Intelligent AI Driven Cloud Native Security Framework for Enterprise Systems Financial Platforms IoT Networks and Real Time Threat Detection
DOI:
https://doi.org/10.15680/IJCTECE.2023.0605013Keywords:
AI-driven security, Cloud-native architecture, Enterprise cybersecurity, Financial platform security, IoT network protection, Real-time threat detection, Zero-trust architecture, Intelligent data governance, Adaptive security, Cyber resilienceAbstract
The increasing complexity and interconnectivity of enterprise systems, financial platforms, and IoT networks have intensified cybersecurity challenges, making traditional security mechanisms inadequate. This research proposes an intelligent AI-driven cloud-native security framework designed to provide proactive, adaptive, and real-time threat detection for modern digital ecosystems. Leveraging artificial intelligence and machine learning algorithms, the framework analyzes large-scale network traffic, transaction data, and IoT device activity to detect anomalies and potential security breaches. Cloud-native technologies, including containerization, microservices, and orchestration platforms, enhance scalability, flexibility, and operational resilience, while zero-trust principles ensure robust access control across all system components. The framework integrates intelligent data governance to enforce regulatory compliance, data privacy, and secure information exchange. By combining real-time threat intelligence, automated response mechanisms, and continuous monitoring, the proposed architecture enhances enterprise resilience against cyberattacks, insider threats, and distributed denial-of-service attacks. The framework also supports adaptive security policies that evolve with emerging threat landscapes, enabling organizations to maintain high levels of operational continuity, system integrity, and customer trust. This study highlights the strategic role of AI-driven security frameworks in modern enterprises, offering a holistic approach to secure digital transformation for financial institutions and IoT-dependent infrastructures.References
1. Ponlatha, S., Umasankar, P., Balashanmuga Vadivu, P., & Chitra, D. (2021). An IOT‐based efficient energy management in smart grid using SMACA technique. International Transactions on Electrical Energy Systems, 31(12), e12995.
2. Sudhan, S. K. H. H., & Kumar, S. S. (2015). An innovative proposal for secure cloud authentication using encrypted biometric authentication scheme. Indian journal of science and technology, 8(35), 1-5.
3. Madathala, H., Barmavat, B., & Thumala, S. (2023). Performance optimization of sap hana using ai-based workload predictions. International Journal of Innovative Research in Science, Engineering and Technology, 12, 15315-15326.
4. Neela Madheswari, A., Vijayakumar, R., Kannan, M., Umamaheswari, A., & Menaka, R. (2022). Text-to-speech synthesis of indian languages with prosody generation for blind persons. In IOT with Smart Systems: Proceedings of ICTIS 2022, Volume 2 (pp. 375-380). Singapore: Springer Nature Singapore.
5. Madhurya, J. A. (2017). A survey on preserving the data privacy and copyrights during image retrieval in cloud (Vol. 04, Issue 05). International Research Journal of Engineering and Technology (IRJET). Retrieved from https://www.irjet.net/archives/V4/i5/IRJET-V4I5800.pdf
6. 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.
7. 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.
8. Inampudi, R. K., Pichaimani, T., & Surampudi, Y. (2022). AI-enhanced fraud detection in real-time payment systems: leveraging machine learning and anomaly detection to secure digital transactions. Australian Journal of Machine Learning Research & Applications, 2(1), 483-523.
9. Rengarajan, A., & Rajagopalan, S. (2021). Chaos Blend LFSR-Duo Approach on FPGA for Medical Image Security. Emerging Technologies in Data Mining and Information Security: Proceedings of IEMIS 2020, Volume 3, 3, 155.
10. Archana, R., & Anand, L. (2023, May). Effective Methods to Detect Liver Cancer Using CNN and Deep Learning Algorithms. In 2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI) (pp. 1-7). IEEE.
11. Dama, H. B. (2023). Designing Highly Available Multi-Cloud Database Architectures for Global Financial Services. International Journal of Research and Applied Innovations, 6(1), 8329-8336.
12. Ande, B. R. (2022). Enhancing AEM performance using edge computing and global CDN strategies. International Journal of Communication Networks and Information Security, 14(3), 1202–1210.
13. 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.
14. 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.
15. Archana, R., & Anand, L. (2023, May). Effective Methods to Detect Liver Cancer Using CNN and Deep Learning Algorithms. In 2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI) (pp. 1-7). IEEE.
