Deep Learning Anomaly Detection for HIPAA-Regulated Azure Cloud Threat Monitoring

Abstract

HIPAA-regulated Azure cloud environments require threat detection models that can identify abnormal activity across identity access, network traffic, runtime behavior, and protected health information workflows in near real time. This article presents a deep learning-based anomaly detection framework for realtime threat identification using Azure AD logs, RBAC activity, NSG flow records, storage access events, key vault operations, Azure Monitor diagnostics, and Sentinel incident signals. The framework converts heterogeneous Azure telemetry into identity anomaly scores, network threat probabilities, PHI accessrisk scores, runtime anomaly classifications, and HIPAA monitoring confidence outputs. The results show that threat detection accuracy, false positive suppression, and alert prioritization precision improve across real-time monitoring cycles, while resource-group analysis reveals distinct identity, network, and PHI access anomaly patterns across clinical applications, patient records, imaging services, analytics workspaces, backup vaults, and identity services. The study concludes that deep learning can strengthen HIPAA cloud security when anomaly detection is linked to healthcare workload context and regulatory risk interpretation.