AI Driven Cloud Native Systems for Secure Financial Healthcare and Enterprise Analytics
DOI:
https://doi.org/10.15680/IJCTECE.2024.0704013Keywords:
Artificial Intelligence, Cloud-Native Architecture, Financial Analytics, Healthcare Systems, Enterprise Analytics, Data Security, Microservices, DevSecOps, Machine Learning, Big DataAbstract
Artificial Intelligence (AI) combined with cloud-native architectures is revolutionizing data-driven decision-making across financial, healthcare, and enterprise domains. This paper explores the design, implementation, and impact of AI-driven cloud-native systems that enable scalable, secure, and intelligent analytics. Cloud-native technologies such as microservices, containerization, and serverless computing provide flexibility, while AI models enhance predictive accuracy, anomaly detection, and automation. In the financial sector, these systems improve fraud detection and risk management; in healthcare, they support diagnostics and patient monitoring; and in enterprise analytics, they optimize operational efficiency and strategic planning. Security remains a critical concern due to the sensitivity of financial and medical data, necessitating advanced encryption, identity management, and compliance frameworks. This study investigates the integration of AI algorithms within cloud-native environments, focusing on architecture, data pipelines, and security mechanisms. Furthermore, it highlights the role of DevSecOps practices in ensuring continuous security and compliance. The findings demonstrate that AI-driven cloud-native systems significantly enhance scalability, resilience, and real-time analytics capabilities, making them essential for modern digital transformation across industries.
References
1. 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.
2. Sampath Kumar Konda, “Fault-Tolerant BMS Modernization in Precision-Controlled Scientific Facilities: Zero-Downtime Migration Architectures”, Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol, vol. 10, no. 2, pp. 1223–1234, Mar. 2024, doi: 10.32628/CSEIT24102257.
3. Sanepalli, Uttama Reddy. (2023). Distributed Multi-Cloud Data Lake Architecture for Enterprise-Scale Workplace Benefits Analytics: A Federated Approach to Heterogeneous Financial Data Integration. International Journal of Computer Engineering and Technology (IJCET), 14(1), 268-282.
4. Ireddy, R. K. (2024). Event-native financial onboarding platforms: A Kafka-centric reference architecture for sub-minute identity and compliance processing. World Journal of Advanced Research and Reviews, 21(2), 2182–2192. https://doi.org/10.30574/wjarr.2024.21.2.0448
5. 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.
6. G. Sarraf, “Autonomous Ransomware Forensics: Advanced ML Techniques for Attack Attribution and Recovery,” Int. J. Adv. Res. Sci. Commun. Technol., vol. 3, no. 3, pp. 1377–1390, Jul. 2023, doi: 10.48175/IJARSCT-11978W
7. Thumala, Srinivasarao. "Building Highly Resilient Architectures in the Cloud." Nanotechnology Perceptions 16.2 (2020).
8. Meka, S. (2023). Empowering Members: Launching Risk-Aware Overdraft Systems to Enhance Financial Resilience. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(6), 7517-7525.
9. Mudunuri, P. R. (2023). Automation-Driven Reliability Engineering for Public-Sector Biomedical Systems. International Journal of Humanities and Information Technology, 5(01), 68-86.
10. 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.
11. Mangukiya, M. (2023). Blockchain-Enabled Traceability and Compliance in Global Electronics Production Networks. International Journal of Computer Technology and Electronics Communication, 6(6), 7999-8004.
12. Rasul, I., Tohfa, N. A., Rahman, M., Hossain, I., Zareen, S., & Shakhawat, M. (2023). Quantum Machine Learning for Early Disease Diagnosis: A Systematic Review and Public Health Innovation Perspective, World Journal of Advanced Research and Reviews, 2023, 19(01), 1668-1674
13. Balaji, K. V., & Sugumar, R. (2023, December). Harnessing the Power of Machine Learning for Diabetes Risk Assessment: A Promising Approach. In 2023 International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI) (pp. 1-6). IEEE.
