Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Systems and methods for cash verification and remote deposit are provided.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A method for reducing a level of private data exposure associated with a selected third party data custodian is provided. The third party data custodian may be associated with a pre-determined entity. The method may be performed on dark web users communications within a dark web portion of the internet. The method may include searching, periodically, the dark web users communications, for retrieving communications that may include text identifying the selected third party data custodian. The method may include identifying one or more communications including the identifying text, and for each identifying text, increasing by one digit a total name count occurrence counter for the selected third party data custodian. when the total name count escalates to a pre-determined magnitude, the method may include, locking the private data residing within a network of the selected third party data custodian from external access.

Abstract: A method for reducing a level of secure data exposure associated with a third party data custodian associated with a pre-determined entity. The method being performed on dark web users electronic communications. The method may include searching the communications that may include text identifying the selected third party data custodian. Simultaneous to the searching, the method may include running a time clock. The method may further include identifying one or more communications including the identifying text and for each identifying text, increasing a total name count by one digit for the selected third party data custodian and assigning a time-stamp of a time of identification of each identifying text. 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 for communications including text identifying the selected third party data custodian.

Abstract: 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.

Abstract: 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.