International Journal of Science, Engineering and TechnologyAuthors: Bhor Shravani R., Professor Bangar Abhishek P., Professor Karad Vandana A., Professor Bhosale Sachin B.
Abstract: Cloud computing gives organizations flexible storage, computing power, and access to services, but it also creates security risks that can change quickly. A one-time secu-rity check may miss a new attack, a dangerous configuration change, or a threat that moves from one connected system to another. This paper reviews six supplied re-search papers and develops a practical model for real-time security risk assessment in cloud computing. The reviewed work covers holistic risk scoring across cloud and edge layers, searchable encryption and secure data deletion, artificial neural networks for threat detection, secure network design, AI-based anomaly detection with Zero Trust, and a systematic mapping of cloud risk assessment methods. Based on these sources, the paper proposes a continuous cycle that identifies important assets, col-lects security signals, detects unusual activity, combines technical findings with busi-ness impact, assigns a risk level, and supports an appropriate response. The model uses simple and explainable risk scoring alongside optional machine-learning alerts, identity checks, network segmentation, encryption, and audit records. The paper is a literature-based design and does not claim to have run a new experiment. It also iden-tifies practical challenges, including false alarms, limited visibility, data privacy, model drift, and the cost of continuous monitoring. The main conclusion is that real-time assessment works best when monitoring, vulnerability information, access con-trol, and response actions are connected in one process rather than operated as sepa-rate tools.
