Target outcomes
- PII leak rate reduced on sampled conversations
- False-positive rate tracked to avoid user distrust
Initiative playbook
Typical delivery arc for this pattern in enterprise programs.
- 1Discovery2 to 4 wks
Define Restricted/PII patterns and acceptable false-positive rate with risk and privacy.
- 2Pilot6 to 8 wks
Apply redaction to all prompts; log findings and review misses weekly.
- 3Scaleongoing
Add DLP integration, allow lists, and user training; enforce policy centrally via gateway.
Business use case
If your policy says “don’t paste Restricted data into external AI tools,” you need a guardrail that works before the model call.
Solution
A local redaction pass (regex + heuristics) that replaces obvious emails/phones/cards and returns a findings report. Pair it with an approval gate for any remaining “Restricted” content.
Where else this applies
Redaction is a practical way to align “don’t paste Restricted data” policies with reality: users will paste it unless you intercept it.
Support intake
Redact emails/phones before messages are summarised or routed by a model.
Meeting notes
Scrub transcripts before extracting actions and decisions.
Ticket enrichment
Protect sensitive identifiers in batch triage pipelines.
Internal copilots
Reduce accidental leakage from staff-facing chat assistants.
Using this stack elsewhere
Local processing avoids sending raw PII to external APIs. Pair with enterprise gateways and logging for compliance.
Live demo
The demo is the same code path described above, not a simplified mock UI. Add keys in .env.local when you are ready; the narrative and diagrams stand on their own without them.
Business
Paste an email/phone/card-like string and watch local redaction happen before any external model call.
Technical
Local redactPII() at /api/demos/vercel-redact returns redacted text + findings report.