Abstract: Systems and methods for optimizing an online chatbot platform are provided. A method may include generating a database of computing jobs associated with chatbot requests and identifying, for each job, a set of features that may include a priority level, a criticality level, a sensitivity level, a set of resources required for the job and a utilization rate for each such resource, and prerequisite interdependencies with other jobs in the database. Features in the set may include structured data and/or unstructured data. The method may include generating a multi-modal knowledge graph representing the database of computing jobs and the set of features for each job in the database. The method may include receiving a series of requests via the online chatbot platform, generating a list of runtime jobs to address the requests, and generating an optimized sequence for running the runtime jobs.

Abstract: Systems, computer program products, and methods are described herein for determining network paths and eliminating channel redundancy using artificial intelligence (AI) and quantum knowledge graphing. The present disclosure is configured to receive and extract network data from one or more network path transmission requests and identify network channel requirements based on at least the network data. The system executes a network scan based on at least the network channel requirements and determines a network channel status for one or more network channels based on at least the network channel requirements. The present disclosure is also configured to generate a network channel matrix based on at least the spatial network relationship map. Furthermore, the present disclosure is configured to determine, using the AI engine, a determined network channel based on the network channel matrix. In addition, the present disclosure is configured to generate and transmit a determined network channel notification.

Abstract: Decentralized system for third-party identity management and access control, leveraging blockchain technology, decentralized identifiers (DIDs), verifiable credentials (VCs), and cryptographic methods are disclosed. Third-party entities generate unique, self-owned DIDs, which are stored on a blockchain to ensure immutability and security. Verifiable credentials issued by trusted authorities, such as banks or regulatory bodies, encapsulate specific attributes, roles, or permissions and are similarly stored on the blockchain for tamper-proof verification. A public key infrastructure (PKI) is integrated to support secure authentication and cryptographic validation, ensuring trusted interactions between entities. The system features an API gateway that verifies DIDs and VCs, enforces granular access controls, and supports temporal access restrictions.

Abstract: A secure data storage solution fragments encrypted data and distributes it across multiple cloud providers, increasing data protection by preventing any single cloud provider from accessing the complete data. Using cryptographic techniques such as Shamir's Secret Sharing or Reed-Solomon coding, the system divides the encrypted data into multiple independent fragments, each of which is stored separately. Each fragment alone is insufficient to reconstruct the original data, and only a minimum threshold of fragments is required for reassembly. By distributing fragments across diverse cloud providers, the invention mitigates risks associated with single-provider failures or breaches. The system employs secure communication protocols to protect fragments during transmission and monitors the integrity of each fragment in its respective storage location, ensuring both data confidentiality and availability.

Abstract: Keyless key management systems that dynamically generates encryption keys on demand using a Deterministic Key Derivation Function (KDF) are disclosed and eliminate the need for stored encryption keys. The system derives unique, session-specific encryption keys based on a combination of biometric data, time-based one-time passwords (TOTP), and real-time environmental data including device location and IP address. By using this diverse set of inputs, the KDF creates a unique key each time access is requested, tightly linking the encryption key to the specific user and access context. This approach minimizes security risks associated with key storage or transmission, as no key is persistently stored or shared. The system's context-aware design and zero-trust model prevent unauthorized access, rendering the keys unusable outside their defined session parameters.

Abstract: The present invention relates to a dynamic remediation approach for resolving browser compatibility issues in Remote Browser Isolation (RBI) environments using Generative AI (GenAI) and machine learning. The system analyzes web technologies, browser extensions, and scripts to identify compatibility issues in real time. It performs cross-browser testing to ensure consistent functionality across various browsers, dynamically remediating discrepancies through alternative API implementations, script adjustments, and polyfill integration. The invention includes feature detection to identify unsupported functionalities and generate alternative approaches, CSS normalization to ensure uniform rendering, and responsive design testing to optimize visual consistency. Additionally, it dynamically converts file formats and adjusts API versions to enhance performance while maintaining security.

