Abstract: Systems, computer program products, and methods are described herein for selective data routing in a distributed network via data throughput analysis in a distributed ledger network. The present invention is configured to receive a request for a data transmission from an endpoint device of a network, determine, via a routing module, a throughput of data associated with the data transmission, determine a classification of the data transmission, and assign the data transmission to an assigned routing channel. The assigned routing channel may include either a first routing channel or a second routing channel based on the classification, wherein the first routing channel is a light load network, and wherein the second routing channel is a heavy load network comprising a first multicore optical fiber.

Abstract: Arrangements for detecting and resolving anomalies are provided. A computing platform may train an artificial intelligence (AI) engine. The computing platform may configure one or more anomaly detection rules and deploy the one or more anomaly detection rules. The computing platform may receive an encrypted screenshot. The computing platform may decrypt the encrypted screenshot. The computing platform may input the decrypted screenshot into the AI engine. The computing platform may output, based on analyzing the decrypted screenshot using the AI engine, an action to resolve an identified anomaly. The computing platform may execute the action to resolve the identified anomaly.

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 database configurations using advanced computational models for data analysis and automated processing. The present disclosure is configured to receive a first container record including data associated with a container and store the first container record in a first database. The present disclosure is configured to receive a trigger, wherein the trigger causes a generation of a second container record. The present disclosure is configured to determine a difference between the first container record and the second container record. The difference may affect at least one of an application, a file, or an additional container associated with the container.

Abstract: Systems, computer program products, and methods are described herein for decommissioning applications in an electronic environment.

Abstract: Aspects of the disclosure relate to using multiple machine learning models and a sand-box environment to detect malicious uniform resource locators (URLs) and login pages stored in a QR code. An application on a computing device will augment QR code data stored in a QR code and the computing device sends the QR code data and computing device metadata to a deep learning computing platform. The QR code data may comprise a URL and communication protocol information. The deep learning computing platform will generate, by multiple machine learning models and a sand-box environment, a sand-box score using the URL, a user-based score using the computing device metadata, and a connections score using the communication protocol information. The deep learning platform then determines whether the computing device should reject and delete the QR code data based on whether the scores are below a pre-determined threshold.

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 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 present invention provide a system for generating on-demand single-use biometric authentication credentials. The system is configured for determining initiation of an authentication request to access a resource from a user device of a user, prompting the user to select a type of authentication credential from one or more types of authentication credentials, receiving a selection of the type of the authentication credential, instantaneously generating a single-use unique authentication credential associated with the type of the authentication credential, via one or more machine learning models, and transmitting the single-use unique authentication credential to the user device for use in authentication of the user in gaining accessing to the resource.

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: Systems, computer program products, and methods are described herein for automatic mapping of application component dependencies using log analysis. The present disclosure is configured to collect connectivity data from various sources, including log files, service logs, and network packet captures, to automatically identify relationships between applications. The system analyzes the collected data to determine communication protocols, interaction types, and data exchange volumes between the applications. Unique application identifiers are extracted to associate each communication with the correct application. The system persists the determined dependencies and associated metadata in a storage device, and generates visual maps of application relationships for user display. The system is further capable of producing on-demand reports based on user-defined parameters, such as relationship depth and specific communication paths.

Abstract: Arrangements for detecting polymorphic botnets using a pattern matching engine are provided. The platform may train a pattern matching engine to generate image pattern statuses based on a training set of historical image patterns. The platform may automatically generate real-time interaction information based on monitoring interactions between user devices and cloud platforms. The platform may generate a potentially malicious image pattern based on the real-time interaction information. The platform may then input the image pattern into the pattern matching engine to generate an image pattern status for the image pattern. The platform may store the image pattern to a pattern storage repository. Based on identifying the image pattern as malicious, the platform may initiate a security action. The platform may update the pattern matching engine based on the image pattern and its image pattern status to detect future polymorphic botnet attacks.

Abstract: Systems, computer program products, and methods are described herein for a secure tunnel proxy with software-defined perimeter for network data transfer. The present disclosure is configured to receive sensor configuration information associated with an Internet of Things (IoT) sensor; receive information associated with an edge computing device configured to receive sensor data from the IoT sensor; configure a secure tunnel proxy between the IoT sensor and the edge computing device based on at least the sensor configuration information and the information associated with the edge computing device; and deploy the secure tunnel proxy between the IoT sensor and the edge computing device.

Abstract: Systems, computer program products, and methods are described herein for monitoring and predicting technology component performance and generating real time alerts. The present disclosure is configured to collect, from at least one source component, metric data at a pre-defined interval; cluster the metric data into at least one bucket based on a type of the metric data and the pre-defined interval; collect historical metric data associated with the at least one source component at a plurality of historical pre-defined intervals; apply the at least one bucket and the historical metric data associated with the at least one source component to a prediction module; and determine, by the prediction module, a predicted trend for the at least one source component.

Abstract: Systems, computer program products, and methods are described herein for software application access management. The present disclosure is configured to receive a trigger; determine a user main account login, wherein the user main account login comprises a user is logged into a main account on a user device; access a peripheral device, wherein the peripheral device is used to determine a physical location of the user, and wherein the peripheral device is communicatively coupled to the user device; determine a user presence, wherein the user presence comprises determining the user is physically near the user device by using data from the peripheral device; retrieve an address from a database, wherein the address is associated with an application, and wherein the address is retrieved via querying the database; and execute a macro, wherein the macro is used to access the application via the address using a network protocol.

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: Systems, computer program products, and methods are described herein for determining and locating network component errors in a distributed network. The present invention is configured to collect, from at least one source component, exception data at a pre-defined interval; group the exception data into a bucket(s) based on the at least one source component, the pre-defined interval, and an exception type; determine, based on the bucket(s) and by an artificial intelligence (AI) pattern module, an average exception count for the bucket(s) over the pre-defined interval; collect historical exception data associated with the bucket(s); compare, by the AI pattern module, the historical exception data associated with the bucket(s) and the average exception count for the bucket(s); and determine, based on the comparison, an outlier exception pattern for the bucket(s) for the pre-defined interval.

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.