Cybersecurity of systems implementing data intensive domains. Approaches to the application of artificial intelligence methods to automate the processes of vulnerability detection, detection and prevention of attacks, response to attacks and recovery after attacks

In the context of the ever-increasing role of automated information support tools in various fields of activity, disruption or termination of such support can lead to serious disruptions to the corresponding business processes. Automated data intensive domains systems (DID systems) mostly belong to the class of critical information infrastructure systems. The implementation of cyber threats can lead to catastrophic consequences. Therefore, ensuring a high level of cybersecurity is one of the priority tasks, for the solution of which artificial intelligence (AI) and machine learning (ML) methods are increasingly used. The article discusses new solutions for automating the processes of ensuring cybersecurity of DID systems using AI and ML. The development of cybersecurity of DID systems is increasingly associated with the massive use of generative neural networks (GNN). The paper lists the main tasks of ensuring cybersecurity, where GNN are used, technologies for their use. The advantage of using the development of GNN – generative adversarial networks (GAN) in the preparation of training data sets is shown. The solutions ensuring the success of their application, the areas of application and the tasks for which it is effective to use the GAN, and the prospects for their further use are defined. The issues of ensuring the necessary qualification of personnel associated with the functioning of the cybersecurity system, the issues of its own security throughout its life cycle are considered.