16. Bhatnagar, G., Rajoria, Y. K., Sakeel, M., Vigenesh, M., Premananthan, G., & Dongre, D. (2023, September). IoT malware detection tool with CNN classification for small devices. In 2023 6th International Conference on Contemporary Computing and Informatics (IC3I) (Vol. 6, pp. 2017-2023). IEEE.
17. Ponlatha, S., Umasankar, P., Balashanmuga Vadivu, P., & Chitra, D. (2021). An IOT‐based efficient energy management in smart grid using SMACA technique. International Transactions on Electrical Energy Systems, 31(12), e12995.
18. Sarraf, G., & Swetha, M. S. (2019, December). Intrusion prediction and detection with deep sequence modeling. In International Symposium on Security in Computing and Communication (pp. 11-25). Singapore: Springer Singapore.
19. 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.
20. Ponlatha, S., Umasankar, P., Balashanmuga Vadivu, P., & Chitra, D. (2021). An IOT‐based efficient energy management in smart grid using SMACA technique. International Transactions on Electrical Energy Systems, 31(12), e12995.
21. 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.
22. C.Nagarajan and M.Madheswaran - ‘Experimental Study and steady state stability analysis of CLL-T Series Parallel Resonant Converter with Fuzzy controller using State Space Analysis’- Iranian Journal of Electrical & Electronic Engineering, Vol.8 (3), pp.259-267, September 2012.
23. Chinthalapelly, P. R., & Mohammed, A. S. (2021). Legal Standards Extraction Using LLMs with CRF-based Sequence Labeling. American Journal of Data Science and Artificial Intelligence Innovations, 1, 801-836.
24. 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.
25. Potel, R. (2022). AI-Driven Security Graphs for Real-Time Breach Containment in Hybrid Cloud Environments. International Journal of AI, BigData, Computational and Management Studies, 3(4), 123-131.
26. Uttama Reddy Sanepalli , " Adaptive Intelligence Framework for Retirement Portfolio Management: Self- Optimizing Infrastructure for Dynamic Asset Allocation and Risk Mitigation" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 8, Issue 6, pp.769-780, November-December-2022. Available at doi : https://doi.org/10.32628/CSEIT22557
27. P. Jothilingam, “Systems and management innovation in Industry 4.0: Redefining organizational models, human– machine collaboration, and process efficiency,” in Proc. Int. Conf. Innovative Trends in Engineering and Technology, India, Jul. 2022, pp. 699–706.
28. 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
29. Panyala, V. R. (2023). AI-augmented DevOps frameworks for accelerating cloud-native platform engineering at scale. International Journal of Research and Applied Innovations, 6(1), 8375–8379.
30. Namdeo, A. (2023). Generative synthetic data pipelines for bias-free BI training. International Journal of Advanced Engineering Science and Information Technology (IJAESIT), 6(1), 10818–10826. https://doi.org/10.15662/IJAESIT.2023.0601003
31. Kasireddy, J. R. (2023). Optimizing multi-TB market data workloads: Advanced partitioning and skew mitigation strategies for Hive and Spark on EMR. International Journal of Computer Technology and Electronics Communication, 6(3), 6982-6990.
32. Mallireddy, S. (2021). Data encryption and policies via digital transformations and services. International Journal of Research and Applied Innovations, 4(5), 1–6.
33. Narayanan, S. (2023). Operationalizing Artificial Intelligence Security in the Cloud: A Practical Integration framework for Enterprise Risk Management. International Journal of Future Innovative Science and Technology (IJFIST), 6(3), 10619.
34. Sarabu, V. B. (2022). Hybrid on-premise to cloud data migration: A controlled one-way synchronization framework for enterprise-scale modernization. International Journal of Science, Research and Technology, 5(5), 19-33.
35. Ande, B. R. (2022). Enhancing AEM performance using edge computing and global CDN strategies. International Journal of Communication Networks and Information Security, 14(3), 1202–1210.
36. Viswanathan, Venkatraman. "AI-Augmented Decision Intelligence for Enterprise Systems: Integrating Cognitive Analytics for Resource and Talent Optimization." (2023).
37. Sheta, S.V. (2021). Security Vulnerabilities in Cloud Environments. Webology, 18(6), 10043–10063.
38. Ireddy, Ravi Kumar. (2023). API-driven interoperability framework for corporate treasury management: A financial data exchange standard implementation with secure data aggregation networks. World Journal of Advanced Research and Reviews, 19(2), 1727–1738. https://doi.org/10.30574/wjarr.2023.19.2.1609
