Security Data Filtering for LLM Anomaly Analysis Context Limits
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Solution Overview
Problem
Existing analysis engines, such as Large Language Models (LLMs), face limitations in processing large amounts of contextual data due to fixed context windows, leading to inefficiencies and computational resource demands, which hinder effective analysis of anomalous security data in cybersecurity applications.
Innovation Solution
Implementing a filtering mechanism to curate a selection of relevant non-anomalous security data items that correspond to the anomalous data, providing sufficient context within the context window of the analysis engine, thereby reducing data overhead and enhancing computational efficiency while maintaining accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large amounts of contextual security data items are provided to the analysis engine, then the accuracy of analyzing anomalous data items is improved, but the computational resources required and processing time increase significantly
Solution Approach 1:
The filtering mechanism extracts only the most relevant non-anomalous security data items from the large dataset, removing unnecessary contextual data before presenting it to the analysis engine. This extraction process maintains the essential context needed for accurate anomaly analysis while dramatically reducing the volume of data processed, thereby lowering computational resource consumption without sacrificing analytical accuracy.
Solution Approach 2:
The contextual security data is segmented into relevant and irrelevant portions through the filtering mechanism. Only the segmented relevant data items that have actual bearing on the anomalous data item are passed to the analysis engine, while irrelevant segments are discarded. This segmentation approach optimizes the balance between providing sufficient context for accurate analysis and minimizing computational overhead.
2Productivity
If the context window of the analysis engine is limited, then computational efficiency is improved, but the amount of contextual information that can be processed is reduced
Solution Approach 1:
The filtering mechanism serves as an intermediary between the large dataset of security data items and the analysis engine's limited context window. It mediates by selecting and curating only the most pertinent non-anomalous data items, ensuring that the context window is filled with high-value information rather than redundant data. This intermediary filtering process maximizes the utilization of the limited context window while minimizing information loss.
Solution Approach 2:
The filtering mechanism performs preliminary action by pre-processing and curating the security data items before they reach the analysis engine. This preliminary filtering ensures that when data enters the context window, it is already optimized for relevance, allowing the analysis engine to operate efficiently within its constraints without losing critical contextual information.
Data Source
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AI summary
This disclosure provides a filtering mechanism to manage anomalous security data items. An anomalous security data item is provided to an analysis engine (such as a Large Language Model (LLM) or another form of generative language model) for interpretation. By curating a selection of one or more relevant non-anomalous security data items to provide with the anomalous data item, the filtering mechanism enables the analysis engine to perform with increased accuracy, without requiring the analyst engine to process large numbers of data items to ascertain their relevance to the anomalous security data item.