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 capacity resolver system in a cloud-based multi-tenant system includes point of presence (POP) systems and a cloud orchestration server. The POPs include hypervisors and the hypervisors includes nodes. A request for provisioning a node in a POP is received. Parameters are received from the hypervisors of the POP. Triggering of parameters above respective threshold values is determined. Downsizing or migration of one or more nodes based on the triggering of the one or more parameters is determined. The downsizing includes reduction in provisioned CPU core utilization or memory utilization that are determined to be underutilized. The migration includes the nodes migrated from the hypervisor to another hypervisor. The downsizing has higher priority of selection than the migration. Based on the selection of the downsizing or the migration of the one or more nodes, the requested node is provisioned at the hypervisor of the POP.

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 capacity resolver system for provisioning and management of nodes at point of presence (POP) in a cloud-based multi-tenant system. The capacity resolver system includes a plurality of POPs and a cloud orchestration server. The POPs include hypervisors that include a plurality of nodes. The cloud orchestration receives a request for provisioning a node. The request is provisioned at the POP based on parameters from the hypervisors of the POP. The parameters include Central Processing Unit (CPU) Core utilization, memory utilization, disk utilization and Virtual File System (VFS) availability of the node. A triggering of one or more parameters above their respective threshold values is determined at the POP. Nodes are identified for downsizing or migration based on the triggering of the one or more parameters. The node is provisioned at the hypervisor of the POP in accordance with a priority for the downsizing or the migration of the nodes.

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.