Abstract: Methods for artificial intelligence-based data access control are disclosed herein. An example computer-implemented method comprises: obtaining information management data associated with a computing environment; preprocessing the information management data to generate a set of user profiles and data asset consumption profiles for the plurality of users; generating a set of access controls for the computing environment including a set of user data access entitlements by inputting the preprocessed information management data to one or more generative artificial intelligence models; obtaining a data access request from a user; identifying a subset of the set of access controls including one or more user data access entitlements assigned to the user; applying the one or more user data access entitlements to the data access request to determine whether the data access request is valid or invalid; and in response to determining the data access request is valid, providing data access to the user.

Abstract: Methods for artificial intelligence-based data access control are disclosed herein. An example computer-implemented method comprises: obtaining contextual data for a virtual computing environment; generating, by the one or more generative AI models, a set of access controls for the virtual computing environment including a set of virtual entity data access entitlements based on the contextual data; simulating the virtual computing by: generating one or more virtual data access requests, and applying the set of access controls to the one or more virtual data access requests; evaluating performance for the set of access controls based on the impact of applying the set of access controls to the one or more data access requests; and based on the evaluation: updating, by the one or more generative AI models, the set of access controls; or approving the set of access controls for use in the computing environment.

Abstract: The methods disclosed herein relate to managing, discovering, and performing transactions on retirement accounts using artificial intelligence and/or blockchain. In one embodiment, a method includes retrieving account data for retirement accounts associated with third-party financial institutions, retrieving external financial data, providing the account data to a trained capture model to cause the trained capture model to generate structured account data, providing the structured account data, the external financial data, and user data associated with the user to a trained deep learning forecast model to cause the trained deep learning forecast model to generate a forecast, and outputting the forecast to the user.

Abstract: The methods disclosed herein relate to managing, discovering, and performing transactions on retirement accounts using artificial intelligence and/or blockchain. In one embodiment, a method includes retrieving account data for retirement accounts associated with third-party financial institutions, retrieving external financial data, providing the account data to a trained capture model to cause the trained capture model to generate structured account data, providing the structured account data, the external financial data, and user data associated with the user to a trained deep learning forecast model to cause the trained deep learning forecast model to generate a forecast, and outputting the forecast to the user.

Abstract: System, method, and apparatus for generating a metaverse are provided. The method comprises: receiving data associated with an object associated with the event; codifying at least a portion of the data to generate a set of codified input associated with the object; determining a three-dimensional (3D) model of the object based on the set of codified input; generating a set of output code based on the set of codified input, the 3D model, and infrastructure as code; and transmitting, to a user device, the set of output code to cause the user device to present a 3D artifact in the metaverse corresponding to the object.

Abstract: Disclosed are various embodiments for garbage collection for object-based storage systems. A first set of objects stored in a data object store is identified as not meeting criteria for deletion or archiving, the criteria specified in a retention policy. A second set of objects is identified by performing a search of the object data store for data objects belonging to a data structure with the first set of objects. The second set of objects is recorded as not meeting the criteria for deletion or archiving. The second set of data objects are retained, based on the recording, to prevent the data structure from being corrupted.

Abstract: Systems and methods are described for managing governance operating parameters through the use of machine learning. The method involves: (i) receiving knowledge data, wherein the knowledge data is indicative of one or more operating parameters; (ii) analyzing, using a nested machine learning model comprising a plurality of agent models trained using composite knowledge data, an input associated with a subset of the one or more operating parameters indicated by the composite knowledge data, wherein the composite knowledge data is generated by one or more data processing machine learning models based on the knowledge data; (iii) determining, based on the analyzing, a compliance action, wherein the compliance action is associated with configuring the subset of the one or more operating parameters associated with the input; and (iv) generating, by the one or more processors using the nested machine learning model, a recommendation associated with the compliance action.

Abstract: A computing system assesses and rationalizes technical debt by collecting data, processing it with AI and learning algorithms, generating actions for asset rationalization, and producing detailed reports and AI-enabled videos. A method involves collecting data, processing with AI, generating rationalization actions, and creating reports and videos. A computer-readable medium includes instructions for performing the method of assessing and rationalizing technical debt.

Abstract: A computing system evaluates and manages technical debt by collecting data, determining coding signals, receiving inputs, assessing business capabilities, evaluating these using a scoring system, and generating a tech debt score. A method involves collecting data, determining coding signals, receiving inputs, assessing business capabilities, evaluating these with a scoring system, and generating a tech debt score. A non-transitory computer-readable medium includes instructions for performing these operations to evaluate and manage technical debt.

Abstract: A method for generating predictive and event-based action recommendations includes receiving, from at least one data source, input data representative of a user; generating, using a first machine learning model, a user behavior pattern for the user based on the input data representative of the user; classifying the user behavior pattern based on one or more personas representative of user characteristics; identifying, based on at least the input data and the one or more personas, an inefficiency in the user behavior pattern impacting a goal of the user; and generating, using a second machine learning model, a personalized recommendation for the user to remediate the inefficiency.

