Dynamic Event Securitization System for Enterprise Data Protection
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Solution Overview
Problem
Current data loss protection software focuses primarily on the content of data shared by employees, neglecting other critical factors such as intent and lacks the technical capability and scalability to examine additional factors for hundreds of thousands of employees, leading to a high risk of 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 analyze risk levels, generating securitized files and modifying security settings based on risk assessments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current data loss protection software focuses only on data content analysis, then the system complexity remains low, but the security reliability deteriorates due to inability to detect unauthorized leaks through multiple factors
Solution Approach 1:
The system segments the security analysis into multiple independent modules: data content analysis module, user behavior analysis module, intent detection module, and risk assessment module. Each module handles a specific aspect of security evaluation, allowing comprehensive analysis while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The patent introduces an intermediary risk assessment engine that synthesizes inputs from multiple analysis modules (content, behavior, intent) and produces a unified security decision. This intermediary layer coordinates the complex interactions between different analysis factors, improving reliability without requiring direct complex integration of all modules.
2Reliability
If the system examines multiple factors beyond data content for hundreds of thousands of employees, then the security reliability improves, but the processing time and productivity deteriorate
Solution Approach 1:
The system performs preliminary analysis by pre-processing and categorizing user behavior patterns, historical data, and access patterns before actual security events occur. This preliminary preparation enables faster real-time decision-making when security events are triggered, maintaining high processing throughput while examining multiple factors.
Solution Approach 2:
The patent implements partial analysis by focusing computational resources on the most critical factors based on risk thresholds. Not all analysis modules are activated for every event; instead, the system selectively engages modules based on event type, user role, and pre-assessed risk levels, maintaining productivity while improving reliability through targeted multi-factor analysis.
3Reliability
If the system implements comprehensive multi-factor analysis for all employees, then the security reliability improves, but the device complexity and computational requirements worsen
Solution Approach 1:
The system applies local quality by tailoring the depth and type of analysis to specific users, data types, and contexts. Different users receive different levels of scrutiny based on their roles, access patterns, and historical behavior. This localized approach improves security reliability for critical areas while reducing computational complexity for low-risk scenarios.
Solution Approach 2:
The patent implements dynamic analysis depth adjustment where the system adapts the complexity of multi-factor analysis in real-time based on detected risk indicators. When unusual patterns are detected, the system dynamically increases analysis depth and activates additional modules. During normal operations, it maintains lighter analysis, reducing overall computational complexity while preserving security reliability when needed.
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.