AI-Driven Threat Intelligence: Transforming Threat Intelligence with AI
14The New Anatomy of Attack
61Chapter 18: Cloud-Native AI-SOC on AWS, Azure, and GCP
15AI-Enhanced Attack Techniques
62AWS AI-SOC Reference Architecture
16The Speed Problem: Why Rules Cannot Keep Up
63Azure AI-SOC Reference Architecture
17Supply Chain and Third-Party Risk: The Expanding Attack Surface
64Google Cloud AI-SOC Reference Architecture
18Cloud-Native Attack Surfaces
65AI-SOC Technology Stack: Component Selection Guide
19Chapter 3: The Human Bottleneck — Alert Fatigue, Analyst Burnout, and the Talent Crisis
66Chapter 19: Introduction to Agentic AI Security Systems
20The Neuroscience of Alert Fatigue
67What Makes an AI System Agentic?
21The Staffing Model Impossibility
68LangChain and Agent Frameworks for Security
22Designing for Human-AI Collaboration
69Chapter 20: Detection Agents — Autonomous Threat Discovery: Building Production Detection Agents
23Chapter 4: The Economics of Cyber Failure — The ROI of Legacy Security Is Collapsing
70Chapter 21: Incident Response Agents — Automated Investigation and Containment: The Incident Response Agent Architecture
24The True Cost of a Security Breach
71Chapter 22: Threat Hunting Agents — Proactive Defense at Scale: Autonomous Threat Hunting at Enterprise Scale
25The ROI Framework for AI-SOC Investment
72Chapter 23: Multi-Agent SOC Systems — Coordination and Orchestration: The Multi-Agent Security Architecture
26Chapter 5: The Case for Transformation — What a Modern AI-SOC Must Deliver
73Chapter 24: Security Copilots — AI-Augmented Human Analysts
27Defining the AI-SOC: Capabilities and Characteristics
74The AI Security Analyst Experience
28The AI-SOC Maturity Model
75AI Security Agent Capabilities Matrix
29AI-SOC Maturity Model
76Chapter 25: The Autonomous SOC Platform — Integration and Operations: Platform Architecture Integration
30Chapter 6: Machine Learning for Threat Detection — From Rules to Intelligence
77Chapter 26: Self-Healing Security Infrastructure: Autonomous Defense and Remediation
31A Taxonomy of Machine Learning in Security
78Chapter 27: AI Red Team vs. AI Blue Team — Adversarial Machine Learning: The Adversarial AI Challenge
32Feature Engineering for Security ML
79Chapter 28: Next-Generation AI Fraud Detection — Real-Time Defense at Transaction Scale
33Behavioral Analytics: Building Baselines That Matter
80The Architecture of AI-Powered Fraud Detection
34ML Model Deployment and MLOps for Security
81Smart Contract Audit Trails and Blockchain for Fraud Evidence
35Chapter 7: Behavioral Analytics and UEBA — The New Perimeter
82Chapter 29: AI-Powered SOC Metrics — Measuring What Matters: The AI-SOC Measurement Framework
36User and Entity Behavior Analytics: Architecture and Implementation
83Chapter 30: SOC Transformation Roadmap — From Legacy to Autonomous
37Insider Threat Detection: The Most Difficult Problem
84The 36-Month Transformation Journey
38Chapter 8: Graph-Based Threat Detection and Attack Path Analysis
85SOC KPI Dashboard: Target Metrics by Maturity Level
39Why Graphs Are Natural Security Models
86Chapter 31: Nation-State AI Cyber Operations: AI in State-Sponsored Cyber Programs
40Attack Graph Analysis and MITRE ATT&CK Mapping
87Chapter 32: AI vs. AI — The Coming Cyber Arms Race: The Automated Offense-Defense Equilibrium
41Chapter 9: Large Language Models in the SOC — The AI Security Analyst
88Chapter 33: Self-Defending Networks and Zero Trust AI: The Architecture of Self-Defending Infrastructure
42LLMs Transform SOC Operations
89Chapter 34: The AI Security Ecosystem — Standards, Governance, and Ethics: Responsible AI in Security Operations
43Building a Security-Specific LLM: Fine-Tuning and RAG
90Chapter 35: The Horizon — Autonomous Cyber Defense in 2030 and Beyond: The Security Future We Are Building
44LLM Security Copilots: Architecture and Implementation
91Appendix A: MITRE ATT&CK Framework and AI Integration
45Chapter 10: AI Threat Hunting — Autonomous Discovery of Hidden Threats: The Evolution of Threat Hunting
92Appendix B: AI-SOC Technology Stack Reference
46Chapter 11: AI Anomaly Detection — Finding What Rules Cannot See
93Appendix C: Implementation Checklist
47Taxonomy of Anomaly Detection Techniques
94Appendix D: Glossary of AI Security Terms
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