Search results for "Generative AI System Design"
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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.
R 4,559.00
Adversarial Symbiosis in Cybersecurity
Apply evolutionary biology to AI-driven cybersecurity attack and defense Offensive AI now discovers vulnerabilities, generates adversarial examples that defeat defensive models, and adapts in real time. Adversarial Symbiosis in Cybersecurity: Co-evolving AI for Attack, Defense and Governance reconceptualizes this landscape as a living ecosystem where defensive AI, offensive AI, and human operators continuously co-evolve through adaptive learning loops, with each side’s adaptations shaping the other’s trajectory. Drawing on predator-prey dynamics and biological co-evolution, the book delivers quantitative models, simulation methods, and practical design principles for building AI systems that adapt dynamically rather than merely detect threats. Later chapters address offensive AI capabilities including automated exploit discovery and adversarial example generation, while governance models balance innovation with ethics, safety, and regulatory compliance. The book also covers: Human-centered approaches integrating cognitive science, decision support design, and trust calibration into AI-augmented security operations workflowsReal-world case studies demonstrating co-evolutionary patterns in advanced persistent threat campaigns, phishing ecosystems, and IoT botnetsPractical architectures for designing adaptive defense systems that respond to evolving offensive techniques through continuous learning loopsMulti-agent reinforcement learning and meta-learning frameworks applied to emergent behaviors within adversarial cybersecurity environmentsGovernance and policy frameworks addressing ethical deployment of offensive AI capabilities alongside regulatory compliance requirements Written for security operations center analysts and managers, threat intelligence specialists, and incident response teams, this book also serves security architects, engineers, and CISOs seeking a rigorous co-evolutionary framework for understanding and operationalizing AI-driven cybersecurity strategy.
R 4,396.00
Artificial Intelligence Paradigms for Smart Cyber-Physical Systems
Cyber-physical systems (CPS) have emerged as a unifying name for systems where cyber parts (i.e., the computing and communication parts) and physical parts are tightly integrated, both in design and during operation. Such systems use computations and communication deeply embedded in and interacting with human physical processes as well as augmenting existing and adding new capabilities. As such, CPS is an integration of computation, networking, and physical processes. Embedded computers and networks monitor and control the physical processes, with feedback loops where physical processes affect computations and vice versa. The economic and societal potential of such systems is vastly greater than what has been realized, and major investments are being made worldwide to develop the technology. Artificial Intelligence Paradigms for Smart Cyber-Physical Systems focuses on the recent advances in Artificial intelligence-based approaches towards affecting secure cyber-physical systems. This book presents investigations on state-of-the-art research issues, applications, and achievements in the field of computational intelligence paradigms for CPS. Covering topics that include autonomous systems, access control, machine learning, and intrusion detection and prevention systems, this book is ideally designed for engineers, industry professionals, practitioners, scientists, managers, students, academicians, and researchers seeking current research on artificial intelligence and cyber-physical systems.
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