Capital One Services, LLC

Krystan R Franzen

Mohamed Seck

Grant Michael Iwan

Shannon Reid

Arunkumar Natarajan

Sudha Shiva Kumar Marri

Yasaswy Rajendraprasad Ravala

Kadhiresan Kanniyappan

Abel Fletcher

Anand Annamalai

Sesha Sowmya Nadiminti

Matthew Blake Ackard

Matthew Zheng

Yasawy Rajendraprasad Ravala

Gayatri Vishnubhotla

Yasaswy Ravala

Emmanuel Obogbaimhe

In some implementations, an exception system may receive an indication of a security vulnerability associated with a cloud environment. The exception system may provide a data structure that represents the security vulnerability to a machine learning model in order to receive a suggested exception justification. The exception system may output, to a user device associated with a user assigned to the security vulnerability, a draft exception request that includes the suggested exception justification. The exception system may receive, from the user device, a request to submit an exception request based on the draft exception request. The exception system may transmit a command to tag the security vulnerability as excepted.

Automated vulnerability exception process

In some implementations, a compliance system may receive, from a pipeline system, a set of properties associated with configuration of a cloud infrastructure. Additionally, the compliance system may receive, from a code repository, a set of computer code associated with the cloud infrastructure. The compliance system may provide the set of properties and the set of computer code to a machine learning model to receive a set of compliance indicators and a set of severity levels. Each compliance indicator in the set of compliance indicators being associated with a corresponding severity level in the set of severity levels. The compliance system may selectively deploy the cloud infrastructure in response to receiving the set of severity levels.

Automatic compliance assessment of cloud infrastructure code

In some implementations, a compliance server may receive a set of data structures associated with one or more compliance activities. The compliance server may apply a machine learning model to the set of data structures in order to generate one or more automation estimates corresponding to the one or more compliance activities. The compliance server may output the one or more automation estimates to a user device. The compliance server may receive, from the user device, an indication of a selected compliance activity from the one or more compliance activities. The compliance server may generate an automation script for the selected compliance activity in response to the indication.

Compliance for cloud-based applications and computer systems using machine learning

In some implementations, a device may cause execution of a batch processing job. The batch processing job may be associated with an application and at least one user. The device may determine that execution of the batch processing job is associated with one or more execution errors. The device may determine, based on the one or more execution errors, an error remediation action for the batch processing job. The device may cause performance of the error remediation action for the batch processing job.

Batch processing and error resolution management

In some implementations, a remediation device may receive, from a database that stores information regarding security vulnerabilities, security vulnerability indicators associated with one or more cloud-based applications. The remediation device may cluster, using at least one machine learning model, the security vulnerability indicators into classes, and may determine, for each class, a corresponding remediation recommendation. The remediation device may transmit, based on a setting, a corresponding message for each class. The remediation device may receive input associated with at least one of the corresponding messages, and may trigger, for at least one of the classes of security vulnerability indicators and based on the input, an automated remediation script based on a corresponding one of the remediation recommendations. The automated remediation script causes a cloud environment to perform an action for a cloud-based application associated with the security vulnerability indicators in the class(es).

Security vulnerability communication and remediation with machine learning

In some implementations, a system may receive dependency indicators associated with a plurality of cloud-based applications and receive change indicators associated with changes to one or more first applications of the plurality of cloud-based applications. The system may receive an indicator associated with an incident ticket based on a problem with a second application of the plurality of cloud-based applications. The device may determine at least one of the change indicators associated with the incident ticket based on dependencies between the one or more first applications and the second application and based on a difference between a time associated with the incident ticket and a time associated with the at least one of the change indicators. The system may, based on determining the at least one of the change indicators, transmit a command to rollback at least one of the changes or to rollforward at least one change.

Software change analysis and automated remediation

In some implementations, a remediation engine may receive an indication of the cloud-based application. The remediation engine may generate the data structure for the cloud-based application. The remediation engine may receive an indication of a corresponding user. The remediation engine may update the data structure to associate the corresponding user with the cloud-based application. The remediation engine may receive an indication of a corresponding category. The remediation engine may update the data structure to associate the corresponding category with the cloud-based application. The remediation engine may receive an indication of a first label and an indication of a first module of the cloud-based application. The remediation engine may update the data structure to associate the first label with the first module of the cloud-based application. The remediation engine may provide the data structure for processing alerts associated with the cloud-based application.

Automated ticket creation and assignment for cloud-based applications

In some aspects, a system may receive security vulnerability indicators associated with one or more cloud-based applications and/or properties associated with one or more cloud-based images used to create cloud instances. The system may determine, for each indicator and/or property, a corresponding remediation recommendation. The system may generate a graphical user interface that provides the indicators and/or properties with the corresponding remediation recommendations. The system may transmit, based on a user setting, a corresponding message for each indicator and/or property. The system may trigger, based on at least one of the indicators and/or properties, an automated remediation script that instructs a cloud environment to perform an action for a cloud-based application and/or image associated with the at least one indicator and/or property. The system may transmit one or more status indicators associated with the automated remediation script. Numerous other aspects are described.

Vulnerability dashboard and automated remediation

In some embodiments, the present disclosure provides an exemplary method that may include steps of identifying at least one computing specification image within a plurality of computing specification images; monitoring each data agent within the plurality of preinstalled data agents for a predetermined period of time to establish a data agent usage baseline associated with each data agent within the plurality of preinstalled data agents; utilizing a chaos engineering algorithm to dynamically perturb each data agent; calculating a usage test score for each data agent within the plurality of preinstalled data agents; calculating an overall data agent-specific usage score associated with each data agent within the plurality of preinstalled data agents based on the plurality of data agent-specific usage test scores; and rejecting at least one data agent within the plurality of preinstalled data agents from being utilized to launch the instance of the software application.

Computer-based systems configured for dynamic performance scoring of software agents and methods of use thereof

A method includes receiving from a transmitting data interface, a data stream mapping of a data input into data shards for transmission in a data stream over a data stream communication channel. Data capacity for a data producing software application from a plurality of data producing software applications is adjusted by increasing or decreasing a number of data shards in the data stream assigned to the data producing software application. An updated data stream mapping of the data input into the plurality of data shards is generated by updating a start hash key and an end hash key in a range for each of the data shards assigned to the data producing software application. The updated data stream mapping is sent to the transmitting data interface for adjusting the data capacity in the data stream transmitted over the data stream communication channel of the data producing software application.

Computer-based systems configured to adjust data capacity in a data stream generated from multiple data producer applications and methods of use thereof

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McLean, VA, United States of America

Krystan R Franzen