Enterprise AI and Cloud Architectures for Predictive Analytics and Secure Data Engineering
DOI:
https://doi.org/10.15680/IJMRSETM.2026.0204001Keywords:
Enterprise AI, cloud architecture, predictive analytics, data engineering, machine learning, data security, cloud computing, big data, data governance, scalable systemsAbstract
The rapid evolution of enterprise systems has been significantly influenced by the convergence of artificial intelligence (AI) and cloud computing, particularly in the domains of predictive analytics and secure data engineering. Organizations are increasingly leveraging cloud-native architectures to process vast volumes of structured and unstructured data, enabling the development of predictive models that support proactive decision-making. This study explores how enterprise AI integrated with cloud architectures enhances data engineering processes, improves scalability, and ensures robust security mechanisms
Predictive analytics, powered by machine learning algorithms, enables enterprises to forecast trends, detect anomalies, and optimize operations. Cloud architectures provide the infrastructure required to deploy and scale these models efficiently while ensuring high availability and fault tolerance. However, the adoption of these technologies introduces challenges related to data privacy, governance, and cyber threats, necessitating the implementation of secure data engineering practices.
This research presents a comprehensive framework that integrates AI-driven analytics with secure cloud-based data pipelines. It also examines architectural components, security models, and implementation strategies. The findings suggest that enterprises adopting AI-enabled cloud architectures achieve enhanced agility, improved decision-making, and stronger data security, thereby gaining a competitive advantage in data-driven environments.
References
1. Rajasekar, M. (2025). Risk-Aware Generative AI and Machine Learning Frameworks for Privacy-Preserving Banking and Trade Analytics over Cloud and 5G Networks. International Journal of Computer Technology and Electronics Communication, 8(4), 11078–11086.
2. Anand, L. (2023). An Intelligent AI and ML–Driven Cloud Security Framework for Financial Workflows and Wastewater Analytics. International Journal of Humanities and Information Technology, 5(02), 87–94.
3. Niture, N. A., & Abdellatif, I. (2020, October). AI based airplane air pollution identification architecture using satellite imagery. In 2020 IEEE Cloud Summit (pp. 150–155). IEEE.
4. Anand, L. (2024). AI-Powered Cloud Cybersecurity Architecture for Risk Prediction and Threat Mitigation in Healthcare and Finance. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(Special Issue 1), 5–12.
5. Gopinathan, V. R. (2023). Cloud-First AI Security Architecture for Protecting Enterprise Digital Ecosystems and Financial Networks. International Journal of Research and Applied Innovations, 6(6), 10031–10039.
6. 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.
7. Vimal, V. R. (2025). Next Generation Enterprise Architecture for SAP Cloud Systems Leveraging AI Driven Analytics and Hybrid Infrastructure. International Journal of Engineering & Extended Technologies Research (IJEETR), 7(6), 11174–11182.
8. Katta, T. B. (2023). Adaptive AI-driven integration pipelines for efficient data and process orchestration in cloud-native environments. International Journal of Research and Applied Innovations (IJRAI), 6(1), 8363–8374. https://doi.org/10.15662/IJRAI.2023.0601010
9. Vayyasi, N. K. (2023). Designing a multi-domain predictive framework using Java and generative AI for financial, retail, and industrial use cases. International Journal of Computer Technology and Electronics Communication (IJCTEC), 6(6), 8060–8069.
10. Kunadi, S. K. (2021). Establishing robust data foundations: Early-stage architecture for scalable data warehousing and analytics systems. International Journal of Engineering & Extended Technologies Research (IJEETR), 3(3), 3078–3088.
11. Kale, A. (2025). RPA for Account Reconciliations: Case Study of 85% Time Reduction. Emerging Frontiers Library for The American Journal of Interdisciplinary Innovations and Research, 7(07), 101–105.
12. Chaturvedi, V. (2025). Disease Diagnostic Systems based on AI-Applications in Healthcare: Models, Challenges, and Future Directions. International Journal of Emerging Research in Engineering and Technology, 6(4), 207–217.
13. Cherukuri, B. R., & Arulkumar, V. (2024, February). Optimization of Data Structures and Trade-Offs with Concurrency Control in Multithread Software Structures Using Artificial Intelligence. In 2024 IEEE International Conference on Computing, Power and Communication Technologies (IC2PCT) (Vol. 5, pp. 1860–1865). IEEE.
14. Loganayagi, S., Hemavathi, R., & VR, V. (2024, March). IoT-driven energy consumption optimization in smart homes. In 2024 International Conference on Trends in Quantum Computing and Emerging Business Technologies (pp. 1–5). IEEE.
15. Appani, C., & Guda, D. P. (2023). Self-supervised representation learning for zero-day attack detection in encrypted network traffic. Computer Fraud & Security, 2023(7), 20–31. Retrieved from: https://computerfraudsecurity.com/index.php/journal/article/view/661
16. 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.
