Artificial Intelligence Based Zero Trust Network

Abstract

Model-based security metrics are an emerging topic of cyber security research that focuses on assessing an information system's risk exposure. We propose an end-to-end solution with the deployment of a zero-trust network utilising Artificial Intelligence in this article to understand the security posture of a system before it is rolled out and as it matures. The major part contains a discussion about the key methods and techniques which was utilized in the development process and simplified operation principles of each developed process. Some developed processes were tested practically to evaluate the problems in the processes. Modules for automatic processing and data analysis were also developed. These modules can be connected in case it is needed. The most important data collection methods were benchmarked to detect problematic situations in the operation in different realistic situations. With the perception from the benchmark test, the problematic parts of the data collection were discovered and proposals for the solution were made which could be developed and tested in the next iterations of the development process. Working Artificial intelligence-based detection and data enrichment methods were created. The results of the article allow multiple continuous research and development projects related to data collection and data analysis with statistical and artificial intelligence-based methods.