Secure Explainable AI on Databricks–SAP Cloud for Risk-Sensitive Healthcare Analytics and Swarm-Based QoS Control

Abstract

The rapid digital transformation of healthcare systems has intensified the need for secure, scalable, and explainable analytics platforms capable of handling sensitive clinical and financial data. This paper presents a secure Explainable AI (XAI) framework deployed on Databricks–SAP Cloud to support risk-sensitive healthcare analytics while ensuring transparency, regulatory compliance, and quality-of-service (QoS) guarantees. The proposed architecture integrates distributed data processing through Databricks with enterprise-grade SAP cloud services to enable real-time analytics, governance, and interoperability across heterogeneous healthcare data sources. Explainable AI models are incorporated to enhance trust, auditability, and interpretability of predictive outcomes, particularly in risk assessment and decision-support scenarios. To address dynamic workload variations and service-level constraints, a swarm-based QoS control mechanism is introduced, leveraging collective optimization and controlled randomization to balance latency, reliability, and resource utilization. Security is enforced through layered access control, encryption, and policy-driven data governance aligned with healthcare compliance requirements. Experimental analysis demonstrates improved risk awareness, transparent decision-making, and adaptive QoS performance under varying operational conditions. The proposed framework provides a robust foundation for trustworthy, scalable, and resilient healthcare analytics in cloud-native environments.