Abstract: Systems, methods, and computer program products are disclosed for automatic code generation and testing. Metadata is determined for a dataset. Data processing code for processing data in the dataset is automatically generated based on the metadata. Test cases are automatically generated for testing the data processing code based on the first data processing code and a data sample generated based on the dataset. The data processing code is automatically executed based on the test cases to generate actual test results. The data processing code is validated by comparing the actual test results to expected test results associated with the test cases.

Abstract: Methods, systems, and computer storage media for providing cognitive query interpretation using a cognitive query interpreter engine of a security management system are described. Cognitive query interpretation refers to the ability of the cognitive query interpretation engine to understand and process queries in a way that mimics human cognitive reasoning. This involves interpreting natural language inputs, identifying the user's intent, and dynamically selecting and querying appropriate data sources—such as logs, APIs, and threat intelligence databases—across different systems and technologies. The cognitive query interpretation engine interprets the query contextually, taking into account factors like user preferences, historical interactions, and the specific security scenario, to provide actionable responses. It leverages contextual analysis and neuro-symbolic reasoning to ensure actionable insights from complex datasets, transforming how users interact with and extract data.

Abstract: In network security systems, graph-based techniques may be employed to generate “thumbprints” of security incidents, which may thereafter be used, e.g., for threat actor attribution or the identification of similar incidents. In various embodiments, each security incident is represented by a graph in which security events correspond to nodes, and which encodes associated metadata in additional nodes and/or node/edge attributes. Graph representation learning may be used to compute node and/or edge embeddings, which can then be aggregated into the thumbprint of the incident.

Abstract: In network security systems, graph-based techniques may be employed to generate “thumbprints” of security incidents, which may thereafter be used, e.g., for threat actor attribution or the identification of similar incidents. In various embodiments, each security incident is represented by a graph in which security events correspond to nodes, and which encodes associated metadata in additional nodes and/or node/edge attributes. Graph representation learning may be used to compute node and/or edge embeddings, which can then be aggregated into the thumbprint of the incident.

Abstract: Machine learning models are used to generate a plan that responds to a user request. The plan includes one or more skills selected from a list of available skills. The prompt may be written in natural language, enabling the user to express their intent without having to know which skills are available or their intricacies. In some configurations, a skill is included in the plan if an embedding representation of an example prompt associated with the skill is within a defined distance of an embedding representation of the user request. Additionally, or alternatively, the embedding distance computations are used to narrow the list of available skills, which is then used to construct a meta-prompt that selects a skill. Skills listed in the meta-prompt may include data types of parameters and return values. This allows the model that processes the meta-prompt to order skills based on data type compatibility.

Abstract: In some examples, a method of generating a security report is provided. The method includes receiving a user query and security data, and providing the user query and security data to a semantic model. The semantic model generates one or more first embeddings. The method further includes receiving, from a data model, one or more second embeddings. The data model is generated based on historical threat intelligence data. The model further includes generating an execution plan based on the one or more first embeddings and the one or more second embeddings, and returning a report that corresponds to the execution plan.

Abstract: Methods, systems, and computer storage media for providing a data analysis pipeline using a data analysis pipeline engine in a data intelligence system are described. A data analysis pipeline refers to a structured sequence of data processing steps that support transforming raw data into meaningful insights or actionable outcomes. The data analysis pipeline engine is an unsupervised learning pipeline based on clustering, topic modeling, and Large Language Models (LLMs). For example, the data analysis pipeline can use advanced machine learning techniques to automatically categorize emails into semantically similar clusters, enabling the data intelligence system to quickly identify and prioritize potentially high-risk emails for further investigation. The data analysis pipeline employs AI agents for context-aware graph induction relevance assessment. The AI agents employ induction and deduction loops to build and refine a data feature hypergraph (e.g.

Abstract: Methods, systems, and computer storage media for providing iterative data processing optimization using an iterative data processing optimization engine in a data intelligence system are described. Iterative data processing refers to handling data where the processing steps are repeated multiple times, across multiple views or modalities, to train machine learning models, filter and score data or generate output. The iterative data processing optimization engine employs expectation step machine learning models that are simple but with fast language models to efficiently and effectively probe and analyze data, while iteratively refining maximization step machine learning models that are optimized and fast to approximate the probing mechanism of the expectation step machine learning models more efficiently, for example, using metadata, external information, and compressed representation.

Abstract: Techniques are described herein that are capable of performing automatic graph-based detection of potential security threats. A Bayesian network is initialized using an association graph to establish connections among network nodes in the Bayesian network. The network nodes are grouped among clusters that correspond to respective intents. Patterns in the Bayesian network are identified. At least one redundant connection, which is redundant with regard to one or more other connections, is removed from the patterns. Scores are assigned to the respective patterns in the Bayesian network, based on knowledge of historical patterns and historical security threats, such that each score indicates a likelihood of the respective pattern to indicate a security threat. An output graph is automatically generated. The output graph includes each pattern that has a score that is greater than or equal to a score threshold. Each pattern in the output graph represents a potential security threat.

Abstract: In some examples, a method of generating a security report is provided. The method includes receiving a user query and security data, and providing the user query and security data to a semantic model. The semantic model generates one or more first embeddings. The method further includes receiving, from a data model, one or more second embeddings. The data model is generated based on historical threat intelligence data. The model further includes generating an execution plan based on the one or more first embeddings and the one or more second embeddings, and returning a report that corresponds to the execution plan.

