Due to the abundance and fragmentation of information in the digital economy, the risk management system of an enterprise and its socio-economic ecosystem should rely on such digital technologies, which can be used to gain time to assess and analyze changes in the economic environment. The purpose of this article is to formulate basic approaches to creating a risk level managerial system, including processes for identifying, evaluating and minimizing the risk level in management decision-making developed using artificial neural networks. Using the methods of operational risk management theory, system economic theory, algorithm theory, in particular, artificial neural networks, and immune response modeling, this study shows that the risk management system of a modern enterprise and its socio-economic ecosystem will be based on the principles of functioning of the immune system by analogy with similar systems in living organisms. We model economic risk management processes within four main interacting subsystems: intentional, expectational, cognitive, and functional. The principles that must be observed for the correct use of artificial neural networks in decision-making and for the accumulation of information about the level of possible risk are highlighted. For wide application of artificial neural networks in enterprises, it is necessary to reach a certain level in digital technologies. It is shown that to create a specialized operating system for managing the risk level of an industrial Internet of things (IoT), enterprise or a digital multi-party business platform as a whole may require a separate digital ecosystem. The presented research may be useful for specialists and managers of enterprises when creating risk management systems and management decision support systems using artificial neural network algorithms. The lack of development of the basic level of information technologies at an enterprise limits the application of the results obtained.