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portada The Developer's Playbook for Large Language Model Security: Building Secure ai Applications
Type
Physical Book
Publisher
Author
Language
English
Pages
200
Format
Paperback
ISBN13
9781098162207
Edition No.
1

The Developer's Playbook for Large Language Model Security: Building Secure ai Applications

Steve Wilson (Author) · O'reilly Media · Paperback

The Developer's Playbook for Large Language Model Security: Building Secure ai Applications - Steve Wilson

New Book Imported to Taiwan
Delivery: 14 Oct - 22 Oct Shipping: 2 to 3 business days.
NT$ 2,081
NT$ 2,081

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Synopsis "The Developer's Playbook for Large Language Model Security: Building Secure ai Applications"

Large language models (LLMs) are not just shaping the trajectory of AI, they're also unveiling a new era of security challenges. This practical book takes you straight to the heart of these threats. Author Steve Wilson, chief product officer at Exabeam, focuses exclusively on LLMs, eschewing generalized AI security to delve into the unique characteristics and vulnerabilities inherent in these models. Complete with collective wisdom gained from the creation of the OWASP Top 10 for LLMs list--a feat accomplished by more than 400 industry experts--this guide delivers real-world guidance and practical strategies to help developers and security teams grapple with the realities of LLM applications. Whether you're architecting a new application or adding AI features to an existing one, this book is your go-to resource for mastering the security landscape of the next frontier in AI. You'll learn: Why LLMs present unique security challenges How to navigate the many risk conditions associated with using LLM technology The threat landscape pertaining to LLMs and the critical trust boundaries that must be maintained How to identify the top risks and vulnerabilities associated with LLMs Methods for deploying defenses to protect against attacks on top vulnerabilities Ways to actively manage critical trust boundaries on your systems to ensure secure execution and risk minimization

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