17. Anbazhagan, K. (2025). AI Driven Zero Trust Security Model for Enterprise Data Protection and Intelligent Infrastructure Management. International Journal of Technology, Management and Humanities, 11(03), 101–107.
18. Dave, B. L. (2025). Advancing Transparency and Responsiveness in Social Work through the SWAN Humanitarian Platform. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 8(3), 12217–12225.
19. Viswanathan, V. (2023). Generative AI for smarter workforce planning and enterprise resource decisions. Journal of Information Systems Engineering and Management, 8(4), e-ISSN 2468-4376.
20. Indurthy, V. S. K. (2025). ETL-Driven Data Integration for Enhanced Pharmaceutical Manufacturer Rebate Processing. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 8(1), 11606–11615.
21. Bheemisetty, N. (2025). Transforming Static Server Allocation into an Adaptive Compute for Enhanced Throughput and SLA Compliance. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 8(3), 12187–12196.
22. Ambalakannu, M. (2025). Accelerating Claims Processing with Observability and Automated Dashboards. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 8(3), 12179–12186.
23. Ganesan, M. (2024). Transforming home electronics customer self-installation experience with AI. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(4), 14319–14327.
24. Nallamothu, T. K. (2024). The Age of Smart Living: How AI Is Shaping Our Daily Lives in Real Time. International Journal of Research and Applied Innovations, 7(5), 11456–11468.
25. Sharma, K. P., Kumar, I., Singh, P. P., Anbazhagan, K., Albarakati, H. M., Bhatt, M. W., ... & Rana, A. (2024). Advancing spacecraft rendezvous and docking through safety reinforcement learning and ubiquitous learning principles. Computers in Human Behavior, 153, 108110.
26. Sengottaiyan, N., Gurusamy, R., Kalyanasundaram, P., Sangameswaran, B. B., Sathesh, M., & Rajasekar, M. (2023, December). Gain Improved Novel Coplanar Waveguide-Fed Sierpinski Carpet Fractal Microstrip Patch Antenna for the Acquisition of Bio-signals. In 2023 2nd International Conference on Automation, Computing and Renewable Systems (ICACRS) (pp. 105–109). IEEE.
27. Jagadeesh, S., & Sugumar, R. (2017). Optimal knowledge extraction system based on GSA and AANN. International Journal of Control Theory and Applications, 10(12), 153–162.
28. Singh, A. (2024). Integration of AI in network management. International Journal of Research and Applied Innovations (IJRAI), 7(4), 11073–11078. https://doi.org/10.15662/IJRAI.2024.0704008
29. Gentyala, R. (2024). From Pipelines to Predictions: An Empirical Study on the Critical Behavioral Markers and Skill Pathways for Effective AI Data Engineering. Journal of Scientific and Engineering Research, 11(11), 187–197.
30. Akash, T. R., Shokran, M., & Ferdousi, J. (2026). Role of Machine Learning in Securing US Digital Advertising Ecosystems Against Fraud and Market Manipulation. American Journal of Economics and Business Management, 9(2).
31. Padala, S. (2026). Voice Biometrics and AI-Driven Automation for Secure Authentication and Claims Processing in Healthcare: A Technical Review. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 7(1), 296–306.
32. Mangukiya, M., Miyani, H., & Yadav, V. (2026). Comment on “Case report: Electrocardiographic (ECG) recording during the hanging process”. Forensic Science, Medicine and Pathology, 1–2.
33. Chachra, B. (2024). Advancing behavioural analytics at scale: Machine learning frameworks for predicting customer intent in large commerce ecosystems. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(6), 11652–11662.
34. Agarwal, S. (2022). Observability in Microservices: From Traditional Monitoring to Distributed System Intelligence. International Journal of Computer Technology and Electronics Communication, 5(6), 16220–16226.
35. Ranjith Rajasekharan. (2019). Hybrid cloud architecture for enterprise database system. International Journal of Science, Research and Technology (IJSRAT), 2(6), 2513–251.
36. Soundappan, S. J. (2022). AI-Based Fault Detection and Isolation for Reliability in Modern Power Systems. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(4), 7106–7110.
37. Kumar, L. M. S. (2025). Developing protocol translation mechanisms for legacy banking systems. International Journal of Innovative Research in Science Engineering, 14(5), 13343–13350.
38. Ireddy, R. K. (2024). Cybersecurity framework for banking systems: A multi-layer defense architecture using machine learning, microservices, and zero-trust principles. World Journal of Advanced Research and Reviews, 24(3), 3629-3638.
39. Sanepalli, U. R. (2025). Autonomous medallion orchestration: A multi-agent reinforcement learning framework for financial ecosystems. International Journal for Multidisciplinary Research (IJFMR).
