Abstract: Embodiments of the present invention provide a system for performing authentication of users connecting to entity resources via external systems. The system is configured for determining that a user is requesting to access an entity resource associated with an entity, initiating a verification mechanism for performing verification of the user, authenticating the user based on performing the verification of the user via the verification mechanism, and allowing or denying access to the entity resource based on successful or unsuccessful authentication of the user.

Abstract: Systems, computer program products, and methods are described herein for determining readiness for managed runtime application program interface (API) applications via artificial intelligence. The present disclosure is configured to scan a set of API applications and their associated custom port hosted within a managed runtime environment using a custom port probe embedded within the managed runtime environment, where the custom port probe is operably coupled to a load balancer, where user traffic within the managed runtime environment is processed by the set of API applications and the custom port; initialize a set of components within the custom port for operation; verify readiness of the set of API application and the custom port probe using an artificial intelligence system; and transmit a notification to the load balancer indicating readiness of the set of API applications.

Abstract: Embodiments of the invention are directed to systems, methods, and computer program products for provisioning a virtual lab environment for software development. In some embodiments, the method includes defining a set of features in the virtual lab environment; receiving, via a user input on a user interface, an unstructured dataset; generating a structured dataset using a generative artificial intelligence (AI) engine, where the unstructured dataset is an input of the generative AI engine; identifying, based on the structured dataset, a preferred configuration, where the preferred configuration comprises a target set of features; initiating a code delivery process, where the code delivery process includes configuring the set of features in the virtual lab environment to match the target set of features. The method may also include identifying at least one available software agent and displaying the at least one available software agent on the user interface.

Abstract: Systems, computer program products, and methods are described herein for reconfiguration authorization protocol and analytics via integrated machine learning. The present disclosure includes receiving a proposed alteration comprising alteration data comprising an application identifier and description data, retrieving at least one configuration item device associated with the application identifier, determining a category of impact based on the alteration data, routing the alteration data to at least one module of a machine learning model, determining dependencies of the proposed alteration based on the application identifier and alteration data, determining a materiality of the proposed alteration, and causing to be displayed, at a first endpoint device, an escalation notice, upon a first condition where the materiality score is above a predetermined threshold.

Abstract: Systems, computer program products, and methods are described herein for enhanced observability within application containers on a container orchestration platform. The present disclosure is configured to: receive an application container within a container orchestration platform, wherein the application container comprises an internal state; embed a container application programming interface (API) within the application container, wherein the container API is linked to an external observability service; transmit a hyper text transfer protocol (HTTP) call from the external observability service to the container API within the application container; observe the internal state of the application container via the container API; and transmit the internal state of the of the application container from the container API to the external observability service.

Abstract: An application-level determination of anomalies in response to issuance of an incident ticket/report. AI including NLP is implemented to decipher the unstructured freeform data in an incident ticket to positively identify the resource event that caused the incident. Once the resource event has been identified, a resource event tracing record associated with the resource event is analyzed to determine one or more anomalies. Analysis of the resource event tracing record may include comparison of the resource event tracing record to other resource event tracing records having one or more similar attributes to determine differences in recorded metrics, which may indicate anomalies. In response to determining the isolated and/or systemic anomalies, an anomaly report that indicates at least a portion of the determined anomalies is generated, and electronic communication is initiated.

Abstract: Embodiments of the present invention provide a system for identifying and remediating small part files in data file systems processing large datasets. The system is configured for continuously monitoring a data file system to identify import of datafiles, determining import of the datafiles into the data file system based on monitoring the data file system, analyzing the datafiles, via an artificial intelligence engine, determining that at least two datafiles of the datafiles are small part files based on analyzing the datafiles via the artificial intelligence engine, and performing auto-remediation of the at least two datafiles, via the artificial intelligence engine.

Abstract: Embodiments of the present invention provide a system for performing proactive latency detection in messaging service queues. The system is configured for continuously monitoring, in real-time, one or more messaging service queues associated with one or more applications of an entity, identifying one or more messages in the one or more messaging service queues, reading header of at least one message of the one or more messages to identify at least one queue attribute, comparing the at least one queue attribute to a predefined queue attribute associated with the one or more applications, determining that the at least one queue attribute exceeds the predefined queue attribute, and transmitting one or more notifications to one or more users associated with the entity.

Abstract: Systems, computer program products, and methods are described herein for automatic requirements capture and generation of system builds using artificial intelligence. The present invention is configured to receive a plurality of architectural requirements for a system build; render an interactive graphical user interface (GUI) for display on user devices, wherein the GUI includes information for a plurality of system builds; receive an input from a user device displaying the GUI, wherein the input includes information for the system build; validate the input for the system build; transmit, upon invalidation of the input, a notification of an invalid input to the user device; transmit, upon validation of the input, the input to networked devices associated with the system build; receive a build output file for the system build; and store the build output file in at least one memory device.

Abstract: Systems, computer program products, and methods are described herein for an AI-powered adaptable drift management system for computer servers.

Abstract: Assisting in the alleviation of Domain Name Service (DNS) request saturation by tracking and identifying DNS queries sent from a server to at least one target server and DNS answers sent from the at least one target server to the server in response to the DNS queries. In response to identifying the DNS queries and DNS answers, (i) destination ports are determined for each DNS answer, with each destination port being associated with a network source, (ii) the network source associated with each destination port is identified, and (iii) the number of connections made between each destination port and the at least one target server is determined. Determining the number of connections between each destination port and the at least one target server can be used, along with DNS caches, to consolidate multiple connections between a destination port and the at least one target server into one persistent connection.