The article considers mathematical models for the collection and processing of voice content, on the basis of which a fundamentally logical scheme for predicting synthetic voice deepfakes has been developed. Experiments have been conducted on selected mathematical formulas and sets of python programming language libraries that allow real-time analysis of audio content in an organization. The software capabilities of neural networks for detecting voice fakes and generated synthetic (artificial) speech are considered and the main criteria for the study of voice messages are determined. Based on the results of the experiments, a mathematical apparatus has been formed that is necessary for positive solutions to problems of detecting voice deepfakes. A list of technical standards recommended for collecting voice information and improving the quality of information security in the organization has been formed.

Keywords: neural networks, detection of voice defects, information security, synthetic voice speech, voice deepfakes, technical standards for collecting voice information, algorithms for detecting audio deepfakes, voice cloning