LLM-Based Threat Detection System
An enterprise-grade LLM-powered cybersecurity system that detects and classifies threats in unstructured log data.
Business Impact
"Reduced mean time to detect (MTTD) security incidents by 67%. False positive rate dropped by 82%, freeing analyst capacity for genuine threats. In a pilot with a 500-seat enterprise, 3 zero-day-style attack patterns were identified in the first week that the existing rule-based system had missed entirely."
The Challenge
Enterprise security teams are buried under millions of log events daily. Legacy SIEM tools miss contextual threats because they rely on rigid rule sets. Analysts spend hours triaging false positives while genuine attack vectors go undetected in the noise.
Our Solution
Yunawise built an LLM-Based Threat Detection System that ingests raw log streams (network, application, authentication) and uses a fine-tuned large language model to classify events, detect anomalous patterns, and surface prioritised alerts with human-readable explanations. The system integrates with existing SIEM pipelines and provides a real-time threat dashboard with drill-down forensics.