Database Compliance via Sensitive Data Sensing and Geographic Routing
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Solution Overview
Problem
Organizations face challenges in maintaining database compliance and security due to evolving threats and regulations, particularly with sensitive data stored in enterprise database systems, which can result in substantial fines and penalties for non-compliance.
Innovation Solution
A computer-implemented method that senses the presence of sensitive data and transfers it to compliant geographies by using a machine learning model to process query rows, extracting geographical information, and routing data to servers that adhere to relevant privacy laws, ensuring data is stored in accordance with applicable regulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If sensitive data is stored in a local database for easy access and processing, then data access speed and processing efficiency are improved, but compliance with data protection regulations deteriorates when the data requires transfer to specific geographies
Solution Approach 1:
The system performs preliminary classification of data sensitivity using a machine learning model before data is stored or accessed. This advance identification allows the system to pre-determine the appropriate database location (local or remote) based on sensitivity and geographic requirements, ensuring compliance is built into the data lifecycle from the start rather than added as a post-processing constraint
Solution Approach 2:
The patent introduces an intermediary classification system that sits between the local database and remote databases. This intermediary layer analyzes data sensitivity and automatically routes queries to the appropriate database location, allowing the system to maintain both local processing efficiency for non-sensitive data and regulatory compliance for sensitive data without requiring direct access to all data locations
2Ease of operation
If all data is processed locally to maintain security control, then security management is simplified, but the ability to meet geographic data protection requirements deteriorates
Solution Approach 1:
The patent segments the database system into multiple geographic locations with different compliance requirements. Instead of treating the database as a single monolithic entity, the system divides data storage across local and remote databases based on sensitivity classification, allowing each segment to operate under its own security and compliance rules while maintaining centralized control through the classification model
3Measurement precision
If data sensitivity classification is performed manually to ensure accuracy, then classification precision is improved, but processing time and operational complexity increase
Solution Approach 1:
The patent replaces manual mechanical classification processes with an automated machine learning model that analyzes data characteristics and determines sensitivity levels programmatically. This substitution maintains high classification accuracy by using sophisticated algorithms while eliminating the time loss and operational burden associated with manual review of each data element
Data Source
AI summary
Embodiments relate to keeping databases compliant with data protection regulations by sensing the presence of sensitive data and transferring the data to compliant geographies. A request including information is received, the request being intended for processing on a local database. A model is used to process the information of the request. Responsive to the model determining that information relates to sensitive data, the request is transferred to a remote database associated with a geography meeting a requirement for the sensitive data in order to execute the request.