Machine learning-based risk determination and recommendations for web access

Abstract

A policy-controlled access system comprising a client device running a local application, A mid-link server monitors network traffic from the client device. The network traffic includes third-party content accessed by a user on the client device. A request for data from the end-user is received using the local application, a category associated with the request for the data is determined, and a policy associated with access to the data is determined based on the category. A risk score associated with the data is determined based on the policy using machine learning models. The machine learning models analyze user activities from the network traffic for the determination of the risk score. The risk score is compared with a threshold value and based on the comparison the request is authorized. Machine learning-based recommendations associated with the data are generated. The recommendations include modifications in the policy for access to the data.