Architecting Graph Neural Network Enabled Cloud for Intelligent Enterprise Identity Management and Secure Data Synchronization

Abstract

As modern enterprises expand across hybrid and multi-cloud environments, traditional Identity and Access Management (IAM) systems suffer from fragmentation, identity silos, and high vulnerability to privilege escalation and lateral movement attacks. Standard perimeter-based and tabular machine learning frameworks analyze log data as isolated rows, failing to model the complex, multi-hop operational relationships between distributed entities. This paper introduces a novel, Graph Neural Network (GNN)-enabled distributed cloud ecosystem designed for unified enterprise identity management and real-time, secure data synchronization. By structuring identities, credentials, user sessions, cloud services, and microservices as dynamic, heterogeneous graphs, the architecture utilizes Relational Graph Convolutional Networks (R-GCN) and Graph Attention Networks (GAT) to continuously learn contextual, topology-aware access baselines. To achieve secure cross-region synchronization without compromising privacy, the model employs a Federated Heterarchical Graph SAGE mechanism coupled with zero-knowledge proof (ZKP) identity verification protocols and post-quantum cryptographic primitives. Empirical evaluations demonstrate that the proposed ecosystem achieves superior anomaly detection precision and recall in detecting zero-day credential compromise compared to baseline sequence-based models. Concurrently, it reduces cross-cloud synchronization latency and bandwidth overhead, providing a resilient, topology-aware zero-trust framework for enterprise-scale multi-cloud infrastructures.