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.

翻译：本研究探讨了传统网络安全意识与培训（CSAT）项目的局限性，并提出了一种利用生成式预训练变换模型（GPT）的创新解决方案来弥补这些不足。传统方法缺乏个性化以及对个体学习风格的适应性。为克服这些挑战，本研究整合GPT模型，以提供高度定制化且动态的网络安全学习体验。通过利用自然语言处理能力，所提出的方法基于个体受训者的画像对培训模块进行个性化调整，有助于确保参与度和有效性。实验采用GPT模型，通过生成定制化的培训内容来提供实时且自适应的CSAT体验。研究结果表明，与传统项目相比，该方法在参与度、动态性和相关性方面有显著提升。基于GPT的CSAT项目提供了一种可扩展且有效的解决方案，通过提供个性化的培训内容来增强网络安全意识，使个体能够在其组织内的特定角色中更有效地应对网络安全风险。