Zero Trust Framework In Financial Sector: The Handling Of Machine Learning Based Trust Management

Acceleration in technology transformation for financial sectors has been impacting the way how relevant institutes should store and protect their data and resources from untrusted entities. Zero Trust as an enhanced security model in which every access request must be verified on a per-session basis is believed to be an effective way of providing fine-tuned and granulated secure access control compared to the traditional network perimeter-based security model, where approval of access requests is based on the network location. In this paper, we proposed a machine learning-based trust management model that is integrated into a Zero Trust framework, aiming to develop a practical example specifically for financial sectors by offering a guide on how trust management could be handled in the zero trust architecture.