US 12,032,706 B2
Application security scoring
Duraimurugan Govindasamy, Round Rock, TX (US); Kavitha Suresh Kumar, Bangalore (IN); and Puthukode G. Ramachandran, Austin, TX (US)
Assigned to Kyndryl, Inc., New York, NY (US)
Filed by Kyndryl, Inc., New York, NY (US)
Filed on Nov. 30, 2021, as Appl. No. 17/538,358.
Prior Publication US 2023/0169179 A1, Jun. 1, 2023
Int. Cl. G06F 21/57 (2013.01); G06F 9/54 (2006.01); G06N 20/00 (2019.01)
CPC G06F 21/577 (2013.01) [G06F 9/54 (2013.01); G06N 20/00 (2019.01); G06F 2221/033 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method, comprising:
receiving, by a computing device, metrics identifying vulnerabilities in an application;
collecting, by the computing device, information related to the vulnerabilities;
determining, by the computing device, a severity of the vulnerabilities by applying artificial intelligence (AI) on the collected information related to the vulnerabilities;
assigning, by the computing device, weights to the metrics based on the determined severity of the vulnerabilities;
applying, by the computing device, a machine learning model on the weighted metrics; and
generating, by the computing device, a predictive score for the vulnerabilities using the machine learning model.