DATA-DRIVEN VULNERABILITY ASSESSMENT AND REMEDIATION PRIORITIZATION FOR MODERN SOFTWARE APPLICATIONS

Abstract

Modern software applications are continuously exposed to evolving cybersecurity threats due to increasing system complexity, cloud adoption, third-party dependencies, and rapid software delivery cycles. Traditional vulnerability assessment approaches often generate large volumes of security findings without considering contextual factors required for effective remediation prioritization. This paper proposes a Data-Driven Vulnerability Assessment and Remediation Prioritization Framework for Modern Software Applications by integrating machine learning, security analytics, vulnerability intelligence, software metrics, and intelligent risk assessment techniques. The proposed framework analyzes vulnerability characteristics, source code information, application behavior, dependency relationships, historical remediation data, and business impact factors to identify and prioritize critical security risks. Machine learning models classify vulnerabilities based on severity, exploitability, and operational impact, enabling optimized remediation strategies. The framework supports continuous security monitoring through DevSecOps integration and automated vulnerability management workflows. Experimental evaluation demonstrates improvements in vulnerability assessment accuracy, remediation prioritization efficiency, risk visibility, and secure software management. Keywords— Data-Driven Security, Vulnerability Assessment, Remediation Prioritization, Machine Learning, Software Security, Risk Analytics, DevSecOps, Vulnerability Management.