Generative AI-Based Incident Management for Cloud Enterprise Systems Through Causal Inference and Automated Remediation
DOI:
https://doi.org/10.15680/IJCTECE.2026.0905001Keywords:
Generative AI, Incident Management, Causal Inference, Automated Remediation, Cloud Enterprise Systems, Root Cause Analysis, AIOpsAbstract
Cloud enterprise systems increasingly depend on distributed applications, interconnected services, and dynamic infrastructure, making incident detection, diagnosis, and resolution complex operational challenges. Traditional incident management approaches frequently rely on predefined rules, manual investigation, and correlation-based analytics, which may struggle to identify underlying causes and recommend effective corrective actions. This research proposes a generative artificial intelligence-based incident management framework that integrates causal inference, intelligent diagnosis, and automated remediation for cloud enterprise environments. The framework collects infrastructure metrics, application logs, distributed traces, configuration changes, and service dependency information to construct a comprehensive representation of operational incidents. Causal inference techniques analyze relationships between system events and performance degradation, helping distinguish potential causes from coincidental correlations. Generative AI processes the resulting evidence to summarize incidents, explain diagnostic findings, recommend remediation procedures, and generate structured response plans. A policy-controlled execution layer validates proposed actions against operational constraints, security permissions, and service availability requirements before implementation. The methodology evaluates incident classification accuracy, root-cause identification quality, remediation success rate, response time, and operational overhead. Comparative experiments against conventional monitoring and rule-based incident management approaches are proposed to assess the framework's effectiveness. Human oversight, auditability, and rollback mechanisms are incorporated to minimize the risks associated with incorrect AI-generated recommendations. The proposed framework aims to improve incident response efficiency, strengthen service reliability, and support evidence-based operational decisions across cloud enterprise systems. Its practical benefits require validation through reproducible experiments and controlled incident scenarios.
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