Abstract: Systems, computer program products, and methods are provided for code revision impact analysis. The method includes generating a system map based on data received from a plurality of network devices; receiving a data transmission including a text file; processing the text file via a natural language processing engine, where an output of the natural language processing engine comprises a plurality of expected updates; determining, based on the system map, at least one downstream effect of the plurality of expected updates; and performing a remedial action.

Abstract: Aspects of the disclosure relate to a collaborative verified user platform using distributed ledger technology. A collaborative verified user platform may identify changes to user information stored at a distributed ledger. The platform may retrieve changes from a remote database. The platform may update the distributed ledger based on the changes. The platform may receive, from a second user device, a request to access the user information. The platform may establish a secure channel. The platform may process one or more secure requests associated with a venture corresponding to both the first user device and the second user device. The platform may detect a permission violation. The platform may initiate security actions based on the permission violation. The platform may receive an updated cyber contract corresponding to the user information. The platform may update permissions based on the updated cyber contract.

Abstract: Systems, computer program products, and methods are provided for automated detection of source code discrepancies. The method includes receiving a data transmission including a text file and a source code file; processing the source code file via a machine learning engine, where an output of the machine learning engine includes a plurality of identified updates; processing the text file via a natural language processing engine, where an output of the natural language processing engine includes a plurality of expected updates; identifying a difference between the plurality of identified updates and the plurality of expected updates; and performing a remedial action.

Abstract: Aspects of the disclosure relate to a collaborative verified user platform using distributed ledger technology. A collaborative verified user platform may receive user information from a first user device. The platform may store the user information to a distributed ledger. The platform may update the distributed ledger based on one or more changes to the user information. The platform may receive a request to access the user information. The platform may determine whether to grant access to the user information based on one or more permissions. The platform may establish a secure channel between a first user device and a second user device. The platform may process secure requests associated with a venture corresponding to both the first user device and to the second user device. The platform may detect a permission violation. The platform may initiate one or more security actions based on the permission violation.

Abstract: A computing platform may train a machine learning model to detect and analyze threat actor activities. The computing platform may generate dynamic honeypotted files and deploy the generated dynamic honeypotted files as adaptive defenses to threat actors in a computing environment. The computing platform may adapt to threat actor activities based on analyzed behavior of the threat actor and any identified tools used by the threat actor to gain access to the computing system. Threat actor activities may be written to a blockchain to publicly record all transactions related to a threat actor's activities for analysis and generation of adaptive defenses to threat actor attacks. The computing platform may cause redirection of the threat actor into a specific computing environment through generation and deployment of dynamic honeypotted files.

Abstract: A computing platform may train a machine learning model to detect and analyze threat actor activities. The computing platform may generate dynamic honeypots and deploy the generated dynamic honeypots as adaptive defenses to threat actors in a computing environment. The computing platform may adapt to threat actor activities based on analyzed behavior of the threat actor and any identified tools used to by the threat actor to gain access to the computing system. The computing platform may cause redirection of the threat actor into a specific computing environment through generation and deployment of dynamic honeypots.

Abstract: Aspects of the disclosure relate to a collaborative verified user platform using distributed ledger technology. A collaborative verified user platform may receive user information from a first user device. The platform may store the user information to a distributed ledger. The platform may update the distributed ledger based on one or more changes to the user information. The platform may receive a request to access the user information. The platform may determine whether to grant access to the user information based on one or more permissions. The platform may establish a secure channel between a first user device and a second user device. The platform may process secure requests associated with a venture corresponding to both the first user device and to the second user device. The platform may detect a permission violation. The platform may initiate one or more security actions based on the permission violation.

Abstract: Systems, computer program products, and methods are described herein for analyzing micro-anomalies in anonymized electronic data. The present disclosure is configured to import or retrieve a first data set, process the first data set to develop at least one event-outcome projection, define an outcome projection data set, import or receive a monitored user data set, anonymize the monitored user data set, define an avatar data set process the avatar data set, wherein the steps of import or receive a monitored user data set, anonymize the monitored user data set, and define an avatar data set are repeated one or more times.

Abstract: Systems, computer program products, and methods are described herein for code revision impact analysis. The present disclosure is configured to generate a system map based on data received from a plurality of network devices; receive a data transmission including a text file; process the text file via a natural language processing engine, where an output of the natural language processing engine comprises a plurality of expected updates; determine, based on the system map, at least one downstream effect of the plurality of expected updates; and perform a remedial action.

Abstract: Systems, computer program products, and methods are described herein for automated detection of source code discrepancies. The present disclosure is configured to receive a data transmission including a text file and a source code file; process the source code file via a machine learning engine, where an output of the machine learning engine includes a plurality of identified updates; process the text file via a natural language processing engine, where an output of the natural language processing engine includes a plurality of expected updates; identify a difference between the plurality of identified updates and the plurality of expected updates; and perform a remedial action.

Abstract: Systems, computer program products, and methods are described herein for analyzing micro-anomalies in anonymized electronic data. The present disclosure is configured to import or retrieve a first data set, process the first data set to develop at least one event-outcome projection, define an outcome projection data set, import or receive a monitored user data set, anonymize the monitored user data set, define an avatar data set process the avatar data set, wherein the steps of import or receive a monitored user data set, anonymize the monitored user data set, and define an avatar data set are repeated one or more times.

Abstract: Systems, computer program products, and methods are described herein for processing data using an optimized machine learning architecture. The present disclosure is configured to monitor usage data for a plurality of network devices; analyze the usage data using a first machine learning engine; determine, based on an output of the first machine learning engine, at least one data trend; and instruct a second machine learning engine to analyze local data associated with the at least one data trend, wherein the second machine learning engine is hosted on a first network device of the plurality of network devices.

Abstract: Systems, computer program products, and methods are described herein for dynamically determining software application coverage in an electronic network. The present invention is configured to determine a potential vulnerability associated with the plurality of application identifiers; generate a coverage score associated with each application identifier of the plurality of application identifiers, wherein the coverage score comprises an ability of at least one of the software application or the component to manage the potential vulnerability; generate a coverage ranking of the plurality of application identifiers, wherein the coverage ranking is based on the coverage score associated with each application identifier of the plurality of application identifiers; and store the coverage ranking of the plurality of application identifiers in a coverage ranking database, wherein the coverage ranking of the plurality of application identifiers is based on the potential vulnerability.