Security Classification by Machine Learning Models
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Solution Overview
Problem
Current systems face challenges in accurately and efficiently determining security classifications for electronic documents and setting appropriate access controls, particularly in large networks with sensitive information, due to the complexity of analyzing vast amounts of data and requiring manual intervention.
Innovation Solution
The system employs machine learning logic to generate classification models based on content, metadata, and contextual factors, using weight parameters to determine security classifications and adjust access privileges, and automatically retrain models to maintain accuracy in the face of data changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual methods are used to determine security classifications for electronic documents, then accuracy can be maintained through human judgment, but productivity is reduced due to the time-consuming nature of manual analysis
Solution Approach 1:
The system enables electronic documents to classify themselves automatically by analyzing their own content, metadata, and contextual factors through machine learning models, eliminating the need for manual human review while maintaining classification accuracy
Solution Approach 2:
Manual human judgment for security classification is replaced with automated machine learning systems that process document content, metadata, and contextual information to determine appropriate security classifications, dramatically increasing productivity
2Measurement precision
If comprehensive analysis of content, metadata, and contextual factors is performed to improve classification accuracy, then measurement precision is improved, but device complexity increases due to multiple inference models and weight parameters
Solution Approach 1:
The classification system is divided into separate inference models for different aspects: content analysis, metadata analysis, and contextual factor analysis. Each model specializes in one aspect and can be independently trained and optimized, reducing overall system complexity while maintaining comprehensive analysis
Solution Approach 2:
The machine learning framework provides a universal platform that handles multiple classification aspects (content, metadata, context) through a unified architecture with weight parameters that can be adjusted based on the importance of different factors, simplifying the integration of multiple analysis functions
3Measurement precision
If machine learning models are retrained frequently to maintain accuracy with changing data, then measurement precision is maintained, but loss of time occurs during the retraining process
Solution Approach 1:
The system implements continuous feedback loops where classification performance is monitored and automatically triggers retraining when accuracy thresholds are not met or when new contextual factors are identified, ensuring models remain accurate without excessive retraining
Solution Approach 2:
Instead of continuous retraining, the system employs periodic retraining cycles triggered by specific conditions such as accumulation of new data, changes in security policies, or performance degradation, reducing time loss while maintaining model accuracy
Data Source
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AI summary
A computer-implemented method that includes a computing system receiving a plurality of data items that include content data. The system then generates a first inference model based on the received content data. The system also receives metadata associated with respective data items and then generates a second inference model based on the received metadata data. The system further determines, based on the first inference model, a first weight parameter associated with the content data, and determines, based on the second inference model, a second weight parameter associated with the metadata. The system then generates a classification model for determining a security classification of at least one data item of the plurality of data items. The classification model is generated based on at least one of the first weight parameter or the second weight parameter.