Adversarial AI Attacks, Mitigations, and Defense Strategies

eBook Details:

• Paperback: 586 pages
• Publisher: WOW! eBook (July 26, 2024)
• Language: English
• ISBN-10: 1835087981
• ISBN-13: 978-1835087985

eBook Description:

Adversarial AI Attacks, Mitigations, and Defense Strategies: A cybersecurity professional’s guide to AI attacks, threat modeling, and securing AI with MLSecOps. Understand how adversarial attacks work against predictive and generative AI, and learn how to safeguard AI and LLM projects with practical examples leveraging OWASP, MITRE, and NIST.

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 Adversarial AI Attacks, Mitigations, and Defense Strategies 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.

• Understand poisoning, evasion, and privacy attacks and how to mitigate them
• Discover how GANs can be used for attacks and deepfakes
• Explore how LLMs change security, prompt injections, and data exposure
• Master techniques to poison LLMs with RAG, embeddings, and fine-tuning
• Explore supply-chain threats and the challenges of open-access LLMs
• Implement MLSecOps with CIs, MLOps, and SBOMs

By the end of this Adversarial AI Attacks, Mitigations, and Defense Strategies book, you’ll be able to develop, deploy, and secure AI systems effectively.