國立高雄大學圖資館 |

Language: English

Back

Securing AI agentsfoundations, frameworks, and real-world deployment /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Securing AI agentsby 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.
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$q(electronic bk.)

Securing AI agentsfoundations, 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$q(electronic bk.)

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

1003508
Expert systems (Computer science)
--Security measures.

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

Dewey Class. No.: 006.33

Securing AI agentsfoundations, frameworks, and real-world deployment /
LDR:03443nmm a2200337 a 4500 001 688385
003 DE-He213
005 20251001130619.0
006 m d
007 cr nn 008maaau
008 260318s2025 sz s 0 eng d
020 $a 9783032021304$q(electronic bk.)
020 $a 9783032021298$q(paper)
024 7 $a 10.1007/978-3-032-02130-4 $2 doi
035 $a 978-3-032-02130-4
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.76.E95 $b H83 2025
072 7 $a KJQ $2 bicssc
072 7 $a COM039000 $2 bisacsh
072 7 $a KJQ $2 thema
082 0 4 $a 006.33 $2 23
090 $a QA76.76.E95 $b H874 2025
100 1 $a Huang, Ken. $3 963569
245 1 0 $a Securing AI agents $h [electronic resource] : $b foundations, frameworks, and real-world deployment / $c by Ken Huang, Chris Hughes.
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2025.
300 $a xxxviii, 373 p. : $b ill., digital ; $c 24 cm.
490 1 $a Advances in data analytics, AI, and smart systems, $x 3091-2741
505 0 $a 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.
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.
650 0 $a Expert systems (Computer science) $x Security measures. $3 1003508
650 0 $a Artificial intelligence $x Security measures. $3 925009
650 1 4 $a Business Information Systems. $3 274346
650 2 4 $a Artificial Intelligence. $3 212515
650 2 4 $a Risk Management. $3 297189
650 2 4 $a Software Engineering. $3 274511
650 2 4 $a Machine Learning. $3 833608
700 1 $a Hughes, Chris. $3 1003506
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a Advances in data analytics, AI, and smart systems. $3 1003507
856 4 0 $u https://doi.org/10.1007/978-3-032-02130-4
950 $a Business and Management (SpringerNature-41169)