Abstract: A computing platform may generate a graphical user interface. The computing platform may embed, into the graphical user interface, steganography, which may include information corresponding to the graphical user interface that is not displayed on the graphical user interface. The computing platform may receive, from a user device of a user, an interface access request and user access credentials. The computing platform may identify, based on the user access credentials, access permissions of the user, which may define portions of the steganography to which the user has access. The computing platform may record, in a change log, interactions of the user with the graphical user interface. The computing platform may compare the interactions in the change log with the access permissions to identify whether or not unauthorized access is detected. Based on detecting the unauthorized access, the computing platform may initiate security actions for the graphical user interface.

Abstract: Arrangements for universal self-service kiosk fraud detection are provided. A request for a transaction may be received via a self-service kiosk. The financial institution associated with the transaction may be identified as part of a consortium of financial institutions. Accordingly, a machine learning model particular to the consortium may be used to analyze the transaction. The model may output a determination of whether fraud or potential fraud exists in the transaction. In some examples, the machine learning model may be trained using historical transaction data from one or more financial institutions that are part of the consortium. If fraud is detected, one or more security actions associated with the transaction may be identified and executed. Further, one or more additional self-service kiosks that may be impacted by the fraud may be identified and security actions may be identified and transmitted to the one or more additional self-service kiosks for execution.

Abstract: A computing platform may train, using historical information access pattern information, a machine learning model to identify unauthorized information access patterns. The computing platform may obscure internal traffic pattern information, and monitor access of the obscured internal traffic pattern information. The computing platform may generate, by inputting information of the access into the machine learning model, a user evaluation output, and may compare the user evaluation output to a first user evaluation threshold. Based on identifying that the user evaluation output meets or exceeds the first user evaluation threshold, the computing platform may modify traffic routing rules corresponding to the user, which may cause activity by the user to be routed to a secure sandbox for further analysis.

Abstract: Aspects of the disclosure relate to using machine-learning models to determine graduated levels of access to secured data for remote devices. In some embodiments, a computing platform may establish a connection with a mobile device. Subsequently, based on establishing the connection, the platform may identify initial device information, device features, and user information. The platform may input the identified information into an authentication model to compute a baseline authentication score and then may identify an initial level of access to secured resources for the mobile device. Thereafter, the platform may receive from the mobile device, AR/VR device information captured by the mobile device. The platform may input the AR/VR device information into the authentication model to compute an augmented authentication score. Based on the augmented score, the platform may identify an augmented level of access to secured resources for the mobile device.

Abstract: A computing platform to generate and manage businesses for users. An artificial intelligence/machine learning (AI/ML) model is continually trained to identify user interests and hobbies that may be transformed into business ventures. The computing platform aggregates electronic data about activities or transactions performed via applications computing systems providing products or services to users. The computing platform facilitates generation of a new business based on prioritized feature sets. The AIMI generates and creates management tools such as business plans that include financial plans, marketing plans, human resource plans, competitor environment analysis, and other management tools for successful operation of the new business.

Abstract: Aspects of the disclosure relate to a method of generating a personalized payment card. In some arrangements, the generated payment card may include on board circuitry and architecture to prevent unauthorized use of the payment card. In some instances, the generated payment card may be a temporary payment card with a fixed transaction limit amount that may or may not be reloadable. The designed personalized payment card may be 3D printed and initialized by a card generating and alerting computing platform.

Abstract: A computing platform to form creative micro-communities of users aggregates electronic data records about electronic activities performed via one or more application computing systems providing products or services to users. An artificial intelligence/machine learning (AI/ML) model is continually trained to identify users with similar interests and group users into micro-communities based on the identified common interests. The computing platform generates micro-communities based on AI determined features or identifiers of a user, or a user may self-identify in a particular category to join the micro-community. Each micro-community may have tiers of participants that may be identified via the AI/ML models. Each micro-community may have customized rules, parameters, or the like, such as protections from certain types of communications.