Abstract: The methods disclosed herein relate to managing, discovering, and performing transactions on retirement accounts using artificial intelligence and/or blockchain. In one embodiment, a method includes retrieving account data for retirement accounts associated with third-party financial institutions, retrieving external financial data, providing the account data to a trained capture model to cause the trained capture model to generate structured account data, providing the structured account data, the external financial data, and user data associated with the user to a trained deep learning forecast model to cause the trained deep learning forecast model to generate a forecast, and outputting the forecast to the user.

Abstract: Systems and methods for detecting, classifying, and predicting a risk severity and/or likelihood from technical debt within an organization are disclosed. An example system obtains input data including internal organization information, third-party tool information, operational risk management information, previous technical debt assessments, previous asset rationalizations, and business capability information. The system processes the input data using a combination of neural networks, generative artificial intelligence models, and reinforcement learning with human feedback. The system generates the risk from technical debt based on the processing, and generates an output of risk information associated with the risk from technical debt.

Abstract: The following generally relates to distributed ledger and artificial intelligence (AI) technology, and more particularly relates to identity verification through distributed ledger and/or AI technology. In some embodiments, one or more processors: receive, from a user device of a user, user-provided identity information; scrape, from a website, data of the user; add the scraped data of the user to a distributed ledger; and attempt to authenticate the user by inputting, into a smart contract stored on the distributed ledger: (i) the user-provided identity information, and (ii) the scraped data of the user added to the distributed ledger.

Abstract: Systems and methods for managing income streams using a distributed ledger are provided. The system may generate at least one transaction including data to: (i) generate at least one soulbound token (SBT) on the distributed ledger, (ii) deposit the at least one SBT into a digital wallet on the distributed ledger associated with a recipient of the income, and (iii) for the each SBT, generate at least one associated smart contract on the distributed ledger. The SBT may be nontransferable and may correspond to an income stream providing an income from an income source. The smart may be configured to automatically distribute at least a portion of the income into the digital wallet. The system may provide the at least one transaction to a node of the distributed ledger for validation and recording of the at least one transaction on the distributed ledger.

Abstract: Systems and methods for managing income streams using machine learning are provided. The system may receive, from a computing device, distribution criteria including information for distributing at least a portion of income received from the income stream to one or more distribution recipients. The system may obtain soulbound token information associated with the soulbound token and the income stream of the owner, and analyze the distribution criteria and the soulbound token information via a machine learning model to generate one or more distribution insights associated with optimizing distribution of at least the portion of the income. The system may generate, by the machine learning model, one or more distribution schemes associated with distributing at least the portion of the income to the one or more distribution recipients, based upon the one or more distribution insights.

Abstract: Systems and methods for identity-based interconnected transactions are disclosed. The system may receive an interconnected transaction request comprising a first financial subsystem identifier, a second financial subsystem identifier, and a transaction request. The first financial subsystem and the second financial subsystem may not be in direct communication with each other. The first financial subsystem may write the interconnected transaction request to an interconnected transaction ledger. The second financial subsystem may retrieve the interconnected transaction request from the interconnected transaction ledger. In response to retrieving the interconnected transaction request the second financial subsystem may complete the transaction request.

Abstract: A computing system assesses, identifies, and tracks technical debt by analyzing code repositories, application architecture diagrams, and process flows, classifying the debt into application and enterprise debt. A method involves analyzing code repositories, classifying technical debt, and utilizing an AI model. A computer-readable medium includes instructions for assessing and classifying technical debt.

Abstract: A computing system includes a processor and memory with instructions to receive user input on redesign objectives, analyze organizational models, generate recommendations for organizational maturity, and output a redesign plan. A method involves receiving user input on redesign objectives, analyzing organizational models, generating recommendations for organizational maturity, and outputting a redesign plan. A computer-readable medium has instructions for receiving user input on redesign objectives, analyzing organizational models, generating recommendations, and outputting a redesign plan.

Abstract: Disclosed are various embodiments for garbage collection for object-based storage systems. A first set of objects stored in a data object store is identified as not meeting criteria for deletion or archiving, the criteria specified in a retention policy. A second set of objects is identified by performing a search of the object data store for data objects belonging to a data structure with the first set of objects. The second set of objects is recorded as not meeting the criteria for deletion or archiving. The second set of data objects are retained, based on the recording, to prevent the data structure from being corrupted.

Abstract: A system includes a memory, and a processing device, operatively coupled to the memory, to perform operations including obtaining user data associated with a user, generating, using at least one machine learning (ML) model processing the user data, a virtual environment associated with an extended reality (XR) representation corresponding to a personalized recommendation for the user, and creating, based on the user data, a virtual avatar for the user reflecting a representation of the user within the virtual environment. The virtual avatar corresponds to a set of attributes representing visual features of the user.