Abstract: Computer-implemented systems and methods are disclosed, including systems and methods utilizing language models for generating data objects and/or updating an ontology. A computer-implemented method may include: employing one or more large language models (“LLMs”) to generate at least a data triple and a classified triple; executing, using the classified triple, a similarity search with reference to an ontology to determine that the classified triple at least partially matches one or more data object types defined in the ontology; in response to the determination, adding into a first database at least a first data object of a first data object type that represents a first entity in the data triple and a second data object of a second data object type that represents a second entity in the data triple.

Abstract: Computer-implemented systems and methods are disclosed, including systems and methods utilizing language models for generating data objects and/or updating an ontology. A computer-implemented method may include: employing one or more large language models (“LLMs”) to generate at least a data triple and a classified triple; executing, using the classified triple, a similarity search with reference to an ontology to determine that the classified triple at least partially matches one or more data object types defined in the ontology; in response to the determination, adding into a first database at least a first data object of a first data object type that represents a first entity in the data triple and a second data object of a second data object type that represents a second entity in the data triple.

Abstract: Computer-implemented systems and methods are disclosed, including systems and methods for automatically solving problems. A computer-implemented method may include: by an agent service configured to interact with an LLM to complete a run: providing an LLM with access to a state machine, executing an initial state of the state machine with the LLM, determining a subsequent state of the state machine based on at least an initial LLM output, and executing the subsequent state of the state machine.

Abstract: Computer-implemented systems and methods are disclosed, including systems and methods utilizing language models for creating and/or updating an ontology. A computer-implemented method may include: receiving tabular data from one or more data sources; generating an interactive graphical representation of at least a portion of the tabular data and connections between the portion of the tabular data; providing, via a user interface, the interactive graphical representation; receiving a user operation via the user interface, updating an ontology and/or generating transformations for adding data objects into a database.

Abstract: Computer-implemented systems and methods are disclosed, including systems and methods utilizing language models for searching a large corpus of data. A computer-implemented method may include: receiving a first user input comprising a natural language query; vectorizing the first user input into a query vector; executing, using the query vector, a similarity search in a document search model to identify one or more similar document portions, where the document search model includes a plurality of vectors corresponding to a plurality of portions of a set of documents; generating a first prompt for a large language model (“LLM”), the first prompt including at least the first user input, and the one or more similar document portions; transmitting the first prompt to the LLM; receiving a first output from the LLM in response to the first prompt; and providing, via a user interface, the first output from the LLM.

Abstract: Computer-implemented systems and methods are disclosed, including systems and methods utilizing language models for generating data objects and/or updating an ontology. A computer-implemented method may include: employing one or more large language models (“LLMs”) to generate at least a data triple and a classified triple; executing, using the classified triple, a similarity search with reference to an ontology to determine that the classified triple at least partially matches one or more data object types defined in the ontology; in response to the determination, adding into a first database at least a first data object of a first data object type that represents a first entity in the data triple and a second data object of a second data object type that represents a second entity in the data triple.

Abstract: A method, performed by one or more processors, includes: receiving an indication of a desired modification to a cybersecurity event detector that is being contemporaneously used for the detection of potential cybersecurity events in a production environment; modifying, in a sandbox environment, the cybersecurity event detector based on the indication of the desired modification to the cybersecurity event detector; and for each system event in a set of system events, determining, in the sandbox environment, whether the respective system event is indicative of a potential cybersecurity event using the modified cybersecurity event detector. Related apparatus are also disclosed.

Abstract: A method, performed by one or more processors, includes: receiving, from search engine software, first data. In some examples, the first data includes one or more data records. In some examples, the method further includes deriving, based on the first data, a data access query for locating second data using data extracted from the one or more data records. In some examples, the second data includes one or more data objects. In some examples, the method further includes sending, to data access software, the data access query to cause the data access software to locate the second data stored in a second data store different from a first data store storing the first data.

Abstract: A method, performed by one or more processors, including: receiving a user query; deriving, based on the user query, a search engine query for use with search engine software and a data access query for use with data access software; sending, to the search engine software, the search engine query to cause the search engine software to locate first data stored in a search engine index; sending, to the data access software, the data access query to cause the data access software to locate second data stored in a distributed data store; receiving, from the search engine software, the first data; and receiving, from the data access software, the second data.

Abstract: A method, performed by one or more processors, including: receiving a user query; deriving, based on the user query, a search engine query for use with search engine software and a data access query for use with data access software; sending, to the search engine software, the search engine query to cause the search engine software to locate first data stored in a search engine index; sending, to the data access software, the data access query to cause the data access software to locate second data stored in a distributed data store; receiving, from the search engine software, the first data; and receiving, from the data access software, the second data.

Abstract: A method, performed by one or more processors, including: receiving a user query; deriving, based on the user query, a search engine query for use with search engine software and a data access query for use with data access software; sending, to the search engine software, the search engine query to cause the search engine software to locate first data stored in a search engine index; sending, to the data access software, the data access query to cause the data access software to locate second data stored in a distributed data store; receiving, from the search engine software, the first data; and receiving, from the data access software, the second data.