Abstract: Generative artificial intelligence (GEN-AI) is utilized to continuously optimize and strengthen the security of a key derivation process in real-time. The GEN-AI model monitors system behavior and user access patterns, dynamically adjusting the parameters of the Deterministic Key Derivation Function (KDF) to respond to detected security threats. Through ongoing analysis of user interactions and environmental data, the AI identifies potential vulnerabilities, enhancing the key generation process to resist evolving attacks. The AI model adapts to detected threats by increasing the complexity of the KDF, adding extra input factors, or altering cryptographic parameters to reinforce security. This proactive adjustment capability ensures that the key derivation process remains resilient and responsive, making the encryption keys harder to predict or replicate.

Abstract: The present invention relates to a hybrid security model for Remote Browser Isolation (RBI) environments to protect against web-based threats while ensuring secure and efficient application execution. The invention integrates sandboxing and application preapproval to enhance security and performance. Sandboxing isolates critical processes, preventing interference and containing malicious activities. A process isolation algorithm assigns dedicated resources, ensuring independent task execution. A resource limitation algorithm restricts CPU, memory, and disk space usage, optimizing resource allocation. Network access control prioritizes network resources for critical tasks, while file system virtualization limits access to specific files. Behavior monitoring algorithms detect and contain unauthorized activities, raising alerts in real time. Application preapproval improves efficiency by allowing trusted applications to bypass the isolation layer.

Abstract: A secure inter-cloud authentication system uses time-synchronized tokens for verifying user access requests across multiple cloud providers. By generating an authentication token that includes a timestamp and time-based one-time password (TOTP), synchronized with a Network Time Protocol (NTP) server, the system ensures that each access request is valid only within a specific time window. When a user requests data access, the token is verified by each participating cloud provider against the synchronized timestamp, allowing secure, time-sensitive validation across platforms. An identity provider (IdP) manages token issuance and synchronization, centralizing the authentication process while maintaining precise time alignment across clouds. This time-sync verification reduces the risk of replay attacks and ensures that only legitimate requests are processed, enabling secure, synchronized data access in multi-cloud environments.

Abstract: A system includes a memory configured to store a set of application environment parameters associated with a software application of a plurality of software applications. The system further includes processors for accessing the set of application environment parameters associated with the software application, identifying, based on the set of application environment parameters, a plurality of potential threats and vulnerabilities associated with an execution of the software application in accordance with the current configuration, and executing one or more generative machine-learning models trained to generate a prediction of one or more cyber threat scenarios based on the set of application environment parameters and the plurality of potential threats and vulnerabilities. The prediction of the one or more cyber threat scenarios includes cyber threat scenarios specific to the software application.

Abstract: A system receives a user request from a user from a user device, determines a task based on the user request, selects a LLM based on the task, uses the LLM to generate a digital content responsive to the user request, and generates digital watermarks for the digital content based on one or more of statistical watermarking, linguistic watermarking, or steganographic watermarking, wherein the statistical watermarking generates a hidden pattern based on portions of the digital content, the linguistic watermarking generates textual information to be inserted into the digital content, and the steganographic watermarking generates hidden information based on the digital content. The system further uses hashing algorithms to generate cryptographic stamps for the digital watermarks, generates a secured first digital content by embedding the cryptographic stamps in the digital content, and sends instructions to the user device for presenting the secured digital content.

Abstract: A system to encrypt, cipher, and compress a data packet is disclosed. The system, by a neural network trained on text patterns, partitions the data packet into data blocks based on text patterns. Each data block may correspond to a distinct text pattern. The system determines that a first data block comprises sensitive information based on a neural network trained on text cues related to sensitive information. In response, the system converts each letter in the determined portion into a mapped letter based on a second encryption key. The system identifies repetitive words in the first data block based on the first text pattern. In response, the system converts the repetitive words into a compressed representation. The system compresses other portions of the first data block. The system aggregates the first data block with other data blocks that may be encrypted, ciphered, compressed as well.

Abstract: Secure user identification and transaction authentication are disclosed. Using an Autonetics true touch sensation engine, AI to identifies and extracts unique human/avatar behavior metadata. This enables precise user authentication for transactions or token reset processes. An avatar touch sensor interface or other extended reality (XR) mechanisms to start user interaction, prompting for traditional PIN authentication. After successful first authentication, it further analyzes the syngeneic avatar behavior metadata, using data collection methods to strengthen security measures. With Hybrid Capture Input (HCI) technology, user behavior is verified to align with pre-established unique behavioral profile(s), to confirm user identity. This self-improving system adapts authentication algorithms dynamically, based on user interaction with the avatar, to create personalized and secure authentication processes.