Abstract: A computing platform to form creative micro-communities of users aggregates electronic data records about electronic activities performed via one or more application computing systems providing products or services to users. An artificial intelligence/machine learning (AI/ML) model is continually trained to identify users with similar interests and group users into micro-communities based on the identified common interests. The computing platform generates micro-communities based on AI determined features or identifiers of a user, or a user may self-identify in a particular category to join the micro-community. Each micro-community may have tiers of participants that may be identified via the AI/ML models. Each micro-community may have customized rules, parameters, or the like, such as protections from certain types of communications.

Abstract: Arrangements for enhanced system and graphical user interface customization based on machine-learned context are provided. In some aspects, historical data may be received from a plurality of data sources and used to train a machine learning model to generate recommended modifications to systems or user interfaces based on user specific data. User specific data may be received from a plurality of data sources. The user specific data may be used as inputs to the machine learning model and, upon execution of the model, a recommendation for one or more modifications to at least one of a system or a user interface may be output. The recommendation may be provided to the user and, if accepted, an instruction causing the recommended modification may be generated and transmitted to one or more computing devices. Additional user specific data may be subsequently received and analyzed to identify additional modifications for recommendation and/or execution.

Abstract: Arrangements for communication channel customization are provided. In some aspects, historical data may be received from a plurality of data sources and used to train a machine learning model to generate recommended categories for association with users and customizations to communication schemes. Upon registering a user, user specific data may be received from data sources. The user specific data may be input to the machine learning model and, upon execution of the model, a recommended category for association with the user may be output. Based on the recommended category, a communication scheme may be retrieved and executed for the user. Subsequent user data may be received and used as inputs in the machine learning model. The model may be executed to output one or more customizations to the communication scheme. The one or more customizations may be transmitted to one or more computing systems and executed to further customize communications.

Abstract: Aspects of the disclosure relate to using machine-learning models to determine graduated levels of access to secured data for remote devices. In some embodiments, a computing platform may establish a connection with a mobile device. Subsequently, based on establishing the connection, the platform may identify initial device information, device features, and user information. The platform may input the identified information into an authentication model to compute a baseline authentication score and then may identify an initial level of access to secured resources for the mobile device. Thereafter, the platform may receive from the mobile device, AR/VR device information captured by the mobile device. The platform may input the AR/VR device information into the authentication model to compute an augmented authentication score. Based on the augmented score, the platform may identify an augmented level of access to secured resources for the mobile device.

Abstract: Aspects of the disclosure relate to computing hardware and software for enhancing collaborative experience security. A computing platform may receive, from a user device, a request to join an enhanced reality collaborative experience involving use of an avatar in an AR or VR environment. The computing platform may verify an authorization channel for the user device, and may grant the user device access to the enhanced reality collaborative experience. The computing platform may receive biometric information corresponding to the user. Using data collected by the user device and the biometric information, the computing platform may perform anomaly detection to detect anomalies in the avatar's participation in the enhanced reality collaborative experience. In response to detecting the anomalies, the computing platform may perform one or more security actions for the enhanced reality collaborative experience.

Abstract: Arrangements for a universal self-service kiosk platform are provided. In some aspects, registration data may be received from a plurality of enterprise organizations and may include self-service kiosk configuration data for each enterprise organization. A request to associate a self-service kiosk with a first enterprise organization may be received and may include identification of the first enterprise organization and the self-service kiosk. In response, a self-service kiosk configuration associated with the first enterprise organization may be retrieved and an instruction causing the self-service kiosk to implement the self-service kiosk configuration associated with the first enterprise organization may be generated. The instruction may be transmitted to the self-service kiosk.

Abstract: Aspects of the disclosure relate to computing hardware and software for enhancing collaborative experience security. A computing platform may receive, from a user device, a request to join an enhanced reality collaborative experience involving use of an avatar in an AR or VR environment. The computing platform may verify an authorization channel for the user device, and may grant the user device access to the enhanced reality collaborative experience. The computing platform may receive biometric information corresponding to the user. Using data collected by the user device and the biometric information, the computing platform may perform anomaly detection to detect anomalies in the avatar's participation in the enhanced reality collaborative experience. In response to detecting the anomalies, the computing platform may perform one or more security actions for the enhanced reality collaborative experience.