Abstract: A computer-implemented method of generating a security language query from a user input query includes receiving, at a computer system, an input security hunting user query indicating a user intention; selecting, using a trained machine learning model and based on the input security hunting query, an example user security hunting query and corresponding example security language query; generating, using the trained machine learning model, query metadata from the input security hunting query; generating a prompt, the prompt comprising: the input security hunting user query; the selected example user security hunting query and the corresponding example security language query; and the generated query metadata; inputting the prompt to a large language model; receiving a security language query from the large language model corresponding to the input security hunting query reflective of the user intention.

Abstract: A computer-implemented method of generating a security language query from a user input query includes receiving, at a computer system, an input security hunting user query indicating a user intention; selecting, using a trained machine learning model and based on the input security hunting query, an example user security hunting query and corresponding example security language query; generating, using the trained machine learning model, query metadata from the input security hunting query; generating a prompt, the prompt comprising: the input security hunting user query; the selected example user security hunting query and the corresponding example security language query; and the generated query metadata; inputting the prompt to a large language model; receiving a security language query from the large language model corresponding to the input security hunting query reflective of the user intention.

Abstract: Machine learning models are used to generate a plan that responds to a user request. The plan includes one or more skills selected from a list of available skills. The prompt may be written in natural language, enabling the user to express their intent without having to know which skills are available or their intricacies. In some configurations, a skill is included in the plan if an embedding representation of an example prompt associated with the skill is within a defined distance of an embedding representation of the user request. Additionally, or alternatively, the embedding distance computations are used to narrow the list of available skills, which is then used to construct a meta-prompt that selects a skill. Skills listed in the meta-prompt may include data types of parameters and return values. This allows the model that processes the meta-prompt to order skills based on data type compatibility.

Abstract: In some examples, a method of generating a security report is provided. The method includes receiving a user query and security data, and providing the user query and security data to a semantic model. The semantic model generates one or more first embeddings. The method further includes receiving, from a data model, one or more second embeddings. The data model is generated based on historical threat intelligence data. The model further includes generating an execution plan based on the one or more first embeddings and the one or more second embeddings, and returning a report that corresponds to the execution plan.

Abstract: Techniques are described herein that are capable of performing automatic graph-based detection of potential security threats. A Bayesian network is initialized using an association graph to establish connections among network nodes in the Bayesian network. The network nodes are grouped among clusters that correspond to respective intents. Patterns in the Bayesian network are identified. At least one redundant connection, which is redundant with regard to one or more other connections, is removed from the patterns. Scores are assigned to the respective patterns in the Bayesian network, based on knowledge of historical patterns and historical security threats, such that each score indicates a likelihood of the respective pattern to indicate a security threat. An output graph is automatically generated. The output graph includes each pattern that has a score that is greater than or equal to a score threshold. Each pattern in the output graph represents a potential security threat.

Abstract: Techniques are described herein that are capable of performing automatic graph-based detection of potential security threats. A Bayesian network is initialized using an association graph to establish connections among network nodes in the Bayesian network. The network nodes are grouped among clusters that correspond to respective intents. Patterns in the Bayesian network are identified. At least one redundant connection, which is redundant with regard to one or more other connections, is removed from the patterns. Scores are assigned to the respective patterns in the Bayesian network, based on knowledge of historical patterns and historical security threats, such that each score indicates a likelihood of the respective pattern to indicate a security threat. An output graph is automatically generated. The output graph includes each pattern that has a score that is greater than or equal to a score threshold. Each pattern in the output graph represents a potential security threat.

Abstract: A computer-implemented method of generating a security language query from a user input query includes receiving, at a computer system, an input security hunting user query indicating a user intention; selecting, using a trained machine learning model and based on the input security hunting query, an example user security hunting query and corresponding example security language query; generating, using the trained machine learning model, query metadata from the input security hunting query; generating a prompt, the prompt comprising: the input security hunting user query; the selected example user security hunting query and the corresponding example security language query; and the generated query metadata; inputting the prompt to a large language model; receiving a security language query from the large language model corresponding to the input security hunting query reflective of the user intention.

Abstract: In network security systems, graph-based techniques can be used to identify, for any given security incident including a collection of security events, other incidents that are similar. In example embodiments, similarity is determined based on graph representations of the incidents in which security events are represented as nodes, using graph matching techniques or incident thumbprints computed from node embeddings. The identified similar incidents can provide context to inform threat assessment and the selection of appropriate mitigating actions.

Abstract: In network security systems, graph-based techniques may be employed to generate “thumbprints” of security incidents, which may thereafter be used, e.g., for threat actor attribution or the identification of similar incidents. In various embodiments, each security incident is represented by a graph in which security events correspond to nodes, and which encodes associated metadata in additional nodes and/or node/edge attributes. Graph representation learning may be used to compute node and/or edge embeddings, which can then be aggregated into the thumbprint of the incident.

Abstract: Techniques are described herein that are capable of performing automatic graph-based detection of potential security threats. A Bayesian network is initialized using an association graph to establish connections among network nodes in the Bayesian network. The network nodes are grouped among clusters that correspond to respective intents. Patterns in the Bayesian network are identified. At least one redundant connection, which is redundant with regard to one or more other connections, is removed from the patterns. Scores are assigned to the respective patterns in the Bayesian network, based on knowledge of historical patterns and historical security threats, such that each score indicates a likelihood of the respective pattern to indicate a security threat. An output graph is automatically generated. The output graph includes each pattern that has a score that is greater than or equal to a score threshold. Each pattern in the output graph represents a potential security threat.