Abstract: A distributed data storage includes data stored in different data shards on nodes of the distributed data storage. A memory stores a dynamic heatmap indicative of a number of data requests for data stored on each of the nodes. A processor receives a request for retrieving the data stored in the distributed data storage. The request includes data ranges and index criteria of the data and location of the nodes in the distributed data storage. In response, the processor determines the nodes of the distributed data storage that include the data using the dynamic heatmap, retrieves the data as determined from the dynamic heatmap; and routes the data to a user associated with the request. When a data retrieval performance is below a desired threshold value, the processor performs dynamic data sharding to reallocate the data shards across the nodes of the distributed data storage.

Abstract: In response to receiving a first request from a user to determine eligibility to perform a data interaction, the user is prompted to provide eligibility information. An eligibility result is determined by inputting the eligibility information into an evaluation algorithm. A zero-knowledge proof of the eligibility result is generated. In response to receiving a second request from the user to perform the data interaction, the zero-knowledge proof is verified. When the zero-knowledge proof is found to be valid, the data interaction requested by the user is initiated.

Abstract: In response to receiving a request from a first computing node of a computing network for a first piece of data, the request is input to an Artificial Intelligence (AI) model. The AI model is trained using a knowledge graph and one or more data transfer constraints to determine a second computing node of the computing network that stores the requested first piece of data. In response to receiving an identity of the second computing node that stores the requested piece of data as a result of the AI model, a transfer is initiated of the first piece of data from the second computing node to the first computing node.

Abstract: A method for identifying configuration drifts in blockchain nodes and remediating the configuration drifts is provided. The method may include monitoring activities at each of a plurality of nodes to identify a possibility of a configuration drift to the required node configuration settings. In response to the monitoring, the method may include identifying a configuration drift on a node, the configuration drift being a deviation between a current node configuration setting and the required node configuration settings. The method may include determining whether an impact level of the configuration drift to the blockchain network is greater than a pre-determined threshold value, and when the impact level is greater than the pre-determined threshold value, electronically communicating with each of the plurality of nodes to temporarily isolate the node comprising the deviation pending a remediation of the configuration drift.

Abstract: In response to detecting that a first data interaction has been performed in relation to a first user, it is determined, based on one or more historical interaction logs associated with previous data interactions performed in relation to the first user, that the first data interaction does not at least partially match with the previous data interactions. In response, it is determined that a potential data breach has occurred. A trained AI model is used to verify the potential data breach to confirm whether the data breach has actually occurred. Upon successfully confirming the potential data breach, one or more remediation methods are determined and implemented to avoid damage to impacted areas because of the data breach.

Abstract: Transactions may be disputed. Accordingly, streamlining and anticipating possible disputes with transactions may help achieve improved transaction processing efficiency and enhance customer satisfaction. In some arrangements, a transaction processing and dispute resolution system may determine a dispute propensity score for a transaction in order to anticipate and possibly avoid possible disputes for a transaction. This determination may be performed by configuring and applying transaction parameters to a neuro-symbolic artificial intelligence (AI) model. Additionally, the transaction processing and dispute resolution system may also automatically identify resolutions to transaction disputes when they arise using smart contracts. In such a system, the transaction-in-dispute may be analyzed using a neuro-symbolic AI model to identify possible updates to the smart contract rules. This allows the model to further adapt and learn based on new contexts and information.

Abstract: Transactions may be disputed. Accordingly, streamlining and anticipating possible disputes with transactions may help achieve improved transaction processing efficiency and enhance customer satisfaction. In some arrangements, a transaction processing and dispute resolution system may determine a dispute propensity score for a transaction in order to anticipate and possibly avoid possible disputes for a transaction. This determination may be performed by configuring and applying transaction parameters to a neuro-symbolic artificial intelligence (AI) model. Additionally, the transaction processing and dispute resolution system may also automatically identify resolutions to transaction disputes when they arise using smart contracts. In such a system, the transaction-in-dispute may be analyzed using a neuro-symbolic AI model to identify possible updates to the smart contract rules. This allows the model to further adapt and learn based on new contexts and information.