AI-Driven Cloud Governance 2.0: Balancing Agility, Compliance, and Operational Efficiency in Hybrid Multi-Cloud Environments

Authors

  • Prashant Kumar Prasad Vice President, USA Author

DOI:

https://doi.org/10.15662/1ssn7f86

Keywords:

Multi-Cloud, Cloud Governance, Hybrid, Agility, Operations, Compliance, AI

Abstract

The paper discusses the way AI-informed cloud governance can enable organisations to control hybrid multi-clouds setting without a disruption in the balance between agility, compliance, and operational efficiency. The paper explains the way AI will aid in policy enforcement, resources control, security surveillance, and automate workloads. The results indicate that AI will cut on manual work, improve the accuracy of the decisions, and develop faster response to risks and performance issues. Resulting challenges also indicate, however, poor quality of data, skills deficiency, and inconsistency in policy implementation. It echoes the findings of the paper that AI-Driven Cloud Governance 2.0 can prove to be a potent asset provided with vivid frameworks, dependable data and ongoing organisational orientation

References

[1]Polu, O. R. (2021). AI-DRIVEN GOVERNANCE FOR MULTI-CLOUD COMPLIANCE: AN AUTOMATED AND SCALABLE FRAMEWORK. International Journal of Cloud Computing, 1(4), 1–13.https://doi.org/10.34218/ijcc_01_04_001

[2] Zhong, Z., Xu, M., Rodriguez, M. A., Xu, C., & Buyya, R. (2021). Machine Learning-based Orchestration of

Containers: A Taxonomy and future directions. arXiv (Cornell University).https://doi.org/10.48550/arxiv.2106.12739

[3] Zhang, X., Lin, Q., Xu, Y., Qin, S., Zhang, H., Qiao, B.,Dang, Y., Yang, X., Cheng, Q., Chintalapati, M., Wu, Y.,Hsieh, K., Sui, K., Meng, X., Xu, Y., Zhang, W., Shen, F.,Zhang, D., Nanjing University, . . . Microsoft. (2019). Crossdataset Time Series anomaly detection for cloud systems. In Proceedings of the 2019 USENIX Annual Technical

Conference.https://www.usenix.org/conference/atc19/presentation/zhang-xu

[4] Hong, J., Dreibholz, T., Schenkel, J. A., Hu, J. A., SimulaMet,& Durham University. (2019). An overview of Multi-CloudComputing. https://webbackend.simula.no/sites/default/files/2024-06/M2EC2019-MultiCloud.pdf

[5] Sauvanaud, C., Kaâniche, M., Kanoun, K., Lazri, K., & DaSilva Silvestre, G. (2018). Anomaly detection and diagnosisfor cloud services: Practical experiments and lessons learned.Journal of Systems and Software, 139, 84–106.

https://doi.org/10.1016/j.jss.2018.01.039

[6] Hasan, M. M., Bhuiyan, F. A., & Rahman, A. (2020). Testingpractices for infrastructure as code. Testing Practices forInfrastructure as Code, 7–12.https://doi.org/10.1145/3416504.3424334

[7] A survey on Hybrid and Multi-Cloud Environments:integration strategies, challenges, and future directions.

(2021). International Journal of Computer Technology andElectronics Communication (IJCTEC), 3219–3220.

https://www.researchgate.net/publication/397430770_A_Sur

vey_on_Hybrid_and_MultiCloud_Environments_Integration_Strategies_Challenges_and_Future_Directions

[8] Vasamsetty, C., Anthem Inc, Palanisamy, P., & SNS Collegeof Technology. (2019). ANOMALY DETECTION IN

CLOUD HEALTHCARE NETWORKS USING DEEPLEARNING [Journal-article]. International Journal ofBusiness Management and Economic Review, Vol. 2(No.02), 54. http://doi.org/10.35409/IJBMER.2019.2088_1

[9] Padur, S. K. R. (2021). From Control to Code : Governance Models for Multi-Cloud ERP Modernization. ijsrcseit.com.

https://doi.org/10.32628/CSEIT218356

[10] Henriques, J., Caldeira, F., Cruz, T., & Simões, P. (2022). Anautomated closed-loop framework to enforce security policies from anomaly detection. Computers & Security, 123, 102949.https://doi.org/10.1016/j.cose.2022.102949

Downloads

Published

2024-03-31

How to Cite

AI-Driven Cloud Governance 2.0: Balancing Agility, Compliance, and Operational Efficiency in Hybrid Multi-Cloud Environments. (2024). International Journal of Engineering & Extended Technologies Research (IJEETR), 6(2), 7848-7851. https://doi.org/10.15662/1ssn7f86