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Generative AI-Driven Cloud Operations through Causal Root-Cause Analysis and Intelligent Incident Remediation

Dr. Mohamed Abd El-Basset Matwalli

Faculty of Computers and Informatics, Zagazig University, Egypt

Abstract

The increasing complexity of cloud computing environments has created significant challenges in infrastructure monitoring, incident detection, root-cause identification, and service recovery. Traditional cloud operations rely heavily on rule-based alerts, predefined troubleshooting procedures, and manual investigation, which may result in delayed incident resolution and increased operational costs. This study proposes a generative artificial intelligence (AI)-driven framework for intelligent cloud operations that integrates causal root-cause analysis, large language models (LLMs), multimodal observability data, and automated incident remediation. The framework combines metrics, logs, distributed traces, configuration records, and deployment events to identify relationships between observed failures and their underlying causes. Causal inference techniques distinguish potential causes from simple statistical correlations, while generative AI interprets diagnostic evidence, produces contextual incident summaries, and recommends corrective actions. A risk-aware remediation mechanism validates proposed interventions through policy checks, simulation, and human approval when necessary before executing approved recovery procedures. The proposed methodology is evaluated conceptually using incident detection accuracy, root-cause identification precision, mean time to resolution, remediation success rate, and operational overhead. The framework aims to improve incident response efficiency, reduce repetitive manual troubleshooting, and strengthen reliability across dynamic cloud infrastructures. By integrating causal reasoning with controlled generative AI capabilities, the proposed approach provides a foundation for explainable, adaptive, and trustworthy cloud operations. Experimental validation is required to quantify its effectiveness under realistic workloads and diverse failure scenarios

Article Information

Journal

International Journal of Science, Research and Technology

Volume (Issue)

Vol. 9 No. 5 (2026): International Journal of Science, Research and Technology (IJSRAT)

DOI

https://doi.org/10.15662/IJSRAT.2026.0905003

Pages

1766-1775

Published

September 15, 2026
Copyright

Open Access

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

How to Cite

Dr. Mohamed Abd El-Basset Matwalli (%2026). Generative AI-Driven Cloud Operations through Causal Root-Cause Analysis and Intelligent Incident Remediation. International Journal of Science, Research and Technology , Vol. 9 No. 5 (2026): International Journal of Science, Research and Technology (IJSRAT) , pp. 1766-1775. https://doi.org/10.15662/IJSRAT.2026.0905003

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