AI SECURITYLABS
Cover of Fundamentals of AI Security
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Fundamentals of AI Security

Securing Models, Applications, and AI Systems Across the Lifecycle

4 parts · 24 chapters · 4 appendices

About the book

A practitioner textbook that treats AI security as a discipline of its own: how learned behaviour changes the attack surface, the threats to models, applications and agents, how to engineer and operate defences, and how to govern AI risk. Every chapter has learning objectives, and the book ends with a capstone that brings threat modelling, architecture review, red teaming and assurance together.

Written for: Students and practitioners starting out in AI security, and security professionals moving into it.

This book is not published yet. This page shows its syllabus; the full text will follow.

Syllabus

Part I. Foundations of SecureAI

  1. Introduction: Why AI Security Matters
  2. SecureAI vs. AI for Cybersecurity
  3. Cybersecurity Foundations for AI Systems
  4. AI and Machine Learning Foundations
  5. The AI System Lifecycle

Part II. The AI Threat Landscape

  1. Threat Modeling AI Systems
  2. Adversarial Machine Learning and Evasion Attacks
  3. Data Poisoning and Training-Time Attacks
  4. Model Inversion, Membership Inference, and Privacy Leakage
  5. Model Extraction and Intellectual Property Risk
  6. Generative AI, LLMs, Prompt Injection, and RAG Security
  7. AI Agents, Tool Use, MCP, and Excessive Agency Risk

Part III. SecureAI Engineering and Defense

  1. Secure AI Architecture and Deployment
  2. Securing AI Pipelines and MLOps Environments
  3. Secure Model Serving, APIs, Identity, and Access Control
  4. AI Monitoring, Logging, Detection, and Incident Response
  5. AI Supply Chain Risk and Third-Party Model Risk
  6. Testing, Evaluation, Red Teaming, and Assurance

Part IV. Governance, Risk, and Workforce Readiness

  1. NIST AI RMF and AI Risk Management
  2. MITRE ATLAS, OWASP GenAI, and AI Security Frameworks
  3. AI Governance, Policy, Compliance, and the EU AI Act
  4. Building AI Risk Registers and Control Mappings
  5. SecureAI Workforce Roles and Career Pathways
  6. Capstone: Threat Model, Architecture Review, Red Team, and Assurance Report

Appendices

  • AI Security Control Checklist
  • AI Risk Register Template
  • AI Security Framework Crosswalk
  • Lab Environment