The field of insider threat detection is moving towards more advanced and innovative approaches, leveraging techniques such as multivariate behavioral signal decomposition, cross-modal fusion, and large language models (LLMs) to improve detection accuracy and efficiency. These methods aim to capture the complex and dynamic nature of insider threats, which often involve subtle and context-dependent behaviors. Furthermore, there is a growing focus on protecting privacy in AI systems, particularly in the context of LLMs, where multi-agent frameworks and simulation-based approaches are being explored to enhance contextual privacy and detect disinformation. Notable papers in this area include: Log2Sig, which proposes a frequency-aware insider threat detection framework, and DMFI, which integrates semantic inference with behavior-aware fine-tuning for LLM-based insider threat detection. Additionally, papers such as ScamAgents and Chimera highlight the potential risks and benefits of using LLMs in security applications, while 1-2-3 Check and MCP-Orchestrated Multi-Agent System demonstrate the effectiveness of multi-agent systems in enhancing privacy and detecting disinformation.