Intelligent Multi-Cloud Security Orchestration Using Machine Learning for Enterprise Infrastructure Protection
Abstract
The rapid adoption of multi-cloud computing has transformed enterprise infrastructure by enabling organizations to distribute applications, data, and workloads across multiple cloud service providers. Although multi-cloud architectures provide flexibility, scalability, resilience, and access to specialized services, they also create complex cybersecurity challenges involving inconsistent security policies, fragmented monitoring, identity management, configuration errors, workload visibility, and incident response. Intelligent security orchestration using machine learning (ML) provides an emerging approach for addressing these challenges by integrating security telemetry from heterogeneous cloud environments, identifying abnormal behavior, prioritizing threats, and supporting automated responses. Machine learning can analyze large volumes of network traffic, identity events, application logs, configuration changes, endpoint activities, and cloud audit records to identify patterns that may indicate malicious or anomalous activity. Security orchestration can then coordinate appropriate actions across cloud platforms, security tools, and enterprise applications. This study examines intelligent multi-cloud security orchestration as an integrated framework for enterprise infrastructure protection. It considers the role of ML-based threat detection, centralized security intelligence, automated incident response, cloud-native security controls, identity and access management, and policy consistency. The study also recognizes challenges related to false positives, model explainability, data quality, interoperability, privacy, adversarial attacks, automation risks, and human oversight. A mixed-methods research methodology is proposed to evaluate how intelligent orchestration influences threat detection, response efficiency, security visibility, policy consistency, and organizational resilience in multi-cloud enterprise environments
Article Information
Journal |
International Journal of Science, Research and Technology |
|---|---|
Volume (Issue) |
Vol. 6 No. 5 (2023): International Journal of Science, Research and Technology (IJSRAT) |
DOI |
|
Pages |
10714-10722 |
Published |
September 19, 2023 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Dr. Randy R. Koon Koon (%2023). Intelligent Multi-Cloud Security Orchestration Using Machine Learning for Enterprise Infrastructure Protection. International Journal of Science, Research and Technology , Vol. 6 No. 5 (2023): International Journal of Science, Research and Technology (IJSRAT) , pp. 10714-10722. https://doi.org/10.15662/IJSRAT.2023.0605026 |
References
2. Koganti, H. (2021). Machine learning-driven performance anomaly detection and auto-tuning in distributed Java full-stack systems: A comprehensive review. International Journal of Research Publications in Engineering, Technology and Management, 4(3), 4977–4986.
3. Badam, L. R. (2023). AI-driven real-time cyberattack detection for critical financial infrastructure. International Journal of Science, Research and Technology (IJSRAT), 6(4), 10374–10383.
4. Punithavathi, R., Selvi, R. T., Latha, R., Kadiravan, G., Srikanth, V., & Shukla, N. K. (2022). Robust node localization with intrusion detection for wireless sensor networks. Intelligent Automation and Soft Computing, 33(1), 143-156.
5. Matrouk, K., V, S., Kumar, S., Bhadla, M. K., Sabirov, M., & Saadh, M. J. (2023). Deep Learning–based Dynamic User Alignment in Social Networks. ACM Journal of Data and Information Quality, 15(3), 1-26.
6. Polamarasetty, V. K. (2022). Enterprise SAP application modernization for post-acquisition business transformation. International Journal of Science, Research and Technology, 5(3), 7777-7783.
7. Ramasamy, M. (2022). A reference architecture for AI-driven network assurance in large-scale enterprise networks. International Journal of Engineering & Extended Technologies Research (IJEETR), 4(1), 4336–4346.
8. Reddy, K. V. (2022). Power-aware verification closure using AI-enhanced UVM and dynamic voltage and frequency scaling models. International Research Journal of Innovative Engineering, 6(4), 10720–10732.
9. Bhattacharjee, B., Khan, M. A. N., Enayet, M. A., Gazi, M. S., Tasnim, M., Jakir, T., ... & Himeluzzaman, M. (2021). Adversarial Machine Learning and Cognitive AI for Autonomous Defense against Advanced Cyber Threats. International Journal of Computer Technology and Electronics Communication, 4(6), 4327-4337.
