Abstract: Methods, apparatus, and systems are provided for using narrow artificial intelligence (“AI”) to detect and secure confidential data. The methods may include using a camera on a user computer to capture a live camera feed of an area adjacent to the user computer. Methods may include identifying one or more unverified data elements in the captured data. In response to determining an unverified data element, methods may include securing the user computer. Methods may include capturing a screenshot of the user computer screen. Methods may include transmitting data extracted from the screenshot through an object identification algorithm. Methods may include identifying confidential data within the screenshot using a pattern analysis model. Methods may include recreating the screenshot using a narrow AI model. Methods may include blurring the confidential data in the recreated screenshot. Methods may include overwriting the screenshot to display the recreated screenshot on the user computer.

Abstract: Systems, methods, and computer program products are provided for monitoring network processing using node analysis. The method includes receiving node operation information relating to a node command from one or more nodes. The one or more nodes are grouped into a cluster. The method also includes determining one or more node characteristics based on the node operation information. The method further includes comparing the node characteristic(s) of the node command to expected node characteristic(s). The method still further includes determining a node outage likelihood. The node outage likelihood indicates the likelihood the given node will experience a node outage. The method also includes determining a cluster node operation plan. The cluster node operation plan is configured to determine the nodes of the cluster that must be in operation in an event of the node outage of the given node.

Abstract: A machine learning computing system identifies a vulnerability associated with a server. Based on information associated with the server and a knowledge base, the computing system schedules an interval for patching the server in a centralized tracking module. Based on the knowledge base and the vulnerability, the computing system creates, validates, and deploys the patch job. During patch job execution, the computing system monitors the status of the patch job at the server and transmits status updates to a user interface module. After expiration of the interval, the computing system generates an assessment report for the executed patch job. The computing system updates the knowledge base based on the assessment report to improve future decisioning processes. Based on the success or failure of the patch job, the computing system, upon a failure indication, automatically reschedules an interval for patching the server.

Abstract: A machine learning computing system identifies a vulnerability associated with a server. Based on information associated with the server and a knowledge base, the computing system schedules an interval for patching the server in a centralized tracking module. Based on the knowledge base and the vulnerability, the computing system creates, validates, and deploys the patch job. During patch job execution, the computing system monitors the status of the patch job at the server and transmits status updates to a user interface module. After expiration of the interval, the computing system generates an assessment report for the executed patch job. The computing system updates the knowledge base based on the assessment report to improve future decisioning processes. Based on the success or failure of the patch job, the computing system, upon a failure indication, automatically reschedules an interval for patching the server.

Abstract: A machine learning computing system identifies a vulnerability associated with a server. Based on information associated with the server and a knowledge base, the computing system schedules an interval for patching the server in a centralized tracking module. Based on the knowledge base and the vulnerability, the computing system creates, validates, and deploys the patch job. During patch job execution, the computing system monitors the status of the patch job at the server and transmits status updates to a user interface module. After expiration of the interval, the computing system generates an assessment report for the executed patch job. The computing system updates the knowledge base based on the assessment report to improve future decisioning processes. Based on the success or failure of the patch job, the computing system, upon a failure indication, automatically reschedules an interval for patching the server.

Abstract: Systems, methods, and computer program products are provided for monitoring network processing using node analysis. The method includes receiving node operation information relating to a node command from one or more nodes. The one or more nodes are grouped into a cluster. The method also includes determining one or more node characteristics based on the node operation information. The method further includes comparing the node characteristic(s) of the node command to expected node characteristic(s). The method still further includes determining a node outage likelihood. The node outage likelihood indicates the likelihood the given node will experience a node outage. The method also includes determining a cluster node operation plan. The cluster node operation plan is configured to determine the nodes of the cluster that must be in operation in an event of the node outage of the given node.

Abstract: A machine learning computing system identifies a vulnerability associated with a server. Based on information associated with the server and a knowledge base, the computing system schedules an interval for patching the server in a centralized tracking module. Based on the knowledge base and the vulnerability, the computing system creates, validates, and deploys the patch job. During patch job execution, the computing system monitors the status of the patch job at the server and transmits status updates to a user interface module. After expiration of the interval, the computing system generates an assessment report for the executed patch job. The computing system updates the knowledge base based on the assessment report to improve future decisioning processes. Based on the success or failure of the patch job, the computing system, upon a failure indication, automatically reschedules an interval for patching the server.

Abstract: A data loss prevention device that includes a data loss prevention engine implemented by a processor. The data loss prevention engine is configured to receive data in transit to a target network device and to identify content within the data. The data loss prevention engine is configured to determine the content of the data comprises an image and to determine an image type for the image based on objects within the image, and to determine whether the image type matches a restricted image type from a set of restricted image types. The data loss prevention engine is further configured to block transmission of the data to the target network device in response to determining that the image type matches a restricted image type and forward the data to the target network device in response to determining that the image type does not match a restricted image type.

Abstract: A machine learning computing system identifies a vulnerability associated with a server. Based on information associated with the server and a knowledge base, the computing system schedules an interval for patching the server in a centralized tracking module. Based on the knowledge base and the vulnerability, the computing system creates, validates, and deploys the patch job. During patch job execution, the computing system monitors the status of the patch job at the server and transmits status updates to a user interface module. After expiration of the interval, the computing system generates an assessment report for the executed patch job. The computing system updates the knowledge base based on the assessment report to improve future decisioning processes. Based on the success or failure of the patch job, the computing system, upon a failure indication, automatically reschedules an interval for patching the server.

Abstract: A data loss prevention device that includes a data loss prevention engine implemented by a processor. The data loss prevention engine is configured to receive data in transit to a target network device and to identify content within the data. The data loss prevention engine is configured to determine the content of the data comprises an image and to determine an image type for the image based on objects within the image, and to determine whether the image type matches a restricted image type from a set of restricted image types. The data loss prevention engine is further configured to block transmission of the data to the target network device in response to determining that the image type matches a restricted image type and forward the data to the target network device in response to determining that the image type does not match a restricted image type.

Abstract: A data loss prevention device that includes a data loss prevention engine implemented by a processor. The data loss prevention engine is configured to receive data in transit to a target network device and to identify content within the data. The data loss prevention engine is configured to determine the content of the data comprises an image and to determine an image type for the image based on objects within the image, and to determine whether the image type matches a restricted image type from a set of restricted image types. The data loss prevention engine is further configured to block transmission of the data to the target network device in response to determining that the image type matches a restricted image type and forward the data to the target network device in response to determining that the image type does not match a restricted image type.

Abstract: A data loss prevention device that includes a data loss prevention engine implemented by a processor. The data loss prevention engine is configured to receive data in transit to a target network device and to identify content within the data. The data loss prevention engine is configured to determine the content of the data comprises an image and to determine an image type for the image based on objects within the image, and to determine whether the image type matches a restricted image type from a set of restricted image types. The data loss prevention engine is further configured to block transmission of the data to the target network device in response to determining that the image type matches a restricted image type and forward the data to the target network device in response to determining that the image type does not match a restricted image type.

Abstract: Embodiments of the invention relate to systems, methods, and computer program products for an automated infrastructure management and remediation in an enterprise-type computing infrastructure that provides for automated deployment of critical updates/patches to enterprise-wide computing servers to insure that such updates occur and within prescribed time limits. Further, the invention provides for automatic extraction data from the various different data sources that contain data relevant to the update/patch process, consolidation and transformation of the data to accommodate reporting needs and analytical research and relying on the data to automatically determine the current state of the servers for the subsequent purpose of determining which of enterprise-wide servers require a pending update/patch.