The Use of Artificial Intelligence and Machine Learning in Zero Trust Networks

Abstract

This paper focuses on the application of Artificial Intelligence (AI) and Machine Learning (ML) within the context of the Zero Trust (ZT) security model to improve Cybersecurity within the ever-evolving digital landscape. Conventional security models that focus on proactively protecting the perimeter and assuming trust within internal networks are often inadequate against these threats. Zero trust can be characterised as a modern approach resulting from the "never trust, always verify" principle; thus, it implies an unceasing process of the users' authentication and access authorisation. Regarding Zero Trust security, this research builds upon the concept by incorporating AI/ML techniques to enhance threat, anomaly, and predictive detection. The first and foremost is the implementation of deep learning models using an optimised Keras framework better suited for the unique dynamics of the Zero Trust environments. Some of these models successfully differentiate and filter network traffic into normal and malicious categories using state-of-the-art features like dropout characters and dense layers. Briefly discuss some problems and solutions, for instance, data shift and model performance decline in conditions that change with time: transfer learning and periodically, for example, perform retraining of the model. Real-world assessments clearly show that incorporating Artificial Intelligence and Machine Learning into the Zero Trust Architectures enhances the capability to identify and mitigate advanced persistent threats and zero-day attacks. Therefore, this research will form a basis for more work in the area of Artificial Intelligence and Cybersecurity by presenting the knowledge required to establish intelligent security systems that can learn to handle new threats as they emerge effectively in real-time. Specifically, the results highlight how these speeds strengthen Zero Trust security solutions against emerging threats.