Dynamic Event Securitization for Data Loss Prevention
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Solution Overview
Problem
Current data loss protection software focuses primarily on the content of shared data, neglecting other critical factors such as employee intent and lacks scalability to handle large enterprises with hundreds of thousands of employees, thereby failing to effectively prevent unauthorized data leaks.
Innovation Solution
A dynamic event securitization and neural network analysis system that secures data before authorization, utilizing a computing platform with event analysis modules, a neural network model, and a decision engine to assess risk levels by examining multiple factors beyond data content, including user intent, access rights, and historical data, and dynamically adjusts security settings based on analysis results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current data loss protection software focuses only on data content analysis, then the system complexity remains low, but the detection accuracy and reliability of unauthorized data leaks deteriorate
Solution Approach 1:
The system segments the data loss protection analysis into multiple independent modules: user behavior analysis module, data content analysis module, intent recognition module, and risk assessment module. Each module processes specific aspects separately and their results are integrated to form a comprehensive security assessment, thereby improving detection accuracy without overwhelming system complexity
Solution Approach 2:
The patent introduces an intermediary neural network model that processes and integrates data from multiple analysis modules. This intermediary layer synthesizes user behavior patterns, data content features, and intent signals into a unified risk assessment, enabling accurate detection while managing computational complexity through structured intermediate processing
2Reliability
If the system examines multiple factors beyond data content for each employee, then the detection capability improves, but the processing time and resource consumption increase
Solution Approach 1:
The system performs preliminary analysis of user behavior patterns and establishes baseline profiles before actual data sharing events occur. By pre-processing user activity data and identifying anomalous patterns in advance, the system reduces the computational burden during real-time event processing, thereby maintaining high detection capability while minimizing processing time delays
Solution Approach 2:
The patent implements continuous monitoring and analysis of user behavior as an ongoing process rather than discrete batch processing. The system continuously updates user profiles and behavioral baselines in the background, allowing it to quickly assess new data sharing events against established patterns without requiring intensive real-time computation for each individual event
3Reliability
If the system analyzes multiple factors for hundreds of thousands of employees, then the security coverage improves, but the scalability and processing efficiency deteriorate
Solution Approach 1:
The patent implements a universal analysis framework that processes multiple types of data (user behavior, content metadata, access patterns, intent signals) through a single integrated neural network model. This multi-functional architecture allows the system to uniformly process security events across all employees regardless of department or role, improving security coverage while maintaining processing efficiency through standardized analysis pipelines
Solution Approach 2:
The system dynamically adjusts analysis parameters and processing depth based on risk levels and user profiles. For low-risk users with established behavioral patterns, the system reduces analysis intensity and processing depth. For high-risk or anomalous events, the system increases scrutiny and computational resources. This adaptive parameter adjustment enables scalable processing across hundreds of thousands of employees while maintaining comprehensive security coverage
Data Source
AI summary
Aspects of the disclosure relate to a dynamic event securitization and neural network analysis system. A dynamic event inspection and securitization computing platform comprising at least one processor, a communication interface, and memory storing computer-readable instructions may securitize event data prior to authorizing execution of the event. A neural network event analysis computing platform comprising at least one processor, a communication interface, and memory storing computer-readable instructions may utilize a plurality of event analysis modules, a neural network, and a decision engine to analyze the risk level values of data sharing events. The dynamic event inspection and securitization computing platform may interface with the neural network event analysis computing platform by generating data securitization flags that may be utilized by the neural network event analysis computing platform to modify event analysis results generated by the event analysis modules.