Data-Driven Predictive Cybersecurity for SAP-Based Healthcare Business Processes in Secure Cloud Platforms

Abstract

Healthcare enterprises increasingly rely on SAP-based business processes to manage clinical, operational, and financial information at scale. Although these platforms improve efficiency and integration, they also expose organizations to sophisticated cybersecurity and data privacy risks arising from large data volumes, tightly coupled workflows, and stringent regulatory requirements. This paper proposes a data-driven cybersecurity and predictive intelligence framework aimed at securing SAP-based healthcare business processes. The approach combines advanced analytics and machine learning techniques to examine system logs, transactional records, and process execution behaviors for early threat identification. Predictive intelligence models enable proactive detection of security incidents and process anomalies before they impact healthcare operations. The framework further supports secure data governance and regulatory compliance while preserving process performance and continuity. Experimental results demonstrate enhanced threat detection accuracy, faster response times, and greater system resilience when compared to conventional rule-based security mechanisms. These findings emphasize the effectiveness of data-driven security intelligence in protecting mission-critical SAP environments within healthcare organizations.