Skip to content

privacy-and-governance.md

Latest commit

 

History

History
151 lines (108 loc) · 6.07 KB

privacy-and-governance.md

File metadata and controls

151 lines (108 loc) · 6.07 KB

🔐 Privacy & Governance for RAG Systems — Practical Runbook

Build trustworthy AI: minimize data, control exposure, and prove compliance without killing velocity.

Quick Nav
Data Contracts · Ops · Patterns: Memory Desync · SCU

Not legal advice. Use this as a technical baseline and align with your counsel.

0) Principles

  1. Minimize: ingest only what you truly need; redact at source.
  2. Fence: per-source prompt fences; cite-then-explain.
  3. Prove: log every decision via data contracts; keep tight retention.
  4. Control: least-privilege access; encrypt at rest/in transit.

1) PII taxonomy & redaction

  • Categories: identifiers (name, email, gov ID), contact, location, financial, health, biometric, free-text PII.
  • Redact at ingest with deterministic tags:
{"text":"Contact Alice at alice@example.com","redactions":[{"span":[8,13],"type":"person"},{"span":[24,43],"type":"email"}]}
  • Keep reversible vault only if business requires it; otherwise irreversible.

2) Storage & access control

  • Encryption: TLS in transit; AES-GCM at rest.
  • Access: service accounts per component; forbid shared tokens; rotate keys.
  • Retention: default 30–90 days for logs; 7–30 days for raw prompts unless required longer.
  • Deletion: implement DSR (data subject request) over doc_id or user_id.

3) Model provider governance

  • Confirm data usage (training vs. inference only).
  • Disable logging on hosted APIs if must not leave boundary.
  • For self-hosted models, pin container images and track model checksum.

4) Prompt governance (SCU-safe)

  • Lock schema: system → task → constraints → citations → answer.
  • Forbid cross-source merges; require line-level citation IDs.
  • Add guard prompts to avoid reproducing secrets or PII unless necessary and consented.

5) Audit & reproducibility

  • Use envelope fields (trace_id, mem_rev, mem_hash) in every record.
  • Keep answer → prompt → citations → chunks chain navigable.
  • Export metrics pack per release (ΔS, λ rates, nDCG, recall).

6) Config template (YAML)

privacy:
  redact_at_ingest: true
  redactors: [pii_email, pii_phone, pii_name]
  reversible_vault: false
  retention_days:
    prompts: 14
    logs: 60
    embeddings: 180
  access:
    roles:
      retriever: [read_chunks]
      reranker: [read_chunks]
      llm: [read_prompts]
      analyst: [read_metrics]
  secrets:
    provider: "aws-kms"   # or gcp-kms, vault
    rotation_days: 90
providers:
  openai:
    share_for_training: false
  claude:
    share_for_training: false

7) Risk scenarios → mitigations

Scenario Risk Mitigation
User uploads PII-heavy PDFs Accidental exposure Redact at ingest; block high-risk types; allow override with consent
Multi-tenant leakage Cross-account data bleed Tenant IDs in chunk keys; per-tenant indices; access policies
Citations reveal secrets SCU or over-inclusion Reduce context window; per-source fences; require justification
Vendor logs prompts Data leaves boundary Use no-log endpoints; self-host; encrypt locally

Acceptance criteria

  • ✅ PII redaction rate ≥ 95% on test corpus; no residual PII in prompts unless approved.
  • ✅ Trace chain present for 100% of answers (citations included).
  • ✅ Secrets rotated within policy; provider log-sharing disabled.
  • ✅ Retention job passes dry-run audit monthly.

🔗 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 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

If this repository helped, starring it improves discovery so more builders can find the docs and tools.
GitHub Repo stars