5.8 KiB
Regulatory Alignment — Guardrails and Fix Pattern
🧭 Quick Return to Map
You are in a sub-page of Governance.
To reorient, go back here:
- 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.
This page defines how to align AI pipelines with existing laws, sector regulations, and compliance regimes.
Most AI failures at scale are not purely technical but compliance drift — your pipeline silently breaks GDPR, HIPAA, or copyright law because logging or schema fences were never enforced.
When to use this page
- Your system must prove compliance with GDPR, HIPAA, CCPA, or EU AI Act.
- Clients demand explainable outputs and data provenance.
- Auditors request reproducibility and risk registers.
- You operate in finance, healthcare, or government sectors with strict controls.
Acceptance targets
- 100% of data sources have a license_id and jurisdiction field.
- Provenance chain covers ingestion → embedding → retrieval → generation.
- Risk register includes bias, privacy, and IP risks with owner assignment.
- Queries and outputs auditable within 5 minutes.
- Alignment tests run weekly against updated compliance checklists.
Common failures → exact fixes
| Symptom | Likely cause | Open this |
|---|---|---|
| Data from EU not separated or anonymized | missing residency fence | data_residency.md |
| Private health data leaks in logs | no PHI redaction | privacy_and_pii_edges.md |
| Citations omit license or source | ingestion lacks rights | license_and_dataset_rights.md |
| Retrieval answers drift from contract | schema not enforced | data-contracts.md |
| Bias audit fails on specific cohorts | no structured probes | eval_playbook.md |
Fix in 60 seconds
-
Residency + anonymization
Partition datasets by region. Strip identifiers. -
Provenance chain
Loglicense_id,jurisdiction,ingest_date,index_hash. -
Bias + privacy probes
Weekly run λ stability tests across demographic variants. -
Risk register
Maintain an owner, severity, and mitigation plan per risk. -
Alignment replay
Prove a query followed rules by replaying citations and logs.
Minimal compliance checklist
- All ingestion jobs include
license_idandjurisdiction. - GDPR/CCPA consent tracked in logs.
- Health/finance data use sector schemas.
- Bias probes run weekly, logged with ΔS and λ.
- Audit replay tested monthly with compliance team.
🔗 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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