AI-Assisted Threat Detection and Continuous Compliance Automation in SAP S/4HANA Cloud Security Environments

Abstract

The increasing adoption of SAP S/4HANA Cloud has transformed enterprise operations by enabling integrated business processes, real-time analytics, and scalable digital services. However, the expansion of cloud-based enterprise environments introduces security challenges, including unauthorized access, identity misuse, configuration vulnerabilities, data breaches, and regulatory compliance violations. Traditional security mechanisms often rely on predefined rules and periodic assessments, limiting their ability to identify emerging threats and continuously validate compliance. This research proposes an AI-assisted security framework that integrates machine learning-based threat detection, behavioral analytics, automated compliance monitoring, and intelligent incident response within SAP S/4HANA Cloud environments. The framework analyzes authentication records, user activities, application logs, configuration changes, and security events to identify suspicious behavior and deviations from established security policies. A continuous compliance automation component evaluates security configurations against organizational policies and applicable regulatory requirements, generating alerts and recommending corrective actions. The methodology combines anomaly detection, risk scoring, policy validation, and automated workflow orchestration to strengthen enterprise security operations. The proposed approach aims to improve threat identification, reduce manual compliance workloads, enhance audit readiness, and support proactive risk management. Evaluation can be conducted using historical security logs, controlled attack simulations, and compliance test scenarios. The framework provides a structured foundation for integrating intelligent security monitoring with SAP cloud governance while maintaining human oversight, accountability, and protection of sensitive enterprise information.