LLM-Based Threat Detection System 

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.

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.

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.