Generative AI in Cybersecurity: A Systematic Literature Review and Meta-Analysis

Generative AI has emerged as a transformative force in cybersecurity, offering both opportunities for innovation and challenges in threat detection and mitigation. This systematic literature review and meta-analysis synthesizes existing research to evaluate the efficacy of generative AI in cybersecurity applications, focusing on detection performance, overall impact, and threat detection metrics. We conducted a comprehensive analysis of peer-reviewed studies, employing rigorous statistical methods to quantify effect sizes and their significance. The results reveal a substantial negative effect size for generative AI detection performance (d = −3.41, 95% CI [−3.42, −3.40], p < 1e−5), indicating a strong but counterintuitive trend that warrants further investigation. In contrast, the overall impact of generative AI on cybersecurity was negligible (d = −0.06, 95% CI [−0.31, 0.20], p = 0.68), suggesting a neutral net effect. However, generative AI demonstrated a statistically significant positive effect on threat detection metrics (d = 0.20, 95% CI [0.06, 0.35], p = 0.005), highlighting its potential to enhance specific security tasks. These findings underscore the dual nature of generative AI in cybersecurity, where its capabilities are context-dependent and require careful implementation. The study provides a foundational framework for future research, emphasizing the need for balanced approaches to harness generative AI’s benefits while mitigating its risks.