Skip to main content

AI-Driven Cyber Risk Intelligence for Adaptive Security Management in Distributed Enterprise Cloud Infrastructure

Dr.K.Dinakaran

Professor, Department of AIDS, S.A.Engineering College, Chennai, India

Abstract

The increasing adoption of distributed enterprise cloud infrastructure has introduced complex cybersecurity challenges associated with dynamic workloads, interconnected applications, distributed identities, and evolving cyber threats. Traditional security management approaches often depend on static policies, predefined threat signatures, and isolated monitoring systems, limiting their ability to identify emerging risks and respond to sophisticated attacks. This research proposes an AI-driven cyber risk intelligence framework for adaptive security management across distributed enterprise cloud environments. The proposed framework integrates artificial intelligence, machine learning, predictive analytics, behavioral monitoring, and threat intelligence correlation to identify, assess, prioritize, and mitigate cybersecurity risks. The architecture collects security information from cloud infrastructure, network traffic, identity management platforms, application logs, and vulnerability assessment systems. Machine learning algorithms analyze historical and real-time security events to identify anomalous activities, predict potential attack scenarios, and estimate infrastructure risk levels. Intelligent risk scoring combines threat severity, vulnerability exposure, access behavior, and asset criticality to support adaptive security decisions. Furthermore, automated policy recommendations enable dynamic access control, threat containment, and continuous security posture optimization. The framework emphasizes explainability, scalability, operational efficiency, and integration with enterprise security operations. By combining predictive cyber intelligence with adaptive policy enforcement, the proposed approach aims to strengthen threat visibility, improve risk prioritization, reduce security response delays, and enhance the resilience of distributed enterprise cloud infrastructure.

Article Information

Journal

International Journal of Emerging Trends in Engineering and Management Research

Volume (Issue)

Vol. 9 No. 5 (2024): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR)

DOI

https://doi.org/10.15662/ijetemr.2024.0905007

Pages

16482-16490

Published

October 16, 2024
Copyright

Open Access

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Dr.K.Dinakaran (%2024). AI-Driven Cyber Risk Intelligence for Adaptive Security Management in Distributed Enterprise Cloud Infrastructure. International Journal of Emerging Trends in Engineering and Management Research , Vol. 9 No. 5 (2024): International Journal of Emerging Trends in Engineering and Management Research (IJETEMR) , pp. 16482-16490. https://doi.org/10.15662/ijetemr.2024.0905007

