Understand how adversarial attacks work against predictive and generative AI, and learn how to safeguard AI projects with practical examples leveraging OWASP, MITRE, and NIST
Key Features
- Understand the connection between AI and security by learning about adversarial AI attacks
- Discover the latest security challenges in adversarial AI by examining GenAI, deepfakes, and LLMs
- Implement secure-by-design methods and threat modeling, using standards and MLSecOps to safeguard AI systems
- Purchase of the print or Kindle book includes a free PDF eBook
Book Description
Adversarial attacks trick AI systems with malicious data, creating new security risks by exploiting how AI learns. This challenges cybersecurity as it forces us to defend against a whole new kind of threat. This book demystifies adversarial attacks and equips cybersecurity professionals with the skills to secure AI technologies, moving beyond research hype or business-as-usual strategies. The strategy-based book is a comprehensive guide to AI security, presenting a structured approach with practical examples to identify and counter adversarial attacks. This book goes beyond a random selection of threats and consolidates recent research and industry standards, incorporating taxonomies from MITRE, NIST, and OWASP. Next, a dedicated section introduces a secure-by-design AI strategy with threat modeling to demonstrate risk-based defenses and strategies, focusing on integrating MLSecOps and LLMOps into security systems. To gain deeper insights, you'll cover examples of incorporating CI, MLOps, and security controls, including open-access LLMs and ML SBOMs. Based on the classic NIST pillars, the book provides a blueprint for maturing enterprise AI security, discussing the role of AI security in safety and ethics as part of Trustworthy AI. By the end of this book, you'll be able to develop, deploy, and secure AI systems effectively.
What you will learn
- Understand how GANs can be used for attacks and deepfakes
- Discover how LLMs change security, including prompt injections and data exposure
- Understand privacy-preserving ML techniques and apply them using Keras and PyTorch
- Explore LLM threats with RAG, embeddings, and privacy attacks
- Find out how to poison LLMs by finetuning APIs or direct access
- Examine model benchmarking and the challenges of open-access LLMs
- Discover how to automate AI security using MLSecOps, including CI, MLOps, and SBOMs practices
Who this book is for
This book tackles AI security from both angles - offense and defense. AI builders (developers and engineers) will learn how to create secure systems, while cybersecurity professionals, such as security architects, analysts, engineers, ethical hackers, penetration testers, and incident responders will discover methods to combat threats and mitigate risks posed by attackers. The book also provides a secure-by-design approach for leaders to build AI with security in mind. To get the most out of this book, you'll need a basic understanding of security, ML concepts, and Python.
