Intelligent Cloud-Native Frameworks for Big Data Analytics MLOps and Secure Enterprise Applications
DOI:
https://doi.org/10.15680/IJCTECE.2025.0806036Keywords:
Cloud-native computing, Big Data Analytics, MLOps, DevSecOps, Kubernetes, Microservices, Data Security, Machine Learning Lifecycle, Distributed Systems, Real-time AnalyticsAbstract
Intelligent cloud-native frameworks are transforming the way enterprises design, deploy, and manage big data analytics, machine learning operations (MLOps), and secure distributed applications. With the exponential growth of data from IoT devices, social platforms, and enterprise systems, traditional monolithic architectures are no longer sufficient to handle scalability, resilience, and real-time processing requirements. Cloud-native paradigms, built on microservices, containers, and orchestration platforms such as Kubernetes, provide elastic scalability and high availability, enabling efficient big data processing pipelines.
This study explores an integrated framework that combines big data analytics, MLOps automation, and enterprise-grade security mechanisms within cloud-native ecosystems. It emphasizes the role of continuous integration/continuous deployment (CI/CD), model lifecycle management, and DevSecOps practices in ensuring reliable and secure AI-driven applications. Furthermore, it investigates how distributed computing frameworks and data streaming technologies enhance real-time analytics capabilities.
The paper also highlights challenges such as data governance, latency optimization, multi-cloud interoperability, and security vulnerabilities in distributed environments. By synthesizing recent advancements, the study proposes a conceptual architecture that supports intelligent workload orchestration, secure data pipelines, and scalable machine learning deployment, ultimately enabling enterprises to achieve operational efficiency and data-driven decision-making.
References
1. Rahman, M. W., & Hossain, M. S. (2024). An explainable AI framework for insider threat detection using behavioral business analytics. An Explainable AI Framework for Insider Threat Detection Using Behavioral Business Analytics, 1(8), 70-97.
2. Soundappan, S. J. (2025). Privacy preserving data analytics frameworks using homomorphic encryption techniques. International Journal of Future Innovative Science and Technology (IJFIST), 8(2), 14531.
3. Vankayala, S. C. (2017). Embedding quality intelligence in API-first architectures: Assurance frameworks for real-time financial transactions. Journal of Scientific and Engineering Research, 4(6), 227-241.
4. Bellundagi, M. (2024). An intelligent digital transformation framework for smart enterprises using AI and cloud computing. International Journal of Science, Research and Technology, 7(4), 12433-12446.
5. Adepu, G. (2025). AI-based epidemiological data platforms for early outbreak detection and real-time health analytics. International Journal of Future Innovative Science and Technology (IJFIST), 8(2), 9–29.
6. Macha, Y., & Pulichikkunnu, S. K. (2023). An explainable AI system for fraud identification in insurance claims via machine-learning methods. International Journal of Advanced Research in Science Communication and Technology, 3(3), 1391-1400.
7. Gurram, S. (2025). Adaptive drift defense: A unified framework for data, task, and user-intent drift in LLM apps. International Journal of Research and Applied Innovations, 8(6), 3721-3729.
8. Kothokatta, L. (2025). A cloud-native test automation framework for secure OTT content delivery systems. International Journal of Research and Applied Innovations, 8(4), 2428-2437.
9. Hajj, A. A., & Rony, M. (2020). Cyber security in the age of COVID-19: An analysis of cyber-crime and attacks. International Journal for Research in Applied Science & Engineering Technology, 8(8), 1476-1480.
10. Kunadi, S. K. (2024). From raw data to revenue intelligence: Architecting GTM data platforms for business impact. International Journal of Future Innovative Science and Technology (IJFIST), 7(2), 12414.
11. Prasad, P. K. (2025). Policy-over-model guardrails — An agentic MLOps control plane for safe autonomy in production engineering and infra. International Journal of Science, Research and Technology (IJSRAT), 8(4), 14610–14614.
12. Akila, R. (2024). A deep reinforcement learning approach for optimizing inventory management in the agri-food supply chain. J. Electrical Systems, 20(4s), 2238-2247.
13. Subramani, V. (2023). Governance led security architecture in large scale enterprise systems. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(4), 9037-9045.
14. Bellundagi, M. (2024). An intelligent digital transformation framework for smart enterprises using AI and cloud computing. International Journal of Science, Research and Technology, 7(4), 12433-12446.
15. Narayanan, S. (2025). Autonomous cyber sovereignty: A dual-control architecture for agentic artificial intelligence in offensive defensive security ecosystems. World Journal of Advanced Research and Reviews, 25(3), 2538–2546.