References

1. Wu, C., Horiuchi, S., Murase, K., Kikushima, H., & Tayama, K. (2021). Intent-driven cloud resource design framework to meet cloud performance requirements and its application to a cloud-sensor system. Journal of Cloud Computing, 10, Article 30. https://doi.org/10.1186/s13677-021-00242-w
2. Collins, C., Dennehy, D., Conboy, K., & Mikalef, P. (2021). Artificial intelligence in information systems research: A systematic literature review and research agenda. International Journal of Information Management, 60, Article 102383. https://doi.org/10.1016/j.ijinfomgt.2021.102383
3. 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.
4. Kalabhavi, V. (2024). Promotional spend analytics using AI for PFME allocation and coupon redemption optimization. International Journal of Emerging Trends in Engineering and Management Research, 9(2), 15406.
5. Sahu, S. K. (2022). Automating insurance benefit interpretation: A domain-intelligence pipeline for document extraction, benefit classification, and plan recommendation. International Journal of Research and Applied Innovations, 5(6), 8252–8259.
6. Kommineni, R. (2022). Monitoring as code for self-hosted Elasticsearch: Independent, delta-based lifecycle management of alerting watchers and dashboards across multi-cloud environments. International Journal of Science, Research and Technology (IJSRAT), 5(2), 7437–7446.
7. Kopparthi, G. S. (2022). AI in healthcare: Unlocking the potential of predictive analytics. Journal of Computational Analysis and Applications, 30(2), 1183–1193. https://eudoxuspress.com/index.php/pub/article/view/5508
8. Randhi, K. S. (2021). Design and implementation of an API-driven hybrid integration framework for real-time utility inventory systems. International Journal of Emerging Trends in Engineering and Management Research, 6(2), 9281–9294.
9. Mole, M. (2024). Modern business intelligence: Big data analytics and artificial intelligence for creating the data-driven value. International Research Journal of Innovative Engineering, 8(4), 15010–15022.
10. 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.
11. Bandari, U., & Thilagaraj, S. V. (2023). Defensive and offensive data strategy in manufacturing M&A: A comparative evaluation of data lake, data fabric, and data mesh integration patterns. International Journal of Research and Applied Innovations, 6(1), 8403–8413.
12. Rengarajan, A. (2022). Developing AI Powered Enterprise Integration Platforms Through Cloud Native Observability and Automated Governance. International Journal of Science, Research and Technology, 5(6), 8970–8977.
13. Ahuja, D. (2023). Multi-cloud management strategies: Tools, challenges, and implementation methods. Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol, 9(10), 493–506.
14. Chinthamani, R. (2023). Engineering resilient microservices for real-time retail order and inventory synchronization. International Journal of Engineering & Extended Technologies Research, 5(6), 7774–7779.
15. Sugumar, R. (2022). Explainable Deep Learning Framework for Financial Fraud Detection and Intelligent Risk Analysis. International Journal of Emerging Trends in Engineering and Management Research, 7(5), 12528.
16. Reddy, K. V. (2021). UVM methodology for industry-specific applications using AI in hardware verification. International Journal of Science, Research and Technology, 4(3), 5731–5744.
17. Shaik, N. (2024). Right-sizing at scale: A data-driven approach to Kubernetes cost optimization. International Journal of Future Innovative Science and Technology, 7(1), 12068–12078.
18. Bandhela, R. R., & Kannan, V. (2021). Leveraging generative AI and large language models for secure and efficient healthcare data management. Journal of Informatics Education and Research, 1(3).
19. Ramasamy, M. (2022). Architecting scalable intent-based networking platforms for enterprise automation. International Journal of Emerging Trends in Engineering and Management Research (IJETEMR), 7(3), 11812–11823.
20. Mathew, A. R. (2019). Cyber-infrastructure connections and smart gird security. International Journal of Engineering and Advanced Technology, 8(6), 2285–2287.
21. Kalakoti, M. K. R. (2023). Interference-aware resource scheduling for cloud microservices on Kubernetes. International Journal of Future Innovative Science and Technology, 6(1), 9900–9909.
22. Puli, S. K. (2023). A fail-closed architecture for cryptographically enforced recipient authorization in clinical trial data delivery. International Journal of Information Technology and Management Information Systems (IJITMIS), 14(1), 148–164.
23. Karrothu, A. (2024). Automated risk-based security controls for vulnerability detection and prevention across cloud-native software development lifecycles. International Research Journal of Innovative Engineering, 8(4), 14994–15009.
24. Selvarajan, K. (2022). Architecting scalable self-service data platforms for enterprise analytics. International Journal of Science, Research and Technology (IJSRAT), 5(2), 7427–7436.
25. Kannan, K. S., Sunitha, G., Deepa, S. N., Babu, D. V., & Avanija, J. (2022). A multi-objective load balancing and power minimization in cloud using bio-inspired algorithms. Computers and Electrical Engineering, 102, 108225.
26. Basavala, S. R. (2022). Security assessment framework for microservices-based applications using automated penetration testing. International Journal of Emerging Trends in Engineering and Management Research, 7(4), 12179–12190.
27. Natta, P. K. (2022). Computer-vision-assisted retail inventory automation: Integrating autonomous scan data with worklist completion systems. International Journal of Science, Research and Technology, 5(6), 8957–8969.
28. Karuturi, Y. (2023). Energy-aware capacity governance for large-scale SAP HANA deployments. International Journal of Research and Applied Innovations, 6(3), 6000–6009.
29. Gopinathan, V. R. (2022). Advanced Predictive Decision Intelligence Using Machine Learning for Strategic Enterprise Management Platforms. International Journal of Research and Applied Innovations, 5(6), 8162–8169.
30. Mysula, S. (2023). Performance optimization of distributed enterprise integration workflows across heterogeneous applications. International Journal of Engineering & Extended Technologies Research, 5(1), 6003–6008.
31. Jinna, K. R., & Subramani, S. K. (2022). Supply-chain-aware CI/CD for regulated enterprises: Integrating artifact signing, secret injection and promotion gates in Kubernetes-native pipelines. International Journal of Computer Technology and Electronics Communication, 5(6), 16275–16285.
32. Soundappan, S. J. (2021). DataOps: Orchestrating Reliable ML Data Pipelines. International Journal of Research and Applied Innovations, 4(4), 5533–5537.
33. Raja, G. V. (2022). AI-Driven Enterprise Architecture Integrating Zero-Trust Security for Secure Scalable and Compliant Cloud-Native Platforms. International Journal of Emerging Trends in Engineering and Management Research, 7(4), 12178.
34. Gadige, C. D. (2023). Scalable CRM automation for high-volume customer onboarding: An event-driven Salesforce architecture. International Journal of Emerging Trends in Engineering and Management Research, 8(6), 14772.
35. Vemireddy, S. (2022). Architecting personalized tax advisory experiences through event-driven customer data integration. International Journal of Research Publications in Engineering, Technology and Management, 5(6), 7923–7929.
36. Chandran, A. S. (2023). A unified Spark ETL stack across Cloudera CDP, Azure HDInsight, and Databricks. International Journal of Future Innovative Science and Technology, 6(6), 11769–11779.