Abstract: Aspects of the disclosure relate to computing hardware and software for uniform security access. A computing platform may receive, from a 5G device, a request to access a collaborative experience service. The computing platform may identify whether the 5G device is authenticated. Based on identifying that the 5G device is authenticated, the computing platform may grant the 5G device access to an API that may be used to access the collaborative experience service. The computing platform may identify that attributes of the 5G device comply with a stored security policy, and grant access to the collaborative experience service in response. The computing platform may receive user interaction information from the 5G device, and may input the user interaction information into an anomaly detection model. Based on identifying that the interactions do not comprise an anomaly for the user, the computing platform may maintain the access to the collaborative experience service.

Abstract: Aspects of the disclosure relate to computing hardware and software for uniform security access. A computing platform may receive, from a 5G device, a request to access a collaborative experience service. The computing platform may identify whether the 5G device is authenticated. Based on identifying that the 5G device is authenticated, the computing platform may grant the 5G device access to an API that may be used to access the collaborative experience service. The computing platform may identify that attributes of the 5G device comply with a stored security policy, and grant access to the collaborative experience service in response. The computing platform may receive user interaction information from the 5G device, and may input the user interaction information into an anomaly detection model. Based on identifying that the interactions do not comprise an anomaly for the user, the computing platform may maintain the access to the collaborative experience service.

Abstract: Aspects of the disclosure relate to energy optimization. A computing platform may receive an event processing request, and may identify its parameters. The computing platform may package, based on the parameters, the event processing request into containers. The computing platform may input the parameters into an energy optimization model, which may identify: a data center computing system and an energy mix for processing the event processing request. The computing platform may encrypt the containers. The computing platform may send, to the data center computing system, the encrypted containers and commands directing the data center computing system to: decrypt the encrypted containers to identify the event processing request, and process the event processing request using the energy mix, which may cause the data center computing system to decrypt the encrypted containers and process the event processing request using the energy mix.

Abstract: Aspects of the disclosure relate to energy optimization. A computing platform may receive a cryptocurrency request indicating a gas price and KPIs of the cryptocurrency request. The computing platform may input the gas price and the KPIs into a mining optimization model, which may cause the mining optimization model to output an energy mix for use in processing the cryptocurrency request. The energy mix may indicate energy sources, capable of providing an amount of energy needed to process the cryptocurrency request and used to support operation of a data center configured to process cryptocurrency requests, and a cost of the amount of energy from the energy mix may be less than a cost of the amount of energy from other available energy sources. The computing platform may send commands directing the data center to process the cryptocurrency request using the energy mix, which may cause it to do so.

Abstract: Aspects of the disclosure relate to energy optimization. A computing platform may receive an event processing request. The computing platform may identify parameters of the event processing request. The computing platform may input the parameters into a global energy optimization model, to identify an edge computing system at which to process the event processing request. The computing platform may route the event processing request to the edge computing system along with commands to process the event processing request using an energy mix identified by a local energy optimization model, corresponding to the edge computing system, which may cause the edge computing system to: input the parameters of the event processing request into the local energy optimization model to identify the energy mix, and process, using the energy mix, the event processing request.

Abstract: Aspects of the disclosure relate to using machine-learning models to determine graduated levels of access to secured data for remote devices. In some embodiments, a computing platform may establish a connection with a mobile device. Subsequently, based on establishing the connection, the platform may identify initial device information, device features, and user information. The platform may input the identified information into an authentication model to compute a baseline authentication score and then may identify an initial level of access to secured resources for the mobile device. Thereafter, the platform may receive from the mobile device, AR/VR device information captured by the mobile device. The platform may input the AR/VR device information into the authentication model to compute an augmented authentication score. Based on the augmented score, the platform may identify an augmented level of access to secured resources for the mobile device.