AI-Driven Vulnerability Management and Intelligent Cyber Risk Mitigation Frameworks

Abstract

The rapidly evolving threat landscape in cyberspace has significantly increased the complexity of securing modern information systems. Traditional vulnerability management approaches, while effective to an extent, often struggle to keep pace with the scale and sophistication of contemporary cyberattacks. This paper introduces an AI-driven perspective on vulnerability management and cyber risk mitigation, highlighting how artificial intelligence and machine learning techniques can enhance the identification, prioritization, and remediation of security weaknesses. By integrating predictive analytics, automated threat detection, and intelligent risk scoring mechanisms, organizations can transition from reactive defense strategies to proactive and adaptive security models. This study explores a range of AI-enabled tools, frameworks, and methodologies that support continuous monitoring and real-time decision-making in cybersecurity environments. Furthermore, it emphasizes the role of data-driven insights in improving vulnerability assessment accuracy and optimizing resource allocation for risk mitigation.