Privacy-Preserving AI and Machine Learning for Enterprise Risk Detection in SAP-Based Cloud Business Processes

Abstract

Enterprises increasingly rely on SAP-based cloud business processes to manage critical financial, healthcare, and operational data, making them attractive targets for sophisticated cyber and operational risks. While artificial intelligence and machine learning enhance risk detection capabilities, they also raise significant concerns regarding data privacy and regulatory compliance. This paper presents a privacy-preserving AI and machine learning framework for enterprise risk detection in SAP-based cloud business processes. The proposed approach combines advanced analytics, knowledge extraction, and privacy-enhancing techniques to analyze transactional data, system logs, and process execution traces without exposing sensitive information. Machine learning models are employed to identify risk patterns, anomalies, and potential security incidents across interconnected enterprise workflows. The framework supports secure data governance, compliance with data protection regulations, and scalable deployment in cloud environments. Experimental evaluation demonstrates improved risk detection accuracy, reduced false positives, and enhanced system resilience compared to traditional rule-based approaches. The results highlight the effectiveness of privacy-preserving AI in enabling trustworthy and intelligent risk management for SAP-driven enterprise cloud ecosystems.