Introduction. Generalization of biological, social, and psychological predictors of self-destructive behavior is one of the main tasks for world health care at the present time. For this reason, the development of an integrative model of self-destructive behavior, taking into account the diversity of biopsychosocial factors, as well as their representation in the virtual environment, will be highly relevant. The integrative model can be used to create automated systems for analyzing socio-cultural risk factors of self-destructive behavior, as well as helping specialists to create prevention, maintenance and correction programs. The purpose of the study is to describe and generalize the factors of the real and virtual environment that mediate the risk of developing self-destructive behavior and to create a hierarchical generalized model of self-destructive behavior based on the synthesis of biopsychosocial and cyberpsychological paradigms. Materials and methods. For a holistic analysis of the basic concepts and the creation of a general model of selfdestructive behavior, we used a combined approach that includes content analysis to describe the factors mediating self-destructive behavior, the method of expert assessments for the selection of significant factors, mathematical and statistical analysis for processing the data obtained (methods of descriptive statistics, cluster analysis (Ward method)). The results of the study. As a result of cluster analysis , the following components of the self-destructive behavior model were identified: 1) "Peculiarities of self-attitude" (23.2%) (the dynamic component, depending on the specific situation, is highly significant for face-to-face diagnosis; 2) "Endogenous factors of self-destructive behavior" (20.2%) (violations of the emotional-volitional sphere); 3) "Exogenous factors of self-destructive behavior" (21.4%) (negative factors of upbringing); 4) "Negative factors of the socio-cultural environment" (17.3%) (traumatic micro- and macrofactors that can provoke the occurrence of self-destructive symptoms); 5) "Individual characteristics developed in the process of personality formation" (14.2%) (individual predictors of self-destruction); 6) "Factors of isolation from the social environment" (3.6%) (a number of personal predictors that prevent open active interaction with others and getting help). Conclusion. A number of new data describing the structure of the generalized model of self-destructive behavior and the main directions of risk analysis have been obtained. The leading factors of the micro- and macro-socio-cultural environment are identified, representing a wide range of markers available for study through the analysis of virtual communication. The possibility of combining a biopsychosocial model and the capabilities of a cyberpsychological approach to create a dynamic digital monitoring model that allows for a general risk assessment for large samples to minimize the resources of individual diagnostics is shown.