Secure Multiparty Healthcare Safety Data Analytics using Federated AI and Cybersecurity Controls on SAP Cloud

Abstract

The increasing digitization of healthcare ecosystems has intensified the need for secure, collaborative analytics across institutions while preserving data privacy and regulatory compliance. This paper proposes a secure multiparty healthcare safety data analytics framework that leverages Federated Artificial Intelligence (AI) and advanced cybersecurity controls on SAP Cloud. The framework enables multiple healthcare stakeholders to collaboratively train intelligent models on distributed safety data without exposing raw patient information. Federated learning is integrated with secure aggregation, access control, and encryption mechanisms to ensure confidentiality, integrity, and accountability throughout the analytics lifecycle. SAP Cloud services provide scalable data orchestration, governance, and interoperability across heterogeneous healthcare systems. The proposed approach supports real-time safety monitoring, adverse event detection, and decision intelligence while complying with healthcare data protection regulations. Experimental evaluation demonstrates improved analytical accuracy, reduced data exposure risk, and efficient multi-institution collaboration. By combining federated AI with cloud-native cybersecurity, the framework establishes a trustworthy foundation for privacy-preserving healthcare safety analytics in distributed and multi-organizational environments.