Search results for "Securing Generative AI, LLMs, and ML using Zero Trust Architecture"
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R 557.28
Generative AI Security
Up-to-date reference enabling readers to address the full spectrum of AI security challenges while maintaining model utility Generative AI Security: Defense, Threats, and Vulnerabilities delivers a technical framework for securing generative AI systems, building on established standards while focusing specifically on emerging threats to large language models and other generative AI systems. Moving beyond treating AI security as a dual-use technology, this book provides detailed technical analysis of three critical dimensions: implementing AI-powered security tools, defending against AI-enhanced attacks, and protecting AI systems from compromise through attacks like prompt injection, model poisoning, and data extraction. The book provides concrete technical implementations supported by real-world case studies of actual AI system compromises, examining documented cases like the DeepSeek breaches, Llama vulnerabilities, and Google’s CaMeL security defenses to demonstrate attack methodologies and defense strategies while emphasizing foundational security principles that remain relevant despite technological shifts. Each chapter progresses from theoretical foundations to practical applications. The book also includes an implementation guide and hands-on exercises focusing on specific vulnerabilities in generative AI architectures, security control implementation, and compliance frameworks. Generative AI Security: Defense, Threats, and Vulnerabilities discusses topics including: Machine learning fundamentals, including supervised, unsupervised, and reinforcement learning and feature engineering and selectionIntelligent Security Information and Event Management (SIEM), covering AI-enhanced log analysis, predictive vulnerability assessment, and automated patch generationDeepfakes and synthetic media, covering image and video manipulation, voice cloning, audio deepfakes, and AI’s greater impact on information integritySecurity attacks on generative AI, including jailbreaking, adversarial, backdoor, and data poisoning attacksPrivacy-preserving AI techniques including federated learning and homomorphic encryption Generative AI Security: Defense, Threats, and Vulnerabilities is an essential resource for cybersecurity professionals and architects, engineers, IT professionals, and organization leaders seeking integrated strategies that address the full spectrum of Generative AI security challenges while maintaining model utility.
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Securing Uncontrolled Gen-AI Upload-Induced Data Leakage
The rapid growth of generative artificial intelligence (Gen-AI) is transforming industries such as healthcare, finance, education, law and software engineering. While Gen-AI enhances productivity and decision-making, it also introduces significant security, privacy and governance challenges, particularly through the unintended exposure of sensitive data. Securing Uncontrolled Gen-AI Upload-Induced Data Leakage examines the risks associated with uploading personal information, intellectual property, confidential communications and proprietary source code to AI systems. It explores key threats, including unsafe prompt engineering, shadow AI usage, prompt injection, model inversion, data poisoning and API misuse, which can result in data leakage, model memorization and regulatory violations. Through real-world case studies, forensic analysis and log tracing, this book demonstrates how sensitive information propagates across AI environments, and how such risks can be identified and mitigated. It also presents governance and forensic frameworks aligned with regulations such as GDPR, HIPAA and CCPA, providing practical guidance for secure, transparent and responsible AI adoption.
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Generative AI
Generative AI (GenAI) is the hottest new area of AI, accelerating innovations affecting all industries and showcasing AI’s ability to present human-like language capabilities with large language models (LLMs). This book builds a solid foundation for the technology underlying GenAI Models, LLM, and foundation and reasoning models for the No-Code audience by explaining the models' workings in simple English. It then takes the reader on a journey to find out where there are gaps to innovate. Each chapter outlines the state of research in each topic and invites learners to build on this research to think about ethical implications and balance it with innovation to benefit everyone. The author covers the technological, product, and societal impact of Generative AI to give a comprehensive guide for this important technology. This book will help the data scientist, product manager, and business leader alike because it covers the API economy and critical industries impacted and offers a framework to think about automation and jobs impacted and what competencies are needed to excel in GenAI and offers an in-depth guide to build GenAI products and applications.
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Oxford Handbook of the Foundations and Regulation of Generative AI
Generative AI technology holds immense transformative potential, with applications spanning from text generation to image creation and scientific discovery. Yet, its rapid development and adoption have introduced significant legal and ethical concerns, such as potential cybersecurity risks, misinformation, unsustainability, and algorithmic discrimination. The Oxford Handbook of the Foundations and Regulation of Generative AI offers theoretical insights and practical recommendations for ensuring the responsible development and application of this emerging technology.Edited and written by leading scholars in law, AI ethics, economics, and computer science, this Handbook brings together a global team of experts to examine the foundational principles and regulatory challenges posed by generative AI systems. The Handbook introduces core technical concepts-including explainable AI (XAI), agentic AI safety, and prompting-and discusses pressing legal and ethical concerns such as AI bias, consumer protection, platform and worker rights, data protection, copyright, and liability for AI-generated speech. The volume also considers how generative AI is transforming industries, from business and legal practice to healthcare, finance, and cybersecurity. Finally, it concludes with in-depth analyses of AI regulation in various jurisdictions, including the EU, US, China, Brazil, India, and Australia. Interdisciplinary in scope and global in coverage, this Handbook is an essential resource for anyone seeking to understand and navigate the evolving landscape of generative AI. It provides an accessible entry point for students, while offering the depth and rigor needed to inform practice, policy, and advanced research.
