Problem

Any AI memory system that stores and retrieves user context will inevitably handle personally identifiable information — names, emails, phone numbers, addresses, SSNs, medical info, financial data. When that context gets sent to an LLM for processing, the PII goes with it.

This is a liability for every production deployment, and there's no clean modular solution that plugs into existing memory pipelines.

Proposal: PII Guard Module

A lightweight, pluggable PII layer that:

1. Detection & Stripping

• Scans text for PII (names, emails, phone numbers, addresses, government IDs, financial info, etc.)
• Uses a combination of NER, regex patterns, and configurable rules
• Runs before any text is sent to an LLM

2. Identity Mapping

• Replaces detected PII with deterministic tokens (e.g., John Smith → [PERSON_A7x3], [email protected] → [EMAIL_A7x3])
• Maintains a local-only identity map that never leaves the user's environment
• Same entity always maps to the same token within a session, so the LLM can still reason about relationships ("PERSON_A7x3 emailed PERSON_B2k9")

3. Restoration on Request

• After the LLM responds, tokens get rehydrated back to real identities before the user sees the output
• User can control restore behavior: always restore, never restore, or ask-per-entity
• Restoration map is encrypted at rest

4. Pluggable Architecture

• Works as middleware — sits between mempalace (or any memory system) and the LLM API call
• Simple interface: sanitize(text) → (clean_text, map) and restore(text, map) → original_text
• Could also work standalone for any AI pipeline, not just mempalace

Why This Matters

• Compliance: GDPR, CCPA, HIPAA all have requirements around PII handling
• Trust: Users storing personal memories/docs need confidence their data isn't leaking to third parties
• Universal need: This isn't mempalace-specific — every AI system sending user context to an LLM has this problem. Building it here as an open module benefits the entire ecosystem.

Open Questions

• Should the identity map persist across sessions, or be ephemeral by default?
• Best approach for multilingual PII detection?
• Should there be a confidence threshold where low-confidence PII gets flagged for user review rather than auto-stripped?

Would love input from anyone working on privacy-preserving AI pipelines.