Abstract: A system comprises a memory communicatively coupled to at least one processor. The at least one processor is configured to receive a first setting configuration associated with a first server of a first data center and receive a second setting configuration associated with a second server of a second data center. Further, the processor is configured to execute a artificial intelligence algorithm to combine the first setting configuration and the second setting configuration into a first combined setting configuration, generate, based at least in part upon the first combined setting configuration, deployment parameters configured to guide provisioning of a third server prior to deployment in a third data center, provision the third server in accordance with the deployment parameters, and deploy a provisioned version of the third server in the third data center.

Abstract: A performance anomaly is detected in relation to a first data center equipment. An AI model is trained based on a plurality of anomaly patterns associated with a plurality of data center equipment and respective remediation processes associated with the anomaly patterns, to determine a remediation process that can resolve the detected performance anomaly associated with the first data center equipment. Upon execution of a machine-learning algorithm, the AI model compares real-time performance indicators recorded for the first data center equipment with performance indicators of the anomaly patterns and determines a matching anomaly pattern. The AI model determines a particular remediation process associated with the matching anomaly pattern. The remediation process is then implemented to resolve the performance anomaly associated with the first data center equipment.

Abstract: An AI model is trained based on a plurality of anomaly patterns associated with a data center equipment to predict a performance anomaly associated with the data center equipment. Upon execution of a machine-learning algorithm, the AI model compares real-time performance indicators associated with the data center equipment to historical performance indicators of the anomaly patterns and determines a matching anomaly pattern. The AI model identifies a performance anomaly associated with the matching anomaly pattern and predicts that the performance anomaly is expected to occur in relation to the data center equipment. A remediation method is then implemented to avoid the performance anomaly from occurring.

Abstract: An AI model is trained to optimize power consumption associated with cooling processing servers of a data center by determining migration of one or more software applications or portions thereof among the processing servers to distribute heat among the processing servers. Upon executing a machine-learning model, the AI model predicts that a scheduled processing of a software application by a first processing server can cause the temperature of the first processing server to equal or exceed a threshold temperature. In response, the AI model generates a recommendation to migrate the processing of the software application from the first processing server to a second processing server. Based on the recommendation, the software application is migrated from the first processing server to the second processing server.

Abstract: A system comprises a memory communicatively coupled to at least one processor. The at least one processor is configured to receive first configuration feedback from a first data center and receive second configuration feedback from a second data center. Further, the at least one processor may be configured to execute the artificial intelligence algorithm to determine that the first data center uses the first resources more efficiently than the second data center uses the second resources in response to determining that the first usage efficiency of the first resources in the first data center is greater than the second usage efficiency of the second resources in the second data center, generate provisioning parameters based on the first configuration feedback, provision the third data center based on the provisioning parameters, and deploy a provisioned version of the third data center based at least in part upon the provisioning parameters.

Abstract: A system comprises a memory communicatively coupled to at least one processor. The at least one processor is configured to receive a first traffic processing availability from a first data center and receive a second traffic processing availability from a second data center. Further, the processor may be configured to execute an artificial intelligence algorithm to determine a first difference of resources between the first unused resources and the first predicted resources to be used, determine a second difference of resources between the second predicted resources to be used and the second unused resources, generate routing commands configured to distribute a portion of the second difference of resources to the first data center, provision the first data center based at least in part upon the routing commands, and deploy a provisioned version of the first data center.

Abstract: An AI model is trained based on a plurality of anomaly patterns associated with a data center to determine that a performance bottleneck has occurred in relation to a data center equipment. Upon execution of a machine-learning algorithm, the AI model compares real-time performance indicators associated with a first data center equipment to historical performance indicators associated with the anomaly patterns and determines a matching anomaly pattern. The AI model identifies a performance bottleneck associated with the matching anomaly pattern and determines that the performance bottleneck has occurred in relation to the first data center equipment. A remediation process is then implemented to resolve the performance bottleneck.

Abstract: Systems, computer program products, and methods are described herein for providing automated command access approval across a network. The method includes receiving a request for a command execution to be performed on a first node. The request for the command execution indicates a user attempting to execute the command on the first node. The method also includes comparing the request for command execution to one or more previous command execution determinations. The one or more previous command execution determinations are associated with the command to be performed. The method further includes determining an execution decision based on the comparison of the request for command execution to one or more previous command execution determinations.

Abstract: Systems, computer program products, and methods are described herein for tracking network traffic data and identifying rogue access patterns in an electronic network. The present invention is configured to receive a plurality of peer user accounts; receiving a plurality of peer user data associated with the plurality of peer user accounts; generating a relational mapping based at least on the predetermined group; and generating a plurality of peer historical data access patterns based on the plurality of peer user data over the historical predetermined period. The present invention may further be configured to receive a primary user account; receive a plurality of primary user data; generate a plurality of primary user access patterns; compare the plurality of peer historical data access patterns and the plurality of primary user access patterns to generate an abnormality score; and determine whether the abnormality score meets the abnormality threshold.

