Adversarial AI Attacks, Mitigations, and Defense Strategies: A cybersecurity pro

Adversarial AI Attacks, Mitigations, and Defense Strategies by John Sotiropoulos

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Format Paperback

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Description 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 NISTKey FeaturesUnderstand the connection between AI and security by learning about adversarial AI attacksDiscover the latest security challenges in adversarial AI by examining GenAI, deepfakes, and LLMsImplement secure-by-design methods and threat modeling, using standards and MLSecOps to safeguard AI systemsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionAdversarial 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 learnUnderstand poisoning, evasion, and privacy attacks and how to mitigate themDiscover how GANs can be used for attacks and deepfakesExplore how LLMs change security, prompt injections, and data exposureMaster techniques to poison LLMs with RAG, embeddings, and fine-tuningExplore supply-chain threats and the challenges of open-access LLMsImplement MLSecOps with CIs, MLOps, and SBOMsWho this book is forThis 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.

Author Biography

John Sotiropoulos is a senior security architect at Kainos where he is responsible for AI security and works to secure national-scale systems in government, regulators, and healthcare. John has gained extensive experience in building and securing systems in roles such as developer, CTO, VP of engineering, and chief architect.A co-lead of the OWASP Top 10 for Large Language Model (LLM) Applications and a core member of the AI Exchange, John leads standards alignment for both projects with other standards organizations and national cybersecurity agencies. He is the OWASP lead at the US AI Safety Institute Consortium.An avid geek and marathon runner, he is passionate about enabling builders and defenders to create a safer future.

Details ISBN 1835087981 ISBN-13 9781835087985 Title Adversarial AI Attacks, Mitigations, and Defense Strategies Author John Sotiropoulos Format Paperback Year 2024 Pages 586 Publisher Packt Publishing Limited GE_Item_ID:160898465; About Us

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