Log Canonicalization Using Natural Language and Vector Embeddings
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Solution Overview
Problem
Computer system logs are often in complex, vendor-specific formats that are difficult for humans to interpret, leading to inefficiencies in diagnosing issues and analyzing data, and require numerous bespoke parsers, complicating data integration and scalability.
Innovation Solution
The conversion of computer system logs into natural language representations using natural language processing (NLP) and subsequent transformation into multi-dimensional vectors, facilitated by large language models and embedding models, standardizes and optimizes log data for analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If computer system logs are kept in vendor-specific formats, then the logs preserve their original detailed information, but they become difficult for humans to interpret and require numerous bespoke parsers for data integration
Solution Approach 1:
The patent introduces natural language as an intermediary representation layer between the original vendor-specific log formats and human analysis. The system translates structured log data into natural language descriptions that are easily interpretable by humans, while preserving the underlying detailed information through bidirectional translation capabilities and reference to original log data.
2Reliability
If numerous bespoke parsers are created for different vendor formats, then each vendor's log format can be processed accurately, but the system complexity increases and scalability decreases
Solution Approach 1:
The patent implements a universal natural language processing framework that can handle multiple vendor-specific log formats through a single system architecture. The approach uses standardized natural language generation and translation mechanisms that work across different log formats, eliminating the need for separate bespoke parsers for each vendor while maintaining accurate processing of vendor-specific details.
3Productivity
If logs are translated into natural language representations, then human interpretability and analysis efficiency improve, but the need for advanced NLP and vector processing infrastructure increases
Solution Approach 1:
The patent applies preliminary action by pre-translating log data into natural language representations and pre-computing vector embeddings during log ingestion rather than performing these computationally intensive operations in real-time during analysis. This approach prepares the data in advance, making subsequent analysis faster and reducing the immediate processing infrastructure requirements during critical analysis phases.
Data Source
AI summary
Provided herein is an exemplary system for canonicalizing computer system logs into natural language processed representations for data analysis, the system including a real-time data collector, a cyber security purpose-based large language model communicatively coupled to the real-time data collector, a multi-dimensional vector generator communicatively coupled to the cyber security purpose-based large language model and a vectorization index and a prediction engine communicatively coupled to the multi-dimensional vector generator.