Generative AI and Large Language Modeling in Cybersecurity

Cybersecurity is encountering new challenges demanding innovative solutions due to the complexity and frequency of cyberattacks progressing. Artificial intelligence (AI), particularly generative AI, has emerged as a promising technology with the potential to revolutionize current cybersecurity modeling and practices. This chapter provides a comprehensive overview of generative AI and large language modeling (LLM) in the context of cybersecurity, highlighting its potential benefits, challenges, and diverse methods. A variety of machine and deep learning techniques including generative adversarial networks (GANs), variational autoencoders (VAEs), and deep neural networks that can mimic and generate data are included. In the realm of cybersecurity, generative AI plays a multifaceted role including the development of realistic honeypots, deceiving adversaries, producing simulated threat data for security system training, and enhancing anomaly detection capabilities. We also explore cybersecurity large language modeling, i.e., “CyberLLM” and discuss multi-stages of our suggested LLM-based framework highlighting its potential to solve diverse cybersecurity issues. This chapter further explores the challenges and opportunities for generative AI emphasizing the potential for enhanced threat mitigation and resilience in a constantly evolving cyber threat environment.