Bank of America Corporation

Peggy J Qualls

Sidy Diop

Ajay Jose Paul

Anthony R Bandos

Ghada I Khashab

Lori Mammoser

Travis E Hoyt

Alexander Thomas Edwards

Cary Hooper

Artificial Intelligence ('AI') apparatus and method are provided that correlate and consolidate operation of discrete vendor tools for detecting cyberthreats on a network. An AI engine may filter false positives and eliminate duplicates within cyberthreats detected by multiple vendor tools. The AI engine provides machine learning solutions to complexities associated with translating vendor-specific cyberthreats to known cyberthreats. The AI engine may ingest data generated by the multiple vendor tools. The AI engine may classify hardware devices or software applications scanned by each vendor tool. The AI engine may decommission vendor tools that provide redundant cyberthreat detection. The AI engine may display operational results on a dashboard directing cyberthreat defense teams to corroborated cyberthreats and away from false positives.

Artificial intelligence vendor similarity collation

Artificial intelligence corroboration of vendor outputs

Artificial intelligence vulnerability collation

A method for reducing a level of secure data exposure within the dark web may be provided. The secure data may be associated with a third party data custodian that may be associated with a pre-determined entity. The method may include searching the communications to identify one or more single locations that may include numerous communications that may include text identifying the selected third party data custodian. Each communication may be assigned a time-stamp in order to calculate a speed of how many communications within a pre-determined time period may be identified to include text identifying the third party data custodian. Simultaneous to the searching, the method may include running a time clock. When a rate per unit of time of identification of each identifying text is equal to or greater than a pre-determined rate per unit of time, increasing a monitoring at the one or more single locations.

Leveraging entity dark web chatter using slope of vendor identifier appearances as a search proxy

Artificial intelligence reverse vendor collation

A method for securely testing a web application is provided. The method may include analyzing each HTTP log entry that may be included in a centralized web server log file of a web application. Each HTTP log entry may include an endpoint. The endpoint may be a URL path correlating to a location on the web application. Based on the analysis, the method may include identifying each endpoint included in the web application and generating a first web application site-map based on each identified endpoint. The method may further include determining one or more endpoints on the first web application site map, that may be absent from a second web application site map. The second web application site map may include each crawled endpoint within the web application identified via a crawling of the web application by a web application attack tool for identifying security vulnerabilities.

HTTP log integration to web application testing

Growing community of inventors

Oswego, IL, United States of America

Peggy J Qualls