Cyberdefense Powered by Generative AI: A Comprehensive State –of- the -Art Review

Cyberattack sophistication is rapidly increasing, leading to a decline in the effectiveness of traditional signature-based security solutions. Consequently, intelligent and adaptive security solutions are now being employed that are powered by generative artificial intelligence. Numerous review papers have been conducted on these solutions, but they focus on particular aspects of cybersecurity, providing not a holistic view of all areas. This paper attempts to investigate the applications of generative AI in cyberdefense by reviewing 113 academic papers clustered according to cyberdefense domains indirectly referring to such generative AI models. The review illustrated generative AI’s effectiveness in transferring security from the reactive security model to a proactive one by greatly increasing the detection of new threats and inducing synthetic data which enhances the quality of training, with numerous papers achieving performance accuracy exceeding 95%. There are uniterable issues to overcome however, including the computing costs, model instability and reliance on data. A new multi-dimensional taxonomical methodology and comparative analysis was undertaken on the various generative AI architectures with a strategic roadmap given to guide and orientate further research towards the adoption of autonomous, effective, resilient and reliable cyberdefenses.