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Abstract

This study explores the limitations of traditional Cybersecurity Awareness and Training (CSAT) programs and proposes an innovative solution using Generative Pre-Trained Transformers (GPT) to address these shortcomings. Traditional approaches lack personalization and adaptability to individual learning styles. To overcome these challenges, the study integrates GPT models to deliver highly tailored and dynamic cybersecurity learning experiences. Leveraging natural language processing capabilities, the proposed approach personalizes training modules based on individual trainee profiles, helping to ensure engagement and effectiveness. An experiment using a GPT model to provide a real-time and adaptive CSAT experience through generating customized training content. The findings have demonstrated a significant improvement over traditional programs, addressing issues of engagement, dynamicity, and relevance. GPT-powered CSAT programs offer a scalable and effective solution to enhance cybersecurity awareness, providing personalized training content that better prepares individuals to mitigate cybersecurity risks in their specific roles within the organization.

Publication Date

2024-06-11

Event

16th World Conference on Information Security Education, WISE 2024

Publication Title

Information Security Education - Challenges in the Digital Age - 16th IFIP WG 11.8 World Conference on Information Security Education, WISE 2024, Proceedings

Publisher

Springer Science and Business Media Deutschland GmbH

ISBN

9783031629174

ISSN

1868-4238

Acceptance Date

2024-04-13

Deposit Date

2024-11-05

Keywords

Cybersecurity Awareness Training (CSAT), Generative Pre-Trained Transformers (GPT)

First Page

3

Last Page

20

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