14. Devarajan, R., Prabakaran, N., Vinod Kumar, D., Umasankar, P., Venkatesh, R., & Shyamalagowri, M. (2023, August). IoT Based Under Ground Cable Fault Detection with Cloud Storage. In 2023 Second International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) (pp. 1580-1583). IEEE.
15. Yashwanth, K., Adithya, N., Sivaraman, R., Janakiraman, S., & Rengarajan, A. (2021, July). Design and Development of Pipelined Computational Unit for High-Speed Processors. In 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1-5). IEEE.
16. Vigenesh, M., Upadhyay, A. K., Murali, M. J., Seth, K., & Shinde, G. R. (2024, June). Exploring the Role of Visual Information in Mixed Media Creation. In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1-6). IEEE.
17. 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.
18. Harish, M., & Selvaraj, S. K. (2023, August). Designing efficient streaming-data processing for intrusion avoidance and detection engines using entity selection and entity attribute approach. In AIP Conference Proceedings (Vol. 2790, No. 1, p. 020021). AIP Publishing LLC.
19. 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.
20. Gurram, S. (2023). Why Data Engineering, Not Model Scale, Became the True Bottleneck in Generative AI. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 6(4), 9028-9036.
21. C.Nagarajan and M.Madheswaran - ‘Performance Analysis of LCL-T Resonant Converter with Fuzzy/PID Using State Space Analysis’- Springer, Electrical Engineering, Vol.93 (3), pp.167-178, September 2011.
22. Sarabhu, V. B., & Balaji, V. (2018). Design and implementation for an improved version of cloud computing architecture by using concept of ontology with query retrieval and refinement mechanism. International Journal of Research and Applied Innovations (IJRAI), 1(1), 8–16.
23. Padala, S. (2023). Intelligent Workforce Management: A Predictive Analytics Approach. American International Journal of Computer Science and Technology, 5(3), 42-47.
24. Vijayakumar, R., & Gireesh, G. (2013, July). Quantitative analysis and fracture detection of pelvic bone X-ray images. In 2013 fourth international conference on computing, communications and networking technologies (ICCCNT) (pp. 1-7). IEEE.
25. Guda, D. P. (2024). Cyber insurance for DevSecOps risks: Pricing models and coverage gaps. Journal of Information Systems Engineering and Management, 9(3).
26. Rajasekharan, R. (2017). The role of DevOps automation in improving enterprise database reliability. International Journal of Humanities and Information Technology (IJHIT), 2(1), 20–29.
27. Devarajan, R., Prabakaran, N., Vinod Kumar, D., Umasankar, P., Venkatesh, R., & Shyamalagowri, M. (2023, August). IoT Based Under Ground Cable Fault Detection with Cloud Storage. In 2023 Second International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) (pp. 1580-1583). IEEE.
28. Mohana, P., Muthuvinayagam, M., Umasankar, P., & Muthumanickam, T. (2022, March). Automation using Artificial intelligence based Natural Language processing. In 2022 6th International Conference on Computing Methodologies and Communication (ICCMC) (pp. 1735-1739). IEEE.
29. 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.
30. Khan, M. F., & Hassan, M. M. (2024). Explainable Ai and Machine Learning Models for Transparent and Scalable Intrusion Detection Systems. J. Inf. Syst. Eng. Manag, 9(4s), 1576-1588.
31. Vimal Raja, G. (2022). Leveraging Machine Learning for Real-Time Short-Term Snowfall Forecasting Using MultiSource Atmospheric and Terrain Data Integration. International Journal of Multidisciplinary Research in Science, Engineering and Technology, 5(8), 1336-1339.
32. Hebbar, K. S. (2022). Machine learning-assisted service boundary detection for modularizing legacy systems. International Journal of Applied Engineering & Technology, 4(2), 401–414.
33. Garg, V. K., Soundappan, S. J., & Kaur, E. M. (2020). Enhancement in intrusion detection system for WLAN using genetic algorithms. South Asian Research Journal of Engineering and Technology, 2(6), 62–64. https://doi.org/10.36346/sarjet.2020.v02i06.003
34. 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.
