Data Loss Prevention Model Using Numerical Event Transformations
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Solution Overview
Problem
Existing data loss prevention methods face challenges in efficiently detecting insider data loss during employee off-boarding due to the complexity and volume of event records, requiring manual analysis that is time-consuming and difficult to perform.
Innovation Solution
A data loss prevention model is developed by transforming event types and data into numerical representations, associating them with indications of data loss events, and using these to determine a risk threshold, allowing for automated detection of potential data loss events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual analysis of event records is performed to detect data loss events, then detection accuracy can be maintained, but the time consumption and operational complexity increase significantly
Solution Approach 1:
The patent replaces manual mechanical analysis of event records with an automated machine learning model. The model transforms event types and event data into numerical representations, processes them through trained algorithms, and automatically generates risk scores for data loss events, eliminating the need for manual operator analysis while maintaining detection accuracy.
Solution Approach 2:
The system enables self-service detection by using historically labeled event records to train a machine learning model that autonomously identifies potential data loss events. The model continuously processes new event records without requiring manual intervention, allowing the system to serve itself in detecting and prioritizing security risks.
2Measurement precision
If manual analysis of each event record is performed, then detailed inspection can be achieved, but the complexity of analysis increases making it difficult to perform
Solution Approach 1:
The patent replaces complex manual analysis procedures with an automated machine learning system. The model handles the complexity of processing multiple event features, transforming them into numerical representations and applying trained algorithms to generate risk assessments, thereby eliminating the operational complexity while maintaining detailed inspection capabilities.
Solution Approach 2:
The system transforms qualitative event characteristics into quantitative numerical representations. By converting event types and event data into numerical formats that can be processed by machine learning algorithms, the system simplifies the analysis process while preserving the detailed information needed for accurate detection.
3Reliability
If a large number of event records are collected for comprehensive analysis, then detection coverage is improved, but the difficulty of analyzing each record increases
Solution Approach 1:
The patent replaces manual analysis of large volumes of event records with an automated machine learning model. The model efficiently processes comprehensive datasets by transforming event records into numerical representations and applying trained algorithms, maintaining high detection coverage while eliminating the complexity associated with manual analysis of large record volumes.
Solution Approach 2:
The system creates numerical representations (copies) of the original event records that preserve all necessary information for analysis. These numerical copies can be processed efficiently by machine learning algorithms without requiring direct manual examination of the original complex event data structures.
Data Source
AI summary
One method for developing a data loss prevention model includes receiving, at a processing device, an event record corresponding to an operation performed on a computing device. The event record includes an event type and event data. The method also includes transforming, using the processing device, the event type to an event number corresponding to the event type. The method includes transforming, using the processing device, the event data to a numerical representation of the event data. The method includes associating an indication of whether the event type and the event data correspond to a data loss event with the event number and the numerical representation. The method also includes determining the data loss prevention model using the indication, the event number, and the numerical representation.