Securex.ai, Inc.

Intel Corporation

Harjinder Singh

Venkata Ramani Yellapragada

Jason A Gayman

Dean J Grannes

Systems and methods for prioritizing various security findings to allow a security platform to focus on a proper subset of (e.g., the most important) one or more software application stacks of an enterprise are described. In one embodiment, a method includes generating a profile for an enterprise that indicates one or more software application stacks and a network architecture for the one or more software application stacks, determining one or more vulnerability features of the one or more software application stacks, generating one or more exploitability scores by a first machine learning model based at least in part on the one or more vulnerability features, determining a proper subset of the one or more software application stacks based at least in part on the one or more exploitability scores, determining one or more vulnerabilities of the proper subset of the one or more software application stacks and one or more vulnerabilities of the network architecture, providing the one or more vulnerabilities of the proper subset of the one or more software application stacks, the one or more vulnerabilities of the network architecture, and the profile as input to a second machine learning model, generating an inference by the second machine learning model that indicates one or more attack paths for an attacker in the proper subset of the one or more software application stacks and the network architecture, and transmitting the inference to a storage location or a security software application.

Systems and methods for prioritizing security findings using machine learning models

Systems and methods for predictive analysis of potential attack patterns based on contextual security information are described. In one embodiment, a method includes generating a profile for an enterprise that indicates one or more software application stacks and a network architecture for the one or more software application stacks; determining one or more vulnerabilities of the one or more software application stacks and one or more vulnerabilities of the network architecture; providing the one or more vulnerabilities of the one or more software application stacks, the one or more vulnerabilities of the network architecture, and the profile as input to a machine learning model; generating an inference by the machine learning model that indicates one or more attack paths for an attacker in the one or more software application stacks and the network architecture; and transmitting the inference to a storage location or a security software application.

Systems and methods for predictive analysis of potential attack patterns based on contextual security information

Systems and methods for predictive analysis of potential attack patterns based on contextual security information are described. In one embodiment, a method includes generating an image for an enterprise that indicates one or more software application stacks of a virtual instance of a cloud provider from an application programming interface of the cloud provider and a network architecture for the one or more software application stacks without installing and utilizing security monitoring software on the cloud provider; determining one or more vulnerabilities of the one or more software application stacks and one or more vulnerabilities of the network architecture from the image; providing the one or more vulnerabilities of the one or more software application stacks, and the one or more vulnerabilities of the network architecture as input to a machine learning model; generating an inference by the machine learning model that indicates one or more attack paths for an attacker in the one or more software application stacks and the network architecture; and transmitting the inference to a storage location or a security software application.

Systems and methods for vulnerability assessment for cloud assets using imaging methods

A circuit for sensing on-die temperature at multiple locations using a minimum number of pins is described. Thermal diodes coupled to pins are placed on a die to measure the temperature at various die locations. Voltage is applied to the pins to determine the temperature at each given diode location. The polarity of the voltage applied across the pins determines what diodes are selected for measurement.

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San Jose, CA, United States of America

Harjinder Singh