  Insider threats are a growing concern for organizations due to the amount of
damage that their members can inflict by combining their privileged access and
domain knowledge. Nonetheless, the detection of such threats is challenging,
precisely because of the ability of the authorized personnel to easily conduct
malicious actions and because of the immense size and diversity of audit data
produced by organizations in which the few malicious footprints are hidden. In
this paper, we propose an unsupervised insider threat detection system based on
audit data using Bayesian Gaussian Mixture Models. The proposed approach
leverages a user-based model to optimize specific behaviors modelization and an
automatic feature extraction system based on Word2Vec for ease of use in a
real-life scenario. The solution distinguishes itself by not requiring data
balancing nor to be trained only on normal instances, and by its little domain
knowledge required to implement. Still, results indicate that the proposed
method competes with state-of-the-art approaches, presenting a good recall of
88\%, accuracy and true negative rate of 93%, and a false positive rate of
6.9%. For our experiments, we used the benchmark dataset CERT version 4.2.
