Abstract: A system and methods for the creation of domain-specific languages that are both domain-agnostic and language-agnostic for use in a multi-language abstract digital simulation model generation and execution, comprising an onboarding module that creates domain specific models from declarative languages, domain-specific language engine, that uses the declarative domain-specific models to create a domain specific language, a meta-model structuring and creation system, meta-model mapping table, remote server, simulation execution process, computer domain-specific language, and methods for user-creation and editing of meta-models, simulation models, and parametrization of simulation environments, actors, objects, and events in real-time using heuristic searching.

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 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 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 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.

Abstract: A reconnaissance engine gathers data about a client's computer network from the client, from devices and systems on the client's network, and from the Internet regarding various aspects of cybersecurity. Each of these aspects is evaluated independently, weighted, and cross-referenced to generate a cybersecurity score by aggregating individual vulnerability and risk factors together to provide a comprehensive characterization of cybersecurity risk using a transparent and traceable methodology. The scoring system itself can be used as a state machine with the cybersecurity score acting as a feedback mechanism, in which a cybersecurity score can be set at a level appropriate for a given organization, and data from clients or groups of clients with more extensive reporting can be used to supplement data for clients or groups of clients with less extensive reporting to enhance cybersecurity analysis and scoring.

Abstract: Cybersecurity reconnaissance, analysis, and scoring uses distributed, cloud or edge-based pools of computing services to provide sufficient scalability for analysis of IT/OT networks using only publicly available characterizations. An in-memory associative array manages a queue of configuration and vulnerability search tasks through at least one public-facing proxy network which uses configurable search nodes to approach the target network with search tools in a desired manner to control certain aspects of the search in order to obtain the desired results, especially when target network behavior adjusts based on counterparty characteristics. A data packet modifier reveals IP addresses of threat actors behind port scans and subsequently block the threat actors.

Abstract: A system and method to identify and prevent cybersecurity attacks on modern, highly-interconnected networks, to identify attacks before data loss occurs, using a combination of human level, device level, system level, and organizational level monitoring.

Abstract: A system and method for providing access and agency to individual entities and people over their data for the purpose of data set validation to facilitate data set and algorithm bias certification and scoring. A first data set is filtered to extract its core information content and to create a certified data set. A certified model is created by training a machine learning algorithm on the certified data set, which certified model is then used to evaluate the bias of subsequent data sets. The data set may be given a value score which represents the overall validity of the data set and its bias characterization. A bias characterization audit can help identify the root causes of bias outcomes from predictive software and algorithms that perform third party tasks and services. The score can be used as a metric to further facilitate market transactions.

Abstract: A system and methods for the creation of domain-specific languages that are both domain-agnostic and language-agnostic for use in a multi-language abstract digital simulation model generation and execution, comprising an onboarding module that creates domain specific models from declarative languages, domain-specific language engine, that uses the declarative domain-specific models to create a domain specific language, a meta-model structuring and creation system, meta-model mapping table, remote server, simulation execution process, computer domain-specific language, and methods for user-creation and editing of meta-models, simulation models, and parametrization of simulation environments, actors, objects, and events in real-time using heuristic searching.

Abstract: A system for fully integrated collection of business impacting data, analysis of that data and generation of both analysis-driven business decisions and analysis driven simulations of alternate candidate business actions has been devised and reduced to practice. This business operating system may be used predict the outcome of enacting candidate business decisions based upon past and current business data retrieved from both within the corporation and from a plurality of external sources pre-programmed into the system. Both single parameter set and multiple parameter set analyses are supported. Risk to value estimates of candidate decisions are also calculated.