Context-Aware Incident Prioritization Using Large Language Models and Automated Remediation in Enterprise Cloud Systems
DOI:
https://doi.org/10.21590/Keywords:
Context-Aware Incident Management, Large Language Models, Automated Remediation, Enterprise Cloud Systems, Incident Prioritization, Artificial Intelligence, Cloud Monitoring, Intelligent Automation, Root Cause Analysis, Site Reliability Engineering, Operational Intelligence.Abstract
The increasing complexity of enterprise cloud environments has resulted in a growing number of operational incidents involving infrastructure failures, application disruptions, security vulnerabilities, performance degradation, and resource availability issues. Traditional incident management systems primarily depend on predefined rules, static severity classifications, and manual investigation, which may limit their ability to understand complex operational contexts and respond effectively to dynamic cloud conditions. This study proposes a context-aware incident prioritization and automated remediation framework integrating Large Language Models (LLMs), cloud monitoring technologies, and intelligent decision-making mechanisms. The proposed framework analyzes incident descriptions, system logs, monitoring metrics, application dependencies, historical incident records, and service-level agreement requirements to determine incident severity and operational priority. Large Language Models are employed to interpret unstructured incident information, identify relevant contextual relationships, generate incident summaries, and recommend appropriate remediation strategies. A context-aware prioritization mechanism evaluates factors such as business impact, urgency, affected services, infrastructure dependencies, security implications, and recovery requirements. Automated remediation agents execute authorized corrective actions, including service restarts, resource scaling, configuration recovery, and workload redistribution. The proposed methodology emphasizes experimental evaluation using incident classification accuracy, prioritization effectiveness, mean time to resolution, remediation success rate, and operational efficiency. The framework aims to reduce manual intervention, accelerate incident resolution, improve service reliability, and strengthen intelligent operational management across enterprise cloud infrastructures.
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