Machine Learning Sensitive Content Detection in Electronic Files
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Solution Overview
Problem
Organizations face challenges in efficiently analyzing and identifying sensitive content within large volumes of unstructured data, such as conversations and communications, due to the complexity and labor-intensive nature of traditional methods, which hinder compliance with data privacy and computer security policies.
Innovation Solution
A system utilizing machine learning techniques, including preprocessing, feature extraction, and multiclass classification, to automatically identify sensitive content in electronic files by converting unstructured data into text format, removing noise, and using machine learning engines trained on specific patterns and context features to classify sensitive information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional methods are used to analyze unstructured data, then data privacy and security policies can be aligned, but the analysis process becomes overly laborious and time-consuming
Solution Approach 1:
The patent replaces manual mechanical analysis methods with automated machine learning systems. The machine learning engine automatically processes unstructured data to identify sensitive content, substituting human labor with computational algorithms that can analyze large volumes of data efficiently while maintaining compliance accuracy.
Solution Approach 2:
The system enables self-service by allowing the machine learning model to autonomously analyze unstructured data and identify sensitive content without requiring manual intervention. The automated classification and detection processes perform the compliance analysis independently, significantly reducing the time and labor required while maintaining reliable policy alignment.
2Measurement precision
If traditional methods are used to analyze unstructured data, then sensitive content can be identified, but the process becomes overly laborious due to large data volumes
Solution Approach 1:
The patent replaces complex manual analysis processes with machine learning-based automated systems. The machine learning engine handles the complexity of processing large unstructured data volumes, maintaining high detection accuracy for sensitive content while simplifying the operational complexity for users through automated processing.
Solution Approach 2:
The system changes the parameters of the analysis process by transitioning from manual rule-based methods to machine learning models with multiple classification engines. This parameter change enables the system to handle large data volumes efficiently while maintaining or improving detection accuracy through advanced algorithms that adapt to different data patterns.
3Measurement precision
If manual analysis methods are used, then sensitive content detection can be performed, but productivity decreases due to labor-intensive processes
Solution Approach 1:
The patent replaces manual analysis mechanics with automated machine learning systems that can process large volumes of unstructured data rapidly. The machine learning engine maintains high detection accuracy for sensitive content while dramatically increasing productivity by eliminating the bottlenecks associated with manual labor-intensive processes.
Solution Approach 2:
The system introduces dynamics by using adaptive machine learning models that can automatically adjust to different types of unstructured data and sensitive content patterns. This dynamic approach enables the system to maintain high detection accuracy across varied data types while achieving high productivity through automated, scalable processing capabilities.
Data Source
AI summary
Systems and methods for identifying sensitive content in electronic files are disclosed. In an embodiment, a request is received to determine whether an electronic file contains sensitive content. The electronic file is preprocessed based on a file type of the electronic file, resulting in a first input file and a second input file. The first input file is inputted to a first machine learning engine, which classifies the first input file for numerical items in the sensitive content. The second input file is inputted to a second machine learning engine, which classifies the second input file for textual items the sensitive content. A report is generated based on a combination of a first output from the first machine learning engine and a second output from the second machine learning engine, where the report indicates items of the sensitive content that are contained in the electronic file.