ML Data Loss Prevention Engine for Image Content Analysis
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Solution Overview
Problem
Existing data access control systems in computer networks employ an all-or-nothing approach based on file types, which is overly restrictive and unable to differentiate between accessible and restricted data, leading to limitations in information distribution and increased vulnerability to data exfiltration.
Innovation Solution
A machine learning-based system that selectively allows documents through a network by identifying and analyzing the contents of images and text within documents, distinguishing between restricted and non-restricted types, thereby enhancing data access control and preventing unauthorized data exfiltration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing systems use all-or-nothing data access control based on file types, then data security is maintained through simple rules, but the system becomes overly restrictive and cannot differentiate between accessible and restricted data
Solution Approach 1:
The patent applies local quality by analyzing specific regions and characteristics within documents rather than treating entire files uniformly. The machine learning model examines local features such as text patterns, image content, and data structures at specific locations within documents to determine restricted information, enabling granular access control decisions for different portions of data while maintaining overall security policies
Solution Approach 2:
The system transitions from discrete file-type parameters to continuous content-based parameters by using machine learning models that analyze document contents, extract features, and determine restricted information based on learned patterns. This parameter transformation enables the system to distinguish between different types of data within the same file format, providing flexible access control while maintaining security
2Reliability
If existing systems block all data transmission based on file format, then unauthorized data exfiltration is prevented, but legitimate data sharing is also restricted
Solution Approach 1:
The system performs preliminary analysis of document contents using machine learning models before making access control decisions. By pre-training models on restricted information patterns and performing content analysis ahead of transmission decisions, the system can quickly identify and block unauthorized data while allowing legitimate data sharing to proceed without restriction, improving both security and productivity
Solution Approach 2:
The patent implements feedback mechanisms where the machine learning model continuously learns from blocked and allowed data transmissions. The system analyzes outcomes of access control decisions and uses this feedback to refine its identification of restricted information, improving the accuracy of data classification over time and reducing both false positives and false negatives in data sharing decisions
3Difficulty of detecting and measuring
If keyword matching techniques are used to identify restricted text, then simple restricted information can be detected, but other types of restricted information such as schematics, flowcharts, and visual representations cannot be identified
Solution Approach 1:
The patent applies universality by using a single machine learning framework that can detect multiple types of restricted information including text, images, schematics, flowcharts, and other visual representations. The system processes different data types through unified model architectures that can identify restricted information across diverse formats and representations, eliminating the need for separate detection mechanisms for each information type
Solution Approach 2:
The system replaces mechanical keyword matching with machine learning-based content analysis. Instead of relying on predefined keyword lists and pattern matching algorithms, the patent uses trained neural networks and deep learning models that can automatically learn and identify restricted information patterns, including visual representations and complex data structures that cannot be detected by traditional text-based methods
4Productivity
If existing systems only process email based on file format presence, then processing is simple and fast, but the actual contents of attached files are not analyzed for restricted information
Solution Approach 1:
The patent applies partial action by selectively analyzing only certain portions and types of content within emails and attachments based on risk assessment. The machine learning model identifies which parts of attached files require detailed analysis and focuses computational resources on those specific regions, rather than processing entire files uniformly. This approach maintains processing efficiency while ensuring thorough security verification of critical content areas
Data Source
AI summary
A data loss prevention device that includes a data loss prevention engine implemented by a processor. The data loss prevention engine is configured to receive data in transit to a target network device and to identify content within the data. The data loss prevention engine is configured to determine the content of the data comprises an image and to determine an image type for the image based on objects within the image, and to determine whether the image type matches a restricted image type from a set of restricted image types. The data loss prevention engine is further configured to block transmission of the data to the target network device in response to determining that the image type matches a restricted image type and forward the data to the target network device in response to determining that the image type does not match a restricted image type.