An End-to-End Cloud AI Framework for Petabyte-Scale Multi-Tenant Data: Azure DevOps–Integrated Fraud Detection and Risk Analytics
DOI:
https://doi.org/10.15662/IJARCST.2024.0701811Keywords:
Cloud AI, Multi-Tenant Data, Petabyte-Scale Analytics, Fraud Detection, Risk Analytics, Azure DevOps, Machine Learning, Enterprise Cloud Systems, Scalable Cloud Framework, Data SecurityAbstract
Enterprises operating in multi-tenant cloud environments face increasing challenges in detecting fraud and managing risk across petabyte-scale datasets. Traditional approaches often lack the scalability, automation, and intelligence required to handle dynamic, high-volume data from diverse sources. This paper proposes an end-to-end Cloud AI framework that integrates Azure DevOps for continuous deployment, monitoring, and management, enabling real-time fraud detection and risk analytics across multi-tenant systems.The framework leverages artificial intelligence and machine learning techniques to analyze transactional, behavioral, and contextual data at scale. Its architecture ensures secure tenant isolation, high availability, and efficient processing of massive datasets. By combining AI-driven analytics with cloud-native tools, the system provides predictive risk scoring, anomaly detection, and actionable insights for proactive fraud prevention. Experimental evaluation demonstrates improved detection accuracy, reduced false positives, and enhanced operational efficiency, highlighting the effectiveness of integrating AI with cloud DevOps practices for enterprise-scale fraud and risk management.
References
1. Kusumba, S. (2022). Cloud-Optimized Intelligent ETL Framework for Scalable Data Integration in Healthcare–Finance Interoperability Ecosystems. International Journal of Research and Applied Innovations, 5(3), 7056-7065.
2. Rao, S. B. S., Krishnaswamy, P., & Pichaimani, T. (2022). Algorithm-Driven Cost Optimization and Scalability in Analytics Transformation for National Health Plans. Newark Journal of Human-Centric AI and Robotics Interaction, 2, 120-152.
3. Pachyappan, R., Vijayaboopathy, V., & Paul, D. (2022). Enhanced Security and Scalability in Cloud Architectures Using AWS KMS and Lambda Authorizers: A Novel Framework. Newark Journal of Human-Centric AI and Robotics Interaction, 2, 87-119.
4. Navandar, P. (2023). The Impact of Artificial Intelligence on Retail Cybersecurity: Driving Transformation in the Industry. Journal of Scientific and Engineering Research, 10(11), 177-181.
5. 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.
6. Udayakumar, R., Chowdary, P. B. K., Devi, T., & Sugumar, R. (2023). Integrated SVM-FFNN for fraud detection in banking financial transactions. Journal of Internet Services and Information Security, 13(3), 12-25.
7. Jayaraman, S., Rajendran, S., & P, S. P. (2019). Fuzzy c-means clustering and elliptic curve cryptography using privacy preserving in cloud. International Journal of Business Intelligence and Data Mining, 15(3), 273-287.
8. Sandeep Kamadi. (2022). Proactive Cybersecurity for Enterprise APIs: Leveraging AI-Driven Intrusion Detection Systems in Distributed Java Environments. IJRCAIT, 5(1), 34-52.
9. Oleti, Chandra Sekhar. (2023). Credit Risk Assessment Using Reinforcement Learning and Graph Analytics on AWS. World Journal of Advanced Research and Reviews. 20. 1399-1409. 10.30574/wjarr.2023.20.1.2084.
10. Praveen Kumar Reddy Gujjala. (2023). Advancing Artificial Intelligence and Data Science: A Comprehensive Framework for Computational Efficiency and Scalability. IJRCAIT, 6(1), 155-166.
11. Joyce, S., Pasumarthi, A., & Anbalagan, B. (2025). SECURITY OF SAP SYSTEMS IN AZURE: ENHANCING SECURITY POSTURE OF SAP WORKLOADS ON AZURE–A COMPREHENSIVE REVIEW OF AZURENATIVE TOOLS AND PRACTICES.||.
12. Meka, S. (2022). Streamlining Financial Operations: Developing Multi-Interface Contract Transfer Systems for Efficiency and Security. International Journal of Computer Technology and Electronics Communication, 5(2), 4821-4829.
13. Sudhan, S. K. H. H., & Kumar, S. S. (2016). Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection. Indian Journal of Science and Technology, 9, 44.
14. Kumar, R., Al-Turjman, F., Anand, L., Kumar, A., Magesh, S., Vengatesan, K., ... & Rajesh, M. (2021). Genomic sequence analysis of lung infections using artificial intelligence technique. Interdisciplinary Sciences: Computational Life Sciences, 13(2), 192-200.
15. Sudhakara Reddy Peram, Praveen Kumar Kanumarlapudi, Sridhar Reddy Kakulavaram. (2023). Cypress Performance Insights: Predicting UI Test Execution Time Using Complexity Metrics. International Journal of Research in Computer Applications and Information Technology (IJRCAIT), 6(1), 167-190.
16. Christadoss, J., Yakkanti, B., & Kunju, S. S. (2023). Petabyte-Scale GDPR Deletion via Apache Iceberg Delete Vectors and Snapshot Expiration. European Journal of Quantum Computing and Intelligent Agents, 7, 66-100.
17. Zaharia, M., Das, T., Li, H., Hunter, T., Shenker, S., & Stoica, I. (2016). Discretized streams: Fault-tolerant stream processing at scale. Proceedings of the 24th ACM Symposium on Operating Systems Principles, 423–438.
18. Paul, D.; Soundarapandiyan, R.; Krishnamoorthy, G. Security-First Approaches to CI/CD in Cloud-Computing Platforms: Enhancing DevSecOps Practices. Aust. J. Mach. Learn. Res. Appl. 2021, 1, 184–225.
19. 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.
20. Muthusamy, M. (2022). AI-Enhanced DevSecOps architecture for cloud-native banking secure distributed systems with deep neural networks and automated risk analytics. International Journal of Research Publication and Engineering Technology Management, 6(1), 7807–7813. https://doi.org/10.15662/IJRPETM.2022.0506014.
21. Vasugi, T. (2022). AI-Enabled Cloud Architecture for Banking ERP Systems with Intelligent Data Storage and Automation using SAP. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(1), 4319-4325.
22. Adari, V. K. (2020). Intelligent Care at Scale AI-Powered Operations Transforming Hospital Efficiency. International Journal of Engineering & Extended Technologies Research (IJEETR), 2(3), 1240-1249.
23. Kumar, R. K. (2023). AI‑integrated cloud‑native management model for security‑focused banking and network transformation projects. International Journal of Research Publications in Engineering, Technology and Management, 6(5), 9321–9329. https://doi.org/10.15662/IJRPETM.2023.0605006
24. Md Al Rafi. (2022). Intelligent Customer Segmentation: A Data- Driven Framework for Targeted Advertising and Digital Marketing Analytics. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(5), 7417–7428.
