Security Control Recommendation via Data Derivation Analysis
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Solution Overview
Problem
In cloud computing environments, customers face challenges in ensuring consistent security controls when moving data between different storage tiers, as they may not specify appropriate controls, and providers struggle to determine necessary controls without analyzing customer data, due to privacy concerns.
Innovation Solution
A system that analyzes customer data to determine similar data instances, generates derivations such as checksums or Bloom filters, and compares these to recommend or automatically apply appropriate security controls, ensuring similar security measures are applied across storage tiers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the provider analyzes customer data to determine appropriate security controls, then the accuracy of security control recommendations is improved, but the customer's privacy is compromised
Solution Approach 1:
The system creates a synthetic representation (derivation) of the customer's data structure and patterns without copying or exposing the actual data. This derivation can be used to determine appropriate security controls while the original data remains private and unexposed to the provider's analysis systems.
Solution Approach 2:
The patent introduces an intermediary representation layer between the customer's actual data and the security control determination process. This intermediary (the derivation) allows the provider to analyze data characteristics and recommend security controls without directly accessing or exposing the customer's sensitive information.
2Reliability
If the customer specifies security controls for each storage location, then security coverage is improved, but the complexity of data management increases
Solution Approach 1:
The system enables self-service by automatically analyzing the data structure and patterns to determine appropriate security controls. The customer's data structure itself serves as the basis for generating security recommendations, eliminating the need for manual specification of controls for each storage location.
Solution Approach 2:
The security control determination process is made universal by using a single analysis mechanism that works across all storage locations. The same derivation and comparison process applies whether data is stored in memory, on disk, or in the cloud, providing consistent security coverage without location-specific complexity.
3Reliability
If the provider uses best practice security controls, then security standards are improved, but the relevance to customer-specific use cases decreases
Solution Approach 1:
The system applies local quality by tailoring security control recommendations to the specific characteristics of each customer's data. Instead of applying uniform best practices, the analysis examines the actual data structure, patterns, and sensitivity to generate customized security recommendations relevant to each customer's specific use case.
Solution Approach 2:
The patent changes the parameters of security control selection by using data-driven analysis rather than fixed best practice guidelines. The security controls are selected based on the actual characteristics of the customer's data (such as data type, structure, and sensitivity patterns) rather than generic security standards.
Data Source
AI summary
Security controls applied to a set of data, such as a data volume, can be recommended based on comparing the contents of the data set against those of other data sets and locating other data sets with the same or similar data. Security controls applied to those other data sets, but not applied to the set of interest, can be recommended for application to the data set. In order to protect customer data, the data to be compared can be sampled from the data set and analyzed for aspects such as present patterns, structures, or data types. In some embodiments various derivations can be generated, as may include hashes, checksums, or summaries based on the underlying data, and these derivations can be compared for purposes of finding data sets including similar data. Security controls can be recommended for tasks such as data placement, storage, or migration.