At&t Intellectual Property I, LP

Brian Miles

William Trost

Daniel Solero

Anthony Ramos

Michelle Barry

Chad Hiestand

Anthony Librera

Elizabeth Wood

A method includes identifying a plurality of assets of an enterprise for which quantum risk is to be assessed, acquiring a plurality of sets of metadata, where each set of metadata describes one asset of the plurality of assets, executing a machine learning model that takes the plurality of sets of metadata as input and generates as output a plurality of scores, where each score of the plurality of scores quantifies a quantum risk associated with one set of metadata of the plurality of sets of metadata that corresponds to one asset of the plurality of assets, and initiating a remedial action for a first asset of the plurality of assets, based on a first score of the plurality of scores that is assigned to a first set of metadata of the plurality of sets of metadata corresponding to the first asset at least meeting a predefined threshold.

Scalable assessment and prioritization of network asset cryptography for quantum risk mitigation

Aspects of the subject disclosure may include, for example, receiving a first request from a first communication orchestrator of a first protected environment to provide a secure and authenticated connection between a first resource of the first protected environment and a second resource of a second protected environment, accessing first encryption information from the first communication orchestrator and second encryption information from a second communication orchestrator of the second protected environment, verifying a capability for secure quantum communications of an encryption technique of the first communication orchestrator and the second communication orchestrator according to the first encryption information and the second encryption information, and enabling the first communication orchestrator and the second communication orchestrator to initiate a secure and authenticated communication channel via quantum communications. Other embodiments are disclosed.

Methods, systems, and procedures for quantum secure ecosystems

Concepts and technologies are disclosed herein for using deep learning models to obfuscate and optimize communications. A request can be received in a first language, from a user device, and at a first computing device storing a first neural network. The request can be translated using the first neural network into a modified request in a custom language. The modified request can be sent to a second computing device hosting an application. The first computing device can receive a modified response that is in the custom language, where the modified response can be created at the second computing device using the second neural network and based on a response from the application. The modified response can be translated into a response in the first language and sent to the user device.

Using deep learning models to obfuscate and optimize communications

A system can receive a guardrail policy request that specifies a guardrail policy to assess for deployment on a server to protect at least a specific port of the server. The system can execute a fingerprint clustering machine learning model using server fingerprint data to generate cluster data that identifies a virtual machine cluster that includes a plurality of virtual machines executed by the server. The system can execute a traffic discovery machine learning model using server traffic data and the cluster data to generate a confidence score indicative of whether deployment of the guardrail policy would have an adverse impact on the server. The system can execute a risk assessment machine learning model using the application type data to generate a risk assessment score. The system can evaluate the confidence score and the risk assessment score and can determine whether the guardrail policy should be deployed on the server.

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Pittsboro, IN, United States of America

Brian Miles