Abstract: The Generative AI Content Verification Exchange systematically registers and stores content generated by AI and real people alike. Upon submission, the system categorizes content into distinct groups, then deconstructs it into multiple segments using various methods. Each segment is assigned a unique hash value, termed a “part identifier,” ensuring individualized identification. This registration process, combining grouping, segmentation, and hashing, enhances content traceability and retrieval. The resulting database not only organizes generated content by groups but also allows for efficient and secure referencing of specific content segments. A content similarity score may be generated by comparing hash values against a large corpus of registered content. The similarity score is indicative of the likelihood, or not, of an input content being in part registered content.

Abstract: The Generative AI Content Verification Exchange processes system obtained data and user input data, creating a set of hash values using a robust hashing algorithm. It then compares these hash values against a registry of content. If an exact match or sufficient similarity is detected, the system indicates that the input content, or component of input content, is likely a copy or generated by an AI tool. This approach enables users to discern whether the content is original and if it is probabilistically likely to have originated from an generative AI process or from humans. It has applications in content authenticity verification, aiding users in identifying content utilization, distribution dynamics, AI-generated content and promoting transparency and trust in online interactions and content.

Abstract: The Generative AI Content Verification Exchange systematically registers and stores content generated by AI-enabled or enhanced services. Upon submission, the system categorizes content into distinct groups, then deconstructs it into multiple segments using various methods. Each segment is assigned a unique hash value, termed a “part identifier,” ensuring individualized identification. This registration process, combining grouping, segmentation, and hashing, enhances content traceability and retrieval. The resulting database not only organizes generated content by groups but also allows for efficient and secure referencing of specific content segments. The systematic registration and storage framework enable streamlined management of diverse generative AI-generated content for various applications, such as registration, analysis, search, and verification by diverse counterparties.

Abstract: A system and method for cybersecurity reconnaissance, analysis, and scoring that uses distributed, cloud-based computing services to provide sufficient scalability for analysis of enterprise IT networks using only publicly available characterizations. The system and method comprise an in-memory associative array which manages a queue of vulnerability search tasks through a public-facing proxy network. The public-facing proxy network has search nodes configurable to present the network to search tools in a desired manner to control certain aspects of the search to obtain the desired results. A distributed data processing engine and cloud-based storage are used to provide scalable computing power and storage. Each of the cloud-based computing services is containerized and orchestrated for management and efficient scaling purposes.

Abstract: A system and method for collaborative cybersecurity defensive strategy analysis that predicts the evolution of new cybersecurity attack strategies and creates a virtual network space that provides a virtual reality environment for collaborative insights into network dynamics during a cyberattack. makes recommendations for cybersecurity improvements to networked systems based on a cost/benefit analysis. The system and method use machine learning algorithms to run simulated attack and defense strategies against a virtual network space model of the networked system created using a virtual network space manager. A simulation interaction server can facilitate secure sharing of virtual network spaces and simulations between and among various real and virtual actors to provide a collaborative space where one or more organization's network can be tested for resilience and mitigation. Recommendations are generated based on an analysis of the simulation results against a variety of cost/benefit indicators.

Abstract: Autonomous management of risk transfer is provided using an automated underwriting processor that creates a contract block by compiling the request into a computational graph-based format, links the contract block to the requester, stores the contract block into memory, retrieves a plurality of available underwriting agreements from memory, and creates an offer list by perform computational graph operations on the contract block to determine viable risk-transfer agreements; and presenting the offer list to the requester.

Abstract: A system and method for the secure and private demonstration of cloud-based cyber-security tools. Using an advanced sandboxing design patterns, isolated instances of virtual networks allow a potential client to compare their existing cyber defense tools against a set of cloud-based tools. Capitalizing on non-persistent and secure sandboxes allow the invention to demonstrate fully functional and devastating cyber-attacks while guaranteeing strict privacy and security to both existing customers and potential ones. Additionally, instantiating separate sandboxed observed systems in a single multi-tenant infrastructure provide each customer with the ability to rapidly create actual representations of their enterprise environment offering the most realistic and accurate demonstration and comparison between products.

Abstract: A system for dynamic predictive analysis of data sets using an actor-driven distributed computational graph, wherein a pipeline orchestrator creates and manages individual data pipelines while providing data caching to enable interactions between specific activity actors within pipelines. Each pipeline then comprises a pipeline manager that creates and manages individual activity actors and directs operations within the pipeline while reporting back to the pipeline orchestrator.

