Abstract: A source-code security system detects and remediates tampering and vulnerabilities in source code in real time. The system tokenizes received source code into numerical sequences and routes the numerical sequences, based on a complexity threshold and a volume threshold, between a long short-term memory (LSTM) model and a quantum long short-term memory (QLSTM) model. LSTM results are stored in a memory cache and QLSTM results are stored in a quantum cache that stores and retrieves a quantum state using entanglement and superposition. Based on the stored results, a hardware processor predicts tampered code, detects a vulnerability, and automatically remediates the tampered code or the vulnerability by modifying the source code. The system may execute the LSTM model and the QLSTM model concurrently with dynamic load balancing and may generate a report identifying each affected area of the source code.

Abstract: A first single board computer deployed at a first server determines a first performance status of a first server based on the performance data relating to the first server and electronically transmits the first performance status of the first server to a first network switch. A second single board computer deployed at the first network switch receives the first performance status of the first server and determines, based on the first performance status of the first server, that the first server has experienced a performance anomaly. The second single board computer receives a first request to perform a first processing task as part of a first data interaction, and in response, causes the first network switch to route the first request to a second server that is also configured to perform the first processing task.

Abstract: Systems and methods for proactive real-time anomaly detection in cross-environment RPC (Remote Procedure Call) communications within computing systems. Utilizing an Intelligent GraphRPC Method, this invention integrates advanced graph analysis techniques to enhance fault detection and workflow management. The method features a dual-graph approach, employing both real-time and aggregated dependency graphs, which allows for continuous monitoring and analysis of RPC interactions to detect and prevent unauthorized or misconfigured RPC calls between staging and production environments. An ingestion pipeline further supports the system by aggregating and archiving call graph data, providing beneficial insights into service dependencies and potential security risks.

Abstract: A method includes receiving and analyzing data items to generate a weight for each data item. In response to determining that a first weight of a first data item is within a first weight range, the method determines that the first data item has a high security level. In response to determining that a second weight of a second data item is within a second weight range, the method determines that the second data item has a medium security level. A first subset of the data items having the high security level are encrypted with a first cryptography algorithm to generate first encrypted data items. A second subset of the data items having the medium security level are encrypted with a second cryptography algorithm to generate second encrypted data items. An artificial intelligence/machine learning model is trained using the first and second encrypted data items as a training data set.

Abstract: Quantum entangled domain map analysis and quantum gradient descent network landscape analysis are designed to enhance inter-domain connectivity and decision-making efficiency across distributed computer networks. Quantum entangled domain map analysis 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 analysis utilizes quantum gradient descent methods 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 analysis reduces computation time significantly while enhancing the accuracy of determining the most effective pathways for data flow.

Abstract: A system includes a memory and a quantum processor operably coupled to the memory and configured to receive, from a computing device, a request to complete execution of an executable interaction with the software application. For each of the plurality of microservices, the quantum processor is configured to execute a quantum particle swarm optimization (QPSO) algorithm configured to generate and associate with first and second computing systems an autonomous compute agent. The autonomous compute agent is configured to execute directional movements over a plurality of communication paths associated with the first and second computing systems to identify a best communication path. The quantum processor is configured to route, using the identified best communication path, the request to the indicated one of the first computing system or the second computing system and instruct the indicated one of the first computing system or the second computing system to execute the associated microservice.

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: A system includes a memory storing a generative AI model and task-specific AI models and processors coupled to the memory. The processors receive a multi-modal input including a first input in a first modality and a second input in a second modality and execute the generative AI model on the multi-modal input to determine a task. The processors execute a first and a second task-specific AI models on the first and second inputs respectively to extract first and second respective features. The processors extract a first subset of first features and a second subset of second features by the generative AI model, generate a unified multi-modal feature representation integrating the first subset of first features and the second subset of second features by the generative AI model, and execute the generative AI model on the unified multi-modal feature representation to output an execution result for the task.

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 includes a memory storing an evaluation algorithm configured to determine eligibility for performing a data interaction based on an eligibility criteria, and a processor communicatively coupled to the memory. The processor is configured to receive, from a computing node associated with a user, a first request to determine eligibility of the user to perform the data interaction; prompt for eligibility information; receive the eligibility information; and determine an eligibility result by inputting the eligibility information into the evaluation algorithm. In response to determining that the user is eligible, the processor transmits an indication of eligibility to the computing node and generates a zero-knowledge proof of the eligibility that does not include the received eligibility information. Upon verifying the zero-knowledge proof by running a verification algorithm and determining the proof is valid, the processor initiates the requested data interaction.

Abstract: A system and method for secure collaborative analytics enable multiple parties to perform joint data analysis without exposing sensitive information. The invention integrates quantum computing, advanced cryptographic protocols, and generative artificial intelligence (GenAI) to ensure privacy, accuracy, and efficiency. Private datasets undergo quantum optimization to retain significant features, followed by encryption using quantum-resistant protocols. Encrypted data is securely transmitted and analyzed collaboratively using quantum homomorphic encryption, allowing computations directly on encrypted datasets. GenAI optimizes feature selection, computation workflows, and privacy-accuracy trade-offs, enabling iterative refinement of analytical outputs. Results are decrypted securely using quantum key authentication and validated against predefined benchmarks.

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.