Sensitive Data Leakage Screening With Real-Time Transmission Blocking
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Solution Overview
Problem
Current data protection methods fail to effectively prevent sensitive data leakage due to human error, system vulnerabilities, or malicious intent, necessitating a comprehensive solution that detects, mitigates, and alerts against unauthorized data disclosure in real-time.
Innovation Solution
A system utilizing machine learning and deep learning to analyze data transmissions, detect ciphertext, and classify data as public, private, or confidential, with real-time decision-making on allowing, holding, or blocking transmissions, and providing an analytics dashboard for investigative entities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional encryption and access controls are used to protect sensitive data, then data security is improved to a certain extent, but the system cannot effectively prevent inadvertent leakage due to human error, system vulnerabilities, or malicious intent
Solution Approach 1:
The system implements continuous monitoring of data transmissions and provides real-time feedback by analyzing outgoing data for sensitive information patterns. The feedback loop enables dynamic adjustment of security measures, automatically blocking transmissions containing sensitive data while allowing legitimate communications to proceed, thereby adapting to prevent various types of unauthorized disclosure including human error and malicious intent
Solution Approach 2:
The patent introduces an intermediary data analysis system positioned between the user and the transmission channel. This intermediary component scans and analyzes data before it leaves the organization, acting as a mediator that can identify and block sensitive information without requiring changes to user behavior or existing encryption protocols, thus enhancing the system's ability to prevent unauthorized disclosure
2Reliability
If comprehensive real-time detection and analysis systems are implemented to detect and prevent data leakage, then data security is significantly improved, but system complexity increases
Solution Approach 1:
The security system is segmented into distinct functional modules: a data collection engine for gathering transmission data, a sensitive data identification component for analyzing patterns, and a decision-making module for determining whether to block transmissions. This segmentation allows each component to perform its specific function efficiently, reducing overall system complexity while maintaining comprehensive security coverage
Solution Approach 2:
The system employs self-service mechanisms through automated pattern recognition and decision-making algorithms that continuously learn and adapt without requiring manual intervention. The automated nature of the system reduces operational complexity while maintaining high security standards, as the system serves itself by automatically updating its detection capabilities based on new data patterns
3Productivity
If automated machine learning models are used to classify data in real-time, then productivity of data security monitoring is improved, but measurement precision of data classification may be compromised
Solution Approach 1:
The system performs preliminary actions by pre-training machine learning models on extensive datasets of sensitive and non-sensitive information patterns before deployment. This preliminary training enables the models to quickly and accurately classify data during real-time transmissions, maintaining both high productivity and measurement precision without requiring complex runtime decision processes
Data Source
AI summary
A comprehensive system for sensitive data leakage protection. Text extracted from documents and images that is to be transmitted/communicated is scanned to detect ciphertext within a document or image. Machine learning models are trained and executed to analyze the datum to determine data classifications and deep learning models are self-trained and executed to detect emerging data points (i.e., new threats affecting the ability to classify data) and feeds such emerging data points back to the machine learning model(s). Intelligence capable of receiving findings from the ciphertext detection component as well both the machine learning and the deep learning is executed to determine a level of sensitive data leakage attributed to each dataset being transmitted/communicated and, in response to determining the level of sensitive date leakage, make real-time decisions on whether to allow, hold or block the data transmission/digital communication.