Book Launch

THE AI Security Book

by Scott Thornton

The most comprehensive guide on AI/ML Security — a must-have for all who develop, implement, support, or secure AI/ML applications. Built on the latest research (2024 and newer), real-world exploits, threats, and vulnerabilities. Vendor-agnostic and focused on practical concepts, proven designs, and battle-tested implementations.

THE AI Security Book cover

Final book art subject to change

ai_security_book.py
#!/usr/bin/env python3
"""
THE AI Security Book - Comprehensive ML/AI Security Guide
"""

class AISecurityBook:
    def __init__(self):
        self.chapters = 40+
        self.word_count = 300_000+
        self.code_examples = {
            'offensive': 50+,
            'defensive': 50+
        }
        self.diagrams = 250+
        self.resources = [
            'github_repo',
            'downloadable_appendices',
            'vulnerability_database'
        ]
    
    def learn_ml_mechanics(self):
        """Teaching you the mechanics of machine learning"""
        return "Understanding how models work fundamentally"
    
    def find_vulnerabilities(self):
        """Where security vulnerabilities hide"""
        return ["model_poisoning", "adversarial_attacks", "prompt_injection"]
    
    def exploit_and_defend(self):
        """How to exploit and defend ML systems"""
        return {
            'red_team': self.code_examples['offensive'],
            'blue_team': self.code_examples['defensive']
        }

# Initialize your AI security journey
book = AISecurityBook()
print("Ready to secure the AI future? 🛡️🤖")

Inside the Book

Attacks
Adversarial Attacks

Evasion, perturbations, jailbreaks, and model extraction.

Defense
Defensive Engineering

Robust training, guardrails, evals, and secure prompts.

Governance
Governance & Compliance

NIST AI RMF, EU AI Act, SOC 2, ISO 42001.

Future
Quantum & Future Risks

Post-quantum crypto, quantum ML, timing channels.

Agents
Multi‑Agent Security

Agent memory poisoning, goal hijacking, inter‑agent threats.

Incidents
Real‑World Incidents

Case studies and lessons learned for practitioners.

Scott Thornton

About the Author

Scott Thornton is a seasoned cybersecurity architect and AI security pioneer with over 25 years of experience protecting enterprise infrastructures from the network layer to the AI application stack. As a Senior Consulting Engineer at Palo Alto Networks, he specializes in securing GenAI applications and defending against adversarial machine learning attacks, bringing practical expertise from the front lines of enterprise AI security.

AI is rewriting the future. Security must keep up.

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