Data Pipeline Security Constraint Enforcement
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Solution Overview
Problem
Conventional data pipeline systems lack the ability to effectively enforce data security constraints, leading to unintended release of sensitive data, as they typically have access to all datasets and cannot restrict data release based on user-defined security constraints.
Innovation Solution
Configuring data pipelines with user-defined data security constraints that filter and verify datasets to ensure compliance, using configuration datasets and filtering functions to control data release and adapt to changes in datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional data pipeline systems access all datasets for processing, then data processing flexibility and automation are improved, but data security and compliance are worsened due to inability to restrict sensitive data release
Solution Approach 1:
The patent segments the data pipeline into multiple stages (ingestion, transformation, verification, release) with independent control mechanisms. Each stage can be configured with specific security constraints, allowing automated processing while maintaining granular control over data flow and preventing unauthorized release of sensitive information.
Solution Approach 2:
The patent introduces configuration datasets and filtering functions as intermediary layers between the data pipeline and source datasets. These intermediaries automatically enforce security constraints by filtering sensitive data before it reaches transformation stages, enabling automated processing without direct access to all underlying data.
2Reliability
If data pipeline systems process all available data, then processing completeness is improved, but compliance with security constraints is worsened due to unintended release of sensitive data
Solution Approach 1:
The patent applies preliminary filtering actions at the configuration stage, before data transformation and release. Configuration datasets define security constraints in advance, and filtering functions automatically apply these constraints to source datasets, ensuring that only compliant data enters the processing pipeline and preventing security violations throughout subsequent stages.
Solution Approach 2:
The patent implements verification stages that provide feedback on whether processed datasets comply with security constraints. The system automatically verifies transformed datasets against configuration datasets and can alert operators or block release if compliance failures are detected, ensuring continuous adherence to security requirements.
3Object-affected harmful factors
If data pipelines are configured to access restricted datasets for security, then data security is improved, but processing flexibility and adaptability are worsened
Solution Approach 1:
The patent creates a universal configuration mechanism using configuration datasets and filtering functions that can be applied across multiple data pipeline stages and different data sources. This single configurable interface provides both security enforcement and processing flexibility, allowing the same framework to adapt to various security requirements and pipeline configurations without sacrificing either protection or versatility.
Data Source
AI summary
A computer-implemented method enforces data security constraints in a data pipeline. The data pipeline takes one or more source datasets as input and performs one or more data transformations on them. The method includes using data defining one or more data security constraints to configure the data pipeline to perform a data transformation on a restricted subset of entries of the source datasets. The restriction is defined by the data defining one or more data security constraints. The method further includes performing the data transformation according to the configuration to produce one or more transformed datasets. The method further includes using the data defining one or more data security constraints to perform a verification on one or more of the transformed datasets to ensure that entries in the one or more of the transformed datasets are restricted as defined by the one or more data security constraints.