Guardrail: mask U.S. personal data (SSN, phone, card)

Guardrail that masks U.S. personal data in AI assistant replies: SSNs, +1 phone numbers, personal emails and card numbers. Rules, 12 tests, code.

Rules

Mask before sending any data that identifies or reaches a person:
- personal email addresses (john.smith@…);
- U.S. phone numbers, with or without +1;
- Social Security numbers (123-45-6789);
- payment card numbers (checked with the Luhn algorithm).
Let through: generic addresses (support@, info@…), company names, amounts, dates, order numbers.

Test cases

Code

import re

PATTERNS = {
    "email": r"[\w.+-]+@[\w-]+\.[\w.-]+",
    "phone": r"(?:\+1[\s.-]?)?\(?\b[2-9]\d{2}\)?[\s.-]?\d{3}[\s.-]?\d{4}\b",
    "ssn": r"\b(?!000|666|9\d{2})\d{3}-(?!00)\d{2}-(?!0000)\d{4}\b",
    "card": r"\b(?:\d[ -]?){13,19}\b",
}
GENERIC = ("support@", "info@", "sales@", "hello@", "contact@")


def luhn(number: str) -> bool:
    digits = [int(c) for c in number if c.isdigit()][::-1]
    total = sum(d if i % 2 == 0 else (d * 2 - 9 if d * 2 > 9 else d * 2) for i, d in enumerate(digits))
    return 13 <= len(digits) <= 19 and total % 10 == 0


def mask(text: str) -> tuple[str, bool]:
    """Replace personal data with [<type> redacted]; return the text and True if anything was masked."""
    found = False
    for kind, pattern in PATTERNS.items():
        def repl(m):
            nonlocal found
            value = m.group(0)
            if kind == "email" and value.lower().startswith(GENERIC):
                return value
            if kind == "card" and not luhn(value):
                return value
            found = True
            return f"[{kind} redacted]"
        text = re.sub(pattern, repl, text)
    return text, found

Estimated cost

Input tokens per call : ≈ 871

Input cost only, excluding the model's answer. Providers' public standard prices (Mistral AI, DeepSeek, OpenAI, Anthropic) converted from USD to euros at the ECB rate of September 29, 2026. Estimate: 1 token ≈ 3.6 characters.

Identifier

contextetech--mask-us-personal-data

Type

Guardrails

File

mask-us-personal-data.guardrail.json

Format

JSON (application/json)

Size

1,928 bytes (1.9 KB)

Encoding

UTF-8

Estimated tokens

≈ 534

Language

English

License

MIT

Commercial use

Allowed

Personal data

Fictional (made up)

Version

1.0

Published on

October 2, 2026 at 9:54 PM

Updated on

October 2, 2026 at 9:54 PM

Storage

in database

Uses

0

Likes

0

mask-us-personal-data.guardrail.json

{
  "format": "contextetech/guardrail/v1",
  "id": "contextetech/mask-us-personal-data",
  "description": "Guardrail that masks U.S. personal data in AI assistant replies: SSNs, +1 phone numbers, personal emails and card numbers. Rules, 12 tests, code.",
  "tags": [
    "pii",
    "ccpa",
    "security"
  ],
  "license": "MIT",
  "language": "en",
  "type": "pii",
  "action": "mask",
  "rules": "Mask before sending any data that identifies or reaches a person:\n- personal email addresses (john.smith@…);\n- U.S. phone numbers, with or without +1;\n- Social Security numbers (123-45-6789);\n- payment card numbers (checked with the Luhn algorithm).\nLet through: generic addresses (support@, info@…), company names, amounts, dates, order numbers.",
  "cases": [
    {
      "input": "You can reach John at john.smith@gmail.com for updates.",
      "expected": "block"
    },
    {
      "input": "Call me at (415) 555-0132 tomorrow.",
      "expected": "block"
    },
    {
      "input": "My cell is +1 212-555-0187.",
      "expected": "block"
    },
    {
      "input": "SSN on file: 219-09-9999.",
      "expected": "block"
    },
    {
      "input": "Charge it to 4111 1111 1111 1111, exp 08/28.",
      "expected": "block"
    },
    {
      "input": "Send the invoice to maria.lopez@acme-corp.com.",
      "expected": "block"
    },
    {
      "input": "Email support@contextetech.com with any question.",
      "expected": "allow"
    },
    {
      "input": "Your order #458712 ships tomorrow.",
      "expected": "allow"
    },
    {
      "input": "The total is $1,250.00 including sales tax.",
      "expected": "allow"
    },
    {
      "input": "Your appointment is on 10/12/2026 at 2 pm.",
      "expected": "allow"
    },
    {
      "input": "Acme Corp approved the quote.",
      "expected": "allow"
    },
    {
      "input": "Hi, how can I help you today?",
      "expected": "allow"
    }
  ]
}

Check a text (Python)

import re

PATTERNS = {
    "email": r"[\w.+-]+@[\w-]+\.[\w.-]+",
    "phone": r"(?:\+1[\s.-]?)?\(?\b[2-9]\d{2}\)?[\s.-]?\d{3}[\s.-]?\d{4}\b",
    "ssn": r"\b(?!000|666|9\d{2})\d{3}-(?!00)\d{2}-(?!0000)\d{4}\b",
    "card": r"\b(?:\d[ -]?){13,19}\b",
}
GENERIC = ("support@", "info@", "sales@", "hello@", "contact@")


def luhn(number: str) -> bool:
    digits = [int(c) for c in number if c.isdigit()][::-1]
    total = sum(d if i % 2 == 0 else (d * 2 - 9 if d * 2 > 9 else d * 2) for i, d in enumerate(digits))
    return 13 <= len(digits) <= 19 and total % 10 == 0


def mask(text: str) -> tuple[str, bool]:
    """Replace personal data with [<type> redacted]; return the text and True if anything was masked."""
    found = False
    for kind, pattern in PATTERNS.items():
        def repl(m):
            nonlocal found
            value = m.group(0)
            if kind == "email" and value.lower().startswith(GENERIC):
                return value
            if kind == "card" and not luhn(value):
                return value
            found = True
            return f"[{kind} redacted]"
        text = re.sub(pattern, repl, text)
    return text, found