Abstract: Systems and methods for novel uses and/or improvements to blockchains and blockchain technology. As one example, systems and methods are described herein for self-validating digital records that may be transferred in real-time through a cross-network registry. For example, in a conventional system, minting a token (e.g., a digital record) involves writing a self-executing program that defines the transfer rules of the digital record. Once the self-executing program is written, it is deployed on a blockchain, and the digital record is minted by publishing it to a blockchain.

Abstract: The technology evaluates the compliance of an AI application with predefined vector constraints. The technology employs multiple specialized models trained to identify specific types of non-compliance with the vector constraints within AI-generated responses. One or more models evaluate the existence of certain patterns within responses generated by an AI model by analyzing the representation of the attributes within the responses. Additionally, one or more models can identify vector representations of alphanumeric characters in the AI model's response by assessing the alphanumeric character's proximate locations, frequency, and/or associations with other alphanumeric characters. Moreover, one or more models can determine indicators of vector alignment between the vector representations of the AI model's response and the vector representations of the predetermined characters by measuring differences in the direction or magnitude of the vector representations.

Abstract: Systems and methods for providing network validations for cloud-based network architectures are described herein. For example, the system may receive a network requirement for a first cloud-based network architecture. The system may receive a first network action that corresponds to the network requirement when facilitated by the first cloud-based network architecture. The system may process the first network action through the first cloud-based network architecture. The system may receive a first indicium of security components used to process the first network action through the first cloud-based network architecture. The system may compare the first indicium to a known indicium for processing the first network action through an approved cloud-based network architecture. The system may generate a first network validation based on comparing the first indicium to the known indicium.

Abstract: The technology evaluates the compliance of an AI application with predefined vector constraints. The technology employs multiple specialized models trained to identify specific types of non-compliance with the vector constraints within AI-generated responses. One or more models evaluate the existence of certain patterns within responses generated by an AI model by analyzing the representation of the attributes within the responses. Additionally, one or more models can identify vector representations of alphanumeric characters in the AI model's response by assessing the alphanumeric character's proximate locations, frequency, and/or associations with other alphanumeric characters. Moreover, one or more models can determine indicators of vector alignment between the vector representations of the AI model's response and the vector representations of the predetermined characters by measuring differences in the direction or magnitude of the vector representations.

Abstract: A system facilitates a process for automatically deploying artificial intelligence (AI) models. The system receives, for a first artificial intelligence (AI) model used by an entity, a first request to deploy the first AI model to make the first AI model available for use in a production environment to process input data and generate corresponding outputs. A first model deployment location for the first model is selected based on a model deployment engine. The system generates scripts to deploy the first AI model to the selected location, then monitors operations parameters associated with the deployment of the first AI model. Based on the values of the operations parameters, the system updates the model deployment engine. In response to a second request to deploy a second AI model, the system uses the updated model deployment engine to select a second model deployment location for the second model.

Abstract: Systems and methods for novel uses and/or improvements to data migration. In particular, systems and methods for data migration of metadata stored in object storage from one container to another, especially in instances when the metadata is destined for ingestion by an artificial intelligence application. The systems and methods ensure that all metadata (e.g., metadata stored in object storage) is preserved during data migration, including metadata such as content type, object lock mode, object lock retain until date, and/or custom metadata.

Abstract: A distributed private ledger function of a server of a first consortium member receives data representing an alias for one of its customers from the customer and also receives data that represents an alias for a customer of a second member replicated by a distributed private ledger function of a server of the second member to all members of the consortium. Thereafter, the distributed private ledger function of the first member's server identifies a recipient account of the second member's customer based on an account pointer associated with the alias of the second member's customer and initiates a transfer of funds from a source account of the first member's customer corresponding to an account pointer associated with the alias for the first member's customer to the identified recipient account of the second member's customer.

Abstract: Systems and methods for generating code for artificial intelligence models without requiring training data that is specific to model components and objectives. For example, the system may receive an original version of a rule engine. The system may input the original version, using a first input condition, into a regeneration model to generate a first regenerated version of the rule engine. The system may determine whether the first regenerated version includes a first hallucination based on comparing the first regenerated version to alternative versions of the rule engine, wherein each of the alternative versions were generated using a respective alternative input condition. The system may, in response to determining that the first regenerated version includes the first hallucination, determining whether the first hallucination comprises a positive mutation.

Abstract: A computer-implemented method for seamlessly processing transactions using distributed ledger technology. The method may comprise: linking one or more conventional accounts hosted in a conventional banking infrastructure to one or more DLT-based client accounts hosted on a distributed ledger, wherein the DLT application comprises a routing address configured to be used in conventional transaction infrastructure using conventional communication protocols; storing one or more wallet identifications for the one or more DLT-based client accounts and a mapping of the one or more wallet identifications to the one or more conventional accounts hosted in the conventional banking infrastructure; exchanging a sequence of messages to execute an asset transfer and complete a transaction lifecycle, the sequence of messages based on the first asset type; updating the distributed ledger based on the asset transfer; and sending appropriate messages to clients.

