Retrieval-Augmented LLMs for Security Incident Analysis
Investigating cybersecurity incidents requires collecting and analyzing evidence from multiple log sources, including intrusion detection alerts, network traffic records, and authentication events. This process is labor-intensive: analysts must sift through large volumes of data to identify relevant indicators and piece together what happened. We present a RAG-based system that performs security incident analysis through targeted query-based filtering and LLM semantic reasoning. The system uses a query library with associated MITRE ATT&CK techniques to extract indicators from raw logs, then retrieves relevant context to answer forensic questions and reconstruct attack sequences. We evalua...
| Authors | Xavier Cadet, Aditya Vikram Singh, Harsh Mamania et al. |
|---|---|
| Primary category | cs.CR |
| Published | 18 March 2026 |
| Last updated at source | 4 May 2026 |
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