Constraint-Based Training Data Generation for Compliance
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Solution Overview
Problem
The collection and use of application data for model training often occur non-specifically, neglecting data compliance requirements, leading to increased violations of regulatory standards and potential penalties.
Innovation Solution
A system for constraint-based training data generation that includes metadata annotation and compliance enforcement, using SDKs and tooling to automatically label data types and generate machine-readable data manifests, coupled with compliance engines to enforce data handling rules across distributed networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If application data is collected and used for model training without specific compliance checks, then productivity and data utility are improved, but data compliance reliability deteriorates leading to regulatory violations
Solution Approach 1:
The system performs preliminary compliance assessment by analyzing data metadata and applying compliance rules before data is used for model training. This proactive approach identifies and flags non-compliant data early in the workflow, preventing regulatory violations while maintaining productive use of compliant data.
Solution Approach 2:
The patent introduces an intermediary compliance assessment system that sits between data collection and model training processes. This intermediary layer evaluates data compliance without blocking productive workflows, acting as a mediator that enables both efficient data utilization and regulatory adherence.
2Reliability
If comprehensive compliance checks are implemented on all application data, then data compliance reliability is improved, but device complexity and processing overhead increase
Solution Approach 1:
The system applies different levels of compliance checking to different data based on their metadata characteristics and sensitivity. Rather than uniformly complex processing of all data, the system tailors compliance assessment depth to local data properties, reducing overall system complexity while maintaining comprehensive coverage.
Solution Approach 2:
The compliance system dynamically adjusts processing parameters based on data metadata and compliance rule requirements. By changing processing intensity, checking depth, and validation strictness according to specific data parameters, the system avoids unnecessary complexity while ensuring adequate compliance verification.
3Ease of operation
If metadata annotation and automated compliance enforcement are implemented, then ease of operation is improved, but device complexity increases due to additional processing requirements
Solution Approach 1:
The system enables self-service compliance management by automatically annotating data with metadata and applying compliance rules without requiring manual intervention. The automated enforcement mechanisms handle compliance verification and enforcement independently, making compliance management easier to operate while the added processing complexity is managed through automation rather than manual procedures.
Data Source
AI summary
In one embodiment, a device may receive a request for training data that is based on application data generated by an application executed at a data collection node, wherein the application data is associated with metadata identifiers. The device may determine one or more training data constraints that restrict use of the application data as training data. The device may generate the training data in part by excluding application data of a particular type from being included in the training data based on a match between its metadata identifier and the one or more training data constraints. The device may provide the training data to be used to train a machine learning model.