Chain-of-Thought Augmented Fine‑Tuning of a Distilled Llama‑8B Model for SIEM Detection Query Generation

Abstract

Writing effective security detection rules for SIEM systems is a complex and timeconsuming task that traditionally requires deep domain expertise.One of the persistent challenges in applying AI to security operations (SecOps) is the scarcity of high-quality, domain-specific datasets that can support the development of accurate and reliable models.This paper addresses that gap by presenting a method to both generate such a dataset and leverage it to fine-tune a compact reasoning-oriented model (DeepSeek R1 Distill Llama 8B) using Low Rank Adaptation (LoRA).As part of our contributions, we detail the creation of a curated, high-quality (of 1206 entries from 106 detection rule) dataset specifically tailored for SIEM text-to-query tasks, which enabled effective fine-tuning of the model.A key feature of this dataset is the augmentation of each training example with chain-ofthought (CoT) rationales: step-by-step explanations linking the natural language description of a detection rule to the resulting Lucene query.These rationales, produced by a stronger teacher model (Claude sonnet 4), are used to supervise the smaller student model.We describe the data pipeline, prompt templates, and LoRA configuration, and we report an initial human evaluation showing that CoT augmentation improves the reliability of textquery generation without increasing computational cost.Despite its compact size, our fine-tuned model outperformed several large proprietary language models in both query accuracy and reasoning quality.DeepSeek R1 Distill Llama 8B was chosen as a small model with reasoning capabilities, while Claude Sonnet 4 was selected for its strong ability to generate rationales.However, the research remains applicable to other small reasoning models and large, more capable models, respectively.