Cloud-Native AI Architecture for Data-Scarce Regions: Dynamic Bayesian Hierarchical Modeling with Threat Intelligence, Lakehouse Analytics, and SAP Integration
DOI:
https://doi.org/10.15662/IJRAI.2025.0806814Keywords:
Cloud-Native Architecture, Dynamic Bayesian Hierarchical Models, Data-Scarce Regions, Threat Intelligence, AI Anomaly Detection, Data Lakehouse, Real-Time Analytics, SAP Integration, Probabilistic Modeling, Digital Transformation, Enterprise Data Management, Quality Assurance, Streaming Pipelines, Cloud ComputingAbstract
Data-scarce regions present unique challenges to organizations that rely on accurate, timely, and scalable insights for decision-making. This paper proposes a cloud-native AI architecture that leverages Dynamic Bayesian Hierarchical Models to enable robust probabilistic inference in environments with limited or inconsistent data availability. The framework integrates threat intelligence pipelines, AI-driven anomaly detection, and lakehouse analytics to unify batch, streaming, and unstructured data within a single scalable environment. SAP workflow integration ensures seamless enterprise adoption, enabling automated data interoperability, quality assurance, and real-time operational visibility. The proposed solution demonstrates improved resilience against data sparsity, enhanced security insights through continuous threat monitoring, and significant performance gains across SAP-enabled business processes. This architecture offers a comprehensive pathway for digital transformation in organizations operating in data-constrained or high-risk regions.
References
1. 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.
2. Binu, C. T., Kumar, S. S., Rubini, P., & Sudhakar, K. (2024). Enhancing Cloud Security through Machine Learning-Based Threat Prevention and Monitoring: The Development and Evaluation of the PBPM Framework. https://www.researchgate.net/profile/Binu-C-T/publication/383037713_Enhancing_Cloud_Security_through_Machine_Learning-Based_Threat_Prevention_and_Monitoring_The_Development_and_Evaluation_of_the_PBPM_Framework/links/66b99cfb299c327096c1774a/Enhancing-Cloud-Security-through-Machine-Learning-Based-Threat-Prevention-and-Monitoring-The-Development-and-Evaluation-of-the-PBPM-Framework.pdf
3. Bairi, A. R., Thangavelu, K., & Keezhadath, A. A. (2024). Quantum Computing in Test Automation: Optimizing Parallel Execution with Quantum Annealing in D-Wave Systems. Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023, 5(1), 536-545.Kandula, N. Evolution and Impact of Data Warehousing in Modern Business and Decision Support Systems
4. Poornima, G., & Anand, L. (2024, April). Effective Machine Learning Methods for the Detection of Pulmonary Carcinoma. In 2024 Ninth International Conference on Science Technology Engineering and Mathematics (ICONSTEM) (pp. 1-7). IEEE.
5. Achari, A. P. S. K., & Sugumar, R. (2024, November). Performance analysis and determination of accuracy using machine learning techniques for naive bayes and random forest. In AIP Conference Proceedings (Vol. 3193, No. 1, p. 020199). AIP Publishing LLC.
6. Karanjkar, R., & Karanjkar, D. Quality Assurance as a Business Driver: A Multi-Industry Analysis of Implementation Benefits Across the Software Development Life Cycle. International Journal of Computer Applications, 975, 8887.
7. Uddandarao, D. P. Improving Employment Survey Estimates in Data-ScarceRegions Using Dynamic Bayesian Hierarchical Models: Addressing Measurement Challenges in Developing Countries. Panamerican Mathematical Journal, 34(4), 2024. https://doi.org/10.52783/pmj.v34.i4.5584
8. Kusumba, S. (2025). Unified Intelligence: Building an Integrated Data Lakehouse for Enterprise-Wide Decision Empowerment. Journal Of Engineering And Computer Sciences, 4(7), 561-567.
9. Kumar, S. N. P. (2025). AI and Cloud Data Engineering Transforming Healthcare Decisions. Journal Of Engineering And Computer Sciences, 4(8), 76-82.
10. Nagarajan, G. (2025). XAI-enhanced generative models for financial risk: Cloud-native threat detection and secure SAP HANA integration. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 8(Special Issue 1), 50–56. https://doi.org/10.15662/IJARCST.2025.0806810
11. Zaharia, M., Chowdhury, M., Franklin, M. J., Shenker, S., & Stoica, I. (2010). Spark: Cluster computing with working sets. Proceedings of the 2nd USENIX Conference on Hot Topics in Cloud Computing (HotCloud).
12. Kumar, R. K. (2023). Cloud-integrated AI framework for transaction-aware decision optimization in agile healthcare project management. International Journal of Computer Technology and Electronics Communication (IJCTEC), 6(1), 6347–6355. https://doi.org/10.15680/IJCTECE.2023.0601004
13. Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 321–357.
14. Xu, Z., Zhang, X., & Yi, S. (2021). Generative approaches for synthetic tabular data: a survey and benchmark. Journal / Proceedings of relevant ML conferences.
15. Suchitra, R. (2023). Cloud-Native AI model for real-time project risk prediction using transaction analysis and caching strategies. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(1), 8006–8013. https://doi.org/10.15662/IJRPETM.2023.0601002
16. Vasugi, T. (2023). AI-empowered neural security framework for protected financial transactions in distributed cloud banking ecosystems. International Journal of Advanced Research in Computer Science & Technology, 6(2), 7941–7950. https://doi.org/0.15662/IJARCST.2023.0602004
17. Pasumarthi, A. (2023). Dynamic Repurpose Architecture for SAP Hana Transforming DR Systems into Active Quality Environments without Compromising Resilience. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(2), 6263-6274.
18. 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
19. Kotapati, V. B. R., & Yakkanti, B. (2023). Real-Time Analytics Optimization Using Apache Spark Structured Streaming: A Lambda Architecture-based Scala Framework. American Journal of Data Science and Artificial Intelligence Innovations, 3, 86-119.
20. Baeza-Yates, R., & Ribeiro, B. (2018). Data and people: Security, privacy and ethics in machine learning and data analytics. Communications of the ACM.
21. 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.
22. Konda, S. K. (2024). AI Integration in Building Data Platforms: Enabling Proactive Fault Detection and Energy Conservation. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(3), 10327-10338.
23. Adari, V. K. (2024). How Cloud Computing is Facilitating Interoperability in Banking and Finance. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(6), 11465-11471.
24. Chiranjeevi, Y., Sugumar, R., & Tahir, S. (2024, November). Effective Classification of Ocular Disease Using Resnet-50 in Comparison with Squeezenet. In 2024 IEEE 9th International Conference on Engineering Technologies and Applied Sciences (ICETAS) (pp. 1-6). IEEE.
25. HV, M. S., & Kumar, S. S. (2024). Fusion Based Depression Detection through Artificial Intelligence using Electroencephalogram (EEG). Fusion: Practice & Applications, 14(2).
26. Sculley, D., Holt, G., Golovin, D., Davydov, E., Phillips, T., Ebner, D., ... & Dennison, D. (2015). Hidden technical debt in machine learning systems. Advances in Neural Information Processing Systems.
