Abstract: Apparatus and methods for using deepfakes defensively to detect fake, spoofed, and hoax phone calls and videoconferences are provided. A program may record a target individual reciting exemplary phrases. The program may analyze the recordings to create a baseline. The program may use deepfake algorithms to create exemplar deepfake audiovisual representations of the target individual. The program may store the data in a database. The program may analyze, in real-time, a phone call or videoconference to determine whether they are legitimate or illegitimate by comparing the audio or audiovisual contents of the phone call or videoconference with the exemplar deepfakes. When the program determines the phone call or videoconference is illegitimate, the program may terminate the call or videoconference and inform the recipient and others.

Abstract: Apparatus and methods for providing an indicator of trustworthiness of a software application are provided. A program may receive information regarding the software application from a developer of the application. The program may analyze the application and determine that the application is trustworthy and secure. The program may then create a unique logo that may include various information to indicate to a user that the program has determined the application is trustworthy. The program may transmit the logo to the developer so the developer may incorporate the logo into the application. In an embodiment, the program may transmit the logo to a third-party application store for display on a page where the application is available.

Abstract: Apparatus and methods for using deepfakes defensively to detect fake, spoofed, and hoax accounts and posts on social media and elsewhere are provided. A program may gather verified images or writings of a target individual. The program may analyze the verified images and writings to create a baseline. The program may use deepfake algorithms to create exemplar deepfake images or writings. The program may store the data in a database. The program may search a network for social media accounts or posts that may meet the baseline and determine whether they are legitimate or illegitimate by comparing the contents of the accounts or posts with the exemplar deepfakes. When the program determines the accounts or posts are illegitimate, the program may initiate a takedown of those accounts or posts. The program may use machine learning algorithms to refine itself and become more accurate.

Abstract: Arrangements for providing software vulnerability analysis and monitoring are provided. In some aspects, software bill of materials (SBOM) data may be received and software attributes may be extracted from the SBOM data. Author data may be received and analyzed using natural language processing and/or machine learning to identify author attributes. Current event or vulnerability data may be received. In some examples, one or more machine learning models may be executed to determine a confidence score associated with the software being analyzed. For instance, software attributes, author attributes, and current event data may be used as inputs in the machine learning model and a confidence score may be output. Based on the confidence score, one or more alerts may be generated and transmitted to one or more enterprise organization computing devices.

Abstract: A plurality of communicatively coupled, networked assets may be threatened or attacked by a cybersecurity attack. The operational resiliency of the computer network determines whether the cybersecurity attack leads to a shutdown of one or more assets, or even the entire computer network. Machines and processes are disclosed to improve operational cybersecurity resiliency of software on the computer network. Machine learning is used to identify potential vulnerabilities from a vulnerability database. Chaos stress testing using a machine learning algorithm can be performed on software to exploit the vulnerabilities. A blast radius can be set to minimize any potential negative side effects of the testing. Software can be remediated to account for responses to the testing by reconfiguring to prevent exploitation of the vulnerabilities. A financial impact of the exploited vulnerabilities can be calculated and reports can be generated.

Abstract: A plurality of communicatively coupled, networked assets may be threatened or attacked by a cybersecurity attack. The operational resiliency of the computer network determines whether the cybersecurity attack leads to a shutdown of one or more assets, or even the entire computer network. Machines and processes are disclosed to improve operational cybersecurity resiliency of software on the computer network. Machine learning is used to identify potential vulnerabilities from a vulnerability database. Chaos stress testing using a machine learning algorithm can be performed on software to exploit the vulnerabilities. A blast radius can be set to minimize any potential negative side effects of the testing. Software can be remediated to account for responses to the testing by reconfiguring to prevent exploitation of the vulnerabilities. A financial impact of the exploited vulnerabilities can be calculated and reports can be generated.