Securing AI Agents
Foundations, Frameworks, and Real-World Deployment
Buch in deiner Nähe kaufen
Beschreibung
This book focuses on agentic AI security, providing a comprehensive guide to the theoretical foundations and practical techniques required to secure the increasingly prevalent AI agent systems. It examines the security challenges posed by multi-agent environments and presents real-world examples of open-source frameworks and commercial solutions to mitigate these risks. It answers key questions, including how to conduct threat modeling for agentic AI systems, how to secure communication and identity within multi-agent environments, and how to leverage open-source frameworks and commercial solutions for effective security.
The book features dedicated chapters on agentic AI threat modeling, identity security, communication security in MAS (Multi-Agent Systems), red teaming, AI agents life cycle security, capability and security benchmarking using GAIA and AIR frameworks, Reinforcement Learning (RL) and security, secure agentic AI deployment strategies, innovative open source security frameworks (Cloud Security Alliance and OWASP examples), and case studies of commercial startups addressing agentic AI security challenges. It also explores the unique threat landscape of agentic AI, the challenges of securing communication and identity within multi-agent systems, and the practical application of security benchmarks and open-source frameworks.
As such, the book equips cybersecurity professionals, AI developers, and researchers with the knowledge and tools to mitigate the unique security risks associated with autonomous agents and multi-agent systems.
This book focuses on agentic AI security, providing a comprehensive guide to the theoretical foundations and practical techniques required to secure the increasingly prevalent AI agent systems. It examines the security challenges posed by multi-agent environments and presents real-world examples of open-source frameworks and commercial solutions to mitigate these risks. It answers key questions, including how to conduct threat modeling for agentic AI systems, how to secure communication and identity within multi-agent environments, and how to leverage open-source frameworks and commercial solutions for effective security.
The book features dedicated chapters on agentic AI threat modeling, identity security, communication security in MAS (Multi-Agent Systems), red teaming, AI agents life cycle security, capability and security benchmarking using GAIA and AIR frameworks, Reinforcement Learning (RL) and security, secure agentic AI deployment strategies, innovative open source security frameworks (Cloud Security Alliance and OWASP examples), and case studies of commercial startups addressing agentic AI security challenges. It also explores the unique threat landscape of agentic AI, the challenges of securing communication and identity within multi-agent systems, and the practical application of security benchmarks and open-source frameworks.
As such, the book equips cybersecurity professionals, AI developers, and researchers with the knowledge and tools to mitigate the unique security risks associated with autonomous agents and multi-agent systems.
Offers a focused and practical guide to securing agentic AI systems Presents practical tools and solutions through GAIA, AIR, open-source frameworks, and real-world examples Provides valuable insights based on the author's deep experience and industry expertise
Autor*in
Ken Huang
Themen in »Securing AI Agents«
Agentic AI Security AI Agent Security Autonomous System Security AI Threat Modeling Agent RL Security (Reinforcement Learning Security) AI Red Teaming GAIA Benchmark Stanford AIR Benchmark Multi-Agent Systems Security (MAS Security)