Generative AI for Vulnerability Management: A Blueprint

Abstract

Cybersecurity threats continue to evolve in complexity and scale every year. The need for more effective vulnerability management has become more
important now than ever. Traditional vulnerability management methods rely heavily on human resources for the time-consuming research aspect. Relying on manual processes and having multiple human touchpoints affects the ability to scale and the velocity of the team. In this paper, a design blueprint for integrating Generative Artificial Intelligence (AI), particularly large language models (LLMs) into the vulnerability management lifecycle are discussed. By leveraging the advanced capabilities of Generative AI, organizations can improve efficiency across various stages of vulnerability management, from intelligence gathering, risk analysis, and prioritization to remediation. Specifically, the paper provides a comprehensive blueprint for applying Generative AI to vulnerability intelligence, research, and remediation. The paper illustrates a logical design for each of these areas and discusses the design, key
components, and functions. The paper also captures the challenges of adopting Generative AI-based solutions and guidance to overcome them. Lastly, the paper outlines future research directions aimed at overcoming challenges and further enhancing the role of AI in vulnerability management.