Abstract: An information-security automated system for authorizing a user request for access to a module and for identifying anomalous user authorization requests may include components for receiving the user request for access to the module from the authorization request tool over a computer network, a computer machine comprising a processor, and computer-executable instructions stored on a computer-readable medium configured to cause the processor to perform steps of receiving a user request for access to a module, generating a peer group proximity dataset, generating a user dataset, calculating a critical score, determining if the critical score is less than an enterprise threshold, and generating an approval or denial of the user request based upon the critical score. In some examples the process may include the step of executing machine learning instructions to generate a second approval or a second denial of a second user request for access to a second module.

Abstract: An information-security automated process for authorizing a user request for access to a module and for identifying anomalous user authorization requests, may be implemented on a computer machine and may include the steps of receiving a user request for access to a module, generating a peer group proximity dataset, generating a user dataset, calculating a critical score, determining if the critical score is less than an enterprise threshold, and generating an approval or denial of the user request based upon the critical score. In some examples the process may include the step of executing machine learning instructions to generate a second approval or a second denial of a second user request for access to a second module. In another examples an information-security automated process for validating a user authorization access profile is disclosed herein.

Abstract: An information-security automated process for authorizing a user request for access to a module and for identifying anomalous user authorization requests, may be implemented on a computer machine and may include the steps of receiving a user request for access to a module, generating a peer group proximity dataset, generating a user dataset, calculating a critical score, determining if the critical score is less than an enterprise threshold, and generating an approval or denial of the user request based upon the critical score. In some examples the process may include the step of executing machine learning instructions to generate a second approval or a second denial of a second user request for access to a second module. In another examples an information-security automated process for validating a user authorization access profile is disclosed herein.

Abstract: Systems, computer program products, and methods are described herein for tracking network traffic data and identifying rogue access patterns in an electronic network. The present invention is configured to receive a plurality of peer user accounts; receiving a plurality of peer user data associated with the plurality of peer user accounts; generating a relational mapping based at least on the predetermined group; and generating a plurality of peer historical data access patterns based on the plurality of peer user data over the historical predetermined period. The present invention may further be configured to receive a primary user account; receive a plurality of primary user data; generate a plurality of primary user access patterns; compare the plurality of peer historical data access patterns and the plurality of primary user access patterns to generate an abnormality score; and determine whether the abnormality score meets the abnormality threshold.

Abstract: Systems, computer program products, and methods are described herein for providing automated command access approval across a network. The method includes receiving a request for a command execution to be performed on a first node. The request for the command execution indicates a user attempting to execute the command on the first node. The method also includes comparing the request for command execution to one or more previous command execution determinations. The one or more previous command execution determinations are associated with the command to be performed. The method further includes determining an execution decision based on the comparison of the request for command execution to one or more previous command execution determinations.

Abstract: Systems and methods for accurately and securely assembling, storing, and leveraging travel data are provided. A method may include detecting a travel event of a user. The method may include recording the travel event in a travel map that is stored in a database. The travel map may be a temporal- and spatial-based record of one or more travel events of the user. The travel map may be stored as a blockchain ledger. The method may include tokenizing sensitive information associated with the user in the database, and calculating a hazard vector. The method may include updating, based at least in part on the hazard vector, a status index associated with the user, and providing the user and a system administrator access to the travel map and the status index. Access may be provided via an online portal configured to be viewed via an application running on a mobile device.

Abstract: Systems and methods for accurately and securely assembling, storing, and leveraging travel data are provided. A method may include detecting a travel event of a user. The method may include recording the travel event in a travel map that is stored in a database. The travel map may be a temporal- and spatial-based record of one or more travel events of the user. The travel map may be stored as a blockchain ledger. The method may include tokenizing sensitive information associated with the user in the database, and calculating a hazard vector. The method may include updating, based at least in part on the hazard vector, a status index associated with the user, and providing the user and a system administrator access to the travel map and the status index. Access may be provided via an online portal configured to be viewed via an application running on a mobile device.

Abstract: Systems and methods for accurately and securely assembling, storing, and leveraging travel data are provided. A method may include detecting a travel event of a user. The method may include recording the travel event in a travel map that is stored in a database. The travel map may be a temporal- and spatial-based record of one or more travel events of the user. The travel map may be stored as a blockchain ledger. The method may include tokenizing sensitive information associated with the user in the database, and calculating a hazard vector. The method may include updating, based at least in part on the hazard vector, a status index associated with the user, and providing the user and a system administrator access to the travel map and the status index. Access may be provided via an online portal configured to be viewed via an application running on a mobile device.

Abstract: Systems and methods for accurately and securely assembling, storing, and leveraging travel data are provided. A method may include detecting a travel event of a user. The method may include recording the travel event in a travel map that is stored in a database. The travel map may be a temporal- and spatial-based record of one or more travel events of the user. The travel map may be stored as a blockchain ledger. The method may include tokenizing sensitive information associated with the user in the database, and calculating a hazard vector. The method may include updating, based at least in part on the hazard vector, a status index associated with the user, and providing the user and a system administrator access to the travel map and the status index. Access may be provided via an online portal configured to be viewed via an application running on a mobile device.