AI-Driven Predictive Threat Detection for Secure Multiparty SAP Business Processes in Broadband Clouds

Abstract

The increasing adoption of cloud-based SAP systems across broadband networks has amplified the complexity and scale of cyber threats targeting enterprise business processes. Traditional security mechanisms often lack the intelligence and adaptability required to detect sophisticated, multiparty, and process-level attacks in real time. This paper proposes an AI-driven predictive threat detection framework designed to secure multiparty SAP business processes deployed in broadband cloud environments. The proposed architecture integrates advanced machine learning models with large-scale data analytics platforms to analyze transactional logs, network traffic, and process execution traces for early threat identification. By leveraging predictive analytics, the framework anticipates anomalous behavior before it propagates across interconnected enterprise systems. The solution supports secure data sharing among multiple stakeholders while preserving process integrity and regulatory compliance. Experimental analysis demonstrates improved detection accuracy, reduced false positives, and enhanced resilience compared to rule-based and reactive security approaches. The results highlight the effectiveness of AI-powered security intelligence in safeguarding mission-critical SAP workflows in high-bandwidth, cloud-native enterprise ecosystems.