10. Selvarajan, K. (2022). Cloud-native architectures for high-throughput distributed data processing. International Journal of Emerging Trends in Engineering and Management Research (IJETEMR), 7(5), 12538–12548.
11. Mishra, D., Behera, D., Challa, A., & Vemu, C. (2021). PDCloudEX software defined network. In Integrated emerging methods of artificial intelligence & cloud computing (pp. 66–70). Springer International Publishing.
12. Kavuru, R. R., & Balaji, R. (2023). Extending the service mesh into a compliance enforcement fabric: A policy-aware architecture for regulated financial platforms. International Journal of Emerging Trends in Engineering and Management Research, 8(3), 13612–13618.
13. Venkatasalam, K., Rajendran, P., & Thangavel, M. (2019). Improving the accuracy of feature selection in big data mining using accelerated flower pollination (AFP) algorithm. Journal of medical systems, 43(4), 96.
14. Shalk, N. (2022). Beyond vendor lock-in: Building intelligent multi-cloud Kubernetes orchestration. International Research Journal of Innovative Engineering, 6(2), 9985–9996.
15. Neela, S. (2022). Semantic Interoperability in Real-Time Enterprise Integration Using Middleware Abstractions. American International Journal of Computer Science and Technology, 4(6), 56-66.
16. Yadavalli, V. R. (2022). Continuous trust scoring for cross-layer access governance in S/4HANA, Fiori, and SAP BTP. International Journal of Engineering & Extended Technologies Research, 4(1), 4347–4357.
17. 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.
18. Devineni, A., Pochincharla, S., & Muppalla, L. K. (2022). A telemetry-fusion framework for proactive incident detection in multi-tenant financial cloud platforms. International Journal of Advances in Signal and Image Sciences, 13–18.
19. Tarigoppula, S. (2022). Zero-trust security architecture for hybrid enterprise cloud environments using AWS native services. International Research Journal of Innovative Engineering, 6(6), 11433–11444.
20. Raja, G. V., & Mali, R. K. (2021). Federated learning frameworks for privacy-preserving artificial intelligence applications. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 4(3), 4946-4950.
21. 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.
22. Basavala, S. R. (2021). Threat modeling and security requirements specifications for enterprise application development. International Research Journal of Innovative Engineering, 5(1), 7498–7508.
23. Roy, S., & Sen, A. (2020, September). Identification of the K-most Vulnerable Entities in a Smart Grid System. In 2020 3rd International Conference on Advanced Communication Technologies and Networking (CommNet) (pp. 1-6). IEEE.
24. Chinnam, N. B. (2022). Engineering real-time supply chain visibility and ETA intelligence platforms for large-scale retail distribution networks. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(6), 7918–7922.
25. 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.
26. RamMohan Reddy Kundavaram. (2019). Utilizing AI and machine learning in cloud systems for advanced automation. Economic Sciences, 15(1), 28–38. https://doi.org/10.69889/4tx3gc35
27. Soundappan, S. J. (2021). Integrated Artificial Intelligence Framework for Enterprise Cloud Modernization and Intelligent Threat Management Systems. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 4(4), 5274-5284.
28. Mathew, A. (2021). Artificial intelligence and cognitive computing for 6G communications & networks. International Journal of Computer Science and Mobile Computing, 10(3), 26-31.
29. Sugumar, R. (2021). Generative AI Pipelines for Safety Validation of Autonomous Driving Models. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 4(5), 5466-5469.
30. 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.
31. Bitragunta, S. L. V. (2022, November 20). High level modeling of high-voltage gallium nitride (GaN) power devices for sophisticated power electronics applications. Journal of Artificial Intelligence, Machine Learning and Data Science, 1(1), 2011–2015.
32. Bhattacharjee, B., Khan, M. A. N., Enayet, M. A., Gazi, M. S., Tasnim, M., Jakir, T., ... & Himeluzzaman, M. (2021). Adversarial Machine Learning and Cognitive AI for Autonomous Defense against Advanced Cyber Threats. International Journal of Computer Technology and Electronics Communication, 4(6), 4327-4337.
33. Alazab, M., et al. (2021). Privacy preservation in federated learning: An insightful survey from the GDPR perspective. Computers & Security, 110, 102402. https://doi.org/10.1016/j.cose.2021.102402