Data Confidence Fabric for Accurate Edge Compliance Reporting
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Solution Overview
Problem
Traditional audit and reporting processes in edge environments fail to consider varying data confidence levels, leading to insufficient or incorrect compliance measurements and inaccurate reporting.
Innovation Solution
Implement a data confidence fabric (DCF) to generate and maintain data confidence scores for edge devices, using trust metadata and confidence scores to assess and report compliance levels, enabling more accurate compliance assessments and reporting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional audit and reporting processes are used, then the process is simple and easy to implement, but the compliance measurement is insufficient or incorrect and reporting is inaccurate
Solution Approach 1:
The system performs preliminary actions by continuously generating and maintaining data confidence scores for all data streams before compliance assessment. Trust metadata is pre-computed and stored, enabling accurate compliance measurements to be made later without requiring complex real-time analysis during audits.
Solution Approach 2:
The patent introduces trust metadata as an intermediary element between data streams and compliance assessments. This intermediary carries confidence scores that mediate the evaluation process, allowing traditional audit processes to achieve higher accuracy by incorporating this additional layer of information without fundamentally redesigning the entire audit framework.
2Reliability
If data confidence scores are incorporated into compliance assessment, then compliance measurement accuracy is improved, but the system complexity increases
Solution Approach 1:
The system segments the compliance assessment process into independent components: data confidence score generation, trust metadata maintenance, and compliance evaluation. This segmentation allows each component to be developed, tested, and optimized independently, reducing overall system complexity while improving reliability through specialized functionality.
Solution Approach 2:
The data confidence score generation mechanism serves multiple functions: it provides compliance assessment data, enables risk evaluation, supports audit trail creation, and facilitates regulatory reporting. This multi-functionality reduces the need for separate systems and improves reliability without proportionally increasing complexity.
Data Source
AI summary
One example method includes receiving, from a DCF (data confidence fabric) including a node that comprises a data source of an edge environment, a confidence score concerning a data stream associated with the data source, correlating the confidence score to a compliance level of the edge environment with regard to a specified requirement for the edge environment, generating, based on the confidence score and compliance information, an audit report that identifies a connection between the confidence score and the compliance level, and for a value of a performance gap between the compliance level and a required compliance level, associated with the specified requirement, that meets or exceeds a threshold, identifying a remedial action which, when implemented in the edge environment, causes a reduction of the performance gap to a value below the threshold.