Real-time Context-based Data Classification
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Solution Overview
Problem
Current data classification methods in computing systems are inefficient as they classify data after it has been received and stored, leading to potential inaccuracies and inappropriate disclosure of sensitive information, necessitating real-time context-based detection and classification to ensure data security.
Innovation Solution
Implementing a method that uses machine learning operations, such as natural language processing and artificial intelligence, to analyze data in real-time and apply contextual classification criteria, inspecting data before storage to identify and protect personal, sensitive, and proprietary information based on jurisdictional laws and regulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data classification is performed after data is received and stored, then the classification process is simpler and faster to implement, but data security is compromised due to potential inaccuracies and inappropriate disclosure of sensitive information
Solution Approach 1:
The patent applies preliminary action by performing data classification before data storage. The system classifies incoming data in real-time as it is being written to the file system, determining security categories and sensitivity levels prior to the data being committed to storage. This ensures that sensitive information is identified and protected before it can be improperly accessed or disclosed, resolving the contradiction by prioritizing security while managing complexity through integrated real-time processing.
Solution Approach 2:
The patent introduces an intermediary classification mechanism that acts as a mediator between data input and data storage operations. This intermediary layer analyzes data characteristics, applies classification rules, and determines security categories before data is written to the file system. The intermediary classification system bridges the gap between simple storage operations and complex security requirements, enabling reliable data protection without significantly increasing overall system complexity.
2Measurement precision
If real-time context-based classification is implemented, then data security and accuracy are improved, but processing time and computational resources increase
Solution Approach 1:
The patent applies partial action by implementing context-based classification selectively rather than uniformly for all data. The system identifies and applies comprehensive context analysis to data that requires high classification accuracy (such as sensitive or uncertain data types), while using faster, rule-based classification for routine data. This approach maintains high measurement precision where needed while minimizing overall processing time by avoiding excessive analysis on all data streams.
Solution Approach 2:
The patent utilizes parameter changes by dynamically adjusting classification depth and processing intensity based on data characteristics. The system monitors data parameters such as file type, source, and content patterns to determine the appropriate level of context analysis required. When data exhibits high uncertainty or sensitivity parameters, the system increases processing depth for accurate classification; when parameters indicate routine data, processing is expedited, thereby balancing accuracy with processing time efficiency.
3Measurement precision
If comprehensive contextual information is analyzed for each data item, then classification accuracy is improved, but system complexity and computational load increase
Solution Approach 1:
The patent applies segmentation by dividing the classification process into distinct modular stages: initial data reception, contextual information extraction, classification rule application, and final categorization. Each stage processes specific aspects of the data independently, allowing the system to analyze comprehensive contextual information accurately while managing complexity through modular architecture. This segmented approach enables high measurement precision by thoroughly analyzing context without overwhelming system complexity, as each module handles a specific aspect of the classification task.
Data Source
AI summary
Various embodiments are provided for providing real-time context-based detection and classification of data in a computing environment are provided. Data may be received from a user. Contextual information may be learned from the data received from a user using a machine learning operation. The data may be classified according to the contextual classification criteria applied to contextual information derived in real time from the data.