Metadata-Based Sensitive Content Prioritization
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Solution Overview
Problem
Current data governance applications in cloud computing systems are inefficient in processing and analyzing large amounts of data, leading to delayed identification and protection of sensitive content, as they lack effective methods to prioritize files based on sensitivity, resulting in excessive time and resource consumption.
Innovation Solution
A system and method that utilize file metadata to estimate the sensitivity of data objects, prioritizing their analysis by generating sensitivity estimates and sorting files accordingly, allowing for efficient processing and protection of sensitive content without needing to analyze the content itself, especially in remote cloud computing systems where bandwidth is a constraint.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current governance applications process and analyze all data from data sources continuously, then complete coverage of sensitive content is achieved, but excessive time and computational resources are consumed
Solution Approach 1:
The system performs preliminary action by analyzing file metadata (name, extension, size, creation date, modification date) before conducting full content analysis. This preliminary assessment generates a sensitivity score that prioritizes which files require detailed inspection, allowing the system to prepare and queue files for analysis in order of suspected sensitivity without transferring all files simultaneously
Solution Approach 2:
The system segments the file analysis process into distinct stages: metadata collection, sensitivity scoring based on metadata patterns, prioritization ranking, and selective content analysis. This segmentation allows the system to process large volumes of files efficiently by only performing resource-intensive content analysis on files that score high for sensitivity based on their metadata characteristics
2Measurement precision
If files are transferred from remote cloud storage for analysis, then complete analysis capability is achieved, but bandwidth consumption increases significantly
Solution Approach 1:
The system applies partial action by transferring and analyzing only a portion of files (those with high sensitivity scores based on metadata) rather than all files from remote storage. Files are scored based on metadata patterns indicating potential sensitivity (file extensions, naming conventions, creation/modification timestamps), and only files exceeding a threshold are transferred for content analysis, significantly reducing bandwidth consumption while maintaining detection accuracy for sensitive content
Solution Approach 2:
The system introduces metadata analysis as an intermediary step between remote storage and full content analysis. This intermediary process evaluates files based on their metadata characteristics and generates sensitivity scores that determine transfer priority, acting as a filter that reduces the volume of data requiring bandwidth-intensive transfer while preserving the ability to accurately identify sensitive files
3Productivity
If metadata analysis is performed to estimate sensitivity, then processing time is reduced, but measurement precision of sensitivity detection may be compromised
Solution Approach 1:
The system segments the sensitivity assessment into two tiers: a rapid metadata-based scoring tier that evaluates file names, extensions, and timestamps to generate initial sensitivity scores, and a secondary content analysis tier that provides definitive sensitivity determination for high-scoring files. This segmentation enables rapid triage of large file volumes while maintaining accurate detection through selective deep analysis
Solution Approach 2:
The system performs preliminary metadata analysis to generate sensitivity estimates and prioritize files before conducting full content analysis. By pre-scoring files based on metadata patterns (such as sensitive file extensions, naming conventions containing keywords, or unusual creation/modification dates), the system identifies candidate files for detailed inspection, achieving both speed through preliminary filtering and precision through subsequent targeted analysis
Data Source
AI summary
Example systems and methods for efficient data governance are disclosed. Metadata associated with file objects is analyzed to estimate, for each file object, the likelihood that the file object includes sensitive data. The estimates are used to prioritize the file objects for analysis of the file objects' content to determine the whether the file objects include data deemed to be of a sensitive nature. In cloud-based systems/methods the estimates are also used to prioritize the file objects for transfer from a remote file storage system to the cloud-based system for analysis of content. The disclosed systems and methods significantly reduce the time required to identify sensitive file content in a large number of file objects.