Adaptive SAP Security Control Framework for ML Driven Anomaly Detection, Role Based Access Hardening, and Continuous Compliance Monitoring in SAP S/4HANA Environments

Abstract

Enterprise SAP systems — deployed across more than 440,000 organisations globally — present a uniquely high value and complex attack surface. The convergence of centralised financial data, deeply integrated business processes, and historically under monitored application layer controls has made SAP environments a primary target for advanced persistent threats, insider fraud, and supply chain compromise. This paper presents the Adaptive SAP Control Security Framework (ASCSF), an integrated security architecture combining intelligent access control analysis, machine learning based anomaly detection, and continuous compliance monitoring for SAP S/4HANA 2020 environments. The framework's two principal algorithms — Algorithm 1 (ASCSF: composite user risk scoring from SoD conflict analysis, authorisation object evaluation, critical access detection, and behavioural modelling) and Algorithm 2 (SSADE: an Isolation Forest + BiLSTM + Autoencoder ensemble for security audit log anomaly detection achieving AUC 0.978) — provide real time, adaptive security coverage across all five layers of the proposed SAP security taxonomy. Experimental evaluation on 18 months of production SAP S/4HANA data spanning 4.9 million security events, 15,284 users, and three industry sectors demonstrate: 98.1% anomaly detection rate, 0.9% false positive rate, 2.1 second mean detection latency (9,143x improvement over weekly manual review), and a 67% reduction in security incidents post deployment. Cross comparison against seven baseline systems including SAP Enterprise Threat Detection, SecurityBridge, and Splunk SIEM confirms statistically significant superiority across all metrics (McNemar's test, p < 0.001). Validation on the SAP Security Baseline   2021 hardening checklist demonstrates 31 percentage point improvement in patch compliance over six months.