AI Driven DevSecOps Security Pipelines for Fraud Detection in SAP Based Business and Marketing Processes
DOI:
https://doi.org/10.21590/ijtmh.09.02.05Keywords:
Artificial Intelligence, DevSecOps, SAP Security, Fraud Detection, Business Processes, Marketing Analytics, Machine Learning, Cloud Security, Multivariate Analysis, Threat IntelligenceAbstract
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.
References
1. Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., … Isard, M. (2016). TensorFlow: A system for large-scale machine learning. Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation, 265–283.
2. Adari, V. K. (2021). Building trust in AI-first banking: Ethical models, explainability, and responsible governance. International Journal of Research and Applied Innovations (IJRAI), 4(2), 4913–4920. https://doi.org/10.15662/IJRAI.2021.0402004
3. Chivukula, V. (2020). Use of multiparty computation for measurement of ad performance without exchange of personally identifiable information (PII). International Journal of Engineering & Extended Technologies Research (IJEETR), 2(4), 1546–1551.
4. Thambireddy, S. (2022). SAP PO Cloud Migration: Architecture, Business Value, and Impact on Connected Systems. International Journal of Humanities and Information Technology, 4(01-03), 53-66.
5. Bishop, C. M. (2006). Pattern recognition and machine learning. Springer.
6. Sudhan, S. K. H. H., & Kumar, S. S. (2015). An innovative proposal for secure cloud authentication using encrypted biometric authentication scheme. Indian journal of science and technology, 8(35), 1-5.
7. Davis, J., & Clark, J. (2019). Data-driven security operations using machine learning. IEEE Security & Privacy, 17(3), 42–49. https://doi.org/10.1109/MSEC.2019.2907158 8. Vimal Raja, G. (2021). Mining Customer Sentiments from Financial Feedback and Reviews using Data Mining Algorithms. International Journal of Innovative Research in Computer and Communication Engineering, 9(12), 14705-14710.
9. Gartner. (2023). DevSecOps: Integrating security into DevOps pipelines. Gartner Research.
10. Meka, S. (2023). Building Digital Banking Foundations: Delivering End-to-End FinTech Solutions with Enterprise-Grade Reliability. International Journal of Research and Applied Innovations, 6(2), 8582-8592.
11. Kshetri, N. (2021). Cybersecurity in finance: Adoption of AI and machine learning. Computer, 54(2), 68–72. https://doi.org/10.1109/MC.2020.3045960
12. Gopalan, R., & Chandramohan, A. (2018). A study on Challenges Faced by It organizations in Business Process Improvement in Chennai. Indian Journal of Public Health Research & Development, 9(1), 337-341.
13. Kumar, S. N. P. (2022). Text Classification: A Comprehensive Survey of Methods, Applications, and Future Directions. International Journal of Technology, Management and Humanities, 8(3), 39–49. https://ijtmh.com/index.php/ijtmh/article/view/227/222
14. Paul, D. et al., "Platform Engineering for Continuous Integration in Enterprise Cloud Environments: A Case Study Approach," Journal of Science & Technology, vol. 2, no. 3, Sept. 8, (2021). https://thesciencebrigade.com/jst/article/view/382
15. Kasaram, C. R. (2020). Platform Engineering at Scale: Building Self-Service Dev Environments with Observability. ISCSITR-INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND ENGINEERING (ISCSITR-IJCSE)-ISSN: 3067-7394, 1(1), 5-14.
16. Ramakrishna, S. (2023). Cloud-Native AI Platform for Real-Time Resource Optimization in Governance-Driven Project and Network Operations. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(2), 6282-6291.
17. Navandar, P. (2022). SMART: Security Model Adversarial Risk-based Tool. International Journal of Research and Applied Innovations, 5(2), 6741-6752.
18. Sivaraju, P. S. (2021). 10x Faster Real-World Results from Flash Storage Implementation (Or) Accelerating IO Performance A Comprehensive Guide to Migrating From HDD to Flash Storage. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 4(5), 5575-5587.
19. NIST. (2020). Security and privacy controls for information systems and organizations (SP 800-53). National Institute of Standards and Technology.
20. Next-Gen Life Sciences Manufacturing: A Scalable Framework for AI-Augmented MES and RPA-Driven Precision Healthcare Solutions. (2023). International Journal of Engineering & Extended Technologies Research (IJEETR), 5(2), 6275-6281. https://doi.org/10.15662/IJEETR.2023.0502004
21. Venkata Krishna Bharadwaj Parasaram. (2021). Assessing the Impact of Automation Tools on Modern Project Governance. International Journal of Engineering Science and Humanities, 11(4), 38–47. Retrieved from https://www.ijesh.com/j/article/view/423
22. Satish Kumar Nalluri, Venkata Krishna Bharadwaj Parasaram. (2019). Software-Centric Automation Frameworks Integrating AI and Cybersecurity Principles. International Journal of Engineering Science & Humanities, 9(1), 30–40. Retrieved from https://www.ijesh.com/j/article/view/539
23. Nagarajan, G. (2023). AI-Integrated Cloud Security and Privacy Framework for Protecting Healthcare Network Information and Cross-Team Collaborative Processes. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(2), 6292-6297.
24. SAP SE. (2022). SAP security best practices for cloud and enterprise systems. SAP Whitepaper.
25. Rajurkar, P. (2020). Predictive Analytics for Reducing Title V Deviations in Chemical Manufacturing. International Journal of Technology, Management and Humanities, 6(01-02), 7-18.
26. Sharma, P., Sengupta, J., & Choo, K.-K. R. (2020). Artificial intelligence for cybersecurity: A survey. Journal of Information Security and Applications, 54, 102526. https://doi.org/10.1016/j.jisa.2020.102526
27. Usha, G., Babu, M. R., & Kumar, S. S. (2017). Dynamic anomaly detection using cross layer security in MANET. Computers & Electrical Engineering, 59, 231-241.
28. Zhang, Y., Chen, M., & Li, X. (2021). Multivariate anomaly detection for cloud security using deep learning. IEEE Transactions on Cloud Computing, 9(4), 1452–1465. https://doi.org/10.1109/TCC.2019.2940404
29. Venkata Krishna Bharadwaj Parasaram. (2022). Quantum and Quantum-Inspired Approaches in DevOps: A Systematic Review of CI/CD Acceleration Techniques. International Journal of Engineering Science and Humanities, 12(3), 29–38. Retrieved from https://www.ijesh.com/j/article/view/424