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Securing 5G and Evolving Architectures
SECURING and EVOLVING ARCHITECTURES 5G initiates a period of technological evolution where the benefits transcend faster data download speeds and enable services that will change the way we all live and consume technology. Leveraging 5G's openness, a new developer ecosystem is building breakthrough services that billions of people will consume, delivering immense value to enterprises and subscribers alike. For 5G to achieve its potential, organizations must embrace multi-layered security that goes far beyond 3GPP specifications. Now, leading security architect Pramod Nair helps network professionals climb the steep learning curve associated with securing 5G, fully understand its threat surfaces, systematically mitigate its risks, and maximize the value of their security investments. This coherent, pragmatic, and vendor-agnostic guide will help you plan for security from the outset, make better choices throughout the lifecycle, and develop the mindset needed to secure new generations of networks. You'll find all you need: from high-level 5G security concepts to in-depth coverage of specific security controls, end-to-end architectural guidance, 5G security use cases, and cutting-edge "quantum proofing." Throughout, practical examples and real-life scenarios help you apply Nair's insights---whether you're a service provider, an enterprise, an industry vertical, a startup, a cybersecurity vendor, a systems integrator, or even in a defense environment. Securing 5G and Evolving Architectures is for technical and management audiences at all levels of 5G experience---from enterprise and security architects to network engineers, cloud computing and data center professionals, to CSO and CTO teams. Explore new 5G security challenges---and why you still need external controls, even with recent 3GPP improvementsImplement network component security controls for RAN, Transport, 5GC, and devicesSafeguard Multi-Access Edge Compute (MEC), SDNs, virtualized 5G cores, and massive IOTProtect Public and Non-Public Networks (Private 5G) deployment scenariosSecure Critical Infrastructure, Vehicle to Everything (V2X), and Smart Factory use casesOptimize end-to-end 5G security architecture across all 5G domains based on zero trustPrioritize 5G security investments in service provider or enterprise environmentsPreview emerging 5G use cases and ML/AI-based security enhancements
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LLM for SoC Design and Security
This book provides a comprehensive guide to using LLMs for secure and efficient chip design, addressing key aspects such as LLM-driven IP development, verification, threat modeling, property generation, and security validation. The authors explore how LLMs can be harnessed for hardware IP verification, security assurance, and protection against emerging threats such as hardware Trojans, side-channel attacks, and supply chain vulnerabilities. By covering both the foundations of SoC design and security and the practical applications of LLMs in IP protection, this book will serve as an essential reference for hardware engineers, security researchers, SoC designers, and IP vendors seeking to integrate AI-driven methodologies into their workflow. The book not only presents the theoretical foundations of using LLMs in chip design but also provides practical insights into real-world implementation, ensuring that readers can apply these techniques effectively in securing next-generation semiconductor devices.
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Generative Adversarial Learning: Architectures and Applications
This book provides a collection of recent research works addressing theoretical issues on improving the learning process and the generalization of GANs as well as state-of-the-art applications of GANs to various domains of real life. Adversarial learning fascinates the attention of machine learning communities across the world in recent years. Generative adversarial networks (GANs), as the main method of adversarial learning, achieve great success and popularity by exploiting a minimax learning concept, in which two networks compete with each other during the learning process. Their key capability is to generate new data and replicate available data distributions, which are needed in many practical applications, particularly in computer vision and signal processing. The book is intended for academics, practitioners, and research students in artificial intelligence looking to stay up to date with the latest advancements on GANs’ theoretical developments and their applications.
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Generative AI
The cybersecurity landscape is changing, for sure. For example, one of the oldest threat variants is that of Phishing. It evolved in the early 1990s, but even today it is still being used as a primary threat variant and has now become much more sophisticated, covert, and stealthy in nature. For example, it can be used to launch ransomware, social engineering, and extortion attacks.The advent of Generative AI is making this much worse. For example, a cyberattacker can now use something like ChatGPT to craft the content for Phishing emails that are so convincing that it is almost impossible to tell the difference between what is real and what is fake. This is also clearly evident in the use of deepfakes, where fake images of real people are replicated to create videos to lure unsuspecting victims to a fake website.But, Generative AI can also be used for the good also, to combat Phishing Attacks. This is the topic of this book. In this, we cover the following:A review of PhishingA review of AI, Neural Networks, and Machine LearningA review of Natural Language Processing, Generative AI, and the Digital PersonA proposed solution as to how Generative AI can combat Phishing Attacks, as they relate to Privileged Access accounts
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