Algorithmic Defense

Building & Running an AI Security Operation Center

By: Ajit Singh

Narrated by: Virtual Voice

About the Book

This book provides a comprehensive, practical, and step-by-step guide to conceptualizing, designing, building, and operating a specialized AI Security Operation Center (AI-SOC). It is singularly focused on the discipline of defending an organization's own AI and Machine Learning models from unique and emerging threats.


Philosophy

The core philosophy of this book is that AI systems should be treated as first-class citizens within a security program, not as black-box applications monitored by traditional tools. We advocate for a proactive, specialized defense strategy. The security of AI cannot be an afterthought; it must be an integrated, operational discipline. This requires a fusion of skills from data science, software engineering, and cybersecurity. Our approach demystifies the threats against AI and provides a clear, operational framework for mitigating them.


Key Features

1. Step-by-Step Guidance: From designing the architecture to writing detection rules and responding to incidents, the book provides clear, sequential instructions.
2. Focus on Implementation: Extensive use of Python, popular libraries (like Scikit-learn, TensorFlow, PyTorch), and open-source security tools.
3. Real-World Case Studies: Analysis of known AI attacks and security incidents to illustrate concepts and demonstrate the need for an AI-SOC.
4. Beginner to Advanced: The content is structured to serve as a primary textbook for B.Tech/M.Tech students while also being a valuable desk reference for seasoned professionals like SOC managers, CISOs, and MLOps engineers.
5. Vendor-Neutral Principles: While specific tools are used for examples, the underlying principles and architectures taught are universally applicable across different technology stacks.
6. Complete Capstone Project: A full, end-to-end DIY project in the final chapter to build and run a minimum viable AI-SOC for monitoring a live ML model.


To Whom This Book Is For

This book is written for a diverse audience, including:

1. B.Tech/M.Tech Computer Science Students: As a primary textbook for courses on Cybersecurity, AI Security, or Secure Software Development.
2. Aspiring AI Security Professionals: For individuals looking to specialize in the new and rapidly growing field of ML security.
3. Cybersecurity Professionals: For SOC analysts, managers, and architects who need to expand their skills to cover AI/ML systems.
4. MLOps and Data Science Professionals: For engineers and data scientists responsible for deploying and maintaining ML models who want to understand how to secure their creations.
5. CISOs and IT Leaders: As a strategic guide for understanding the risks associated with AI and establishing a governance and operational framework to mitigate them.

Disclaimer: Earnest request from the Author.

Kindly go through the table of contents and refer kindle edition for a glance on the related contents.

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