Generative Artificial Intelligence for Cyber Security: Literature Review and Challenges

The digital transformation era has witnessed substantial technological advancements, resulting in significant global changes. However, the increasing reliance on interconnected systems and data-driven processes emphasizes the criticality of cybersecurity. This paper explores the utilization of Generative Artificial Intelligence (Generative AI) within the domain of cybersecurity, underscoring its capacity to augment threat detection, facilitate anomaly identification, and optimize incident response. Through an extensive evaluation of existing scholarly work and methodologies, the investigation delineates principal applications such as intrusion detection, malware analysis, and the synthesis of artificial data for training objectives. The results indicate that Generative AI has the potential to markedly enhance the precision of cybersecurity systems while concurrently automating repetitive processes, thus improving overall operational efficacy. Nevertheless, obstacles persist, including susceptibility to adversarial attacks, ethical dilemmas, and the requirement for considerable computational resources. This research offers novel perspectives on how Generative AI may be employed to confront intricate cybersecurity challenges, highlighting the necessity of reconciling innovative applications with robust defensive strategies. This research presents pathways for future investigations into secure and ethical applications of Generative AI technologies in cybersecurity.