Sathyanjana, W. W. N. C.Gunawardhane, H. M. K. T.Kumara Samantha, B. T. G. S.Perera, S.2026-01-112025-10-10978-624-6010-14-02783 – 8862https://rda.sliit.lk/handle/123456789/4501Logs are important for diagnosing and understanding the security and operations of IT systems. In these spectacles, the sheer volume of data and their inherent complexity do not allow for an isolated approach. Issues of scalability and adaptability majorly divest most rule-based systems in log analysis. This paper proposes an automatic approach that employs state-of-the-art Large Language Models to detect anomalies, suggest parsing templates, and improve log quality. The suggested system will try to integrate and analyse an Anomaly Detection module for identifying outliers and threats to security, a Pattern Recognition Engine for identifying semantic relations, and a Log Parsing Module for deriving structured patterns. All three collectively serve to enhance efficiency, adaptability, and real-time detection of the log analysis process. Before any LLM-based processing, the results of these preprocessing steps are put through tokenization and normalization. The system was evaluated in a combination of 16 log sources with over 32,000 entries. The model attained an accuracy of 96% in lassification; this shows that it performs well in identifying complex log structures and detecting anomalies. Compared to standard approaches, the framework reduces manual processes and increases interpretability on a large scale across diverse environments of IT. The paper describes a structured approach in AI-powered log analysis, which automates essential procedures to offer improved system reliability, as well as real-time security monitoring. Further directions include real-time streaminganalysis, addressing ethical concerns in log data processing, and enhancing explain ability.enAnomaly DetectionLarge Language Models (LLM)Log AnalysisArticlehttps://doi.org/10.54389/NIAW2551