# Insight-LLM: LLM-enhanced Multi-view Fusion in Insider Threat Detection

arXiv: https://arxiv.org/abs/2509.01509 · HTML: https://mlsanity.com/paper/2509.01509/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Sep 2025.

## Built on by (most cited first, 1 of 1)

- [LLMs for Zero-Shot Threat Detection via Structured Risk Indicators](https://mlsanity.com/paper/2608.16508/) (2026)

## Abstract (arXiv)

Insider threat detection (ITD) requires analyzing sparse, heterogeneous user behavior. Existing ITD methods predominantly rely on single-view modeling, resulting in limited coverage and missed anomalies. While multi-view learning has shown promise in other domains, its direct application to ITD introduces significant challenges: scalability bottlenecks from independently trained sub-models, semantic misalignment across disparate feature spaces, and view imbalance that causes high-signal modalities to overshadow weaker ones. In this work, we present Insight-LLM, the first modular multi-view fusion framework specifically tailored for insider threat detection. Insight-LLM employs frozen, pre-nes, achieving state-of-the-art detection with low latency and parameter overhead.