Citibank, N.a.

James Iacona

Adar Kiran Danait

Maneet Sharma

Flora Sah

Ashima Gupta

Tracy L Yager

Crystal Lynn Magruder

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 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

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Yardley, PA, United States of America

James Iacona