Advanced Machine Learning-Driven Vulnerability Management: A Scalable Approach to Securing Dynamic Cloud Environments

With the evolution of cloud computing and more businesses moving their computing needs from on-premises datacenters to the cloud, security challenges have become a critical concern, especially with the rise of sophisticated cyber threats and the incorporation of artificial intelligence in attacks. This study presents an advanced framework for improving cloud security by addressing vulnerabilities through automated threat detection, machine learning-based vulnerability assessment, and rapid response mechanisms. The proposed approach differentiates itself from traditional vulnerability management by incorporating continuous monitoring, anomaly detection, and automated patch management. Public data sources, such as the National Vulnerability Database (NVD), and proprietary logs from a cloud service provider (CSP) are used to implement and evaluate this framework. Machine learning algorithms and auto-patching systems are employed to process large volumes of security data, resulting in faster identification and remediation of vulnerabilities than conventional methods. By integrating both known vulnerabilities from the NVD and real-time anomalies, the framework significantly reduces exposure time and enhances overall security posture. Numerical results are presented to demonstrate the framework’s improved accuracy and efficiency in mitigating threats. This study also highlights the limitations of existing frameworks, emphasizing the need for a more proactive, high-level approach to managing vulnerabilities in dynamic cloud environments.