Security Language Embeddings for Natural-Language Threat Hunting
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Solution Overview
Problem
Existing security data analysis systems require structured query languages like SQL or KQL, limiting their effectiveness in analyzing large, unstructured security datasets, and generative models are constrained by context windows, hindering efficient cyber threat detection.
Innovation Solution
Training a security-specific large language model (LLM) to combine security and natural language, allowing unstructured data access through natural language queries and embedding-based retrieval, eliminating the need for structured formats and database parsing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If structured query languages like SQL or KQL are used for security data analysis, then query precision is improved, but device complexity and ease of operation deteriorate due to requiring structured formats and database parsing knowledge
Solution Approach 1:
The patent introduces an intermediary layer between the user and the security data. A natural language processing system translates user-friendly natural language queries into structured query formats, eliminating the need for users to learn SQL or KQL while maintaining query precision. This intermediary translates between different language paradigms.
Solution Approach 2:
The system changes the parameter of query input from structured syntax (SQL/KQL) to natural language syntax. By transforming the input format parameter, the system makes the interface more accessible while preserving the ability to retrieve precise security data through the underlying structured query engine.
2Ease of operation
If generative models are used for security data analysis, then ease of operation is improved through natural language queries, but context window constraints limit the ability to analyze large datasets efficiently
Solution Approach 1:
The patent segments large security datasets into manageable chunks that fit within model context windows. By dividing the data into smaller segments, the system enables analysis of large datasets while respecting the contextual limitations of generative models, allowing progressive processing of extensive security information.
Solution Approach 2:
The system introduces an intermediate embedding space dimension between natural language queries and security data. By projecting data into this embedded representation space, the system can efficiently retrieve relevant information without requiring the model to process entire large datasets at once, effectively working around context window constraints.
3Measurement precision
If security data is parsed and ingested into a database system, then measurement precision is improved, but use of energy and computing resources increase
Solution Approach 1:
The patent extracts the essential information from security data and represents it in an embedded format that captures semantic meaning. By taking out only the critical information features and representing them in a compact embedded form, the system reduces the need for extensive data parsing, indexing, and database ingestion operations, thereby lowering computing and storage resource requirements while maintaining data accessibility.
Data Source
AI summary
Methods and apparatuses for improving the speed, quality, and relevance of automated responses provided by a question answering system for security data are described. The question answering system may generate and utilize a large language model that is trained to combine the language of security data, such as the language found in security logs and alerts, with natural language text. Given an input prompt (or a search query) from an end user of the question answering system, the question answering system may identify relevant content from the security data and display a response based on the relevant content. The question answering system may allow the end user of the question answering system to query security logs using natural language text without requiring the end user to provide a structured query and without requiring the security data be parsed and ingested into a database system.