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.

本研究探讨了传统网络安全意识培训计划的局限性，并提出了一种创新解决方案，使用生成型预训练转换器 (GPT) 来解决这些问题。该研究将 GPT 模型与自然语言处理能力相结合，根据个体培训者的个人资料个性化定制培训模块，从而提供高度个性化和动态的网络安全学习体验。通过使用 GPT 模型提供实时和自适应的网络安全意识培训内容，实验证明了与传统计划相比，在引发兴趣、动态性和相关性等方面都取得了显著改善。GPT 强化的网络安全意识培训计划为提升网络安全意识提供了可扩展且有效的解决方案，为个体在组织内的特定角色中更好地应对网络安全风险提供了个性化的培训内容。