Large Language Model Based Security Operations for Automated Enterprise Cloud Incident Analysis and Response
DOI:
https://doi.org/10.15662/IJEETR.2024.0605027Keywords:
large language models, security operations, cloud security, automated incident response, enterprise cybersecurity, incident analysis, retrieval-augmented generation, threat intelligence, security orchestration, artificial intelligence, cloud incident managementAbstract
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.
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