Sieve uses an LLM to generate executable queries from natural language security questions grounded by auto-extracted log-format context, cutting error rates over 3x on complex temporal and cross-event tasks versus manual scripting across 133 queries and 5 log types.
Using large language models for template detection from security event logs.International Journal of Information Security, 24, 2025
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Systematic review of 145 papers on LLM-based log analysis, providing a unified taxonomy, common design patterns, evaluation practices, and challenges for deployment under drift and limited labels.
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Parser-Free Querying of Security Logs
Sieve uses an LLM to generate executable queries from natural language security questions grounded by auto-extracted log-format context, cutting error rates over 3x on complex temporal and cross-event tasks versus manual scripting across 133 queries and 5 log types.
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LLM4Log: A Systematic Review of Large Language Model-based Log Analysis
Systematic review of 145 papers on LLM-based log analysis, providing a unified taxonomy, common design patterns, evaluation practices, and challenges for deployment under drift and limited labels.