Auto-learning Data Classification for DLP
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Solution Overview
Problem
Current data loss prevention systems face challenges in efficiently classifying and maintaining data classification, as they require extensive resources and time, and often lack prior knowledge of what data is private or public, making it difficult to distinguish between internal and external communication effectively.
Innovation Solution
The implementation of auto-learning schemes that monitor and classify data in real-time, using Bayesian methods to identify keywords and styles indicative of organizational groups, allowing for dynamic classification and access control to regulate data transmission, thereby preventing data loss without pre-defined dictionaries or a priori information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional DLP systems classify all data in advance, then data classification accuracy is improved, but resource consumption and time requirements increase significantly
Solution Approach 1:
The system performs preliminary classification by learning traffic flow characteristics and group patterns from historical data before actual data loss prevention operations. This preliminary learning phase enables the system to pre-establish group relationships and communication patterns, so that during runtime, classification decisions can be made quickly based on pre-learned models rather than analyzing all data from scratch
Solution Approach 2:
The system automatically learns and updates its classification models without requiring manual intervention or pre-defined dictionaries. The auto-learning mechanism continuously monitors data flow, extracts features, and refines group classifications autonomously, eliminating the need for manual data annotation and reducing ongoing maintenance resources
2Speed
If pre-defined dictionaries are used for data classification, then classification speed is improved, but adaptability to new data types decreases
Solution Approach 1:
The system employs dynamic classification models that continuously adapt to new data patterns through ongoing learning from monitored traffic flows. Instead of relying on static pre-defined dictionaries, the classification boundaries and group definitions are updated automatically as the system encounters new data types and communication patterns, maintaining both speed through caching of learned patterns and adaptability through continuous learning
Solution Approach 2:
The system incorporates feedback loops where classification results and actual data flow patterns are continuously monitored and fed back into the learning model. This feedback mechanism allows the system to refine its understanding of group relationships and adjust its classification criteria dynamically, improving adaptability to new data while maintaining efficient classification through learned patterns
3Measurement precision
If manual data classification is performed, then classification accuracy is improved, but time consumption and operational complexity increase
Solution Approach 1:
The system replaces manual mechanical classification processes with automated machine learning algorithms that analyze traffic flow characteristics and data patterns. The auto-learning mechanism uses computational models to automatically extract features, identify group relationships, and perform classification without human intervention, achieving both high accuracy through sophisticated pattern recognition and time efficiency through automated processing
4Reliability
If all data is monitored and classified a priori, then data loss prevention coverage is improved, but system complexity and resource requirements increase
Solution Approach 1:
The system extracts and focuses only on the most critical features and patterns from the data flow for classification purposes, rather than analyzing all data attributes equally. By identifying and monitoring key traffic flow characteristics and group-specific patterns, the system achieves comprehensive data loss prevention coverage while reducing computational complexity through feature selection and focused monitoring of relevant data aspects
Data Source
AI summary
Disclosed are methods for automatic categorization of internal and external communication, the method including the steps of: defining groups of entities that transmit data; monitoring data flow of the groups; extracting the data, from the data flow, for learning traffic-flow characteristics of the groups; classifying the data into group flows; upon the data being transmitted, checking the data to determine whether the data is designated as group-internal; and blocking data traffic for data that is group-internal. Preferably, the step of monitoring includes assigning data weights to the data using Bayesian methods. Most preferably, the step of classifying includes classifying the data using Bayesian methods for evaluating the data weights. Preferably, the step of blocking includes blocking data traffic between members of two or more groups. Preferably, the method further includes the step of: enabling an authorized entity to unblock the data traffic.