US 11,816,476 B2
Systems and methods for risk awareness using machine learning techniques
Per Karlsson, Saint Johns, FL (US); Benjamin Wellmann, Boca Raton, FL (US); Sheel Saket, Wheeling, IL (US); and Vida Lashkari, Atlanta, GA (US)
Assigned to Fidelity Information Services, LLC, Jacksonville, FL (US)
Filed by Fidelity Information Services, LLC, Jacksonville, FL (US)
Filed on Sep. 23, 2021, as Appl. No. 17/448,561.
Prior Publication US 2023/0091520 A1, Mar. 23, 2023
Int. Cl. G06F 8/71 (2018.01); G06N 20/00 (2019.01); G06F 11/32 (2006.01)
CPC G06F 8/71 (2013.01) [G06F 11/327 (2013.01); G06N 20/00 (2019.01)] 18 Claims
OG exemplary drawing
 
1. A method comprising, performing by one or more processors, operations including:
automatically determining, using a trained machine-learning based model, a risk level for a proposed modification to a system, wherein the proposed modification includes a modification to code of a software component of the system,
determining whether the code is a critical code segment or a non-critical code segment, and
performing at least one of:
providing a suggested action for reducing the determined risk level for the proposed modification when the code is determined to be a non-critical code segment and the determined risk level is above a non-critical code predetermined threshold, or
blocking the proposed modification from being implemented when the code is determined to be a critical code segment and the determined risk level is above a critical code predetermined threshold.