Compliance
March 6, 2026 · View on GitHub
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Think of this page as a desk within a ward.
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Guardrails and fix patterns for aligning enterprise knowledge management with legal, regulatory, and internal compliance requirements. Use this page when retrieval, storage, or reasoning drifts into non-compliant outputs even though the raw system looks healthy.
When to use this page
- Retrieved snippets contain PII or sensitive records without redaction.
- AI answers include restricted regulatory clauses without context.
- Citations point to outdated compliance frameworks (e.g., GDPR 2016 vs 2021).
- Access logs show gaps in provenance or audit trails.
- External connectors sync content that violates retention policy.
Core acceptance targets
- ΔS(question, retrieved) ≤ 0.45 within compliant scope.
- Coverage ≥ 0.70 for allowed data, <0.05 for redacted or forbidden scopes.
- λ convergent across three paraphrases and two seeds.
- Every snippet carries
compliance_tag,retention_scope, andaudit_hash.
Typical compliance problems → exact fix
| Symptom | Likely cause | Open this |
|---|---|---|
| Leaked PII (emails, phone numbers) | No compliance redaction contract | data-contracts.md, retrieval-traceability.md |
| Citations reference outdated law text | Missing version control on KB | chunking-checklist.md |
| Audit trail gaps | Index or connector does not log hashes | bootstrap-ordering.md |
| Over-redaction (useful data removed) | Regex-only redactors collapse semantics | logic-collapse.md |
Fix in 60 seconds
- Check ΔS against compliant anchor text.
- Validate snippet contracts: enforce
{snippet_id, compliance_tag, retention_scope, audit_hash}. - Rebuild compliance filter: replace regex-only redaction with semantic filters.
- Re-index with version locks for regulatory text.
- Verify λ stability across paraphrases within compliant sections only.
Copy-paste schema (YAML)
snippet_id: "KB-98231"
section_id: "SEC-77"
compliance_tag: "gdpr_2021"
retention_scope: "7_years"
audit_hash: "sha256:..."
text: "..."
Escalate when
- ΔS ≥ 0.60 persists after re-index and contract rebuild.
- Sensitive snippets reappear across paraphrases.
- Legal team confirms regulatory mismatch.
Escalation path: rebuild with eval_rag_precision_recall.md and audit via retrieval-traceability.md.
🔗 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 |
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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