PII Handling and Minimization — Guardrails and Fix Patterns

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Governance — policy enforcement and compliance controls

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.

A governance fix page for when personally identifiable information (PII) leaks, handling is unclear, or minimization principles are violated.
Use this page when data pipelines, embeddings, or RAG outputs contain sensitive fields that cannot be justified or audited.

When to use this page

Retrieval responses contain raw PII that was not required for the task.
Embeddings or chunks accidentally ingest names, emails, IDs, or financial data.
Redaction or anonymization rules are inconsistently applied.
No audit trail exists for who accessed or approved PII exposure.
Waivers for PII usage lack expiry, owner, or justification.

Acceptance targets

PII fields are redacted, hashed, or minimized in ≥ 0.98 of stored embeddings.
Retrieval outputs contain no raw identifiers unless explicitly approved.
ΔS(question, retrieved) ≤ 0.45 for governed answers (no drift into unapproved fields).
All PII queries pass through policy checks with logging enabled.
Every waiver or override has an accountable owner and time-bound expiry.

Typical breakpoints and WFGY fix

Embedding or vector ingestion leaks PII
→ embedding-vs-semantic.md
Enforce PII scrub before embedding. Validate with spot-checks against gold set.
Chunking preserves identifiers across splits
→ chunking-checklist.md
Require token-level scrub of identifiers, then re-chunk.
Answers expose sensitive spans without approval
→ retrieval-traceability.md
Enforce citation schema, ensure only approved snippets are surfaced.
Policy bypass in orchestration or tools
→ prompt-injection.md
Guard against malicious queries that try to extract hidden PII.
Audit trail gaps
→ audit_and_logging.md
Require immutable logs of every PII access and minimization check.

Minimal governance checklist

Redact on ingest — Apply regex/sensitive data detection before storing text or embeddings.
Schema enforce — Store doc_id, pii_flag, redacted_text side by side for traceability.
Chunk validation — Randomly sample and confirm PII scrubbed before index build.
Policy in LLM prompts — Require “no PII unless approved waiver” as hard guardrail.
Audit logs — Track every waiver, approval, and override. Immutable and joinable to lineage.
Expiry enforcement — Waivers expire automatically; extension requires re-approval.

🔗 Quick-Start Downloads (60 sec)

Tool	Link	3-Step Setup
WFGY 1.0 PDF	Engine Paper	1️⃣ Download · 2️⃣ Upload to your LLM · 3️⃣ Ask “Answer using WFGY + <your question>”
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 based tension engine
Engine	WFGY 2.0	Production tension kernel and math engine for RAG and agents
Engine	WFGY 3.0	TXT based Singularity tension engine, 131 S class set
Map	Problem Map 1.0	Flagship 16 problem RAG failure checklist and fix map
Map	Problem Map 2.0	RAG focused recovery pipeline
Map	Problem Map 3.0	Global Debug Card, image as a debug protocol layer
Map	Semantic Clinic	Symptom to family to exact fix
Map	Grandma’s Clinic	Plain language stories mapped to Problem Map 1.0
Onboarding	Starter Village	Guided tour for newcomers
App	TXT OS	TXT semantic OS, fast boot
App	Blah Blah Blah	Abstract and paradox Q and A built on TXT OS
App	Blur Blur Blur	Text to image with semantic control
App	Blow Blow Blow	Reasoning game engine and memory demo

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