De-Risking Data Treatment for Confidential ML Data Export
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Solution Overview
Problem
The challenge of efficiently processing confidential data for machine learning while maintaining privacy and compliance with regulatory restrictions, particularly in cloud-based environments, is exacerbated by the need to de-risk data and preserve referential integrity across diverse databases with varying data treatments, which is time-consuming and labor-intensive.
Innovation Solution
A data treatment apparatus and method that streamlines the data security workflow by using a de-risking database to manage data requests, apply approved data treatments, and ensure referential integrity, including tokenization and mapping, to facilitate secure data export and processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is exported from primary database for machine learning purposes, then data utility for training/testing is improved, but confidential information may be exposed
Solution Approach 1:
A de-risking database is introduced as an intermediary layer between the primary database and machine learning workflows. This database stores pre-approved data treatments and tokenization rules for different columns, allowing automated security checks without manual review for each data request, thus maintaining confidentiality while enabling ML utility.
Solution Approach 2:
Data treatments and tokenization rules are pre-configured and approved in the de-risking database before actual data export occurs. Security users establish the rules in advance, and the system automatically applies these pre-approved treatments to data requests, eliminating the need for time-consuming iterative approvals for each export request.
2Reliability
If manual security review is performed for each data request, then data security is improved, but processing time and labor are increased
Solution Approach 1:
The system enables self-service data export by allowing security users to pre-configure approval rules in the de-risking database. Once configured, the system automatically evaluates and approves future data requests against these rules without requiring manual security review for each request, reducing both time and labor while maintaining security standards.
Solution Approach 2:
The de-risking database provides automated feedback on data requests by checking them against pre-configured rules. The system automatically determines whether a request complies with established security policies and either approves or rejects it immediately, eliminating iterative manual review cycles and significantly reducing approval time.
3Object-affected harmful factors
If diverse data treatments are applied to different columns, then data confidentiality is improved, but referential integrity across databases is compromised
Solution Approach 1:
Different tokenization rules are applied to different columns based on their specific confidentiality requirements. The de-risking database stores column-specific treatment rules that are locally optimized for each data type, allowing diverse treatments (e.g., full tokenization, partial masking, no treatment) to be applied appropriately to different columns while maintaining overall system integrity.
Solution Approach 2:
The de-risking database serves multiple functions: it stores tokenization rules, manages data treatments, validates referential integrity, and coordinates across diverse databases. This universal platform handles various data treatment scenarios while ensuring that referential relationships between columns across different databases are maintained despite different local treatments.
Data Source
AI summary
Apparatus and methods for automating a de-risking data security workflow that supports machine learning pipelines. The system receives a data export request via a communications interface and queries a de-risking database for prior requests linked to the requested columns. When a match is found, tokenization rules are automatically applied to produce treated columns while preserving confidentiality. The processor then outputs the transformed dataset, optionally routing it to one or more processing nodes that serve as inputs to downstream machine-learning models for inference.