16. Anand, L., Rane, K. P., Bewoor, L. A., Bangare, J. L., Surve, J., Raghunath, M. P., ... & Osei, B. (2022). Development of machine learning and medical enabled multimodal for segmentation and classification of brain tumor using MRI images. Computational Intelligence and Neuroscience, 2022(1), 7797094.
17. Raja, G. V. (2023). Modernizing enterprise systems using AI with machine learning and cloud computing for intelligent systems. International Journal of Future Innovative Science and Technology (IJFIST), 6(6), 11713.
18. Sengupta, J., Alzbutas, R., Iešmantas, T., Petkus, V., Barkauskienė, A., Ratkūnas, V., ... & Džiugys, A. (2024). Detection of subarachnoid hemorrhage using CNN with dynamic factor and wandering strategy-based feature selection. Diagnostics, 14(21), 2417.
19. Shewale, V. (2025). Demystifying the MITRE ATT&CK framework: A practical guide to threat modeling. Journal of Computer Science and Technology Studies, 7(3), 182-186.
20. Pothuri, M. K. (2025). Designing a metadata-driven framework for automated data profiling, data analysis, data management, integration at scale in Medicaid healthcare ecosystems. International Journal of Multidisciplinary Research and Growth Evaluation, 6(4), 1413-1418.
21. Fung, J., & Panyala, V. R. (2020). Automating multi-region scalable CI/CD framework for managing AWS CloudWatch alerts. International Journal of Engineering & Extended Technologies Research, 2(5), 1854–1858.
22. Yamsani, N. (2016). Advancing data consistency and control across global financial institutions by enterprise master data platforms. International Journal of Technology, Management and Humanities, 2(01), 22-35.
23. Gopinathan, V. R. (2024). Cyber-resilient digital banking analytics using AI-driven federated machine learning on AWS. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(4), 8419-8426.
24. Kasireddy, J. R. (2025). The transformative role of AI and machine learning in financial risk analysis. World Journal of Advanced Research and Reviews, 26(1), 1246–1256. https://doi.org/10.30574/wjarr.2025.26.1.1177
25. Sarabu, V. B. (2023). Preventing circular data update loops in distributed systems: A source-controlled synchronization model for enterprise data integrity. International Journal of Research and Applied Innovations (IJRAI), 6(3), 371–386.
26. Indurthy, V. S. K. (2024). The surge in AI-powered data analytics revolutionizing business intelligence. International Journal of Future Innovative Science and Technology (IJFIST), 7(6), 13964.
27. Sugumar, R., & Murugeshwari, B. (2016). An efficient MChord based authentication for vehicular ad-hoc networks.
28. Vayyasi, N. K. (2023). Retail fraud analytics using generative intelligence and Java cloud frameworks. International Journal of Science, Research and Technology, 6(4), 10324-10337.
29. Boddupally, H. L. (2024). Cognitive decision automation framework integrating LLMs with SQL datastores and enterprise rule engines. SSRN. https://doi.org/10.2139/ssrn.6250878
30. Rongali, L. P. (2025). Utilizing AI-driven DevOps for predictive maintenance and anomaly detection in smart grids. Journal of Science and Technology, 10(4), 27–33. https://doi.org/10.46243/jst.2025.v10.i04.pp27-33
31. Parupalli, A. (2022). KPI-driven business intelligence: A review of frameworks and visualization tools. Asian Journal of Computer Science Engineering, 7(4), 4.
32. Adepu, R. (2023). Zero trust architecture for large-scale enterprise infrastructure security. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(6), 171–187.
33. Mulla, F. A. (2024). Modern mobile testing tools: A comprehensive guide to quality assurance and automation. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 10(6), 10-32628.
34. Pasumarthi, H. (2023). A deep dive into enterprise B2B integrations: Designing high-availability file and API workflows with IBM Datapower and Autosys. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(2), 8363-8370.
35. 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.
36. Mallireddy, S. (2024). ServiceNow's critical role in payroll management. International Journal of Computer Technology and Electronics Communication, 7(6), 226-232.
37. Anbazhagan, R. S. K. (2016). A proficient two level security contrivances for storing data in cloud. International Journal of Advanced Research in Computer Science, 7(3), 239–242.
38. Gurram, S. (2025). Adaptive drift defense: A unified framework for data, task, and user-intent drift in LLM apps. International Journal of Research and Applied Innovations, 8(6), 3721-3729.
39. Soundappan, S. J. (2025). Privacy preserving data analytics frameworks using homomorphic encryption techniques. International Journal of Future Innovative Science and Technology (IJFIST), 8(2), 14531.
40. Mali, R. K. (2024). A decentralized security model for preventing data breaches in distributed environments. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(1), 9989-9999.
