AI File Transmission Screening for Real-Time Data Loss Prevention
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Solution Overview
Problem
Existing data loss prevention systems lack efficient and effective methods to determine whether files should be transmitted, especially in real-time, using artificial intelligence to assess various criteria and take appropriate actions.
Innovation Solution
An apparatus and method utilizing a processor and memory to detect files being transmitted, apply a machine learning model with multiple checks, and perform actions based on the model's output, including regular expression checks and deep content inspection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional data loss prevention systems are used to detect and evaluate files for transmission, then the system can identify potential data loss risks, but the system lacks efficiency and effectiveness in real-time decision-making
Solution Approach 1:
The patent replaces traditional mechanical rule-based DLP systems with an AI-based system that uses machine learning models to automatically evaluate files. The AI model analyzes file content, metadata, and context to make intelligent transmission decisions, substituting the mechanical keyword-matching approach with adaptive intelligent processing that achieves both high reliability and real-time performance.
Solution Approach 2:
The system changes the evaluation parameters from simple keyword matching to multi-dimensional AI analysis including file content semantics, metadata patterns, user behavior context, and transmission risk probabilities. This parameter transformation enables the system to maintain high security effectiveness while processing files in real-time through optimized AI inference.
2Measurement precision
If multiple checks and deep content inspection are performed on files, then the accuracy of transmission decisions is improved, but the processing time and system complexity increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and indexing file metadata, maintaining trained AI models ready for inference, and pre-establishing transmission policies before actual file evaluation occurs. This preliminary preparation enables the AI model to perform rapid real-time analysis without delays during the actual transmission decision process.
Solution Approach 2:
The evaluation process is segmented into multiple independent AI checks that can be executed in parallel: content analysis, metadata verification, risk assessment, and policy compliance checking. This segmentation allows the system to perform comprehensive multi-dimensional evaluation while maintaining real-time performance through concurrent processing of different analysis components.
3Adaptability or versatility
If an AI model with multiple checks is implemented for file evaluation, then intelligent decision-making capability is enhanced, but the device complexity increases
Solution Approach 1:
The AI model serves multiple functions simultaneously: it acts as a content analyzer, risk assessor, policy interpreter, and transmission decision-maker. This multi-functionality consolidates what would otherwise require separate specialized systems into a single unified AI platform, enhancing adaptability while managing complexity through functional integration rather than proliferation of separate components.
Solution Approach 2:
The AI model acts as an intermediary layer between the file transmission request and the decision-making process. It receives raw file data and transmission context, processes them through intelligent analysis, and outputs standardized evaluation results that drive transmission decisions. This intermediary architecture simplifies the overall system by providing a single point of intelligent processing that mediates between diverse inputs and decision requirements.
Data Source
AI summary
Apparatuses, methods, systems, and program products are disclosed for techniques for artificial intelligence based data loss prevention. An apparatus includes a processor and a memory that is coupled to the processor. The memory includes instructions that are executable by the processor to detect a file being transmitted from a device, provide the file to a model prior to transmission, the model comprising a plurality of checks to determine whether the file is allowed to be transmitted from the device, and perform an action associated with the file based on output from the model.