AI & Cybersecurity: The Intelligent Defender’s Handbook
From Foundations to Frontlines
Master offensive and defensive AI for cybersecurity. Learn AI-powered phishing detection, malware analysis, intrusion detection, adversarial ML, LLM security, and post-quantum cryptography with real case studies and hands-on labs.
Foundations— Cybersecurity + AI fundamentals.
The Cyber Battlefield in the Age of AI
Understanding how AI transformed cybersecurity from script kiddies to nation-state AI agents
Cybersecurity Fundamentals for the AI Engineer
Core security concepts, networking, cryptography, and the MITRE ATT&CK framework
AI & Machine Learning Fundamentals for Security
The ML toolkit, security datasets, Python stack, and model evaluation for security engineers
AI as Attacker— Offensive AI and threat landscape.
AI-Powered Phishing & Identity Attacks
From Nigerian princes to hyper-personalized AI phishing, deepfakes, and credential attacks
AI-Enabled Malware
Autonomous, adaptive, and invisible — polymorphic malware, RaaS, zero-days, and AI kill chains
Supply Chain Attacks
Poisoning the well — software, AI model, and hardware supply chain threats
Nation-State Attacks and APTs
Advanced persistent threats, AI-enhanced nation-state operations, and cyber warfare
AI as Defender— ML-driven defense systems.
ML for Intrusion Detection Systems
Building ML-powered network intrusion detection and user behavior analytics
AI-Powered Threat Detection
Malware analysis at scale — static, dynamic, deep learning, and NLP approaches
AI-Driven Security Operations Center
The modern AI-SOC — SIEM/SOAR integration, threat hunting, and automated incident response
AI for Vulnerability Management
Intelligent scanning, AI-assisted pentesting, fuzzing, and red team AI agents
Zero Trust Architecture
AI as the enforcement engine — IAM, microsegmentation, and step-by-step implementation
Securing AI— Adversarial ML, LLM security, governance.
Adversarial Machine Learning
Attacking and defending AI models — evasion, poisoning, extraction, and robustness
Securing Large Language Models
LLM security — prompt injection, data leakage, agent security, and best practices
AI Governance and Compliance
EU AI Act, NIST AI RMF, responsible AI, risk assessment, and secure AI development
Advanced Domains— Cloud, IoT, post-quantum.
Cloud Security and AI
Defending the new perimeter — CSPM, Kubernetes security, and SASE architecture
IoT and OT Security
When cyberattacks become physical — ICS/SCADA security, anomaly detection, and edge security
Post-Quantum Cryptography
The coming cryptographic revolution — quantum threats, NIST PQC standards, and crypto-agility
Operations— Security engineering, DFIR, threat intel.
Security Engineering
Building secure systems by design — SSDLC, threat modeling, DevSecOps, and architecture patterns
Digital Forensics and Incident Response
DFIR with AI — memory forensics, network forensics, malware reverse engineering
Threat Intelligence and AI
Predicting the next attack — CTI operations, ML for threat intel, and actor profiling
Future Frontiers— Autonomous agents, ethics, career.
Autonomous AI Agents in Security
The next frontier — agentic AI in SOCs, multi-agent systems, and AI safety meets security
The Future AI Security Engineer
Ethics, career roadmap, continuous learning, and the 10-year outlook
Where the book lands in practice.
ML for Intrusion Detection Systems
Building ML-powered network intrusion detection and user behavior analytics
Open chapterAI-Driven Security Operations Center
The modern AI-SOC — SIEM/SOAR integration, threat hunting, and automated incident response
Open chapterAI for Vulnerability Management
Intelligent scanning, AI-assisted pentesting, fuzzing, and red team AI agents
Open chapter98 sections. Begin with one.
Chapter 1 — The Cyber Battlefield in the Age of AI — is where every reader starts.