One of the toughest cybersecurity issues today are insider threats for which no good solution has been provided to limit their damage through commonly used security solutions. Which could cause serious damage to the assets of the organization. This article begins by presenting a comprehensive multidisciplinary survey of insider threats and the various AI technologies in use. Then, we proposed a method to detect internal threats by analyzing user behavior. A series of activities and events that are analyzed to extract features to effectively detect malicious activity from normal activity. The selected feature vectors are used to train the model. The convolutional neural network (CNN) is used during the implementation phase to detect insider threats of fixed-size feature vectors. The data used to conduct the experiments is public data related to internal threats cert r4.2. The experimental results of the proposed model show that it can successfully detect insider threats with an accuracy of 0.9558 in the best cases.