Smart Cyber Intelligence and Machine Learning Models for Secure Cloud Native Enterprise Data Platforms
Abstract
Smart cyber intelligence and machine learning models have become essential components of secure cloud-native enterprise data platforms in the era of digital transformation and intelligent computing. Modern enterprises increasingly depend on cloud-native infrastructures to support scalable analytics, automated operations, cybersecurity resilience, and real-time decision-making. This study explores the development and implementation of smart cyber intelligence frameworks integrated with machine learning models for securing cloud-native enterprise data platforms across financial, healthcare, industrial, and business environments. The research focuses on how artificial intelligence, cloud computing, predictive analytics, cybersecurity mechanisms, and automation technologies collectively improve enterprise security, operational efficiency, and intelligent governance. The study also examines the role of zero-trust security architectures, cloud-native microservices, real-time threat intelligence, anomaly detection systems, and intelligent orchestration frameworks in protecting enterprise digital ecosystems. A comprehensive literature review highlights recent advancements in cloud-native cybersecurity, AI-driven threat intelligence, machine learning analytics, and secure enterprise data engineering. The proposed methodology introduces a multi-layered intelligent cloud-native architecture integrating cyber intelligence, machine learning analytics, governance, automation, and adaptive security services within a unified enterprise framework. The findings indicate that smart cyber intelligence systems significantly improve threat detection, predictive security analytics, operational transparency, scalability, and business continuity. However, challenges related to data privacy, infrastructure complexity, ethical AI concerns, interoperability, and regulatory compliance continue to influence enterprise adoption and management strategies.Article Information
Journal |
International Journal of Science, Research and Technology |
|---|---|
Volume (Issue) |
Vol. 7 No. 6 (2024): International Journal of Science, Research and Technology (IJSRAT) |
DOI |
|
Pages |
13273-13283 |
Published |
December 21, 2024 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Mohanaad Shakir (%2024). Smart Cyber Intelligence and Machine Learning Models for Secure Cloud Native Enterprise Data Platforms. International Journal of Science, Research and Technology , Vol. 7 No. 6 (2024): International Journal of Science, Research and Technology (IJSRAT) , pp. 13273-13283. https://doi.org/10.15662/IJSRAT.2024.0706013 |
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2. Bellundagi, M. (2022). Performance Optimization Techniques for Enterprise Java Applications Using Middleware and Messaging Systems. International Journal of Computer Technology and Electronics Communication, 5(3), 5158-5168.
3. 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.
4. Kale, P. (2024). A Deep Learning-Based Platform Engineering Framework for Predictive CI/CD Pipeline Optimization and Developer Productivity Enhancement. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 5(2), 194-202.
5. 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
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8. Gangina, P. (2024). Intelligent Cost Optimization Strategies for Multi-Tenant SaaS Platforms Using Machine Learning. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 7(1), 9976-9988.
9. Vimal Raja, G. (2024). Intelligent Data Transition in Automotive Manufacturing Systems Using Machine Learning. International Journal of Multidisciplinary and Scientific Emerging Research, 12(2), 515-518.
10. Adepu, R. (2023). Zero trust architecture for large-scale enterprise infrastructure security. International Journal of Engineering & Extended Technologies Research (IJEETR), 5(6), 171–187.
11. 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.
12. 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.
13. 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.
14. Hema Latha Boddupally. (2020). EnterpriseScale Data Quality Improvement Using Machine Learning: Frameworks, Validation Strategies, and Operational Insights. European Journal of Advances in Engineering and Technology, 7(8), 138–149. https://doi.org/10.5281/zenodo.18083539
15. Raja, G. V. (2022). Integrating network forensics with data mining for advanced cybercrime investigation. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(5), 5321-5326.
16. Mallireddy, S. (2022). Business value of ServiceNow for health care and education services. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(1), 191-196.
17. Vootla, A. (2023). Continuous Accessibility Assurance through DevSecOps-Integrated Testing Pipelines. International Journal of Research and Applied Innovations, 6(6), 9975-9984.
18. 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.
19. Mathew, A., & Alex, H. (2023). From Code to Cure: The Role of AI in Accelerating Drug Discovery. Advances and Challenges in Science and Technology Vol. 2, 94-102.
20. Suvvari, S. K. (2023). Shift Left: Moving the Inclusion of Accessibility Functionalities to the Left in Agile Product Development Life Cycle. Journal of Computational Analysis and Applications, 31(4).
21. Nijaguna, G.S.; Manjunath, D.R.; Abouhawwash, M.; Askar, S.S.; Basha, D.K.; Sengupta, J. Deep Learning-Based Improved WCM Technique for Soil Moisture Retrieval with Satellite Images. Remote Sens. 2023, 15, 2005.
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23. Elminir, H. K., Sabbeh, S. F., ElSoud, M. A., & Gamal, A. (2012). Multi feature content based video retrieval using high level semantic concept. International Journal of Computer Science Issues (IJCSI), 9(4), 254.
24. Balamuralidhar Sarabu, V. (2021). System-of-record governance in enterprise retail platforms: Architectural design principles for financial data ownership and consistency. International Journal of Engineering & Extended Technologies Research (IJEETR), 3(2), 1–16.
25. Ali, M., Hossain, M. S., Rahman, M. W., & Hossain, M. S. (2022). Leveraging Business Analytics to Enhance Supply Chain Resilience and Reduce Disruptions in Critical US Industries. Journal of Business and Management Studies, 4(4), 239-263.
26. Vayyasi, N. K. (2023). Optimizing factory maintenance and downtime prediction through Java-driven AI pipelines. International Journal of Research and Applied Innovations (IJRAI), 6(3).
27. Vankayala, S. C. (2023). Observability-Driven QA for Serverless and PaaS Architectures: A Trace-Informed, SLO-Oriented Benchmarking Framework. International Journal of Science, Engineering and Technology, 11(5).
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29. Prasad, P. K. (2024). AI-driven cloud governance 2.0: Balancing agility, compliance, and operational efficiency in hybrid multi-cloud environments. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(2), 7848–7851.
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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, G. (2023). Intelligent digital government platforms: Leveraging machine learning and cloud architecture for social service delivery. International Journal of Computer Technology and Electronics Communication (IJCTEC), 6(3), 75–92.
33. Yamsani, N. (2021). Governance by design: Secure role delegation and approval structures in enterprise master data systems. International Journal of Science, Engineering and Technology, 9(2). https://doi.org/10.5281/zenodo.18296977
34. Namdeo, A. (2024). Digital twin-driven predictive quality analytics. International Journal of Engineering & Extended Technologies Research (IJEETR), 6(2), 7852–7862. https://doi.org/10.15662/IJEETR.2024.0602009
35. Myakala, P. K., & Naayini, P. (2023). Bridging the Gap: Leveraging Transfer Learning for Low-Resource NLP Tasks. International Journal of Computer Techniques, 10(5).
36. Lanka, S. (2022). Building smarter security systems with AI: Inside Citrix analytics for security. Journal of Advanced Research Engineering and Technology (JARET), 1(2), 93–109. https://doi.org/10.34218/JARET_01_02_009
37. 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.
38. 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
39. 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.
40. Anbazhagan, K., Kumar, R., Thilagavathy, R., & Anuradha, D. (2024, March). Shortest Job First with Gateway-based Resource Management Strategy for Fog Enabled Cloud Computing. In 2024 4th International Conference on Data Engineering and Communication Systems (ICDECS) (pp. 1-6). IEEE.