Abstract: This invention relates to a quantum cloud-based processing system for managing transaction data across multi-application environments with enhanced security, adaptability, and efficiency. The system receives transaction data and metadata, encrypts it using dynamically generated quantum encryption keys, and queries downstream applications to retrieve data structure requirements. It creates a hierarchical map to trace data transformations, tokenizes data into standardized units with encoded metadata, and performs multi-level data augmentation using advanced machine learning techniques. The system ensures seamless integration with applications through a soft commit mechanism, detects and categorizes anomalies, and generates comprehensive discrepancy and anomaly impact reports. Dynamic updating modules adapt to changes in application requirements, while logging and storage modules maintain secure and auditable records of all workflows.

Abstract: Systems, computer program products, and methods are described herein for providing reusable objects in a process flow via refresh tokens. Embodiments of the present disclosure are configured to receive, via a network interface, a transaction from a system of origin, input the transaction into a transaction process flow, where the transaction process flow includes N transaction steps, generate, at each transaction step of the N transaction steps, a plurality of objects, ingest, at each transaction step of the N transaction steps, the plurality of objects in a model, store, if the transaction reaches step N of the transaction process flow, a first subset of objects in one or more systems of record, where the first subset of objects includes objects from each plurality of objects, and apply one or more objects of the first subset of objects to an associated transaction.

Abstract: Systems, computer program products, and methods are described herein for providing reusable objects in a process flow via refresh tokens. Embodiments of the present disclosure are configured to receive, via a network interface, a transaction from a system of origin, input the transaction into a transaction process flow, where the transaction process flow includes N transaction steps, generate, at each transaction step of the N transaction steps, a plurality of objects, ingest, at each transaction step of the N transaction steps, the plurality of objects in a model, store, if the transaction reaches step N of the transaction process flow, a first subset of objects in one or more systems of record, where the first subset of objects includes objects from each plurality of objects, and apply one or more objects of the first subset of objects to an associated transaction.

Abstract: This invention provides a system and method for managing and validating transaction data in decentralized networks, ensuring accuracy, contextual relevance, and security. The system detects missing data using a pre-trained custom model and predicts missing elements via a k-Nearest Neighbors (KNN) algorithm. It establishes contextual relationships using advanced semantic analysis, generating multiple transaction versions filtered for relevance and validity. Each validated version is transformed into cryptographic digital tokens encapsulating both content and context. A Secure Multi-Party Engine (SMPE) refines validation by generating application-specific sub-tokens, which are authenticated by decentralized applications. Responses are consolidated into a unified authentication token to ensure integrity. The event-driven architecture facilitates low-latency token distribution and validation across the network, while the hierarchical sub-token structure enhances security and granularity.

Abstract: Generating and processing electronic content in a decentralized finance network using distributed ledger technology is disclosed. Transaction security and efficiency is enhanced by integrating generative AI with hash-based security and NFTs in a DeFi network. User devices capture a customer prompt specifying a financial transaction request, which a generative AI module analyzes to extract attributes and determine a transaction type. A webhook transmits the attributes to distributed nodes, and a security module generates a hash-based security token for attribute-level security. A content assembly module creates a JSON object based on node responses and smart contract rules. The generative artificial intelligence module generates a dynamic electronic form, prepopulating fields with network data. User input populates the form, which a tokenization module mints as a non-fungible token with a validity period.

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: Embodiments of the invention are directed to systems, methods, and computer program products for processing network simulations. In some embodiments, the method includes identifying, from one or more datasets, a plurality of microsegments; generating a graph neural network (GNN) comprising a plurality of nodes and a plurality of edges, where each node is associated with a microsegment; using a generative artificial intelligence (AI) engine to generate a marker token for each node, where the marker token is associated with an exposure level; executing at least one quantum algorithm, where executing the at least one quantum algorithm comprises processing a plurality of simulations; and based on one or more outputs of the quantum algorithm, causing a user device to execute a remedial action.

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 determining process compliance using advanced computational models for data analysis and automated decision-making. The present disclosure is configured to generate one or more intermediate control layers, wherein the one or more intermediate control layers comprises one or more artificial intelligence models; generate one or more control objectives, wherein each of the one or more control objectives are associated with one or more governance processes; generate, in response to the one or more control objectives, one or more manifests; receive, in response to the one or more manifests, one or more artifacts, wherein the one or more artifacts comprise data associated with the one or more governance processes; capture data from the one or more artifacts; compare the captured data from the one or more artifacts with the one or more intermediate control layers; and approve the one or more artifacts.

