Abstract: Techniques facilitating maintenance of tribal knowledge for accelerated compliance control deployment are provided. In one example, a system includes a memory that stores computer executable components and a processor that executes computer executable components stored in the memory, wherein the computer executable components include a knowledge base generation component that generates a knowledge graph corresponding to respective commitments created via tribal exchanges, the knowledge graph comprising a semantic level and an operational level; a semantic graph population component that populates the semantic level of the knowledge graph based on identified parties to the respective commitments; and an operational graph population component that populates the operational level of the knowledge graph based on tracked status changes associated with the respective commitments.

Abstract: A system includes a memory that stores computer executable components and neural network data, and a processor executes computer executable components stored in the memory. An assessment component assesses a computer network, and classifies the computer network relative to M network classifications stored in a repository, wherein M is an integer greater than one. A risk component determines risk of vulnerability subject to change impact regarding protection against a computer virus or cyber-attack based on historical information regarding vulnerability exposure and vulnerability remediation changes relative to the classification of the computer network. A recommendation component that generates recommendations and best action to mitigate risk and impact, and remediate the vulnerabilities based on the risk assessment and business priorities.

Abstract: Techniques facilitating maintenance of tribal knowledge for accelerated compliance control deployment are provided. In one example, a system includes a memory that stores computer executable components and a processor that executes computer executable components stored in the memory, wherein the computer executable components include a knowledge base generation component that generates a knowledge graph corresponding to respective commitments created via tribal exchanges, the knowledge graph comprising a semantic level and an operational level; a semantic graph population component that populates the semantic level of the knowledge graph based on identified parties to the respective commitments; and an operational graph population component that populates the operational level of the knowledge graph based on tracked status changes associated with the respective commitments.

Abstract: Techniques facilitating relational patch orchestration based on impact analysis are provided. In one example, a computer-implemented method comprises creating, by a device operatively coupled to a processor, patch execution plans for one or more pending patches associated with a computing environment; quantifying, by the device, impact of respective ones of the patch execution plans based on dependencies associated with the respective ones of the patch execution plans; and optimizing, by the device, a patch execution plan from the patch execution plans based on the impact of the respective ones of the patch execution plans.

Abstract: A method and system of identifying a computing device vulnerability is provided. Social media communication is monitored. Social media threads that are related to a vulnerability, based on the monitored social media communication, are identified, filtered, and categorized into one or more predetermined categories of computing device vulnerabilities. Upon determining that a number of social media posts related to the vulnerability is above a first predetermined threshold, one or more dependable social media threads in a same one or more categories as the vulnerability are searched. One or more possible root causes of the vulnerability are determined from the searched dependable social media threads. A validity score for each of the one or more possible root causes is assigned. A possible root cause from that has a highest validity score that is above a second predetermined threshold is selected to be the root cause of the vulnerability.

Abstract: A method and system of identifying technical experts for an identified vulnerability is provided. One or more technical experts for each of one or more categories of the vulnerability are identified. Questions are sent to and answers are received from the one or more identified technical experts for each of the one or more categories of vulnerabilities, via a chatbot module. Answers to parameters that are missing for a Common Vulnerability Scoring System (CVSS) for the identified vulnerability are determined from the received answers to the parameters. The answers to the parameters are validated and a CVSS score is calculated based on the validated determined answers.

Abstract: A method, a computer program product, and a computer system for receiving and sharing files in a group messaging environment. One or more servers in an infrastructure computing environment receive a file from a mobile device of a first user in a group of users in a session of group messaging. The one or more servers determine locations, battery levels, and data download speeds of mobile devices of the users. The one or more servers determine co-located users, based on the locations. The one or more servers select a second user from the co-located users, based on the battery levels and the data download speeds. The one or more servers send the file to a mobile device of the second user. The mobile device of the second user sends the file to mobile devices of the co-located users through a short-range communication medium.

Abstract: Techniques that facilitate group patching recommendation and/or remediation with risk assessment are provided. In one example, a system includes a vertical stack component, a horizontal stack component and a risk classification component. The vertical stack component identifies a first patch profile from a software system associated with a computer system environment. The horizontal stack component identifies a second patch profile from a hardware system associated with network nodes of the computer system environment. The system learns over time to identify repetitive patterns using machine learning techniques. Then, the risk classification component performs a machine learning process to determine a risk classification for the computer system environment based on the first patch profile and the second patch profile.

Abstract: Techniques for dynamic server groups that can be patched together using stream clustering algorithms, and learning components in order to reuse the repeatable patterns using machine learning are provided herein. In one example, in response to a first risk associated with a first server device, a risk assessment component patches a server group to mitigate a vulnerability of the first server device and a second server device, wherein the server group is comprised of the first server device and the second server device. Additionally, a monitoring component monitors data associated with a second risk to the server group to mitigate the second risk to the server group.

Abstract: A computer receives a first information related to a communication sent to a user device of a second person. The computer determines a user device of a first person using the first information. The computer sends a context information request to the user device of the first person. The computer receives a context information reply responsive to the context information request. The computer sends context information of the context information reply to the user device of the second person. The computer releases the communication to the user device of the second person.

Abstract: A computer receives a plurality of records each relevant to a relationship between a first person and a second person. The computer determines a communication score of a communication from the first person to the second person, wherein the score is based on at least one of the plurality of records. The computer compares the communication score to an established mediation threshold. Responsive to determining that the communication score violates the established mediation threshold, the computer passes the communication to a user device of the second person. Further, the computer prioritizes the plurality of records according to a set of priorities, and presents the plurality of records to the first person or to the second person.

Abstract: A computer receives a first attempt at communication made via a first communication channel and stores a first attempt record corresponding to the first attempt at communication in an attempts file. The computer receives a second attempt at communication made through a second communication channel that is different from the first communication channel and stores a second attempt record corresponding to the second attempt at communication in the attempts file. The computer queues at least a portion of the first attempt record and at least a portion of the second attempt record. The computer determines that the first attempt at communication and the second attempt at communication were created by a common sender, determines a third communication channel for replying to the common sender, and transmits a third attempt at communication to the common sender via the third communication channel.

Abstract: A computer receives a first information related to a communication sent to a user device of a second person. The computer determines a user device of a first person using the first information. The computer sends a context information request to the user device of the first person. The computer receives a context information reply responsive to the context information request. The computer sends context information of the context information reply to the user device of the second person. The computer releases the communication to the user device of the second person.

Abstract: A computer receives a plurality of records each relevant to a relationship between a first person and a second person. The computer determines a communication score of a communication from the first person to the second person, wherein the score is based on at least one of the plurality of records. The computer compares the communication score to an established mediation threshold. Responsive to determining that the communication score violates the established mediation threshold, the computer passes the communication to a user device of the second person. Further, the computer prioritizes the plurality of records according to a set of priorities, and presents the plurality of records to the first person or to the second person.

Abstract: A computer receives a first attempt at communication made via a first communication channel and stores a first attempt record corresponding to the first attempt at communication in an attempts file. The computer receives a second attempt at communication made through a second communication channel that is different from the first communication channel and stores a second attempt record corresponding to the second attempt at communication in the attempts file. The computer queues at least a portion of the first attempt record and at least a portion of the second attempt record. The computer determines that the first attempt at communication and the second attempt at communication were created by a common sender, determines a third communication channel for replying to the common sender, and transmits a third attempt at communication to the common sender via the third communication channel.