AI Driven DevSecOps Security Pipelines for Fraud Detection in SAP Based Business and Marketing Processes

Abstract

The increasing adoption of SAP-based cloud platforms for enterprise business and marketing processes has amplified the scale, complexity, and sophistication of fraud and cybersecurity threats. Traditional perimeter-based and rule-driven security mechanisms struggle to provide real-time protection across distributed SAP landscapes, particularly in environments governed by continuous integration and continuous deployment (CI/CD) practices. This paper proposes an AI-driven DevSecOps security pipeline that integrates machine learning–based fraud detection, multivariate analytics, and automated threat intelligence into SAP-centric business and marketing workflows. By analyzing heterogeneous data sources—including SAP transaction logs, marketing campaign data, user behavior metrics, and system telemetry—the framework enables early detection of anomalous activities, financial fraud, and process-level abuse. The proposed approach embeds security controls throughout the DevSecOps lifecycle, ensuring continuous risk assessment, policy enforcement, and rapid incident response. Experimental evaluation demonstrates improved fraud detection accuracy, reduced false positives, and enhanced resilience of SAP-enabled enterprise systems operating in cloud-native environments.