GPT-Enabled Cybersecurity Training: A Tailored Approach for Effective Awareness
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866911869699096576 |
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| author | Al-Dhamari, Nabil Clarke, Nathan |
| author_facet | Al-Dhamari, Nabil Clarke, Nathan |
| contents | 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 expe-riences. Leveraging natural language processing capabilities, the proposed approach personalizes training modules based on individual trainee pro-files, 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 en-gagement, dynamicity, and relevance. GPT-powered CSAT programs offer a scalable and effective solution to enhance cybersecurity awareness, provid-ing personalized training content that better prepares individuals to miti-gate cybersecurity risks in their specific roles within the organization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_04138 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | GPT-Enabled Cybersecurity Training: A Tailored Approach for Effective Awareness Al-Dhamari, Nabil Clarke, Nathan Cryptography and Security Artificial Intelligence 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 expe-riences. Leveraging natural language processing capabilities, the proposed approach personalizes training modules based on individual trainee pro-files, 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 en-gagement, dynamicity, and relevance. GPT-powered CSAT programs offer a scalable and effective solution to enhance cybersecurity awareness, provid-ing personalized training content that better prepares individuals to miti-gate cybersecurity risks in their specific roles within the organization. |
| title | GPT-Enabled Cybersecurity Training: A Tailored Approach for Effective Awareness |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2405.04138 |