Data Sensitivity-Based Backup and Access Control
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Solution Overview
Problem
Current methods for addressing ransomware threats, such as backing up all data equally and regularly updating access permissions, are inefficient and resource-intensive, failing to effectively mitigate the impact of malware attacks on sensitive data.
Innovation Solution
A method that determines the sensitivity level of data instances and conditionally performs backups and adjusts access permissions based on predefined policies, prioritizing sensitive data for more frequent backups and enhanced security measures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all data is backed up equally and regularly, then data security is improved, but resource consumption and backup time increase significantly
Solution Approach 1:
The patent applies local quality by differentiating backup strategies based on data sensitivity levels. Instead of uniformly backing up all data, the system identifies sensitive data through analysis (e.g., detecting PII, financial information, or critical business data) and applies enhanced backup protections specifically to those portions. This allows the system to maintain high security for critical data while reducing resource consumption for less sensitive data, directly resolving the contradiction between comprehensive security and backup efficiency.
Solution Approach 2:
The patent segments the data backup process into multiple sensitivity levels. Data is classified into different categories (e.g., highly sensitive, moderately sensitive, low sensitivity) and each category receives appropriate backup treatment. This segmentation enables the system to focus resources on protecting critical data while using less intensive methods for other data, thereby improving overall backup efficiency without compromising security for sensitive information.
2Reliability
If access permissions are updated for all data, then security is improved, but processor utilization and time increase
Solution Approach 1:
The patent applies local quality by updating access permissions selectively based on data sensitivity. Instead of uniformly updating permissions for all data, the system identifies sensitive data and applies permission updates specifically to those portions. This targeted approach maintains security for critical data while minimizing the time and processor utilization required for permission management.
Solution Approach 2:
The patent segments the permission management process into sensitivity-based categories. Data is divided into different sensitivity levels, and permission update operations are applied differently to each segment. Highly sensitive data receives frequent and rigorous permission updates, while less sensitive data receives less intensive permission management, thereby reducing overall time consumption and processor utilization.
3Productivity
If sensitive data is identified and protected preferentially, then resource efficiency is improved, but the complexity of data analysis increases
Solution Approach 1:
The patent applies self-service by enabling data to essentially identify its own sensitivity characteristics through automated analysis. The system uses machine learning models and pattern recognition to automatically detect sensitive data types (such as personal information, financial data, or proprietary information) without requiring manual classification. This automated self-identification reduces the perceived complexity for users while maintaining high resource efficiency in protecting sensitive data.
Solution Approach 2:
The patent implements feedback mechanisms where the data analysis system continuously learns from identified sensitive data patterns and improves its classification accuracy over time. The system analyzes data characteristics, determines sensitivity levels, and uses this information to refine future identification processes. This feedback loop manages analysis complexity by automating and optimizing the identification process, allowing the system to become more efficient at recognizing sensitive data without increasing operational complexity.
Data Source
AI summary
A computer-implemented method according to an aspect includes determining a sensitivity level for an instance of data, comparing the sensitivity level to one or more policies, and conditionally performing a backup of the instance of data, based on the comparing.