Deep Learning–Based Risk Prediction for Distributed Cloud and Serverless Systems in Cyber and Healthcare Domains

Abstract

The increasing adoption of distributed cloud and serverless architectures has introduced new challenges in managing cyber and operational risks, particularly in sensitive domains such as healthcare. Traditional rule-based and static risk assessment methods are inadequate to address the scale, complexity, and dynamic behavior of modern cloud-native systems. This paper presents a deep learning–based risk prediction framework designed for distributed cloud and serverless environments, with a focus on cyber and healthcare domains. The proposed framework integrates network telemetry, system logs, application metrics, and contextual risk indicators to enable proactive risk detection and prediction. Advanced deep learning models are employed to capture temporal and spatial dependencies across distributed components, enabling accurate identification of emerging threats and system vulnerabilities. The framework supports scalable deployment using cloud-native and serverless paradigms, ensuring low latency and real-time inference. Experimental analysis demonstrates improved prediction accuracy and robustness compared to conventional machine learning approaches. The results highlight the framework’s effectiveness in enhancing cyber resilience, operational reliability, and risk-aware decision-making in cloud-based healthcare systems.