Security Report Generation Using Semantic Threat Matching
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Solution Overview
Problem
Security analysts face challenges in manually combining security data with threat intelligence, which often fails to match similar but not exactly the same entities across different data sources, leading to a brittle process that is inefficient and prone to errors.
Innovation Solution
An artificially intelligent (AI) agent analyzes security data, automatically pulls in relevant threat intelligence, performs fuzzy and semantic matching, and generates real-time reports to enrich security investigations, without requiring parsers for different data types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual combining of security data with threat intelligence is performed, then security analysts can achieve security goals, but the process is brittle and fails to match similar but not exactly the same entities
Solution Approach 1:
The patent replaces the manual mechanical process of security analysts combining security data with threat intelligence with an automated semantic processing system. The system uses semantic parsing to extract entities and relationships from both security data and threat intelligence, then automatically matches them using semantic similarity algorithms, eliminating the need for manual entity matching while improving accuracy.
Solution Approach 2:
The patent introduces semantic embeddings as an intermediary representation layer between raw security data and threat intelligence. By converting both data types into semantic vector representations, the system enables automated similarity comparison and matching, serving as a mediator that bridges the gap between different data formats and structures.
2Productivity
If automated processing is implemented, then efficiency is improved, but the system fails to handle similar but not exactly the same entities
Solution Approach 1:
The patent transforms the entity matching problem from exact string matching to semantic similarity comparison by changing the parameter space. Instead of comparing discrete text values, the system converts entities into continuous semantic vector representations, allowing for gradient-based similarity measurements that can handle variations and approximations in entity descriptions.
Solution Approach 2:
The patent replaces traditional automated text-matching algorithms with semantic processing and embedding-based comparison. This substitution enables the automated system to understand contextual meaning and semantic relationships, allowing it to correctly match similar but not identical entities while maintaining high processing speed through vectorized operations.
3Adaptability or versatility
If parsers for different data types are used, then data processing is possible, but the system becomes complex and requires multiple specialized components
Solution Approach 1:
The patent implements a universal semantic parsing framework that can handle multiple data types (security logs, threat intelligence feeds, incident reports) through a single unified approach. The semantic parser extracts entities and relationships regardless of the input data format, and the embedding model processes all types of text data uniformly, eliminating the need for separate specialized parsers for each data type.
Solution Approach 2:
The patent introduces semantic embeddings as a universal intermediary representation that decouples the input data format from the processing logic. By converting all diverse data types into a common semantic vector space, the system simplifies the architecture while maintaining the ability to handle multiple data types, as the embeddings serve as a format-agnostic bridge between input and processing.
Data Source
AI summary
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.