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Securing AI agents = foundations, frameworks, and real-world deployment /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Securing AI agents/ by Ken Huang, Chris Hughes.
Reminder of title:	foundations, frameworks, and real-world deployment /
Author:	Huang, Ken.
other author:	Hughes, Chris.
Published:	Cham :Springer Nature Switzerland : : 2025.,
Description:	xxxviii, 373 p. :ill., digital ;24 cm.
[NT 15003449]:	Part 1: Foundations of Agentic AI Security -- Introduction to Agentic AI: Foundations, Drivers, and Risks -- Agentic AI Threat Modeling -- Agentic AI Identity Security -- Agentic AI Communication Protocols and Security -- Part 2: Advanced Security Techniques and Frameworks -- AI Agents Life Cycle and Security Considerations -- Agentic AI Capability and Security Benchmark -- Agentic AI Reinforcement Learning and Security -- Agentic AI Red Teaming -- Securing Multi-Modal Agentic AI Systems -- Part 3: Guidance on Real-World Deployment and Commercial Landscape of Agentic AI Security -- Deploying Agentic AI in Enterprise Environments -- Agentic AI Security in Critical Sectors - Finance, Healthcare, and Autonomous Driving -- The Commercial Landscape of Agentic AI Security.
Contained By:	Springer Nature eBook
Subject:	Expert systems (Computer science) - Security measures. -
Online resource:	https://doi.org/10.1007/978-3-032-02130-4
ISBN:	9783032021304

Securing AI agents = foundations, frameworks, and real-world deployment /
Huang, Ken.

Securing AI agentsfoundations, frameworks, and real-world deployment /[electronic resource] :by Ken Huang, Chris Hughes. - Cham :Springer Nature Switzerland :2025. - xxxviii, 373 p. :ill., digital ;24 cm. - Advances in data analytics, AI, and smart systems,3091-2741. - Advances in data analytics, AI, and smart systems..

Part 1: Foundations of Agentic AI Security -- Introduction to Agentic AI: Foundations, Drivers, and Risks -- Agentic AI Threat Modeling -- Agentic AI Identity Security -- Agentic AI Communication Protocols and Security -- Part 2: Advanced Security Techniques and Frameworks -- AI Agents Life Cycle and Security Considerations -- Agentic AI Capability and Security Benchmark -- Agentic AI Reinforcement Learning and Security -- Agentic AI Red Teaming -- Securing Multi-Modal Agentic AI Systems -- Part 3: Guidance on Real-World Deployment and Commercial Landscape of Agentic AI Security -- Deploying Agentic AI in Enterprise Environments -- Agentic AI Security in Critical Sectors - Finance, Healthcare, and Autonomous Driving -- The Commercial Landscape of Agentic AI Security.

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.

ISBN: 9783032021304

Standard No.: 10.1007/978-3-032-02130-4doiSubjects--Topical Terms:

3791235
Expert systems (Computer science)
--Security measures.

LC Class. No.: QA76.76.E95 / H83 2025

Dewey Class. No.: 006.33

Securing AI agents = foundations, frameworks, and real-world deployment /
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520 $a 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.
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