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 multiple administrator accounts of the end-user is identified based on the category. The multiple administrator accounts are associated with multiple policies to access the data. A correspondence is identified between multiple policies of the multiple administrator accounts. A set of policy conflicts are identified among the policies based on the correspondence and a notification is generated to administrator having the policy conflicts. The policy conflicts are resolved based on suggestions from machine learning (ML) models or the administrators. The request is authorized to access the data.

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 plurality of policies associated with access to the data is determined based on the category. A machine-learning based Uniform Resource Locator (URL) score associated with the data is determined based on URLs extracted from user activities. A machine learning based policy engine preference is generated based on priority levels of the plurality of policies. The access to the data is provided based on the machine-learning based URL score in accordance with the machine learning based policy engine preference.

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 plurality of policies associated with access to the data is determined based on the category. A machine-learning based Uniform Resource Locator (URL) score associated with the data is determined based on URLs extracted from user activities. A machine learning based policy engine preference is generated based on priority levels of the plurality of policies. The access to the data is provided based on the machine-learning based URL score in accordance with the machine learning based policy engine preference.

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.

Abstract: A policy-controlled access system comprising a client device running a local application, a secure tunnel between a client endpoint of the client device, and a mid-link endpoint of a mid-link server to provide network traffic from the client device to the mid-link server. The mid-link server monitors the network traffic, identifies a plurality of policies corresponding to the third-party content, the plurality of policies is based on parental control configuration set by a parent user, stores the plurality of policies corresponding to the third-party content in a local cache on the client device, and receives a request for data from the child user. After the request is made, the local application correlates the third-party content with the plurality of policies stored in the local cache, identifies a policy associated with the request for the data based on correlation, and authorizes the request for the data based on the identified policy.