Citibank, N.a.

Maneet Sharma

Adar Kiran Danait

Robin Jose Kurian

Vitthal Ramling Betgar

Anantha Veerasami

Balaji Kumar

Mayank Vinayaka

Richard Eric Lawton

Flora Sah

Ryan Peterman

Badari Narayana Shanka Prasad

James Iacona

Ashima Gupta

Crystal Lynn Magruder

Tracy L Yager

Swati Mittal

Paul Joseph Zakharia

Pavithra Araleri Visweswariah

Djona Fegnem

Rama Koteswara Rao Kandimalla

Krishnendu Chatterjee

Paul Edward Hibbs

Methods and descriptions are described herein for applying cascading machine learning models to command prompts. In particular, the system may receive a query indicating a computing process to be performed. The system may input a command prompt based on the query into a first instance of an LLM, which may output activities for performing the process. The system may input a first activity into a second instance of the LLM, which may output vulnerabilities associated with the first activity. The system may input a first vulnerability into a third instance of the LLM, which may output indications of available control tools for addressing the first vulnerability. The system may input a first control tool into a fourth instance of the LLM, which may output indications of monitoring tools for monitoring the first control tool. The system may then cause implementation of the first control tool and the first monitoring tool.

Applying cascading machine learning models to command prompts

Systems and methods for enhanced software development lifecycle management using a pipeline of machine learning and adaptive models for anomaly detection and action generation. In some aspects, the system may generate a system data stream for an SDLC management platform that stitches together source data from multiple sources, generates a query to a first machine learning model for a user story identifier to generate trend information and anomalies, based on output from a second machine learning model including dynamic thresholds for the anomalies, determining at least one anomaly that satisfies a corresponding dynamic threshold, processing using an adaptive model the at least one anomaly to generate recommended actions to address the at least one anomaly, and generating for inclusion in the graphical user interface the one or more recommended actions.

Machine learning-based anomaly detection and action generation system for software development lifecycle management

Systems and methods for enhanced software development lifecycle management using a pipeline of machine learning and adaptive models for anomaly detection and termination. In some aspects, the system may generate a system data stream for an SDLC management platform that stitches together source data from multiple sources, generates a query to a first machine learning model for a user story identifier to generate trend information and anomalies, based on output from a second machine learning model including dynamic thresholds for the anomalies, determining at least one anomaly that satisfies a corresponding dynamic threshold, based on output from an adaptive model not including recommended actions to address the at least one anomaly, executing a kill switch for the corresponding user story identifier and generating for inclusion in the graphical user interface an indication of the kill switch.

Systems and methods for a software development lifecycle traceability tool. In some aspects, the system may implement a traceability tool that generates a system data stream for an SDLC management platform that stitches together source data from multiple sources, trains a machine learning model to generate progress information and execution risks for a user story on the SDLC management platform, generates a graphical user interface with information for each user story and corresponding progress tracking indicia based on output from the machine learning model, and generates an event-based view of prior actions for each user story and one or more recommended actions to address execution risks.

Software development lifecycle traceability tool

A custom system data stream stitches together aspects of data streams from source systems. In one implementation, an analytics circuit extracts from a first data stream received from a system quality assurance (QA) management system, test case data. The analytics circuit parses metadata, such as a user story identifier, from the test case data. Based on the metadata, the analytics circuit parses, from a second data stream received from a system development lifecycle (SDLC) management computing system, SDLC item data, such as a project identifier. Based on the SDLC item data, the analytics circuit determines at least one impacted computer application. The custom system data stream is bound to a graphical user interface. In some implementations, the interface includes a plurality of side-by-side smart dials.

Management and presentation of system control data streams

Systems and methods are described for a three-tiered artificial intelligence architecture that synchronizes processing statement deployment across diverse computer networks. The system may receive, at a user interface, a first request for a first recommendation. The first recommendation includes a set of standardized processing statements for implementing on a function. The system may input the first request into a first network component. The first network component includes first, second, and third models. The first model is trained to generate a library of standardized processing statements. The second model is trained to generate a plurality of standardized processing characteristic values for each standardized processing statement in the library. The third model is trained to generate recommendations of sets of standardized processing statements to functions. The system receives an output from the first network component and generates for display the first recommendation based on the output.

Systems and methods for synchronizing processing statement deployment across diverse computer networks using a three-tiered artificial intelligence architecture

A custom system data stream stitches together aspects of various data streams from source systems. In one implementation, an analytics circuit of a computing system extracts, from a first data stream received from a system quality assurance (QA) management system, test case data. The analytics circuit parses metadata, including a user story identifier, from the test case data. Based on the user story identifier, the analytics circuit parses, from a second data stream received from a system development lifecycle (SDLC) management computing system, SDLC item data that includes a project identifier. Based on the project identifier from the second data stream, the analytics circuit determines at least one computer application impacted by activity associated with the test case data from the first data stream. Items from the source data streams are dynamically identified for inclusion in the custom system data stream. The custom system data stream is bound to a graphical user interface. In some implementations, the interface includes a plurality of side-by-side smart dials.

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Irving, TX, United States of America

Maneet Sharma