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Solutions . Data Governance

Data Governance for Retrieval-Augmented Generation (RAG)

Eight governance keys that make RAG systems accurate, explainable, and safe at enterprise scale.

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Overview

From governance keys to grounded answers

Retrieval-Augmented Generation only performs in production when the data feeding it is discoverable, secure, well-defined, and traceable. The eight chapters below map each Provectus data governance practice to the specific RAG capability it unlocks — from query routing and access control to lineage, quality, and prompt grounding.

01

Data Discovery

Routing

Leverages data catalogs with enterprise-confirmed definitions to enhance RAG query routing, resulting in higher precision in RAG system query processing.

Tools
  • AWS Glue Data Catalog
  • Open Data Discovery
  • DataHub
  • Apache Atlas
Ownership

Incorporates data ownership information into RAG systems to help users navigate the data landscape and escalate questions to the right owners.

Tools
  • Amazon DataZone
  • Open Data Discovery
  • DataHub
  • Amundsen
Explainability

Uses Data Catalogs to augment RAG explainability with rich, detailed descriptions of data assets so end-users understand the origin of every retrieved answer.

Tools
  • AWS Glue
  • Amazon DataZone
  • Open Data Discovery
  • DataHub
  • Amundsen
  • OpenMetadata
  • Apache Atlas
02

Data Security

Role-Based Access Control (RBAC) and Policy-Based Access Control (PBAC)

Manages data access through Role-Based Access Control and Policy-Based Access Control for dynamic rights adaptation as users, roles, and policies evolve.

Tools
  • AWS IAM
  • Amazon Verified Permissions
  • AWS Verified Access
  • Cedar
  • AWS Cognito
  • Apache Ranger
Policy enforcement with GenAI

Enhances RAG with an internal GenAI engine for dynamic user permission assessment that considers roles and data context at query time.

Tools
  • Amazon Verified Permissions
  • Amazon DataZone
  • Amazon Cognito
  • Apache Ranger
  • Open Data Discovery
03

Data Glossary

Context enrichment with Business Terms

Implements Data Glossaries to create an enterprise-wide set of business terms for RAG systems, aligning model output with the language the business actually uses.

Tools
  • Open Data Discovery
  • DataHub
  • Amundsen
  • OpenMetadata
  • Apache Atlas
Knowledge Graphs

Enhances query interpretation in enterprise SQL databases. Knowledge graphs increase the accuracy of LLM-based responses from 16% to 54%.

Tools
  • AWS Neptune
  • Open Data Discovery
04

Master Data

Reference Data Management

Enriches RAG with lookup data for categories, hierarchies, and business translations so retrieval respects the enterprise's reference frame.

Tools
  • Amazon DataZone
  • Open Data Discovery
Bias-Mitigated Single Source of Truth

Establishes an MDM system that prioritizes the most reliable and curated data sources, thereby reducing the risk of biased data feeding into RAG retrieval.

Tools
  • AWS Data Exchange
  • AWS Glue
05

Data Cost

Efficiency analysis

Integrates data cost aspects so RAG can consider cost factors when retrieving and utilizing data, balancing answer quality against operational spend.

Tools
  • AWS Cost Explorer
  • AWS Cost and Usage Reports
06

Data Quality

Enrichment with data quality metrics

Supplements RAG with detailed data quality metrics and confidence scores so the system can weight retrieved chunks by their reliability.

Tools
  • AWS Glue Data Quality
  • Great Expectations
  • dbt-expectations
  • Deequ
  • Data Quality Gate
  • Open Data Discovery
Detecting biased data

Establishes mechanisms to identify and document biased data, including missing segments and skewness in the underlying corpus.

Tools
  • Amazon SageMaker Clarify
  • AWS Glue Data Catalog
  • Open Data Discovery
  • DataHub
  • Amundsen
User Feedback Loop

Integrates a feedback mechanism allowing users to report issues with data, raise questions, and provide objections — closing the loop between retrieval and curation.

Tools
  • Amazon Bedrock
  • Amazon Comprehend
07

Data Lineage

Enrichment with data origins

Provides detailed information about the origins of data and its subsequent transformations, surfacing provenance alongside RAG answers.

Tools
  • OpenLineage
  • Open Data Discovery
  • DataHub
  • Amundsen
  • OpenMetadata
  • Apache Atlas
Root cause analysis and explainability

Employs Data Lineage for identifying origins of data issues, such as biases, outdated information, or missing data — making RAG failures debuggable.

Tools
  • OpenLineage
  • Open Data Discovery
  • DataHub
  • Amundsen
  • OpenMetadata
  • Apache Atlas
08

Data Modeling

Zero-shot Prompting

Leverages data catalogs, especially those enriched with detailed data models, to provide contextual groundwork for zero-shot prompting against the enterprise corpus.

Tools
  • AWS Glue Data Catalog
  • Open Data Discovery
  • DataHub
  • Amundsen
Few-shot Prompting

Employs Data Governance tools to store, manage, and curate examples for few-shot prompting, anchoring LLM outputs in vetted enterprise patterns.

Tools
  • Open Data Discovery
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Bring the right data governance foundation to your RAG initiative. Our team will help you map keys to capabilities.
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