FileNet Archive Extraction for Confidential Data Detection
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Solution Overview
Problem
Current systems lack the ability to efficiently scan and manage confidential data within globally accessible FileNet archives, posing risks of data exposure and regulatory compliance issues.
Innovation Solution
An automated apparatus and method for FileNet data extraction using a specialized script that queries metadata, applies slicing mechanisms, and utilizes argument-based systems to identify and extract confidential data from FileNet archives, enabling secure storage and purging of such data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If files are archived in FileNet from globally accessible SharePoint sites, then file storage and archiving capability is improved, but confidential data exposure risk increases
Solution Approach 1:
The patent extracts confidential data from archived files by implementing automated scanning of FileNet archives using regex patterns and data loss prevention (DLP) rules. The system identifies and extracts sensitive information such as social security numbers, credit card numbers, and other confidential data types from archived SharePoint files, separating them from the archived content for secure handling.
Solution Approach 2:
The patent introduces an intermediary automated scanning system that acts as a mediator between FileNet archives and confidential data identification. This intermediary layer uses Python scripts, regular expressions, and DLP rules to scan archived files without requiring direct access to the original SharePoint sites, thereby preventing confidential data exposure while maintaining archiving capabilities.
2Measurement precision
If automated scanning is implemented to identify confidential data in FileNet archives, then confidential data detection capability is improved, but system complexity increases
Solution Approach 1:
The patent implements self-service automation where the system automatically scans FileNet archives, identifies confidential data using predefined patterns and rules, and generates reports without requiring manual intervention. The automated Python scripts execute scanning operations, apply regex patterns for data identification, and produce confidential data inventories autonomously, reducing the need for complex manual monitoring systems.
Solution Approach 2:
The patent utilizes parameter changes by implementing configurable scanning parameters including date ranges, file types, and sensitivity levels. The system allows adjustment of detection thresholds and scanning parameters to balance detection precision with system complexity, enabling organizations to tailor the scanning intensity and data types detected based on their specific compliance requirements.
3Ease of operation
If manual monitoring of FileNet archives is performed, then system simplicity is maintained, but productivity and detection efficiency decrease
Solution Approach 1:
The patent implements continuous automated scanning operations that continuously monitor FileNet archives for confidential data without interruption. The automated system performs ongoing scans of archived files, maintaining constant surveillance for confidential information, which significantly improves detection efficiency compared to periodic manual monitoring while preserving ease of operation through automated execution.
Solution Approach 2:
The patent replaces manual mechanical monitoring operations with automated computational systems. Python scripts and automated scanning tools substitute for manual review processes, using algorithmic pattern recognition and regex matching to identify confidential data. This substitution dramatically increases detection efficiency and productivity while maintaining operational simplicity through automated execution.
Data Source
AI summary
An apparatus and method for evaluating and removing confidential data within a FileNet archive is provided. The disclosure may include a compilation of a list of globally accessible sites that contain archived FileNet links and files potentially containing confidential data. The disclosure may include a FileNet Document Extraction script that may be designed to facilitate an extraction of archived files from a list of open sites stored in FileNet repositories. The disclosure may also include a comprehensive metadata compilation accomplished by querying a FileNet archive with a parameter-based approach. The disclosure may include a script that intelligently extracts a document name from metadata of a file or a source and applies a slicing mechanism to isolate a file type. In addition, the disclosure may include an argument-based system that allows users to customize FileNet code script behavior according to specific user needs and requirements.