Abstract: Systems and methods for detecting tampering in software are disclosed. The system includes a preprocessor that converts source code into a minimal intermediate representation and extracts semantic and syntactic features using word embedding algorithms. The preprocessed data is then fed into two machine learning models: a classical LSTM model and a quantum LSTM model. The classical LSTM model detects basic tampering patterns, while the QLSTM model leverages quantum principles to enhance analysis and prediction of more complex tampering attempts. The system also includes a quantum cache for efficient data retrieval and manipulation, enabling real-time or near-real-time analysis. The combination of these features provides improved accuracy and effectiveness in detecting tampering, enabling timely intervention and mitigation of security threats. Remediation may be performed automatically or manually and can be based on historically determined or dynamically generated solutions.

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: Quantum entangled domain map processing and quantum gradient descent network landscape processing are designed to enhance inter-domain connectivity and decision-making efficiency across distributed computer networks. Quantum entangled domain map processing uses quantum entanglement principles to create a comprehensive mapping of network domain features to identify connectivity gaps between domains and to adaptively provide cross-domain optimizations to the network. Quantum gradient descent network landscape processing utilizes quantum gradient descent processes to intelligently navigate the optimization landscape of inter-domain relationships in the distributed network. By minimizing cost functions associated with network connectivity, quantum gradient descent network landscape processing reduces computation time significantly while enhancing the accuracy of determining the most effective pathways for data flow.

Abstract: Systems and methods are disclosed for optimizing database join operations in large-scale relational databases using an integrated framework of quantum annealing, swarm intelligence, and genetic algorithms. The system employs quantum annealing to learn data distribution patterns, normalizing and transforming data to identify optimal join strategies and correlations across tables. Swarm intelligence predicts access patterns by deploying artificial ants to navigate data blocks, converging on high-relevance areas for prefetching. Genetic algorithms estimate join selectivity, calculating expected record counts to allocate resources efficiently. Learned index structures, dynamically created and tuned based on identified patterns, enhance retrieval speed with minimal storage. Additional features include learned Bloom filters for accurate join candidate selection, predictive page loading to reduce I/O wait times, and join order optimization to minimize intermediate result sizes.

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: A system includes a memory configured to store one or more cyber threat scenarios associated with a software application of a plurality of software applications. The system further includes processors for accessing the one or more cyber threat scenarios, identifying, based on the one or more cyber threat scenarios, an actual cyber threat associated with an execution of the software application in accordance with the current configuration, and, in response, executing a dynamic remote based isolation (RBI) engine configured to perform a dynamic reconfiguration of the software application and the system components in response to the identified actual cyber threat. The dynamic reconfiguration is different from the current configuration of the software application and the system components. The processors further cause the software application to be executed in accordance with the dynamic reconfiguration of the software application and the system components.

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: Apparatus and methods for high-dimension validation of large language model (“LLM”) output. The apparatus and methods may leverage the power of Hybrid AI/ML and Quantum GANs to scrutinize LLM output from various angles. The apparatus and methods may discriminate between LLM-generated and verified facts. The methods may include analyzing the information provided by LLMs through different lenses, including metaphors, emotions, video, image analysis, cultural context, and simulations, utilizing Hybrid AI/ML models to identify patterns and relationships. This may enable a more accurate understanding of the data and help identify any inconsistencies or discrepancies in the output. The apparatus and methods may incorporate analysis to evaluate the implications of the information provided by LLMs and ensure that it aligns with standards and guidelines, utilizing Quantum GANs to generate alternative scenarios and assess the reliability of the data.

Abstract: A system is provided for real-time monitoring and remediation of network intrusion using an intelligent application programming interface. In particular, the system may monitor and track, in real time, the various computing devices within a distributed networked system. The system may use one or more trained artificial intelligence models to analyze incoming network requests and detect anomalies within the body of network requests, and based on the analysis, implementing one or more countermeasures (e.g., request throttling, rate limiting, allocation of additional computing resources, and/or the like) in response. In some embodiments, the one or more AI models may be configured to generate intrusion mitigation and/or remediation plans in response to any detected anomalies. The output of the AI models may then be wrapped with additional data that may enhance the anomaly detection process.

Abstract: A system for protecting source code from unauthorized access is disclosed. The system is configured to scan the source code and identify code segments, including function code. The system may perform code obfuscation, code separation, and code encryption. The system may extract a first set of code features from the function code. The first set of code features indicates a first task associated with the function code. The system accesses a training dataset comprising a sample code that is associated with a second task and a template code. The system compares the first task with the second task. In response to determining that the first task corresponds to the second task, the system obfuscates the function code with the template code. In response, the system updates the source code to include the obfuscated function code.

Abstract: A system includes a memory configured to store a software application and a plurality of application programming interfaces (APIs) associated with the software application and a processor operably coupled to the memory. The processor is configured to receive, by a single federated gateway, a request to execute an interaction with the software application to satisfy the request, partition the request into at least a first subsidiary request and a second subsidiary request based on a content of the request, route the first subsidiary request to a first API and first set of independent software services and the second subsidiary request to a second API and second set of independent software services, and receive, by the single federated gateway, the first subset of data and the second subset of data. The processor is further configured to generate a response and provide the response to a computing device.

Abstract: In response to determining that a real-time latency associated with performing a process workflow in a computing network equals or exceeds a threshold, an artificial intelligence (AI) model determines a sequence of tasks associated with the process workflow, determines a server of the computing network that can perform each identified task, and establishes a server group including the identified servers. The server group is configured to perform the process workflow. Upon receiving a subsequent request, the process workflow is performed based on the configuration of the server group.

Abstract: Dynamic Digital Immune Systems/Processes (DDIS) improve data integrity and quality in Explainable AI (XAI) systems. Operating in real time, it monitors data from distributed sources during federated training, detecting and flagging corrupted, poisoned, or low-quality data with dynamic anomaly detection algorithms. The system includes automated data cleaning to remove redundant and irrelevant data, enhancing AI model performance. It features dynamic encryption that adapts to data sensitivity, ensuring robust security. Swarm intelligence-based task distribution allows decentralized auditing of data integrity, while a traceability matrix tracks data lineage for transparency. The DDIS also incorporates a self-healing mechanism that automatically corrects identified anomalies, ensuring continuous reliability of AI outputs. This comprehensive system protects against data corruption, enhances processing efficiency, and safeguards the accuracy and integrity of XAI systems in high-stakes environments.

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.