Agentic AI in 6G Wireless Security: Methods, Applications, and Challenges

Zekra Sakr, Faculty of Computers and Informatics, Zagazig University, Zagazig 44519, Sharqiyah, EgyptFollow
Mona Mohamed, Higher Technological Institute, 10th of Ramadan City 44629 EgyptFollow

ORCID

Zekra Sakr: https://orcid.org/0009-0009-4072-9991

Mona Mohamed: https://orcid.org/0000-0002-8212-1572

Keywords

Agentic AI, 6G Networks, Cybersecurity, Reinforcement Learning, Multi-Agent Systems, Federated Learning, Explainable AI, Network Security, Threat Detection

Article Type

Review Article

Abstract

The move from 5G to 6G networks brings new opportunities but also major challenges, especially in security. Static machine learning and deep learning models are two examples of traditional AI techniques that frequently fail because they are unable to adequately adapt to rapidly evolving threats or provide a clear explanation for their choices. Agentic AI presents an achievable strategy to enhance the security, adaptability, and reliability of 6G systems through its capacity for perception, reasoning, and autonomous action. In this study, we present a comprehensive evaluation of 6G cybersecurity in relation to agentic AI. We evaluate current research, point out any gaps, and arrange it in an understandable framework that connects agent capabilities with different layers of the 6G network and real-world deployment settings. Finally, we identify future research opportunities and talk about unresolved issues like scalability, safety, and human oversight. Our objective is to provide a concise, well-organized overview of how agentic AI can serve as a foundation of intelligent and safe 6G ecosystems.

How to Cite

Sakr, Zekra and Mohamed, Mona (2025) "Agentic AI in 6G Wireless Security: Methods, Applications, and Challenges," Sustainable Machine Intelligence Journal: Vol. 13: Iss. 1, Article 5.
DOI: https://doi.org/10.63689/3005-3617.1076