Abstract: A system for repeatable and flexible predictive analysis of very large data sets using a distributed computational graph using a domain specific language for pipeline and resource declaration has been developed. Data receipt software receives data from one or more sources. In a declared data pathway, data formalization software formats input data for storage, enrichment, or continued processing. An event analysis server inspects stored data for trends, situations, or knowledge. Aggregated data is passed to event message store and data stream handler software. Transformation pipeline software stores, triggers, executes and monitors declared transformation steps and pipelines to manipulate and act on bulk data and data streams, provides results and enables dynamic model and routine re-training and learning.

Abstract: Detection and mitigation of data source compromises in an adversarial information environment, featuring the ability to scan for, ingest and process, and then use relational, wide column, and graph stores for capturing entity data, their relationships, and actions associated with them. Metadata is gathered and linked to the ingested data, which provides a broader contextual view of the environment leading up to and during an event of interest. Data quality analysis is conducted as data is ingested in order to identify if a data source may be compromised. The results are used to manage the reputation of the contributing data sources.

Abstract: A system and method for the detection and mitigation of Kerberos golden ticket, silver ticket, and related identity-based cyberattacks by passively monitoring and analyzing Kerberos and authentication operations within the network. The system and method provide real-time detections of identity attacks using time-series data and data pipelines, and by transforming the stateless Kerberos protocol into stateful protocol. A packet capturing agent is deployed on the network where captured time-series Kerberos and related event and log information is processed in distributed computational graph (DCG) stages where declarative rules determine if an attack is being carried out and what type of attack it is.

Abstract: A system and method for holistic network cybersecurity evaluation and risk rating that takes into account the operation of the entire target network environment comprising hardware, software, operating systems, and network connections. Not only are the hardware, software, operating system, and network evaluated separately for cybersecurity concerns, their interaction and operation as a whole are also evaluated and scored. The results of such analyses may be used, for example, by underwriters of cybersecurity insurance policies to determine policy terms and rates.

Abstract: A system and methods for authentication attack detection with embedded authentication and delegation is provided, comprising an authentication object inspector configured to observe a new authentication object generated by an identity provider, and retrieve the new authentication object, wherein subsequent access requests accompanied by authentication objects are validated by comparing identifiers for each authentication object to previous identifiers.

Abstract: Automated analysis of legal documents within and across different fields is disclosed. An extraction processor identifies and extracts knowledge from data contained in documents and transforms it into a common data form. An analysis processor develops local and global knowledge graphs containing the key entities, relationships and concepts encoded in the text.

Abstract: A semantic search system integrates with an AI platform to provide advanced search capabilities by leveraging automatically generated ontologies and knowledge graphs. The system employs natural language processing, machine learning, and large language models to create, update, and align ontologies from diverse data sources. It supports context-aware query interpretation, personalized results, and complex reasoning by incorporating user context, feedback, and domain knowledge. The system optimizes search performance and efficiency through indexing techniques, distributed computing, and continuous learning. With a modular architecture and scalable infrastructure, the semantic search system enables users to retrieve relevant, meaningful, and context-specific information from vast amounts of structured and unstructured data.

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: A system and methods for detecting and mitigating authentication object forgery and manipulation attacks against services is provided, comprising a policy manager configured to observe a new authentication object generated by an identity provider, and retrieve the new authentication object; and a hashing engine configured to create a security cookie for each valid authentication session; wherein subsequent access requests accompanied by authentication objects are validated by checking for a valid security cookie.

Abstract: A system and method for implementation of zero trust computer network security combined with stateful authentication object tracking, authentication object manipulation and forgery detection, and assessment of authentication and identity attack surface. The methodology involves gathering all authentication objects issued by a network, storing the authentication objects in a master ledger for use in stateful deterministic authentication object tracking, and running detection functions that compare authentication objects presented for access to network resources with the master ledger. In an embodiment, an authentication object agent is installed at the domain controller level. In another embodiment, a log extension utility is installed at the local host computer level to provide additional log data for additional cyberattack detections.

Abstract: A system and method for dynamic authentication attack detection an enforcement at the network, application, and host level that enables zero trust network security principles when combined with stateful authentication object tracking, authentication object manipulation and forgery detection, and assessment of authentication and identity attack surface. The methodology involves gathering all authentication objects issued by a network, storing the authentication objects in a centralized location for use in stateful deterministic authentication object tracking, scoring the completeness of the authentication risk observations, and intervening in authentication processes when the potential risk is too great.

