Mckinsey & Company, Inc.

Sriram Venkatesan

Rares Ioan Almasan

Sastry Vsm Durvasula

Sonam Jha

Neema Uthappa

Jaspreet Singh

Anthony Esposito

Suraj Sharma

Sastry V S M Durvasula

Hugo Sarrazin

Sayan Chowdhury

A method includes collecting current data and architecture state, collecting future data and architecture state; analyzing the current and/or future data and architecture state to generate deployment options; and causing the summary of options to be displayed. A computing system includes a processor and a memory comprising instructions, that when executed, cause the system to collect current data and architecture state, collect future data and architecture state; analyze the current and/or future data and architecture state to generate deployment options; and cause the summary of options to be displayed. A non-transitory computer-readable storage medium includes executable instructions that, when executed by a processor, cause a computer to collect current data and architecture state, collect future data and architecture state; analyze the current and/or future data and architecture state to generate deployment options; and cause the summary of options to be displayed.

Machine learning techniques for environmental discovery, environmental validation, and automated knowledge repository generation

The following relates generally to computer security, and more particularly relates to computer security in a virtual environment, such as a metaverse. In some embodiments, one or more processors: receive a set of known events (e.g., security threats) including event classifications; receive data of layers of the virtual environment; detect events in the data of the layers of the virtual environment; and determine correlations between the events in the data of the layers of the virtual environment. The correlations may be between events in different layers of the virtual environment. The one or more processors may also predict future events by analyzing the detected events.

Systems and methods for collecting and analyzing virtual data to find correlations between events

ML methods and systems for cataloging and making recommendations based on domain-specific knowledge are disclosed. An example method includes: cataloging, using knowledge engines, data to develop knowledge repositories for respective domains; obtain current domain state data; obtain future domain state data; analyze, using first ML models, one or more of (i) data from the knowledge repositories, (ii) the first domain state data, and (iii) the second domain state data to identify a recommended set of one or more regulations, standards, policies and/or rules for a desired second domain state; analyze, using second ML models, (i) the recommended set and (ii) a current data and architecture state for a current computing environment to generate a summary of one or more cloud deployment options for migrating a current computing environment to a future computing environment for a future domain state; and cause the summary to be displayed on a computing device.

Machine learning methods and systems for cataloging and making recommendations based on domain-specific knowledge

A computer-implemented method includes receiving user input including codified knowledge management information and/or engine data; and processing the input using one or more trained machine learning models to generate one or more living documents. A computing system includes one or more processors; and a memory having stored thereon instructions that, when executed, cause the computing system to receive user input including codified knowledge management information and/or engine data; and process the user input using one or more trained machine learning models to generate one or more living documents. A non-transitory computer-readable storage medium includes executable instructions that, when executed by a processor, cause a computer to receive user input including codified knowledge management information and/or engine data; and process the user input using one or more trained machine learning models to generate one or more living documents.

Intelligent knowledge management-driven decision making model

Systems and methods for analyzing virtual data, predicting future events, and improving computer security

Machine learning methods and systems for developing security governance recommendations are disclosed. An example method includes: accessing threat assessment knowledge data representative of security threats and threat patterns; accessing security solution knowledge data representative of security solutions; obtaining first security state data representing a current security state of a current computing environment; obtaining second security state data representing a future security state for a future secured-by-design computing environment; analyzing, using one or more first trained machine learning (ml) models, one or more of (i) the threat assessment knowledge data, (ii) the security solution knowledge data, (iii) the first security state data, or (iv) the second security state data to develop one or more aspects of a security governance recommendation for the future secured-by-design computing environment; and causing the one or more aspects of the security governance to be displayed on a computing device.

Machine learning methods and systems for developing security governance recommendations

A method includes receiving a plurality of user use cases; analyzing the use cases using an AI engine to order the use cases; generating an optimized machine learning model; and causing an optimized deployment option to be displayed. A computing system includes a processor; and a memory comprising instructions, that when executed, cause the computing system to: receive a plurality of user use cases; analyze the use cases using an AI engine to order the use cases; generate an optimized machine learning model; and cause an optimized deployment option to be displayed. A non-transitory computer-readable storage medium stores executable instructions that, when executed by a processor, cause a computer to: receive a plurality of user use cases; analyze the use cases using an AI engine to order the use cases; generate an optimized machine learning model; and cause an optimized deployment option to be displayed.

Artificial intelligence-based use case model recommendation methods and systems

A method includes receiving first input, analyzing the first input using a first model, receiving second input, analyzing the second input using a second model; and generating infrastructure-as-code. A computing system includes a processor; and a memory comprising instructions, that when executed, cause the computing system to: receive first input, analyze the first input using a first model, receive second input, analyze the second input using a second model; and generate infrastructure-as-code. A non-transitory computer-readable storage medium storing executable instructions that, when executed by a processor, cause a computer to: receive first input, analyze the first input using a first model, receive second input, analyze the second input using a second model; and generate infrastructure-as-code.

Automated cloud data and technology solution delivery using machine learning and artificial intelligence modeling

A method includes receiving user inputs; receiving codified knowledge management information; receiving engine data; and processing the user inputs, the codified knowledge management information and engine data using a trained machine learning model to generate a living document. A computing system includes one or more processors; and a memory comprising instructions that, when executed, cause the computing system to: receive user inputs; receive codified knowledge management information; receive engine data; and process the user inputs, the codified knowledge management information and engine data using a trained machine learning model to generate a living document. A non-transitory computer-readable storage medium includes executable instructions that, when executed by a processor, cause a computer to: receive user inputs; receive codified knowledge management information; receive engine data; and process the user inputs, the codified knowledge management information and engine data using a trained machine learning model to generate a living document.

Growing community of inventors

Princeton Junction, NJ, United States of America

Sriram Venkatesan