Omnissa, LLC

Arjun Kochhar

Ramani Panchapakesan

Suman Aluvala

Rohit Pradeep Shetty

Amit Kumar Yadav

Pavan Rajkumar Rangain

Rajneesh Kesavan

Aman Srivastava

Examples described herein include systems and methods for performing distributed encryption across multiple devices. An example method can include a first device discovering a second device that shares a network. The device can identify data to be sent to a server and calculate a checksum for that data. The device can then split the data into multiple portions and send a portion to the second device, along with a certificate associated with the server for encrypting the data. The first device can encrypt the portion of data it retained. The first device can receive an encrypted version of the second portion of the data sent to the second device. The first device can merge these two portions and send the merged encrypted data to the server, along with the checksum value. The server can decrypt the data and confirm that it reflects the original set of data.

Distributed encryption

Disclosed are various approaches for remote working experience optimization. In some examples, a management service receives task execution data and corresponding user data for multiple client devices. The management service inputs target user context data along with the task execution data and the corresponding user data into a prediction model to identify a task and a task schedule that indicates a time to download and cache task content. Instructions are transmitted for a management agent to download and cache the task content according to the task schedule.

Remote working experience optimization systems

Disclosed are various examples for securing enterprise resources using a virtual private network. At least one computing device that can authenticate a client device for a virtual private network (VPN) connection based on a first device identifier received from the client device and a second device identifier received from a remote management service. The at least one computing device can determine that a network event associated with the client device has been observed and execute a machine learning routine to identify a pattern of access for the client device. A network access anomaly is determined in response to a network interaction of the client device deviating from the pattern of access for the client device. A remedial action is performed based on an anomaly type associated with the network access anomaly.

Dynamic variance mechanism for securing enterprise resources using a virtual private network

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Bengaluru, India

Arjun Kochhar