AI Data Virtualization for Automated Governance Compliance
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Solution Overview
Problem
Data virtualization systems lack integrated data governance, leading to potential security risks and legal complexities due to the manual and time-consuming application of data governance policies, which are often applied after data access, and may not account for varying legal jurisdictions and data types.
Innovation Solution
An AI-based data virtualization system that uses machine learning models to predict and apply relevant data governance rules and policies dynamically, integrating data governance into the data access process through a governance model and query model, ensuring compliance with regulations like GDPR and CCPA, and masking sensitive data as needed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual data governance policies are applied, then data security and compliance can be maintained, but the process is time-consuming and error-prone
Solution Approach 1:
The system enables self-service by using machine learning models to automatically predict and apply data governance policies without requiring manual intervention. The governance ML model autonomously analyzes data access requests, identifies relevant governance rules, and applies them automatically, allowing the system to serve itself rather than requiring continuous manual governance application.
Solution Approach 2:
The patent replaces the mechanical manual process of governance policy application with an automated machine learning-based system. The governance ML model and query ML model substitute for manual governance operations, using computational intelligence to predict applicable policies and generate executable commands that apply governance rules automatically to data access requests.
2Speed
If data governance is applied after data access, then data can be retrieved quickly, but security risks and legal complexities increase
Solution Approach 1:
The system performs preliminary action by predicting and applying data governance policies before data retrieval occurs. The governance ML model analyzes the data access request and predicts relevant governance rules in advance, allowing the query ML model to generate executable commands that incorporate governance constraints before the actual data retrieval happens, thus preventing security risks and legal issues rather than addressing them after the fact.
Solution Approach 2:
The patent introduces an intermediary layer consisting of the governance ML model and query ML model that mediates between data access requests and actual data retrieval. This intermediary layer predicts applicable governance policies and generates executable commands that incorporate governance constraints, acting as a buffer that ensures security and compliance are enforced before data is accessed, thereby eliminating the harmful effects of post-access governance application.
3Adaptability or versatility
If heterogeneous data sources are integrated, then data accessibility is improved, but governance complexity increases
Solution Approach 1:
The system achieves universality by using a single governance ML model that can handle multiple data sources, governance frameworks, and policy types. The model is trained to predict governance rules across different jurisdictions and data types, providing a unified approach that works for heterogeneous data sources without requiring separate governance management systems for each source or regulation type.
Solution Approach 2:
The patent applies parameter changes by using the governance ML model to dynamically adapt governance policy predictions based on the specific characteristics of each data source and request. The model analyzes contextual parameters such as data source type, location, and request characteristics to predict appropriate governance rules, allowing the system to handle heterogeneous data sources with varying governance requirements through parameter-based adaptation rather than complex manual configuration.
Data Source
AI summary
Embodiments are disclosed for a method. The method includes determining a context of a data access request for a data virtualization engine. The method also includes determining data sources that are relevant to the data access request by using a governance machine learning model trained to predict the data sources based on the request and the context. Additionally, the method includes determining data governance rules-policies that are relevant to the request, by using the governance machine learning model, further trained to predict the data governance rules-policies based on the data sources and the context. Further, the method includes generating, by using a query machine learning model, a data access command executable by the data virtualization engine and configured to retrieve data from the data sources and apply the data governance rules-policies.