> tpl_air_025
Knowledge-Source Inventory and Authority Register
Authoritative data governance register for Retrieval-Augmented Generation (RAG) and enterprise AI systems indexing corporate knowledge sources, establishing content ownership, authority ranking, confidentiality classification, sync frequency, and deprecation sunset lifecycle.
Data governance register indexing RAG knowledge sources, authority weighting, refresh cadences, and access classifications.
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Problem Solved
RAG applications blindly index contradictory, outdated, or confidential corporate documents from Confluence, Google Drive, and SharePoint, leading to AI hallucinations, out-of-date customer advice, and internal security leaks.
When to Use
- •Establishing the single source of truth inventory of documents eligible for ingestion into enterprise RAG vector databases
- •Assigning authority weights and conflicting source arbitration rules (e.g. Approved Policy doc overrides team meeting notes)
- •Governing document refresh frequencies, automated sync webhooks, and deprecation sunset timelines
When NOT to Use
- •For text chunking experiments, token size tuning, and overlap benchmarks (use TPL-AIR-027)
- •For vector database indexing performance benchmarks and search algorithm selection (use TPL-AIR-029)
5 Template Sections & Structural Outline
Categorizing knowledge assets: Official Policies, Standard Operating Procedures (SOPs), Architecture ADRs, Product Documentation, and Support FAQs. Establishing strict gate criteria for RAG ingestion.
Assigning explicit authority weightings: Tier 1 Canonical (Board-approved policy, Product Truth), Tier 2 Operational (Standard SOPs), Tier 3 Supplemental (KB articles), and Tier 4 Informal. Defining conflict resolution logic.
Mapping information classification (Public, Internal, Confidential, Restricted) to RAG metadata filters, ensuring role-based access control (RBAC) enforcement at retrieval time.
Codifying update cadences: Real-time webhooks, daily delta crawls, monthly full re-indexes. Enforcing expiration dates and flagging unverified documents older than 180 days.
Assigning a designated named Business Owner and Technical Custodian to every knowledge source. Structuring bi-annual re-certification and cryptographic purge workflows.
Completion Instructions
Independent Review Checklist
- All mandatory sections completed
- No secrets or passwords included
- Executive sponsor sign-off obtained
Knowledge-Source Inventory and Authority Register - Worked Case Study
Fictional Entity: Global Pharmaceutical Regulatory Affairs & Clinical Trial Operations
Real-world production case study demonstrating complete operational adoption for Global Pharmaceutical Regulatory Affairs & Clinical Trial Operations.
- •Indexed 14,000 regulatory documents across 6 global operating entities into a unified RAG knowledge register
- •Established 4-tier authority hierarchy eliminating 92% of contradictory clinical protocol AI responses
- •Enforced RBAC security metadata filters, completely preventing restricted clinical trial data leakage
Frequently Asked Questions
Why does an enterprise RAG system need an authoritative knowledge register before indexing documents?
Without a knowledge register, RAG ingestion pipelines blindly scrape outdated drafts, duplicate files, and conflicting policies from shared drives. The register defines which documents are officially authorized, who owns them, how authoritative they are when conflicts arise, and what security clearances are required to retrieve them.
How does source authority ranking resolve contradictory documentation in RAG answers?
When documents conflict (e.g. an old 2022 internal wiki says "vacation is 15 days" while the 2025 Board Policy says "vacation is 20 days"), the RAG ranking engine uses the Authority Register weighting (Tier 1 vs Tier 4) to prioritize or exclusively retrieve the Tier 1 policy, eliminating hallucinations and misinformation.
How does this register support Role-Based Access Control (RBAC) in vector search?
During document ingestion, the register enriches each text chunk with metadata attributes (e.g. department: "Finance", sensitivity: "Confidential", permitted_groups: ["hr-execs"]). When a user queries the RAG system, the vector database pre-filters search vectors against the user's authenticated security tokens.
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Authoritative Sources
- DAMA-DMBOK: Data Management Body of Knowledge (2nd Edition)DAMA International • OFFICIAL REQUIREMENT
- ISO/IEC 27001:2022 Control A.5.12 Classification of InformationISO/IEC • OFFICIAL REQUIREMENT
- Dublin Core Metadata Element Set, Version 1.1: Reference DescriptionDublin Core Metadata Initiative • OFFICIAL REQUIREMENT