WFGY/ProblemMap/GlobalFixMap/Enterprise_Knowledge_Gov/retention_policy.md

Retention Policy — Enterprise Knowledge Governance

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You are in a sub-page of Enterprise_Knowledge_Gov.
To reorient, go back here:

• Enterprise_Knowledge_Gov — corporate knowledge management and governance
• WFGY Global Fix Map — main Emergency Room, 300+ structured fixes
• WFGY Problem Map 1.0 — 16 reproducible failure modes

Think of this page as a desk within a ward.
If you need the full triage and all prescriptions, return to the Emergency Room lobby.

Guardrails and fix patterns for enterprise knowledge retention. Use this page when AI systems over-retain, delete too early, or mix expired data with active knowledge.

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When to use this page

• AI responses reference documents that should have been deleted per policy.
• Retained snippets do not respect jurisdictional time limits (e.g., GDPR 3 years).
• Knowledge base or embeddings store does not purge revisions.
• RAG answers mix archived with active content.

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Core acceptance targets

• ΔS(question, expired_snippet) ≥ 0.70 → expired content must not surface.
• All snippets carry {expiry_date, retention_scope, audit_hash} fields.
• Coverage ≥ 0.70 within active retention window only.
• λ remains convergent across three paraphrases and two seeds.

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Typical retention problems → exact fix

Symptom	Likely cause	Open this
Expired docs still retrieved	Store never purged embeddings	vectorstore-fragmentation.md
Wrong answer mixes expired + active	Snippets missing expiry_date field	data-contracts.md
AI cites “archived only” docs as live	Retrieval trace missing retention scope	retrieval-traceability.md

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Fix in 60 seconds

1. Check ΔS to expired content: run probe with expired snippets, expect ΔS ≥ 0.70.
2. Schema enforcement: require expiry_date and retention_scope in every snippet.
3. Index purge: remove expired embeddings before next RAG run.
4. Audit λ: if λ flips when expired vs active co-exist, clamp with BBAM and enforce contracts.

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Copy-paste schema (JSON)

{
  "snippet_id": "KB-5532",
  "expiry_date": "2025-12-31",
  "retention_scope": "eu-3y",
  "audit_hash": "sha256:...",
  "text": "..."
}

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Escalate when

• Expired content continues to surface after purge.
• ΔS < 0.70 against expired content → embeddings contamination.
• Audit requires full deletion trace and cannot be reproduced.

Use retrieval-playbook.md for deep purge testing and eval_rag_precision_recall.md to validate coverage.

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🔗 Quick-Start Downloads

Tool	Link	3-Step Setup
WFGY 1.0 PDF	Engine Paper	1️⃣ Download · 2️⃣ Upload to your LLM · 3️⃣ Ask “Answer using WFGY + ”
TXT OS (plain-text OS)	TXTOS.txt	1️⃣ Download · 2️⃣ Paste into any LLM chat · 3️⃣ Type “hello world” — OS boots instantly

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Explore More

Layer	Page	What it’s for
⭐ Proof	WFGY Recognition Map	External citations, integrations, and ecosystem proof
⚙️ Engine	WFGY 1.0	Original PDF tension engine and early logic sketch (legacy reference)
⚙️ Engine	WFGY 2.0	Production tension kernel for RAG and agent systems
⚙️ Engine	WFGY 3.0	TXT based Singularity tension engine (131 S class set)
🗺️ Map	Problem Map 1.0	Flagship 16 problem RAG failure taxonomy and fix map
🗺️ Map	Problem Map 2.0	Global Debug Card for RAG and agent pipeline diagnosis
🗺️ Map	Problem Map 3.0	Global AI troubleshooting atlas and failure pattern map
🧰 App	TXT OS	.txt semantic OS with fast bootstrap
🧰 App	Blah Blah Blah	Abstract and paradox Q&A built on TXT OS
🧰 App	Blur Blur Blur	Text to image generation with semantic control
🏡 Onboarding	Starter Village	Guided entry point for new users

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