Asset Sensitivity Inference Using Metadata and Activity Lineage
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Solution Overview
Problem
Existing methods for determining the sensitivity of assets in an organization require manual inspection of content, which is labor-intensive and prone to human error, and may not be feasible for assets containing intellectual property information.
Innovation Solution
A system and method to automatically infer asset sensitivity using file system metadata and user activities, employing machine learning models to generate a sensitivity score and DLP alerts without inspecting the asset content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection of asset content is used to determine sensitivity, then accuracy of sensitivity determination is improved, but labor intensity and time consumption increase significantly
Solution Approach 1:
The patent extracts and analyzes file system metadata (access patterns, modification history, ownership information) separately from the actual asset content. By taking out the metadata analysis from manual content inspection, the system achieves automated sensitivity determination without requiring labor-intensive human review of actual files, thereby reducing time consumption while maintaining accuracy through multiple metadata features.
Solution Approach 2:
The patent introduces file system metadata as an intermediary between the asset content and sensitivity determination. Instead of directly inspecting asset content manually, the system uses metadata as a mediator that reflects asset characteristics and usage patterns, enabling automated analysis that reduces time consumption while preserving accuracy through indirect but informative measurement.
2Measurement precision
If manual inspection of asset content is used to determine sensitivity, then accuracy of sensitivity determination is improved, but labor intensity increases
Solution Approach 1:
The patent enables the system to determine asset sensitivity automatically by analyzing file system metadata without requiring manual human intervention. The system serves itself by collecting, processing, and interpreting metadata features (access patterns, modification history, ownership) to generate sensitivity scores, thereby eliminating labor-intensive manual inspection while maintaining determination accuracy.
Solution Approach 2:
The patent replaces the mechanical process of manual content inspection with an automated computational system that analyzes file system metadata. By substituting human manual review with algorithmic processing of metadata features, the system reduces labor intensity while achieving accurate sensitivity determination through automated pattern recognition and scoring.
3Measurement precision
If manual inspection methods are used, then sensitivity determination can be performed, but scalability to large numbers of assets is limited
Solution Approach 1:
The patent creates a universal system that can determine sensitivity across diverse asset types by analyzing common file system metadata features. The same metadata analysis framework (access patterns, modification history, ownership information) applies universally to different file types and storage locations, enabling scalable deployment across large numbers of assets while maintaining consistent sensitivity determination capability.
Solution Approach 2:
The patent changes the measurement parameters from direct content analysis to metadata-based indicators (access frequency, modification patterns, ownership hierarchy). By transforming the determination parameters from content-specific to structure-based metadata features, the system achieves scalability across large asset volumes while preserving sensitivity determination accuracy through alternative but informative measurement dimensions.
Data Source
AI summary
A method for implementing data loss prevention (DLP) includes: generating an asset lineage map from file system metadata; identifying, based on the asset lineage map, an input feature linked to the asset, a type of the asset, and a plurality of activities linked to the asset; obtaining a sensitivity score for the asset based on the input feature and the type of the asset; obtaining, based on the plurality of activities, a malicious score and a data loss score for the asset; determining a user level of a user; and initiating implementation of a first DLP policy for the user based on the user level, the malicious score, the data loss score, and the sensitivity score.