An Intelligent Cybersecurity Architecture for SAP Systems Using Risk-Aware Predictive Analytics for Financial Data Protection

Abstract

SAP systems are integral to modern enterprise operations, supporting critical business processes across finance, supply chain, human resources, and customer management. Their complexity and centrality make them high‑value targets for sophisticated cyber threats, including misconfiguration exploits, privilege abuse, insider attacks, and advanced persistent threats (APTs). Traditional security approaches — largely signature‑based and reactive — struggle to detect novel or subtle threat behaviors embedded in large volumes of operational and system telemetry. This paper proposes an Intelligent Cybersecurity Architecture for SAP Systems that leverages risk‑aware predictive analytics to enhance threat detection and proactive defense.
 
The architecture combines real‑time log ingestion, feature engineering, machine learning‑based risk scoring, contextual correlation, and adaptive response mechanisms within a cloud‑compatible security fabric. By correlating SAP application logs, user behavior, configuration changes, and network/cloud telemetry, the system builds dynamic risk profiles and continuously assesses threat likelihood. Predictive models trained on historical incident patterns and unsupervised anomaly detection algorithms enable early identification of high‑risk events, improving detection accuracy and reducing response latency.
 
A modular, scalable design supports both on‑premises and hybrid cloud deployments, with auditability and governance aligned to compliance standards such as SOX and GDPR. Evaluation using simulated SAP threat scenarios demonstrates improved detection metrics (precision, recall) over traditional rule sets and underscores trade‑offs between model complexity, latency, and false positive rates. The paper concludes with insights on operational integration and future extensions.