Authors - Saurabh Nimje, Anup Bhitre, Sudhir Agarmore, Utkarsha Pacharaney Abstract - Insider threat is a great danger to business security because of trust rights granted insiders, it is not easy to notice their malicious or careless work through the available security programs. This paper is going to examine how Natural Language Processing (NLP) can be used to detect insider threats proactively by analyzing the communication of employees such as emails, chat messages, and internal reports. Applying the CERT Insider Threat Dataset and simulated logs, a multi-level system was created, which includes text preprocessing, feature extraction based on sentiments and semantics, and classification of machine learning models- Random Forest Mean Square Error, SVM, and LSTM. Out of them, LSTM model performed best (92.6% accuracy and overall performance) since it was able to capture contextual and sequential patterns of communication. The most notable indicators of behaviors were sentiments of negativity, passively aggressive language, and frequency of communication efficiency, which indicated a high relationship with insider threat. SHAP (Shapley Additive Explanations) was also used in the given research to allow enhancing insights into model decisions. The results prove the viability of NLP-based solutions as scalable, context-sensitive, and explainable systems to detect insider threats extending the understanding of organizations to perceive behavioral anomalies and reduce the risks to a minimum.
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