Cloud computing generate highly interconnected security data involving users, virtual machines, containers, applications, network flows, authentication events, and resource interactions. Conventional cyber threat detection techniques frequently represent such information as independent records, thereby limiting their ability to capture relational dependencies among entities and events. This paper proposes a Graph Neural Network (GNN)-based framework for intelligent cyber threat detection in cloud computing environments. The proposed approach models cloud infrastructure as a dynamic attributed graph in which nodes represent cloud entities and edges encode communication, access, dependency, or behavioral relationships. The methodology integrates graph construction, sparse dependency estimation, feature learning, GNN-based representation propagation, threat classification, and adaptive alert prioritization. The theoretical foundation is derived from graph networks and relational inductive biases, while sparse graphical-model techniques provide a principled basis for identifying meaningful dependencies in high-dimensional cloud telemetry. Optimization mechanisms based on shrinkage and distributed optimization are incorporated to improve computational efficiency and scalability. The framework is conceptually aligned with the graph-based deep learning approach reported by Marri et al. (2025), while extending the underlying perspective toward an integrated cloud-security architecture. The analytical findings indicate that relational modeling can improve contextual threat identification, reduce dependence on isolated event signatures, and support more interpretable security reasoning. However, challenges remain regarding dynamic graph construction, computational overhead, class imbalance, adversarial manipulation, and temporal concept drift. The study establishes a research-oriented foundation for intelligent, scalable, and relationship-aware cyber threat detection in cloud environments.