PII Redaction for Customer Support AI

Customer support tickets are inherently unstructured and filled with sensitive data. Customers routinely paste passwords, home addresses, phone numbers, and full credit card details directly into chat windows.

Support Tickets Are PII Minefields

If you connect an AI agent to your Zendesk, Intercom, or Salesforce instance via the Model Context Protocol (MCP) to auto-resolve tickets, that agent will read the raw PII and stream it directly to an external LLM. This violates corporate privacy policies and risks exposing user data to third-party model training sets.

Panovista eliminates this risk by intercepting support workflows and sanitizing unstructured text before it ever reaches the context window.

Real-Time Chat Sanitization

Deploy Panovista directly in front of your customer support CRM’s MCP interface. When the AI agent requests the history of a support ticket, Panovista acts as an ultra-low-latency inline filter.

Using advanced context-aware scanning and regular expressions, it scrubs the unstructured text of all accidental PII disclosures. The LLM receives the technical context of the user’s problem without ever seeing the user’s home address or leaked password, allowing the agent to generate a helpful reply securely.

Support Desk Redaction Schema

Security operations teams can deploy declarative JSON policies to target common customer data leaks:

{
  "version": "1.0",
  "policy_name": "support_ticket_sanitization",
  "rules": [
    {
      "field": "unstructured_chat",
      "type": "regex",
      "pattern": "(?i)(password|pwd)\\s*[:=]\\s*\\S+",
      "replacement_token": "[REDACTED_PASSWORD_LEAK]"
    },
    {
      "field": "unstructured_chat",
      "type": "semantic",
      "intent": "home_address",
      "replacement_token": "[REDACTED_ADDRESS]"
    }
  ]
}

Seamless Reverse-Injection

Enterprise security should never ruin the customer experience. Panovista employs a bidirectional proxy architecture to ensure that the end-user receives highly personalized support without compromising data sovereignty.

[Zendesk] ──(Raw Data)──► [Panovista Tokenizer] ──(Tokens)──► [LLM Context]
[Zendesk] ◄──(Raw Data)── [Panovista Re-Injector] ◄──(Tokens)── [LLM Output]

When the LLM generates a response containing the synthetic placeholder tokens (e.g., "I can send a replacement to [REDACTED_ADDRESS]"), Panovista intercepts the reply on the way back to the CRM.

It seamlessly reverse-injects the true data from its volatile, in-memory state mapping. The customer receives a perfectly coherent response ("I can send a replacement to 123 Main St"), the AI model generates accurate advice, and your security team maintains total data sovereignty.