A4 Refereed article in a conference publication
LLM-Augmented Approach for Learning of Human-Centric Cybersecurity
Authors: Isoaho, Jouni; Rahman, Naeemur; Mohammad, Tahir
Editors: Arabnia, Hamid R.; Deligiannidis, Leonidas; Amirian, Soheyla; Mohammadi, Farid Ghareh; Shenavarmasouleh, Farzan
Conference name: International Conference on The AI Revolution: Research, Ethics, and Society
- Publisher: Springer
Publication year: 2026
Journal: Communications in Computer and Information Science
Book title : AI Revolution : Research, Ethics and Society : International Conference, AIR-RES 2025, Las Vegas, NV, USA, April 14–16, 2025, Proceedings, Part I
Volume: 2721
First page : 157
Last page: 172
ISBN: 978-3-032-12312-1
eISBN: 978-3-032-12313-8
ISSN: 1865-0929
eISSN: 1865-0937
DOI: https://doi.org/10.1007/978-3-032-12313-8_12
Publication's open availability at the time of reporting: No Open Access
Publication channel's open availability : No Open Access publication channel
Web address : https://doi.org/10.1007/978-3-032-12313-8_12
Artificial intelligence, particularly Large Language Models (LLMs), is rapidly transforming the field of cybersecurity. This transformation introduces new challenges and security risks while simultaneously providing powerful and easily accessible tools for building and teaching cybersecurity. This article addresses the human-centric training of cybersecurity professionals to analyze and mitigate risks arising from human activity. This article evaluates the integration of large language models into the pedagogical curriculum for various thematic content areas of the training: threats and assets, privacy and surveillance, risk management, and human differences. The performance was assessed from both the cybersecurity learning and AI usage perspectives among the students. Furthermore, the article identifies key areas for improvement in the continued development of cybersecurity education.
