Content-Aware Cloud Storage Migration Security Compliance
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Solution Overview
Problem
Existing cloud object storage migration techniques often fail to preserve security requirements during data migration, leading to potential security breaches and compliance issues due to differences in security levels between source and target cloud object data stores.
Innovation Solution
The Content-Aware Storage Classification (CASC) system identifies security requirements of data blobs based on their content and metadata, determines suitable target cloud object data stores that match these requirements, and assigns data blobs accordingly, using artificial intelligence techniques like natural language processing and machine learning to ensure security compliance with regulations such as GDPR and HIPAA.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is migrated between cloud object data stores using existing techniques, then data migration is achieved, but security requirements are not preserved
Solution Approach 1:
The system applies different security measures to different data blobs based on their specific security requirements. Each data blob is evaluated individually and assigned to target cloud object data stores that match its specific security needs, rather than applying a uniform security level to all data. This resolves the contradiction by making the security approach adaptable to local (individual data) requirements while maintaining overall security reliability.
Solution Approach 2:
The system changes the security parameter (security level) of the target cloud object data store to match the security requirements of the source data. By dynamically adjusting security parameters based on data classification and requirements, the system preserves security reliability while adapting to different security levels across various target stores.
2Reliability
If security requirements are strictly enforced during migration, then security compliance is improved, but migration flexibility and target store selection are reduced
Solution Approach 1:
The system segments the migration process into distinct phases: security requirement identification, target cloud object data store determination, and data blob assignment. This segmentation allows security compliance to be enforced systematically at each stage while maintaining flexibility in target store selection. The segmentation resolves the contradiction by making security enforcement manageable and adaptable rather than rigid.
Solution Approach 2:
The system dynamically determines target cloud object data stores based on real-time assessment of security requirements and available target stores. Rather than statically restricting migration options, the system adaptively selects from multiple potential targets that satisfy security requirements, thus maintaining both security compliance and migration flexibility.
3Measurement precision
If manual security assessment is performed for each data migration, then security accuracy is improved, but computing resources and time are increased
Solution Approach 1:
The system enables data blobs to effectively self-assess their security requirements through automated analysis of their characteristics and metadata. The cloud object data store itself participates in determining its security requirements, reducing the need for extensive manual assessment while maintaining high accuracy in security requirement identification.
Solution Approach 2:
The system performs security requirement identification and target store determination as preliminary actions before actual data migration begins. By preparing and classifying data security requirements in advance, the system achieves accurate security assessment without increasing the time and resources needed during the actual migration execution.
Data Source
AI summary
A pending data migration is detected, that is related to a set of one or more data blobs. The set of data blobs are stored on a source cloud object data store. The set of data blobs are retrieved, from the source cloud object data store. A security requirement for the set of data blobs is identified based on the set of data blobs and based on the source cloud object data store. A set of one or more potential target cloud object data stores from a set of additional cloud object data stores is determined. The set of data blobs is assigned, in response to the determination, to a first potential target cloud object data store of the set of potential target cloud object data stores. The assignment is based on the security requirement.