Automating Vulnerability Prioritization Using Machine Learning and Financial Impact Analysis

Santosh Kumar Kande

Abstract: Vulnerability management is a vital element of cybersecurity. While many organizations depend on external data sources such as threat intelligence platforms to prioritize vulnerabilities, the potential for automation through machine learning (ML) is underutilized. This paper presents a framework that employs ML models to automate vulnerability prioritization, enhancing the relevance and timeliness of decision - making. By integrating internal asset data, threat intelligence, network communication patterns, and financial risk assessments, organizations can more efficiently prioritize vulnerabilities in real - time, reducing risk and optimizing security investments.

Keywords: Vulnerability management, Machine Learning, Financial Impact, Threat Intelligence, asset prioritization, cybersecurity