Abstract: Presented herein are systems and methods for the employment of machine learning models for image processing as may be performed by computing devices associated with an end user. A method may include obtaining video data comprising a plurality of frames including a document of a document type. The method may include executing an object recognition engine of a machine-learning architecture using image data of the plurality of frames, the object recognition engine trained to detect edges of documents. The method may include identifying, based on the edge detection, a plurality of boundaries for the document. The method may include validating, based on the plurality of boundaries, the document as the document type. The method may include transmitting via one or more networks, to a computer remote from the computing device, responsive to the validation of the type of document, the image data for the plurality of frames depicting the document.

Abstract: A computer-implemented method comprising receiving a request to execute a transaction transferring transaction data from a first account to a second account associated with a second computing device in communication with a distributed ledger, the request comprising an identification of the first account and the second account; identifying a third account associated with a third computing device in communication with the distributed ledger; retrieving first account data for the first account and third account data for the third account from the distributed ledger; comparing a first value from the first account data and a third value from the third account data to a threshold; determining the transaction satisfies a transaction policy; and generating a record in the distributed ledger indicating the transaction transferring the transaction data from the first account to the second account in response to the determination that the transaction satisfies the transaction policy.

Abstract: Systems and methods for streamlining risk modeling in software development using natively sourced kernels are described. The system may receive a native kernel for the first model, wherein the native kernel comprises a native code sample and a native description of the native code sample. The system may input the native code sample into an artificial intelligence model to generate a first output. The system may filter the first output based on the native description to generate a first validation assessment for the first model. The system may generate for display, in the user interface, the first validation assessment.

Abstract: Systems and methods for streamlining risk modeling in software development using natively sourced kernels are described. The system may receive a native kernel for the first model, wherein the native kernel comprises a native code sample and a native description of the native code sample. The system may input the native code sample into an artificial intelligence model to generate a first output. The system may filter the first output based on the native description to generate a first validation assessment for the first model. The system may generate for display, in the user interface, the first validation assessment.

Abstract: Presented herein are systems and methods for enabling a secure browsing session. Embodiments may include a computing device that executes software routines to receive a first indication to enter a secure browsing mode of a session; present data associated with a user for display on the display of the electronic device; cause the display to obscure one or more personally identifiable information of the data on the display of the electronic device; receive a second indication to reveal the one or more personally identifiable information; present the one or more personally identifiable information for display on the display of the electronic device.

Abstract: Systems and methods for managing resources across a global and/or cloud network. In particular, systems and methods for mitigating issues related to providing services while resources are off-line (or may potentially go off-line). For example, the systems and methods may mitigate issues related to providing services while resources are off-line (or may potentially go off-line) by monitoring network services at an aggregate level.

Abstract: Methods and systems are described herein for monitoring fault events at a fiber optical network. In particular, a system may receive, from components of an optical network, corresponding component data structures comprising optical measurements. The system may extract, from a component data structure, a set of component metrics for light transmission signals being transmitted or received via fiber optic transmission lines at a corresponding component and input the component data structure into a first machine learning model to obtain an indication of an occurrence of an event at one or more components. The system may generate a prompt for input into a second machine learning model configured to identify corrective actions for addressing any events within optical networks to obtain one or more corrective actions for addressing the occurrence of the event.

Abstract: Systems and methods may use one or more artificial intelligence models that predict an effect of a predicted event on a current state of the system. The systems and methods may use one or more artificial intelligence models that predict an effect and/or occurrence of a predicted event based on the current state of the system. In order to generate responses that are both timely and pertinent (e.g., in a dynamic fashion), the system must determine, both quickly (i.e., in real-time or near real-time) and accurately, the predicted event.

Abstract: An identity verification system enables peer-to-peer authentication in a potentially insecure channel by leveraging a secure channel communication. The system authenticates a user via an identity verification application. The system provides a validation code to the user. The user communicates the validation code to a counterparty of the peer-to-peer communication. The system receives a request to authenticate the counterparty with the validation code and counterparty authentication data. The system authenticates the counterparty and sends the user the authentication of the counterparty. Alternatively, the user device communicates a request to generate a secure code for participants in a first insecure group application session. The user device selects an authenticated counterparty to receive the secure code from a list of authenticated counterparties. The user creates a second application session using the secure code as a password.

Abstract: A computer can connect with a middleware computing system. The middleware computing system may use application programming interfaces (APIs), webhooks, file-based integration, database replication, message queues, websockets, or direct integration to establish connections with different computing devices. The computer may request a data structure classification for an external data structure stored in a remote computing device. The middleware computing system can receive the request, identify the connection that the middleware computing device has with the remote computing device, and retrieve records for transactions performed by the external data structure from the remote computing device. The middleware computing system can use metadata in the records to automatically determine a data structure type of the data structure. The middleware computing system can generate instructions that cause the computer to link the external data structure with the profile.

Abstract: A model checking system detects violations and conflicts in security and verification policies by running model checking processes. The system detects privilege escalation attacks in misconfigured identification and access management (“IAM”) policies by modeling security policy documents and IAM actions as logical formulas and then running model checking on the model. The system translates non-Boolean variables, such as string variables, into Boolean variables in order to apply an SAT model checker. The model checker also determines whether a policy violation can be achieved in a finite number of steps by elevating privileges of some compromised principal over multiple iterations of the model checking process, or proves absence thereof.