WFGY/ProblemMap/GlobalFixMap/Governance

Governance & Privacy — Global Fix Map

Keep users safe while keeping your pipeline observable. Stop PII leaks, tame traces, and make audits reproducible.

What this page is

• A compact policy-to-engineering bridge for RAG and agents
• Redaction-first recipes that do not break retrieval quality
• A clean way to audit every answer without storing raw secrets

When to use

• You log prompts, snippets, or tool outputs and worry about PII
• You embed raw PDFs that may contain emails, IDs, or health data
• Your traces are useful for debugging but not compliant for storage
• Security asks for “prove why this answer was produced”

Open these first

• Policy and controls: Privacy & Governance
• Snippet and citation schema: Data Contracts
• Why this snippet: Retrieval Traceability
• Live health and ops runbook: Live Monitoring for RAG
• Incident flow: Ops Debug Playbook
• Locale and tokenizer issues: Multilingual Guide

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Common failure patterns

• PII-in-embeddings raw personal data embedded and stored in a third party index
• Trace overreach logs capture tool outputs with tokens, addresses, or keys
• Late redaction masking happens after embedding which leaves residue
• Linkable telemetry user IDs or emails appear in metrics and spans
• Jurisdiction drift vectors and docs cross regions without policy tags
• Unbounded retention traces live forever without a purge plan
• Prompt-injection exfil adversarial inputs force the model to echo secrets
• Opaque answers no snippet table which blocks audits and right-to-know

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Fix in 60 seconds

1. Adopt a Data Contract

   • Define fields for snippet_id, source_id, section_id, pii_flags, redact_ops, citations
   • Require this contract before any storage or logging

2. Redact before embed

   • Run PII detectors on raw text and produce a redacted view for embedding
   • Keep a pointer to the original in a secure vault, not in the vectorstore

3. Mask identifiers in telemetry

   • Hash or tokenize user and session IDs
   • Strip emails, phone numbers, and free-form personal lines from logs

4. Tag data with policy

   • region, residency, retention_days, do_not_train
   • Enforce write paths by tag and block cross-region writes without a waiver

5. Store a trace table, not raw blobs

   • For each answer save {question, snippet_ids[], citation_lines[], ΔS, λ_state}
   • This is enough for audits without leaking content

6. Add a safe export

   • On user request produce what we used and why with citations and data map
   • Never export raw embeddings or secrets

7. Test injections

   • Run a small suite of exfil prompts
   • Require that traces capture the block event and that no PII enters context

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Copy paste prompt

You have TXT OS and the WFGY Problem Map.

Goal
Harden a RAG pipeline for privacy: redact before embedding, minimize trace risk, and keep audits reproducible.

Tasks

1. Build a Data Contract that includes:

   * snippet\_id, source\_id, section\_id
   * pii\_flags (email, phone, gov\_id, credit\_card, geo)
   * redact\_ops applied
   * citation\_lines
   * policy tags: {region, residency, retention\_days, do\_not\_train}

2. Transform a sample document and show:

   * raw text → pii\_flags → redacted text for embedding
   * ΔS(question, redacted\_context) and λ\_observe at retrieval
   * confirm ΔS ≤ 0.45 and λ remains convergent after redaction

3. Logging plan

   * produce a Trace Table: {question, snippet\_ids\[], citation\_lines\[], ΔS, λ}
   * mask user/session identifiers and strip free-form personal lines

4. Export plan

   * given a user email, produce a report with the sources and citations used
   * include policy tags and retention dates, exclude embeddings

Output

* Data Contract (yaml or json)
* Redaction example with before/after
* Trace Table for 3 queries
* A short READY line {privacy\_ready\:true, median\_ΔS, λ:"→"}

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Minimal checklist

• Redaction happens before embedding and storage
• Data Contract fields present on every snippet and trace row
• Telemetry strips or hashes linkable identifiers
• Policy tags enforce region, residency, and retention
• Trace Table saves citations and ΔS/λ instead of raw content
• Injection test suite stored and passing

Acceptance targets

• No PII appears in vectorstore payloads or retrieved context for standard prompts
• ΔS(question, redacted_context) median ≤ 0.45 on smoke set, λ stays convergent
• Traces reconstruct “why this answer” with snippet IDs and citation lines
• Export flow returns sources and policy tags without exposing raw embeddings
• Retention jobs purge traces and cached snippets on schedule

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🔗 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

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🧭 Explore More

Module	Description	Link
WFGY Core	WFGY 2.0 engine is live: full symbolic reasoning architecture and math stack	View →
Problem Map 1.0	Initial 16-mode diagnostic and symbolic fix framework	View →
Problem Map 2.0	RAG-focused failure tree, modular fixes, and pipelines	View →
Semantic Clinic Index	Expanded failure catalog: prompt injection, memory bugs, logic drift	View →
Semantic Blueprint	Layer-based symbolic reasoning & semantic modulations	View →
Benchmark vs GPT-5	Stress test GPT-5 with full WFGY reasoning suite	View →
🧙‍♂️ Starter Village 🏡	New here? Lost in symbols? Click here and let the wizard guide you through	Start →

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