AIOps for log anomaly detection in the era of LLMs: A systematic literature review

Modern IT systems generate large volumes of log data that challenge timely and effective anomaly detection. Traditional methods often require intensive feature engineering and struggle to adapt to dynamic operational environments. This Systematic Literature Review (SLR) analyzes how Artificial Intelligence for IT Operations (AIOps) benefits from advanced language models, emphasizing Large Language Models (LLMs) for more effective log anomaly detection. By comparing state-of-art frameworks with LLM-driven methods, this study reveals that prompt engineering – the practice of designing and refining inputs to AI models to produce accurate and useful outputs – and Retrieval Augmented Generation (RAG) boost accuracy and interpretability without extensive fine-tuning. Experimental findings demonstrate that LLM-based approaches significantly outperform traditional methods across evaluation metrics that include F1-score, precision, and recall. Furthermore, the integration of LLMs with RAG techniques has shown a strong adaptability to changing environments. The applicability of these methods also extends to the military industry. Consequently, the development of specialized LLM systems with RAG tailored for the military industry represents a promising research direction to improve operational effectiveness and responsiveness of defense systems. • We review LLM-based techniques for anomaly detection in AIOps systems. • We explores RAG to enhance LLMs’ task-specific reasoning in IT operations. • We identify key research gaps in AI-powered log anomaly detection. • We propose a framework for LLM and RAG use in IT system diagnostics.