Abstract: This article provides an overview of modern methods of information protection, for the implementation of which wavelet signal conversion is used. The main area of application of wavelet transforms is the analysis and processing of signals that are nonstationary in time or inhomogeneous in space, when the result of the analysis must contain not only the general frequency response of the signal, but also information about certain local coordinates on which certain groups of frequency components appear or their rapid changes take place. Prospects for the use of wavelet analysis methods to protect images by embedding watermarks in images and to protect the transmission of speech information, because of speech information often contains personal data, personal information about human life, i.e., information that is not subject to wide publicity, and sometimes contains classified information about the financial or production activities of organizations. Today, there are several methods to protect the transmission of information. Along with digital processing methods, analog methods are used, but analog methods of information protection provide a lower degree of protection than digital. The task is to solve the problm of optimal protection of information in communication channels by the simplest and fastest methods. Wavelet analysis is a promising area of signal processing, as it has a number of advantages over other methods, including Fourier transform. We can identify further promising areas of research, namely the development of more advanced algorithms for protecting information using wavelet transforms, creating wavelet filters that best meet the problem, testing different types of wavelets for different tasks, because of using different types of wavelets allows to detect different features of the signals, so the selection of the best algorithm is subjective. It is necessary to conduct a comparative analysis of different wavelets for one task and choose the wavelet that is most suitable for a particular problem.