Zero Node Privacy Firewall
PII redaction API for AI and LLM applications.
Detect and replace sensitive values before your application sends text or structured JSON to an external model. Keep useful context with stable tokens, then restore authorized values in the response.
Protect the model call without breaking the workflow
Redact
Send text or JSON to Zero Node before it crosses the boundary to an AI provider.
Process safe tokens
The LLM receives values such as <EMAIL_1> instead of the original PII.
Rehydrate once
Your authorized application restores tokens through a short-lived, single-use session.
Redact PII with one API request
The authenticated endpoint accepts plain text or nested JSON and returns masked previews rather than original findings.
curl -X POST https://zero-node.com/api/v1/redact \
-H "X-API-Key: YOUR_KEY" \
-H "Content-Type: application/json" \
-d '{
"text": "Email Jane at [email protected]",
"options": {
"replacement_mode": "token"
}
}'Detection designed for constrained and contextual data
Deterministic validation
Email, phone, payment cards, IBAN, IP addresses, supported secrets, dates, and labelled identifiers use format and context rules.
Private contextual models
Local English and multilingual models add beta person, organization, and location coverage without calling an external NER API.
Policy controls
Choose detection types, minimum confidence, replacement mode, and bounded request-scoped custom patterns.
Common AI privacy use cases
- Sanitize customer-support conversations before summarization.
- Protect CRM records used for AI-assisted drafting.
- Remove credentials and URL secrets before observability or analysis.
- Redact sensitive string values inside nested JSON while preserving its schema.
- Build an application-controlled privacy boundary around an external LLM.
What the API can and cannot guarantee
Zero Node reduces unnecessary sensitive-data exposure. It does not guarantee detection of every value, certify legal compliance, or replace encryption, authorization, retention controls, and secure storage. Evaluate detection on representative synthetic or governed data before production use.
Review the detection coverage, request limits, and security architecture.
Start with one AI workflow
Use the free playground with synthetic data, or request an early-access key for an authenticated integration.