PROACTIVE CLOUD RISK MANAGEMENT : AN INTELLIGENT FRAMEWORK LEVERAGING MACHINE LEARNING AND EXPLAINABLE AI

Abstract

Cloud computing has transformed the management of data and applications by enabling scalability, flexibility, and cost-efficiency across organizational infrastructures. However, it simultaneously introduces new and complex security challenges, including data breaches, configuration vulnerabilities, and unauthorized access. This study presents the design and implementation of an intelligent, machine learning–driven risk assessment framework for cloud-based systems that proactively identifies, evaluates, and mitigates cybersecurity threats. The framework employs supervised and unsupervised learning techniques such as Random Forest, Support Vector Machines, and Long Short-Term Memory (LSTM) networks to detect anomalies and predict risk levels. Data were sourced from benchmark datasets (e.g., UNSW-NB15, CICIDS2017) and synthetic cloud simulations to ensure robustness and realism. Results from experimental evaluations demonstrate that the proposed model achieves high detection accuracy and adaptability, outperforming conventional static risk assessment techniques. By integrating explainable AI (XAI) methods such as SHAP and LIME, the framework enhances interpretability and compliance alignment with standards like NIST SP 800-53 and ISO/IEC 27001. The findings contribute to advancing proactive, automated, and intelligent risk management strategies for secure and resilient cloud computing environments.