Overview

AI has already changed cyber warfare - and most SOCs are already behind. Attackers are using AI today to generate polymorphic malware, automate reconnaissance, scale phishing and deepfake fraud, and bypass signature-based defenses faster than human teams can respond. Meanwhile, many defenders are experimenting with AI tools they don't fully trust, can't properly measure, and hesitate to deploy in production. AI for Cyber Security is written for security professionals who must make AI work in the real world - inside live Security Operations Centers where mistakes lead to outages, breaches, regulatory exposure, and career-level risk. This is not a vendor pitch, research survey, or theoretical overview. It is a practical, engineering-grade playbook for designing, governing, and operating AI systems across SOCs, cloud environments, and security platforms - from first pilot to safe, human-in-the-loop automation, and ultimately toward autonomous defense. INCLUDED IN THIS BOOK The Crawl-Walk-Run Adoption Roadmap: A phase-by-phase execution guide to move your SOC from experimental AI pilots to autonomous defense without breaking production. 5+ Battle-Tested Security Frameworks: High-stakes decision models including the R.A.I.L.S. automation safety test, the A.I.M. threat modeling system, and the R.E.A.L. vulnerability prioritization method. 25+ Practitioner-Ready Prompts: Engineering-grade prompts to de-obfuscate malware, generate advanced SIEM queries (KQL/SPL), and simulate Red Team behavior. The AI Governance & Risk Toolkit: Ready-to-use templates for AI Acceptable Use Policies, Model Cards, and a no-nonsense Vendor Due Diligence Checklist. 10+ Operational Checklists & Playbooks: From data hygiene audits to incident response playbooks for prompt injection, model poisoning, and RAG data leakage. The Security Metrics Dashboard: KPIs that prove value beyond ""accuracy,"" including False Positive Rate, Human Override Rate, and Cost Per Alert. 15+ Real-World Cyber Scenarios: Deep dives into AI-driven attacks such as deepfakes and polymorphic malware - and the architectural countermeasures used to stop them. WHAT'S INSIDE THIS BOOK Part I: Foundations Why security is shifting from signatures to semantics, how ML and LLMs actually work, and why most security data is toxic to AI without proper grounding. Part II: Operating AI in the SOC Designing human-in-the-loop workflows, moving from probabilities to decisions, securing MLOps pipelines, and measuring what truly matters. Part III: Defensive Applications Practical AI use cases across SOC triage, anomaly detection, malware and phishing defense, cloud and identity security, AppSec, and SOAR automation. Part IV: Adversarial Reality How attackers exploit models themselves, weaponize generative AI, and execute AI-powered attacks - and how to defend against them. Part V: Strategy & Governance Build-vs-buy decisions, AI governance aligned with NIST and emerging regulation, and proving ROI while solving the junior-analyst crisis. Parts VI & VII: Playbooks, Case Studies & The Future Detailed SOC case studies, AI incident response when the model becomes the threat, and preparing for autonomous defense and post-quantum realities. If AI is now part of both the attack surface and the defense stack, this book shows you how to stay in control. Written for SOC analysts, security engineers, architects, and CISOs who need operational clarity, measurable results, and defensible decisions - not hype.

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