Abstract
Cloud computing has transformed modern digital infrastructure by enabling scalable, cost-effective, and flexible computing services for organizations across multiple industries. Enterprises increasingly depend on cloud platforms for data storage, software deployment, remote collaboration, and infrastructure management. Despite these advantages, cloud environments continue to face significant cybersecurity challenges due to increasing cyberattacks, data breaches, insider threats, and advanced persistent threats. Traditional security mechanisms designed for standalone systems are often insufficient for highly distributed and virtualized cloud ecosystems. The dynamic nature of cloud computing introduces vulnerabilities associated with multi-tenancy, shared infrastructure, misconfigured services, insecure APIs, and unauthorized access. Consequently, organizations require intelligent risk assessment frameworks capable of continuously identifying, evaluating, and mitigating cybersecurity risks in cloud infrastructures. This paper presents a comprehensive cybersecurity risk assessment framework for cloud computing environments. The proposed framework integrates threat modeling, vulnerability analysis, access control evaluation, and real-time risk scoring mechanisms to support proactive cloud security management. The system additionally incorporates machine learning-assisted anomaly detection for identifying suspicious activities and abnormal network behavior.Experimental evaluation demonstrates that the proposed framework improves threat visibility, reduces incident response delays, and enhances cloud infrastructure resilience compared to conventional security assessment models. The study contributes toward secure cloud adoption and intelligent cybersecurity governance in modern digital enterprises.