Abstract: A system and method for automated cybersecurity defensive strategy analysis that predicts the evolution of new cybersecurity attack strategies and makes recommendations for cybersecurity improvements to networked systems based on a cost/benefit analysis. The system and method use machine learning algorithms to run simulated attack and defense strategies against a model of the networked system created using a directed graph. Recommendations are generated based on an analysis of the simulation results against a variety of cost/benefit indicators.

Abstract: A system and methods for cybersecurity rating using active and passive external reconnaissance, comprising a web crawler that send message prompts to external hosts and receives responses from external hosts, a time-series data store that produces time-series data from the message responses, and a directed computational graph module that probes, scans, and fingerprints devices within a cyber-physical graph and analyzes the results as time-series data to produce a weighted score representing the overall cybersecurity state of an organization.

Abstract: A system and method for an AI-controlled sensor network for threat mapping and characterization. The system deploys a network of honeypots and sensors across various geographic locations and network segments, collecting and aggregating data on network traffic and potential threats. An AI orchestrator analyzes this data using advanced machine learning models, generating dynamic honeypot profiles and a comprehensive threat landscape. The system can adapt in real-time to emerging threats, optimize resource allocation, and provide actionable intelligence. By correlating data across multiple points, the system offers enhanced threat detection capabilities and proactive cybersecurity measures, surpassing traditional security information and event management (SIEM) tools.

Abstract: A system and method for correlating network event anomalies to identify attack information, that identifies anomalous events within the network, identifies correlations between anomalies and other network events and resources, generates a behavior graph describing an attack pathway derived from the correlations, and determines an attack point of origin using the behavior graph.

Abstract: A system and method for generating and applying meta-models in simulated environments, in which an agent simulation is selected, one or more agent goals are received, and agents are created which are individual instances of the agent simulation with each agent having at least one of the agent goals, wherein the agents are used in the execution of an environment simulation which dynamically changes based on the collective behavior of the agents. The agents operate in the environment simulation using meta-models which describe how the agents interact with other agent and how the agents interact within the simulation.

Abstract: A system for probe-based risk analysis for multi-factor authentication having a multi-dimensional time series data server configured to monitor and record a network's traffic data and to serve the traffic data to other modules and a directed computational graph module configured to probe connection destinations for a response, analyze any received responses, and determine a verification score needed before granting access based at least in part on the analysis of the received responses. A plurality of verification methods build up a user's verification score to required level to gain access.

Abstract: A system and method for AI-driven advertising and content generation that integrates connectionist and symbolic AI techniques to deliver personalized, contextually relevant user experiences. The invention leverages specialized agent networks, knowledge graphs, and retrieval-augmented generation to create dynamic, adaptive content with seamlessly integrated advertisements. It employs a sophisticated ad integration layer and experience broker to ensure relevance and engagement. The system prioritizes security, traceability, and user preferences while optimizing ad placement and content delivery. By bridging connectionist and symbolic AI, the platform enables immersive, tailored interactions across multiple scenarios and time horizons, enhancing user engagement and advertiser value in an AI-driven digital landscape.

Abstract: A system and method for correlating network event anomalies to identify attack information, that identifies anomalous events within the network, identifies correlations between anomalies and other network events and resources, generates a behavior graph describing an attack pathway derived from the correlations, and determines an attack point of origin using the behavior graph.

Abstract: Automatically computing and managing a cybersecurity risk score. The cybersecurity risk score and cyber-physical graph for a network are retrieved and analyzed to identify potential improvements that can be made to network topography and device configurations, changes are applied automatically and an updated cyber-physical graph reflecting the applied changes is produced, and the updated cyber-physical graph is reassessed to determine the effect of the changes that were applied.

Abstract: A system and method for correlating network event anomalies to identify attack information, that identifies anomalous events within the network, identifies correlations between anomalies and other network events and resources, generates a behavior graph describing an attack pathway derived from the correlations, and determines an attack point of origin using the behavior graph.

Abstract: A system and method for automated cybersecurity defensive strategy analysis that predicts the evolution of new cybersecurity attack strategies and makes recommendations for cybersecurity improvements to networked systems based on a cost/benefit analysis. The system and method use machine learning algorithms to run simulated attack and defense strategies against a model of the networked system created using a directed graph. Recommendations are generated based on an analysis of the simulation results against a variety of cost/benefit indicators.

Abstract: A system and method that detects and mitigates zero-day exploits and other vulnerabilities by analyzing event logs and external databases, forcing reauthentication of at-risk and comprised systems and accounts during an identified threat or potential security risk.