Shared GUI Auto-Redaction for Real-Time Sensitive Data Protection
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Solution Overview
Problem
Existing systems fail to effectively and efficiently auto-redact sensitive data in real-time shared graphical user interface (GUI) environments, posing a security risk by inadvertently sharing confidential information during online communications.
Innovation Solution
A system utilizing generative AI and natural language processing (NLP) models to analyze and redact sensitive data in real-time, flagging and removing such data before transmission to networked devices, thereby enhancing security and reducing computational resource usage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If real-time data stream transmission is implemented, then communication speed is improved, but data security deteriorates due to exposure of sensitive information
Solution Approach 1:
The system performs preliminary analysis of data elements before they are transmitted in the real-time stream. A language model predicts whether each data element is sensitive and generates redaction decisions in advance, allowing the system to maintain real-time transmission speed while preventing sensitive data exposure through pre-computed redaction masks
2Object-affected harmful factors
If comprehensive data analysis is performed to identify sensitive data, then data security is improved, but computational resource consumption increases
Solution Approach 1:
Instead of analyzing all data elements exhaustively, the system uses a language model to predict sensitivity and selectively applies detailed analysis only to data elements flagged as potentially sensitive. This partial action approach reduces computational resource consumption while maintaining effective security by focusing resources on the most critical data elements
3Object-affected harmful factors
If manual review of sensitive data is implemented, then data security is improved, but productivity deteriorates due to time consumption
Solution Approach 1:
The system implements self-service through automated redaction where the language model independently analyzes data elements, predicts sensitivity, and generates redaction decisions without requiring manual intervention. The system automatically applies redaction masks to sensitive data elements, maintaining high data security while preserving productivity by eliminating the time-consuming manual review process
Data Source
AI summary
Systems, computer program products, and methods are described herein for auto-redacting data in a shared-GUI environment. The present invention is configured to receive an input request to transmit a data stream; analyze the available data to identify sensitive data; flag the sensitive data to generate flagged data; generate a dataset comprising data elements of the flagged data and location data of the flagged data; for each data element of the flagged data: analyze the data element to determine whether it is a sensitive data element; generate a label for the sensitive data element; and store the generated label, the sensitive data element, and the location data of the sensitive data element in a data structure; redact sensitive data elements from the available data to generate a redacted data stream; and transmit the redacted data stream.