Abstract: A system for continuous contextual policy-aware vulnerability mapping, security posture determination and attack planning and simulation, comprising an indexing service configured to create a dataset by processing and indexing source code of a project by a developer, perform a code audit on the indexed source code, store results from the code audit in the dataset, gather additional information relating to the provided project as intended and as operated, store the additional information in the dataset, and store the dataset into memory; and a monitoring service configured to continuously monitor the project for source code and operational changes and performance and make changes to the dataset as needed.

Abstract: A system and method for complex IT process annotation and tracing, analysis, and simulation, comprising at least a generative simulation platform, optimization engine, and metric engine, which is able to model and simulate a variety of simulations and develop adaptive models, and can be used more specifically for IT/OT infrastructure and technology enabled process simulation to manage performance and risk in a network enabled business infrastructure, perform load-testing and quality control tests, and determine the overall health to known attacks and potential interruptions as a system or network or process topography changes and updates.

Abstract: An AI-powered music registry, collaboration, and workflow management platform that addresses the challenges faced by music industry participants in the digital age. The system comprises a segmentation and hashing subsystem for musical pieces, segments, and isolated elements, enabling the evaluation of uniqueness and the consideration of individual creators' contributions. An artificial intelligence (AI) and machine learning (ML) subsystem is employed for extracting and isolating individual instruments, vocals, and performer contributions, while a component-level tracking module enables enhanced crediting and royalty distribution.

Abstract: A system and methods for enhanced cybersecurity using an automated planning service is provided, comprising an automated planning service that receives a network analysis job, constructs a simulation using known network and threat information, assigns individual actions to worker nodes for processing, analyzes the results and records new threat mitigation plans as they are produced to expedite future operations, and produces an action plan as output.

Abstract: Modeling multi-peril catastrophe using a multidimensional timeseries data server that creates a first dataset by retrieving previously gathered and analyzed data based on a plurality of perils, and create a second dataset by retrieving from memory synthetically generated data based at least on the plurality of perils; and a directed computational graph service configured to retrieve the first dataset and second dataset from the multidimensional time series data server, and perform graph analysis on the first dataset and second dataset to find links amongst the plurality of perils.

Abstract: A system and method for multi-model generative simulation modeling of complex adaptive systems, comprising a generative simulation platform, a multidimension time series datastore, and a directed computational graph, capable of running a multitude of simulations with complex and shifting model data, and an optimization engine which can introduce changes into a simulation to represent unforeseen or random changes and events to introduce changes and shifts in the simulation that might not otherwise occur.

Abstract: A composite AI system and method for advanced reasoning and automation that integrates symbolic knowledge graphs and algorithms with non-symbolic, or connectionist, models such as neural embeddings. A hierarchical architecture enables dynamically distributed, cooperative reasoning through layperson and expert-led challenge-based verification, model blending, model fitness and retraining and selection, comprehensive feedback loops at individual model or model blend or process flow with or without supervision, and specialized routing of processing to account for various operational risk, regulatory, legal, privacy, or economic considerations. Models, datasets, knowledge bases, simulations and simulation components, and embeddings are iteratively refined using knowledge graph elements and model, process, simulation or flow/process optimal hyperparameters which are recorded and tracked. Extraction of symbolic representations from connectionist models links them to curated ontologies of facts and principles.

Abstract: A system for analyzing graph databases using intelligent reasoning systems including scalable collection of, and transformation of, graph data into facts suitable for use with programming logic languages doing deductive reasoning. A graph analyzer ingests disparate graph data from across the Internet and transforms the graph data into a fact table. In order to reduce latency and processing congestion, a stream processing engine and sharding strategy are employed to ensure scalability through parallelized processing of programming logic queries. Transformed graph data, now relational data, is utilized with programming logic languages that allow for hypothetical queries whereby an inference engine can deduce new information to satisfy such a query. Furthermore, the self-contained nature of inputs, outputs, and transformations of the system means strict data provenance can be observed and adhered to.

Abstract: A system for insurance process management employing an advanced insurance management platform has been developed. A high speed data retrieval and storage module retrieves insurance related data from a plurality of sources. A data analysis module determines an activeness metric for an object, such as a physical asset, in order to categorize risk and also receives a plurality of individual, entity, and object data to create one or more cognitive maps which may analyzed to determine a propensity for risk. The data analysis module generates a cognitive density metric based on the activeness metric and cognitive map. A predictive analytics module performs predictive analytics functions on normalized insurance related data and using the cognitive density metric. A predictive simulation module performs predictive simulation functions on normalized insurance related data. As a result, the system can produce various models to determine risk and loss associated with an insured physical asset.