rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Generative AI security
    sub title: defense, threats, and vulnerabilities
  name:
    name part: Rana, Shaila
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Chicone, Rhonda
    role:
      role term: author.
  type of resource: text
  origin info:
    place:
      place term: nju
    date issued: 2026
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource
  abstract: 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 selection Intelligent Security Information and Event Management (SIEM), covering AI-enhanced log analysis, predictive vulnerability assessment, and automated patch generation Deepfakes and synthetic media, covering image and video manipulation, voice cloning, audio deepfakes, and AI's greater impact on information integrity Security attacks on generative AI, including jailbreak…
  table of contents: Chapter 1: -- Abstract -- 1.1: What is Generative AI? -- 1.2: The Evolution of AI in Cybersecurity -- 1.3 Overview of GAI in Security -- 1.4 Current Landscape of Generative AI Applications -- 1.5 A Triangular Approach -- Chapter 1 Summary -- Hypothetical Case Study: The Triangular Approach to AI Security -- References -- Chapter 2: Understanding Generative AI Technologies -- Abstract -- 2.1: ML Fundamentals -- 2.2 Deep Learning and Neural Networks -- 2.3 Generative Models -- 2.4 NLP in Generative AI -- 2.5 Computer Vision in Generative AI -- Conclusion -- Chapter 2 Summary: -- Case Study: -- References -- Chapter 3: Generative AI as a Security Tool -- Abstract -- 3.1 AI-Powered Threat Detection and Response -- 3.2 Automated Vulnerability Discovery and Patching -- 3.3 Intelligent SIEMs -- 3.4 AI in Malware Analysis and Classification -- 3.5 Generative AI in Red Teaming -- 3.6 J-Curve for Productivity in AI-Driven Security -- 3.7 Regulatory Technology (RegTech) -- 3.8 AI for Emotional Intelligence (EQ) in Cybersecurity -- Chapter 3 Summary: -- Case study: GAI as a Tool -- References -- Chapter 4: Weaponized Generative AI -- Abstract -- 4.1 Deepfakes and Synthetic Media -- 4.2 AI-Powered Social Engineering -- 4.3 Automated Hacking and Exploit Generation -- 4.4 Privacy Concerns -- 4.5 Weaponization of AI: Attack Vectors -- 4.6 Defensive Strategies Against Weaponized Generative AI -- Chapter 4 Summary: -- Case Study 1: Weaponized AI in a Small-Sized Organization -- Case Study 2: Weaponized AI in a Large Organization -- References -- Chapter 5: Generative AI Systems as a Target of Cyber Threats -- Abstract -- 5.1 Security Attacks on Generative AI -- 5.2 Privacy Attacks on Generative AI -- 5.3 Attacks on Availability -- 5.4 Physical Vulnerabilities -- 5.5 Model Extraction and Intellectual Property Risks -- 5.6 Model Poisoning and Supply Chain Risks -- 5.7 Open-Source GAI Models -- 5.8 Application-specific Risks -- 5.9 Challenges in Mitigating Generative AI Risks -- Chapter…
  note: Shaila Rana and Rhonda Chicone.
  subject:
    topic: Generative artificial intelligence
    topic: Security measures
  classification: Q335 .R36 2026
  classification: 006.3/1
  identifier: 9781394368532
  identifier: 1394368534
  identifier: 9781394368501
  identifier: 139436850X
  identifier: 9781394368518
  identifier: 1394368518
  identifier: 9781394368488
  identifier: 1394368488
  identifier: 11229970 IEEE
  identifier: http://proxy.library.tamu.edu/login
  record info:
    record content source: NhCcYBP
    record creation date: 251105
    record change date: 20260215001445.3
    record identifier: in00005690759
    language of cataloging:
      language term: eng