This study investigated the potential of Generative Pre-trained Transformers (GPTs), a state-of-the-art large language model, in generating cybersecurity policies to deter and mitigate ransomware attacks that perform data exfiltration. We compared the effectiveness, efficiency, completeness, and ethical compliance of GPT-generated Governance, Risk and Compliance (GRC) policies, with those from established security vendors and government cybersecurity agencies, using game theory, cost-benefit analysis, coverage ratio, and multi-objective optimization. Our findings demonstrated that GPT-generated policies could outperform human-generated policies in certain contexts, particularly when provided with tailored input prompts. To address the limitations of our study, we conducted our analysis with thorough human moderation, tailored input prompts, and the inclusion of legal and ethical experts. Based on these results, we made recommendations for corporates considering the incorporation of GPT in their GRC policy making.
McIntosh et al. (Fri,) studied this question.