Abstract: Systems, computer program products, and methods are described herein for automatically generating multi-layered diffusion images for application programming interface requests in a distributed server system. The present disclosure is configured to identify at least one header associated with at least one of a sender server or a recipient server, wherein the at least one header comprises text data; apply the at least one header to a first diffusion engine; generate, by the first diffusion engine, a first diffusion image based on the text data; apply the first diffusion image to a second diffusion engine; and generate, by the second diffusion engine, a second diffusion image based on the recipient server and the first diffusion image.

Abstract: A computing platform may train, using historical information migration error information, an information reconciliation engine to predict information migration errors. The computing platform may detect migration of information from a source information system to a target information system. The computing platform may generate, by inputting the information into the information reconciliation engine, a list of predicted migration errors. The computing platform may sample the migrated information to identify a list of real time migration errors. The computing platform may identify corrective actions to address the list of predicted migration errors and the list of real time migration errors. The computing platform may generate, based on the corrective actions, a configuration file.

Abstract: A system is provided including a host processor, a plurality of service processors, a universal processor associated with the host processor and capable of exchanging data with each service processor, a first user device of the first user and a second user device of a second user. The universal processor receives a request from the first user device for a data transaction between the host processor and a service processor, and in response generates a code that can be interpreted by the service processor. Universal processor transmits the code to the first user device, which transmits the code to the second user device. The second user device transmits the code to the service processor. The service processor decodes the code and requests the universal processor to perform the data transaction with the host processor. In response, the universal processor sets up the data transaction.

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: Hardware security modules (HSM) may be implemented at distributed edge devices to provide faster and dynamic processing of data. HSM-implemented edge devices may be used to process data to enhance data security. For example, HSM edge devices may provide encryption, decryption, data masking, tokenization, or anonymization. The HSM edge devices may interface with one or more blockchains to invoke smart contracts, and to record data transaction events. In some examples, smart contracts may be used to provide uniform policies and functions across multiple edge devices and their corresponding end user devices.

Abstract: Hardware security modules (HSM) may be implemented at distributed edge devices to provide faster and dynamic processing of data. HSM-implemented edge devices may be used to process data to enhance data security. For example, HSM edge devices may provide encryption, decryption, data masking, tokenization, or anonymization. The HSM edge devices may interface with one or more blockchains to invoke smart contracts, and to record data transaction events. In some examples, smart contracts may be used to provide uniform policies and functions across multiple edge devices and their corresponding end user devices.

Abstract: Hardware security modules (HSM) may be implemented at distributed edge devices to provide faster and dynamic processing of data. HSM-implemented edge devices may be used to process data to enhance data security. For example, HSM edge devices may provide encryption, decryption, data masking, tokenization, or anonymization. The HSM edge devices may interface with one or more blockchains to invoke smart contracts, and to record data transaction events. In some examples, smart contracts may be used to provide uniform policies and functions across multiple edge devices and their corresponding end user devices.

Abstract: Systems, computer program products, and methods are described for a cryptographic hash signature architecture aimed at error pattern recognition and automated recovery. This framework receives live error data, generates unique cryptographic hash signatures (checksums), and matches these against a stored checksum database to identify known error patterns. If a match exists, predefined scripts execute corrective actions. Otherwise, machine learning algorithms assess the error to suggest solutions. The system employs a rules engine applying predefined algorithms to ensure solutions match system protocols. It updates a historical database with error and resolution data, refining the knowledge base. Successful resolutions are verified, with outcomes enhancing the machine learning model's future accuracy. This innovative approach boosts system reliability and user satisfaction by reducing manual interventions and downtime.

Abstract: Systems, computer program products, and methods are described herein for leveraging a distributed network system for secure data transfers. The present disclosure is configured to receive a data transfer from an entity where the entity is at least partially implicated within the data transfer, where the data transfer comprises participants; record the data transfer within a distributed network system, where the distributed network system comprises previous data transfers; verify identities of participants associated with the data transfer; and trigger the data transfer between participants.

Abstract: Embodiments of the present invention provide a system for detecting real-time anomalies in transmission path of resources within an entity network via DNA computing. The system is configured for determining that an application received a resource from a source system to be transmitted to an end application via an entity network, extracting metadata from the resource, wherein the metadata is in a binary format, converting the metadata in the binary format to a DNA format, generating predicted molecular sequence associated with predicted transmission path of the resource within the entity network based on the DNA format of the metadata, monitoring real-time transmission path of the resource, generating real-time molecular sequence associated with the real-time transmission path of the resource, comparing the predicted molecular sequence with the real-time molecular sequence to determine an anomaly, and transmitting alerts associated with the anomaly.

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.