PII-safe support assistant with policy enforcement

The Problem

Customer support copilots process sensitive text (emails, addresses, order data, account identifiers). Without safeguards, prompts may leak PII or generate non-compliant responses.

The Flashback Pattern

Combine:

1. Repository-scoped AI access (isolated keys),

2. AI policies (log / alert / block by risk),

3. Application redaction before model calls,

4. Violation monitoring for audits.

Prerequisites

• AI repository and API key dedicated to support workflows.

• AI policy configured for PII and restricted disclosures.

• Ticketing payload schema with fields that can contain PII.

References:

• AI Policy API reference

• AI LLM configuration

Implementation blueprint

1

Create and scope policy

Define policies at repository scope for support use cases:

• block full payment-card patterns,

• alert on personal addresses and phone numbers,

• disallow speculation outside official KB.

Use policy actions by severity:

• Block for critical data exfiltration patterns,

• Alert for risky but reviewable outputs,

• Log for observability-only checks.

2

Redact sensitive input in app layer

import re

def redact_pii(text: str) -> str:
    text = re.sub(r"\b\d{16}\b", "[REDACTED_CARD]", text)
    text = re.sub(r"[\w\.-]+@[\w\.-]+", "[REDACTED_EMAIL]", text)
    text = re.sub(r"\+?\d[\d\s\-]{7,}\d", "[REDACTED_PHONE]", text)
    return text

Always keep the original payload only in your secure system of record.

3

Enforce answer boundaries

System message example:

You are a customer support assistant.
Use only approved knowledge snippets provided in context.
Never reveal secrets, internal IDs, or personal user data.
If missing information, ask for escalation.

Keep this instruction template versioned.

4

Invoke AI through Flashback endpoint

curl -sS "$FB_OPENAI_BASE_URL/chat/completions" \
  -H "Authorization: Bearer $FB_API_KEY_SECRET" \
  -H "Content-Type: application/json" \
  -d '{
    "model":"gpt-4.1-mini",
    "messages":[
      {"role":"system","content":"You are a compliant support assistant..."},
      {"role":"user","content":"Help me answer this ticket safely."}
    ]
  }'

5

Monitor violations and alerts

Operationalize daily review:

• policy violations trend,

• blocked request samples,

• false positives requiring policy tuning,

• escalations triggered by assistant uncertainty.

Integrate alerts into Slack/PagerDuty if violation rate spikes.

Expected outcome

A support assistant architecture with clear compliance guardrails, auditable controls, and reduced sensitive-data exposure risk.