US 12,452,061 B2
System and method for distributed and authenticated provisioning of encrypted electronic data
Saurabh Arora, Gurugram (IN); Sandeep Kumar Chauhan, Hyderabad (IN); Bhagya Lakshmi Sudha Lavanya Mallidi, Hyderabad (IN); Puneetha Polasa, Telangana (IN); and Sanchit Taggar, New Delhi (IN)
Assigned to BANK OF AMERICA CORPORATION, Charlotte, NC (US)
Filed by BANK OF AMERICA CORPORATION, Charlotte, NC (US)
Filed on Nov. 18, 2022, as Appl. No. 17/989,751.
Prior Publication US 2024/0171397 A1, May 23, 2024
This patent is subject to a terminal disclaimer.
Int. Cl. H04L 9/32 (2006.01); G06F 21/56 (2013.01); H04L 9/40 (2022.01); H04L 47/83 (2022.01); H04L 67/568 (2022.01); H04L 67/5681 (2022.01)
CPC H04L 9/321 (2013.01) [G06F 21/566 (2013.01); H04L 9/3297 (2013.01); H04L 47/83 (2022.05); H04L 63/0876 (2013.01); H04L 63/1425 (2013.01); H04L 67/568 (2022.05); H04L 67/5681 (2022.05)] 14 Claims
OG exemplary drawing
 
1. A system for distributed and authenticated provisioning of encrypted electronic data, the system comprising:
at least one non-transitory storage device; and
at least one processor coupled to the at least one non-transitory storage device, wherein the at least one processor is configured to:
continuously monitor, using an intelligent monitoring engine, one or more actions of an endpoint device, the one or more actions comprising one or more data requests submitted using the endpoint device;
identify, using a machine learning module, one or more access patterns associated with the endpoint device from the one or more data requests and metadata associated with the one or more data requests, wherein identifying one or more access patterns further comprises offloading processing of a function to the endpoint device based on the one or more access patterns identified and pushing specific types of data to the endpoint device based on the types of data frequently requested by a user and timeframes at which the user requests such data, wherein the machine learning module is configured to establish a baseline of authorized activity for the endpoint device, and wherein the machine learning module is configured to analyze historical activity associated with the endpoint device to establish the baseline of authorized activity;
based on the one or more access patterns, transmit an encrypted data set to the endpoint device, wherein transmitting the encrypted data set to the endpoint device comprises transmitting the encrypted data at a time in which a computing load of a server associated with the encrypted data set is submaximal and at a time in which the endpoint device is idle, and wherein transmitting the encrypted data set to the endpoint device comprises, based on the timeframes associated with the one or more data requests, transmitting the encrypted data before a predicted access time of the endpoint device;
identify the baseline for authorized activity on the endpoint device based on the one or more access patterns; and
based on the baseline for authorized activity, authenticate the user associated with the endpoint device for accessing the encrypted data set on the endpoint device, wherein authenticating the user further comprises detecting, via an unauthorized activity monitor, that a recent activity associated with the endpoint device deviates from the baseline for authorized activity, determining that the recent activity is unauthorized activity, and blocking the endpoint device from executing decryption logic to decrypt the encrypted data set to ensure that data of the encrypted data set is inaccessible to unauthorized users.