LLM-Based Threat Detection System
AI-Driven Cybersecurity Intelligence Powered by a Custom-Trained LLM
A cybersecurity intelligence system that uses a custom-trained large language model to analyze logs, network traffic, and threat intelligence feeds, flagging anomalies and emerging threats in natural language.
Understanding the Problem
Security teams are overwhelmed by high volumes of raw log and alert data from traditional rule-based systems, leading to alert fatigue and missed or delayed detection of novel, non-signature-based threats.
Engineering the Solution
Developed a custom-trained LLM pipeline that ingests security logs, network events, and threat intel feeds, correlates patterns beyond static signature matching, and generates human-readable threat summaries and prioritized alerts for analysts, reducing the manual triage burden.
Driving Business Impact
Improved detection of nuanced/novel threats missed by rule-based systems, while cutting analyst triage time through natural-language threat summaries instead of raw log review.