Combating Cyberattacks Targeting the AI Ecosystem : Assessing Threats, Risks, and Vulnerabilities

This book explores in detail the AI-driven cyber threat landscape, including inherent AI threats and risks that exist in Large Language Models (LLMs), Generative AI applications, and the AI infrastructure. The book highlights hands-on technical approaches to detect security flaws in AI systems and a...

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Autor principal: Sood, Aditya K.
Format: Livre numérique
Idioma:Anglais
Publicat: Boston, MA : Mercury Learning and Information 2024.
Paris : Cyberlibris
Accés en línia:Accès Université d'Orléans et IFPM
Nota: Couverture. https://static2.cyberlibris.com/books_upload/136pix/9781501520549.jpg
Cyberlibris (ScholarVox) corpus Informatique
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Edition sous un autre format:• Combating Cyberattacks Targeting the AI Ecosystem, Assessing Threats, Risks, and Vulnerabilities, Aditya K. Sood, Boston, MA, Mercury Learning and Information, 2024, 1 vol. (253 p.), 978-15-0152-054-9
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520 |a This book explores in detail the AI-driven cyber threat landscape, including inherent AI threats and risks that exist in Large Language Models (LLMs), Generative AI applications, and the AI infrastructure. The book highlights hands-on technical approaches to detect security flaws in AI systems and applications utilizing the intelligence gathered from real-world case studies. Lastly, the book presents a very detailed discussion of the defense mechanisms and practical solutions to secureLLMs, GenAI applications, and the AI infrastructure. The chapters are structured with a granular framework, starting with AI concepts, followed by practical assessment techniques based on real-world intelligence, and concluding with required security defenses. Artificial Intelligence (AI) and cybersecurity are deeply intertwined and increasingly essential to moderndigital defense strategies. The book is a comprehensive resource for IT professionals, business leaders, and cybersecurity experts for understanding and defending against AI-driven cyberattacks. Features: Includes real-world case studies with detailed examples of AI-centric attacks and defense mechanisms. Features hands-on security assessments with practical techniques for evaluating the security of AI systems. Demonstrates advanced defense strategies with proven methods to protect LLMs, GenAI applications, and the infrastructure 
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