Abstract
Cloud computing for business-critical enterprise workloads poses considerable security, compliance, and operations risks. Organizations must overcome these risks to use cloud-based systems securely and successfully. This research provides a structured risk mitigation framework that aims to quantify, assess, and counteract threats in multi-clouds. This research offers an end-to-end approach to secure cloud adoption by discovering crucial risk factors, analyzing countermeasure solutions, and evaluating performance impacts. The framework integrates artificial intelligence (AI)-based risk modeling, predictive analytics, and compliance automation to support better decision-making. AI-based risk assessment facilitates proactive vulnerability detection, whereas predictive analytics identifies likely failures in advance. Moreover, compliance automation guarantees round-the-clock conformity to regulatory norms, minimizing the intricacies involved in manual security management. Firms can use this model in various cloud environments to increase resiliency, automate security features, and augment compliance efforts. The research also evaluates the effectiveness of different risk avoidance techniques within real-world cloud implementations, with empirical evidence for best practices. Based on the study, dynamic based on the study, dynamic risk evaluation and automated response strategies are essential tools in securing business cloud infrastructures. This research contributes to the knowledge base by providing an AI-based, scalable approach to cloud risk management. The proposed approach allows organizations to move to the cloud confidently, with security, regulatory compliance, and business efficiency in a dynamic digital world.
Keywords
Cloud Migration, Enterprise Workloads, Risk Mitigation, AI-Driven Risk Modeling, Compliance, Multi-Cloud Security
Citations
IRE Journals:
Adedamola Abiodun Solanke, Ph.D.
"Cloud Migration for Critical Enterprise Workloads: Quantifiable Risk Mitigation Frameworks" Iconic Research And Engineering Journals Volume 4 Issue 11 2021 Page 295-309
IEEE:
Adedamola Abiodun Solanke, Ph.D.
"Cloud Migration for Critical Enterprise Workloads: Quantifiable Risk Mitigation Frameworks" Iconic Research And Engineering Journals, 4(11)
About IRE Journals
IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
Important Links
For Authors
Volume 7:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 6:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 5:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 4:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 3:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12