A DevOps-Enabled AI Analytics Pipeline for Fraud Detection and Cyber Risk Mitigation in SAP HANA Cloud
DOI:
https://doi.org/10.15662/IJARCST.2024.0701807Keywords:
DevOps, SAP HANA Cloud, Fraud Detection, Cyber Risk Mitigation, Machine Learning, Deep Learning, MLOps, DevSecOps, Cloud Security, AI AnalyticsAbstract
The rapid digitalization of banking and enterprise systems has significantly increased exposure to financial fraud and cyber risks. Modern platforms such as SAP HANA Cloud process massive volumes of real-time transactional and operational data, making them attractive targets for sophisticated cyber-attacks and fraud schemes. This paper proposes a DevOps-enabled AI analytics pipeline for fraud detection and cyber risk mitigation deployed on SAP HANA Cloud. The framework integrates machine learning (ML) and deep learning (DL) models with continuous integration and continuous deployment (CI/CD), real-time analytics, and automated security controls. By embedding AI-driven fraud detection within a DevSecOps lifecycle, the proposed pipeline enables rapid model iteration, real-time threat intelligence ingestion, and proactive cyber defense. The architecture leverages SAP HANA’s in-memory computing, cloud-native services, and containerized MLOps workflows to ensure scalability, resilience, and compliance. Experimental analysis demonstrates improved fraud detection accuracy, reduced detection latency, and enhanced cyber risk visibility compared to traditional rule-based systems. The study highlights how DevOps principles enhance AI governance, model reliability, and security posture in enterprise cloud environments. The findings contribute to both academic research and industry practice by presenting a scalable, secure, and continuously adaptive framework for fraud and cyber risk management in SAP HANA Cloud ecosystems.
References
1. Bolton, R. J., & Hand, D. J. (2002). Statistical fraud detection. Statistical Science, 17(3), 235–255.
2. Nagarajan, G. (2022). Optimizing project resource allocation through a caching-enhanced cloud AI decision support system. International Journal of Computer Technology and Electronics Communication, 5(2), 4812–4820. https://doi.org/10.15680/IJCTECE.2022.0502003
3. Malarkodi, K. P., Sugumar, R., Baswaraj, D., Hasan, A., & Kousalya, A. (2023, March). Cyber Physical Systems: Security Technologies, Application and Defense. In 2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS) (Vol. 1, pp. 2536-2546). IEEE.
4. S. Roy and S. Saravana Kumar, “Feature Construction Through Inductive Transfer Learning in Computer Vision,” in Cybernetics, Cognition and Machine Learning Applications: Proceedings of ICCCMLA 2020, Springer, 2021, pp. 95–107.
5. Archana, R., & Anand, L. (2023, September). Ensemble Deep Learning Approaches for Liver Tumor Detection and Prediction. In 2023 Third International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS) (pp. 325-330). IEEE.
6. Adari, V. K., Chunduru, V. K., Gonepally, S., Amuda, K. K., & Kumbum, P. K. (2023). Ethical analysis and decision-making framework for marketing communications: A weighted product model approach. Data Analytics and Artificial Intelligence, 3 (5), 44–53.
7. Thangavelu, K., Keezhadath, A. A., & Selvaraj, A. (2022). AI-Powered Log Analysis for Proactive Threat Detection in Enterprise Networks. Essex Journal of AI Ethics and Responsible Innovation, 2, 33-66.
8. Surampudi, Y., Kondaveeti, D., & Pichaimani, T. (2023). A Comparative Study of Time Complexity in Big Data Engineering: Evaluating Efficiency of Sorting and Searching Algorithms in Large-Scale Data Systems. Journal of Science & Technology, 4(4), 127-165.
9. Dhanorkar, T., Vijayaboopathy, V., & Das, D. (2020). Semantic Precedent Retriever for Rapid Litigation Strategy Drafting. Journal of Artificial Intelligence & Machine Learning Studies, 4, 71-109.
10. 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.
11. Mani, R. (2022). Enhancing SAP HANA Resilience and Performance on RHEL using Pacemaker: A Strategic Approach to Migration Optimization and Dual-Function Infrastructure Design. International Journal of Computer Technology and Electronics Communication, 5(6), 6061-6074.
12. Kumar, R. K. (2022). AI-driven secure cloud workspaces for strengthening coordination and safety compliance in distributed project teams. International Journal of Research and Applied Innovations (IJRAI), 5(6), 8075–8084. https://doi.org/10.15662/IJRAI.2022.0506017
13. Chandola, V., et al. (2009). Anomaly detection survey. ACM Computing Surveys.
