Context-Based Data Scrutinization Crawler Bot
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Solution Overview
Problem
Conventional data storage security systems are reactive, failing to proactively identify and remediate anomalies containing unobscured private data, which can lead to data vulnerabilities and exposure.
Innovation Solution
A context-based data scrutinization and capture system that employs a crawler bot to monitor data storage locations, scan for unobscured private data, identify artifact types, and temporarily move sensitive artifacts to a quarantine storage location for remediation, using a machine learning engine for training and context rule set generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional reactive data storage security systems are used, then system simplicity is maintained, but data security is insufficient due to failure to proactively identify and remediate anomalies containing unobscured private data
Solution Approach 1:
The system performs preliminary scanning and identification of artifacts containing unobscured private data before actual data exposure occurs. The crawler bot continuously monitors storage locations, scans artifacts, and identifies potential security issues in advance, allowing remediation to be performed proactively rather than reactively after a breach is detected
Solution Approach 2:
The security system is divided into distinct functional modules: a crawler bot for monitoring and scanning, a context identification component for classifying artifacts and generating rule sets, and a remediation component for capturing and moving suspicious artifacts. This segmentation allows each component to specialize in specific tasks, improving overall effectiveness while maintaining manageable system complexity
2Reliability
If continuous monitoring and scanning of data storage locations is implemented, then data exposure risks are reduced, but processing time and computational resources increase
Solution Approach 1:
The system replaces manual, mechanical security review processes with automated electronic scanning and analysis. The crawler bot automatically navigates storage locations, the context identification component automatically classifies artifacts using machine learning, and the system automatically moves suspicious files - eliminating the time consumption of human review while maintaining thorough security checking
Solution Approach 2:
When an artifact is captured, the system creates a copy and moves it to a quarantine storage location rather than permanently deleting or modifying the original immediately. This copying approach allows for analysis and verification while preserving the original data integrity, enabling efficient processing without permanent loss errors
Data Source
AI summary
A system for context-based data scrutinization and capture is provided. The system comprises: a memory device with computer-readable program code stored thereon; a communication device connected to a network; a processing device, wherein the processing device is configured to execute the computer-readable program code to: monitor a data storage location using a crawler bot configured for scanning an artifact stored in the data storage location; scan the artifact, using the crawler bot, for one or more data fields, wherein at least one of the one or more data fields comprises unobscured private data; identify an artifact type for the artifact based on the one or more data fields; and capture the artifact from the data storage location based on the artifact and the unobscured private data, wherein capturing the artifact comprises temporarily removing the artifact from the data storage location.