Large Language Model Based Security Operations for Automated Enterprise Cloud Incident Analysis and Response

Dr. T. Nalini; Dr. T. Nalini

doi:10.15662/IJSRAT.2024.0705013

Large Language Model Based Security Operations for Automated Enterprise Cloud Incident Analysis and Response

Dr. T. Nalini

Professor, Department of Computer Science & Engineering, SIMATS Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Chennai, India

Abstract

The increasing complexity of enterprise cloud environments has created significant challenges for security operations teams responsible for identifying, analyzing, and responding to cyber incidents. Modern cloud infrastructures generate large volumes of heterogeneous information through audit logs, security alerts, application events, identity records, network telemetry, vulnerability reports, and incident-management systems. Conventional security operations frequently depend on manual investigation and rule-based correlation, resulting in alert fatigue, delayed response, and inconsistent decision-making. Large language models provide an emerging capability for interpreting heterogeneous security information, summarizing incidents, correlating evidence, generating investigation hypotheses, and supporting automated response. This study investigates a large language model-based security operations framework for automated enterprise cloud incident analysis and response. The proposed methodology integrates cloud telemetry, security alerts, threat intelligence, incident records, and contextual asset information into a unified analytical pipeline. Large language models are evaluated for incident classification, evidence correlation, root-cause analysis, threat summarization, response recommendation, and controlled remediation. Retrieval-augmented generation, structured prompts, tool-assisted reasoning, and confidence-aware validation are incorporated to reduce unsupported outputs and improve contextual accuracy. The research evaluates precision, recall, F1-score, response accuracy, investigation time, alert-reduction efficiency, remediation success, hallucination frequency, and computational overhead. Security, governance, explainability, and human-oversight mechanisms are also examined. The study aims to demonstrate how LLM-enabled security operations can augment analysts and automate selected cloud incident-response activities while maintaining appropriate safeguards against erroneous or unsafe actions

Article Information

Journal	International Journal of Science, Research and Technology
Volume (Issue)	Vol. 7 No. 5 (2024): International Journal of Science, Research and Technology (IJSRAT)
DOI	https://doi.org/10.15662/IJSRAT.2024.0705013
Pages	12889-12899
Published	October 10, 2024
Copyright	All rights reserved
Open Access	This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite	Dr. T. Nalini (%2024). Large Language Model Based Security Operations for Automated Enterprise Cloud Incident Analysis and Response. International Journal of Science, Research and Technology , Vol. 7 No. 5 (2024): International Journal of Science, Research and Technology (IJSRAT) , pp. 12889-12899. https://doi.org/10.15662/IJSRAT.2024.0705013

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