VM Backup Sensitivity Tagging and Adaptive Encryption
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Solution Overview
Problem
Current backup systems for virtual machines lack efficient methods to prioritize and secure data based on sensitivity, leading to inefficient use of computing resources and potential data security vulnerabilities.
Innovation Solution
A method that utilizes a classification engine within the virtual machine to analyze data and generate sensitivity tags, which are then used by a backup server to implement appropriate security measures such as encryption and replication, ensuring that sensitive data is prioritized and securely stored.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all data is treated equally in backup operations, then the backup process is simple and fast, but sensitive data lacks adequate security protection
Solution Approach 1:
The patent applies different security measures to different data based on their sensitivity classification. High-sensitivity data receives encryption and enhanced protection, while low-sensitivity data uses standard backup procedures. This local differentiation resolves the contradiction by providing targeted security where needed without unnecessarily complicating the entire backup process.
Solution Approach 2:
The system performs preliminary classification of data before backup operations to determine appropriate security measures. By classifying data sensitivity levels in advance, the system can automatically apply appropriate protection strategies without requiring complex real-time decision-making during backup operations.
2Reliability
If comprehensive security measures are applied to all backup data, then data protection is maximized, but computing resources are wasted on non-sensitive data
Solution Approach 1:
The patent implements selective security measures based on data sensitivity classification. Only high-sensitivity data receives comprehensive security protection including encryption, while standard backup procedures are applied to low-sensitivity data. This resolves the contradiction by maximizing data protection where needed while avoiding wasted computing resources on non-sensitive data.
Solution Approach 2:
The system applies partial security measures only to the portion of data that requires them (high-sensitivity data), rather than applying comprehensive security to all data. This partial action approach maximizes protection effectiveness while minimizing computing resource consumption.
3Reliability
If data classification and sensitivity analysis are performed, then appropriate security measures can be applied, but additional processing time is required
Solution Approach 1:
The system performs data classification and sensitivity analysis as a preliminary step before backup operations. By completing the classification phase in advance, the system can quickly proceed to backup operations without time-consuming real-time analysis, thus minimizing overall processing time while maintaining appropriate security measures.
Solution Approach 2:
The classification system operates continuously to maintain data sensitivity labels, allowing backup operations to proceed without interruption for re-classification. This continuous classification approach ensures security measure appropriateness while minimizing time losses during backup processing.
Data Source
AI summary
A method for performing a backup operation includes obtaining, by a backup server, a backup request, wherein the backup request specifies a virtual machine to be backed up, wherein the virtual machine is hosted by a production host, and in response to the backup request: obtaining classification data from the backup agent, initiating a backup classification on an unprocessed backup associated with the virtual machine based on the classification data to obtain a sensitivity tag, and initiating a data processing on the unprocessed backup based on the sensitivity tag.