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Key benefits

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

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.

Who is this book 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.

What you will learn

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

What do you get with a Packt Subscription?

Free for first 7 days. $19.99 p/m after that. Cancel any time!

Unlimited ad-free access to the largest independent learning library in tech. Access this title and thousands more!

50+ new titles added per month, including many first-to-market concepts and exclusive early access to books as they are being written.

Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.

Thousands of reference materials covering every tech concept you need to stay up to date.

Subscribe now

View plans & pricing

Frequently bought together

Google Machine Learning and Generative AI for Solutions Architects

₱2551.99

₱1529.99 ~~₱2040.99~~

Adversarial AI Attacks, Mitigations, and Defense Strategies

₱1785.99 ~~₱2551.99~~

Total ₱ 5,867.97 7,144.97 1,277.00 saved

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Dwayne Natwick Sep 02, 2024

I recently received a copy of @Packt Publishing’s Adversarial AI Attacks, Mitigations, and Defense Strategies from @John Sotiropoulos. This book outlines that various types of AI attacks along with the anatomy and design of these attacks. The author provides information on the architecture and setup that can be used for detailed analysis, mitigation, and defense of these attacks. There is also a complete section on generative AI and how it is used for a new level of attacks. Throughout this book, the author has done a great job of developing understanding and knowledge for the reader about these attacks, how they are created, and how you can protect your environment against them. This book is highly recommended for anyone that is looking for a level of understanding about how to architect, monitor, and defend against AI attacks.

Amazon Verified review

T J Coup Aug 01, 2024

A highly necessary book in the field, this comprehensive guide to AI security offers a structured understanding of key issues, complete with hands-on examples. A must-read for all IT professionals this summer.

Matthew Kiely Oct 28, 2024

This book is an essential read for anyone looking to deepen their understanding of adversarial AI.It goes beyond merely explaining how these attacks operate, it shows you how to set up a test environment to simulate these attacks and observe their impact on machine learning models.It’s indepth and not for the faint hearted!The hands-on approach allows you to see how adversarial techniques can corrupt AI systems.It is a well-rounded resource for both aspiring and seasoned AI professionals

Andy Aug 29, 2024

The author really knows his stuff and lays it out in a very approachable way. The writing and graphics are good, and the layout is very logical. That said, you better have solid AI design and cybersecurity in your recent past. Sample code for setting up the environment and defenses against the top AI attacks for predictive and generativeAI environments. Guidance is provided for DevSecOps, MLOps, and LLMOps, so you can build security in from the planning stage or apply mitigation strategies to environments already in operation. I love that he provides reference architecture diagrams that include the potential attacks (the snipped graphic on this review is from the book) and on which part of the architecture different attacks focus.The book is just under 600 pages, there isn’t any fluff, and it is very hands-on. It hits multiple audiences with various role focuses. That said, it is more like an encyclopedia for teams involved in AI at a company than it is a book for an individual, one that should be read end-to-end and then referenced as needed. Nice job.

Brandon Lachterman Sep 06, 2024

I really enjoyed this book, and it will be a fixture on my virtual shelf for reference. In a subject just getting more traction, this book gets down to brass tax and covers the subjects any reader is looking for, while leaving out the extra fluff. Very technical, but clear to understand. Highly recommend.

Adversarial AI Attacks, Mitigations, and Defense Strategies: A cybersecurity professional's guide to AI attacks, threat modeling, and securing AI with MLSecOps

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