Enterprise Data Validation Server for Sensitive Message Protection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional systems in cloud-based environments fail to provide adequate data management and security, leading to risks of inadvertently sending sensitive information to unintended recipients, particularly in finance or HR departments, due to the need for frequent switching between conversation windows.
Innovation Solution
An enterprise data validation server captures conversation snippets, creates a topic model using machine learning (LDA modeling), determines topic distributions, and calculates a bias value to alert users before sending messages, preventing sensitive information from being sent to incorrect recipients.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If users frequently switch between multiple conversation windows to communicate with different people, then communication efficiency is improved, but the risk of sending sensitive information to unintended recipients increases
Solution Approach 1:
The system performs preliminary analysis of conversation history and topic distributions before the user sends a message. By pre-establishing the expected topic profile for each conversation partner based on historical data, the system can detect anomalies in real-time and alert users before sensitive information is inadvertently sent to the wrong recipient.
Solution Approach 2:
The system provides immediate feedback to users by comparing the current message's topic distribution against the established profile for that conversation partner. When a significant deviation is detected, the system alerts the user, allowing them to correct the mistake before sending, thus creating a feedback loop that prevents data leakage while maintaining communication efficiency.
2Reliability
If conventional systems monitor message content to prevent data leakage, then data security is improved, but user privacy and system performance deteriorate
Solution Approach 1:
The system extracts only essential metadata from conversations (topic distributions and conversation patterns) rather than monitoring full message content. By analyzing topic models and statistical distributions rather than actual message text, the system can detect potential data leakage risks without accessing or storing sensitive user communication content, thus maintaining security while preserving privacy.
Solution Approach 2:
The system replaces traditional keyword-based or content-based monitoring with a statistical topic modeling approach. Instead of mechanically scanning message content for sensitive keywords, the system uses probabilistic topic distributions to identify anomalies, substituting invasive content analysis with non-invasive statistical pattern recognition that maintains security without compromising privacy.
Data Source
AI summary
Methods and systems for protecting sensitive data using conversational history are described herein. An enterprise data validation server may receive conversation snippets and create a topic model. The enterprise data validation server may detect a message is being sent from a first user to a second user, determine a topic distribution between the first user and the second user, and a topic distribution of the message. The enterprise data validation server may determine a bias value associated with the message by comparing the topic distribution of the message and the topic distribution between the first user and the second user. Accordingly, based on a determination that the bias value exceeds a threshold, the enterprise data validation server send an alert containing a warning message.