Telephonic Conversation Screening for Predictive Data Exposure Prevention
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Solution Overview
Problem
Existing systems fail to effectively identify and prevent the unintentional disclosure of sensitive information during telephonic conversations, which can lead to security breaches and privacy violations.
Innovation Solution
A system that converts voice data to text, extracts intent and entities, predicts sensitive information, compares the text data to predicted sensitive information, and intersects the voice data to prevent transmission, generating warnings and allowing user control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time analysis of telephonic conversations is implemented to detect sensitive information, then data security is improved, but system complexity increases
Solution Approach 1:
The system segments the telephonic conversation analysis into distinct functional modules: voice-to-text conversion, intent extraction, entity recognition, sensitive information prediction, and comparison. Each module handles a specific aspect of the analysis, making the overall complex system manageable and maintainable while achieving real-time detection of sensitive information
Solution Approach 2:
The patent introduces intermediary components such as the voice-to-text converter that transforms voice data into text data, and the intent extractor that serves as a bridge between raw conversation data and sensitive information detection. These intermediaries simplify the detection process by preprocessing data into structured formats that are easier to analyze for sensitive information
2Loss of information
If automated detection system is deployed to prevent sensitive information exposure, then loss of information is reduced, but device complexity increases
Solution Approach 1:
The system performs preliminary actions by extracting intent and entities from the conversation before the actual sensitive information disclosure occurs. The intent extractor identifies the user's purpose, and the entity extractor identifies key elements, allowing the system to predict potential sensitive information in advance and prevent its exposure before it happens
Solution Approach 2:
The system implements feedback mechanisms where the comparison module continuously compares extracted entities against predicted sensitive information, and the system adjusts its detection parameters based on the results. This feedback loop enables the automated system to learn from previous detections and improve its accuracy in preventing sensitive information exposure
Data Source
AI summary
An embodiment converts, by a voice-to-text converter, voice data to text data. The embodiment extracts, by an extractor, an intent and an entity from the text data. The embodiment predicts, by a predictor, based on the intent and the entity, a sensitive information. The embodiment compares, by an intersector, the text data to the predicted sensitive information. The embodiment determines, by the intersector, responsive to the comparing, whether the text data includes the predicted sensitive information. The embodiment intersects, by the intersector, responsive to a determination that the text data includes the predicted sensitive information, the voice data.