LLM-driven automation in vulnerability management
University of Cairo, Egypt.
Review
Open Access Research Journal of Science and Technology, 2024, 12(01), 030–051.
Article DOI: 10.53022/oarjst.2024.12.1.0114
Publication history:
Received on 30 July 2024; revised on 04 September 2024; accepted on 07 September 2024
Abstract:
The integration of Large Language Models (LLMs) into vulnerability management processes marks a transformative shift in cybersecurity. By automating the identification, prioritization, and remediation of vulnerabilities, LLMs enhance the efficiency and accuracy of these critical tasks. This chapter explores the potential of LLM-driven automation in vulnerability management, highlighting the benefits, challenges, and future directions of this technology. It delves into the methods through which LLMs can be leveraged to mitigate security risks, improve response times, and reduce human error, thereby strengthening overall security postures.
Keywords:
Automation; Cybersecurity; LLM; Vulnerability Management; Vulnerability Remediation
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0