A Predictive Analytics–Driven AI Model for Secure and Risk-Aware SAP Healthcare Systems in Cloud Environments
DOI:
https://doi.org/10.15662/IJARCST.2023.0606022Keywords:
Predictive Analytics, Artificial Intelligence, Cloud Security, SAP Healthcare Systems, Risk Awareness, Machine Learning, ComplianceAbstract
The digital transformation of healthcare organizations has led to the widespread adoption of SAP systems deployed in cloud environments to support critical clinical, administrative, and financial operations. While cloud-based SAP platforms offer scalability and agility, they also introduce complex security risks and compliance challenges associated with sensitive healthcare data. This paper proposes a predictive analytics–driven AI model for enabling secure and risk-aware SAP healthcare systems in cloud environments. The proposed model leverages machine learning techniques to analyze historical system logs, access patterns, configuration changes, and operational metrics in order to proactively predict security threats, performance anomalies, and compliance risks. By integrating predictive intelligence into cloud security controls, the framework supports early risk detection, automated alerting, and informed decision-making for system administrators. Experimental evaluation demonstrates improved risk prediction accuracy, reduced incident response time, and enhanced security posture compared to traditional reactive security approaches. The results indicate that AI-driven predictive analytics can significantly strengthen security, resilience, and operational reliability of SAP systems in healthcare cloud ecosystems.
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