API Governance Validation via User Profile Analysis
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Solution Overview
Problem
API developers face challenges in ensuring that application programming interfaces (APIs) adhere to numerous data governance policies, particularly when sharing data externally, as manually identifying the correct data assets and enforcing security measures can be time-consuming and error-prone.
Innovation Solution
An API development platform that interfaces with a data catalog to validate APIs against data governance rules using user profiles and data asset profiles, performing fetch and insert/update analyses to recommend compliance and suggest data masking or access restrictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual identification and enforcement of data governance policies is used, then data security can be maintained, but the process becomes time-consuming and error-prone
Solution Approach 1:
The system performs preliminary actions by automatically identifying data classes and applying governance rules during API development, before the API is deployed. This includes pre-validating data access patterns, pre-applying masking rules, and pre-identifying compliance issues, thereby reducing the time required for manual validation while maintaining security standards.
Solution Approach 2:
The API development platform provides self-service capabilities that automatically enforce data governance policies without requiring manual intervention. The system autonomously validates APIs against governance rules, identifies data classes, and applies appropriate security measures, eliminating the time-consuming manual process while ensuring consistent compliance.
2Reliability
If comprehensive data governance validation is performed, then data security is improved, but the complexity of the validation process increases
Solution Approach 1:
The patent introduces an intermediary validation layer between the API developer and the data governance policies. This intermediary automatically interprets complex governance rules, maps them to specific data classes, and applies appropriate controls without requiring the developer to understand the underlying complexity. The system acts as a mediator that simplifies the validation process while maintaining comprehensive security checks.
Solution Approach 2:
The validation process is segmented into distinct automated steps: data class identification, governance rule matching, compliance validation, and recommendation generation. Each segment handles a specific aspect of the validation process independently, reducing overall complexity while ensuring thorough coverage of all governance requirements.
3Productivity
If automated validation using user profiles and data asset profiles is implemented, then productivity is improved, but the system complexity increases
Solution Approach 1:
The system implements universal user profiles and data asset profiles that can be reused across multiple APIs and validation scenarios. These profiles serve multiple functions: they define data classes, establish access permissions, and encode governance rules in a standardized format. This multi-functionality reduces the need for custom validation logic for each API, thereby improving productivity while managing system complexity through reuse.
Data Source
AI summary
A computer-implemented method includes: receiving, by a computing device and from a user device, a request to validate an application program interface (API); validating, by the computing device, the API by performing a fetch analysis using different user profiles; returning, by the computing device and to the user device, a result of the fetch analysis; validating, by the computing device, the API by performing an insert/update analysis using the different user profiles; and returning, by the computing device and to the user device, a result of the insert/update analysis.