Shared GUI Auto-Redaction for Real-Time Sensitive Data Masking
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Solution Overview
Problem
Existing systems fail to effectively and efficiently redact sensitive data in real-time during shared graphical user interface (GUI) environments, such as online meetings, posing a security risk by inadvertently sharing confidential information.
Innovation Solution
A system utilizing generative artificial intelligence (AI) and natural language processing (NLP) models to analyze and redact sensitive data on a GUI before sharing, reducing the need for manual intervention and optimizing resource usage by dynamically determining and redacting sensitive data during continuous data transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual redaction of sensitive data is performed in shared-GUI environments, then security is improved, but productivity deteriorates due to time-consuming manual processes
Solution Approach 1:
The system implements automated redaction where the software itself identifies and redacts sensitive data without requiring manual user intervention. The AI model autonomously scans the shared GUI, detects sensitive information patterns, and applies redaction measures, allowing the system to serve its own security needs without external assistance.
Solution Approach 2:
The patent replaces manual mechanical redaction processes with an automated AI-based system. Instead of users manually reviewing and redacting sensitive data, an artificial intelligence model performs the detection and redaction automatically, substituting human effort with computational processing.
2Productivity
If automated redaction systems are implemented, then productivity is improved, but device complexity worsens due to additional processing requirements
Solution Approach 1:
The system performs preliminary analysis of the shared GUI data stream before transmission or display. The AI model is trained in advance on sensitive data patterns and is pre-configured to recognize and redact various types of sensitive information, allowing the system to handle complexity beforehand rather than during real-time operation.
Solution Approach 2:
The patent introduces an intermediary AI processing layer between the GUI and the transmission/display system. This intermediary component handles the complex analysis and rediction tasks, isolating the complexity from the main system and allowing it to be managed as a separate, specialized module.
3Reliability
If real-time analysis of continuous data stream is performed, then security is improved, but use of energy worsens due to continuous processing
Solution Approach 1:
The system maintains continuous monitoring and redaction capabilities throughout the data stream transmission. Rather than periodic checks, the AI model operates continuously on the flowing data, ensuring uninterrupted security protection while optimizing processing to maintain steady-state energy consumption rather than repeated startup cycles.
Solution Approach 2:
The patent applies selective rediction only to identified sensitive data portions rather than processing the entire data stream uniformly. The AI model identifies and focuses computational resources only on segments containing sensitive information, performing partial rediction actions that reduce overall energy consumption compared to blanket processing of all data.
4Reliability
If comprehensive data analysis is performed to identify all sensitive data, then security is improved, but loss of time worsens due to extensive processing required
Solution Approach 1:
The system performs preliminary training of the AI model on comprehensive sensitive data patterns before deployment. The model is pre-loaded with knowledge of various sensitive data types, formats, and contexts, enabling it to quickly recognize and redact sensitive information during real-time operation without requiring extensive analysis time during actual use.
Solution Approach 2:
The patent replaces time-consuming manual or rule-based data analysis with AI-based pattern recognition. The artificial intelligence model processes and understands complex data patterns, contexts, and variations in sensitive information much faster than traditional systematic analysis methods, significantly reducing the time required for comprehensive security checks.
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 continuous 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 continuous data stream; generate a prompt requesting acceptance of the redacted continuous data stream; and transmit the redacted continuous data stream.