Advancing Autonomous Cyber Threat Detection and Incident Response Using Explainable Artificial Intelligence and Large Language Models in Enterprise Computer Networks
DOI:
https://doi.org/10.21590/Keywords:
Explainable Artificial Intelligence (XAI), Large Language Models (LLMs), Autonomous Cyber Threat Detection, Incident Response Automation, Enterprise Network Security, Threat Intelligence, Cybersecurity Analytics.Abstract
The rapid evolution of enterprise cyber threats has exposed the limitations of conventional security systems that rely heavily on static rules and signature-based detection. This study explores the integration of Explainable Artificial Intelligence (XAI) and Large Language Models (LLMs) to advance autonomous cyber threat detection and incident response in enterprise computer networks. The proposed framework combines explainable machine learning, intelligent threat correlation, natural language reasoning, and automated incident response to improve the accuracy, transparency, and timeliness of cybersecurity operations. By enabling interpretable decision-making and contextual analysis of security logs, network traffic, and threat intelligence, the framework enhances analyst trust while reducing response time to sophisticated cyberattacks. Furthermore, the study examines the role of XAI in mitigating black-box limitations and investigates how LLMs support security operations through automated threat analysis, root cause identification, and adaptive defense strategies. The findings suggest that integrating explainable intelligence with autonomous cybersecurity systems strengthens enterprise resilience, improves operational efficiency, and provides a scalable foundation for next-generation cyber defense in increasingly complex digital environments.
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