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.

Regeln

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.

Testfälle

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

Geschätzte Kosten

Eingabe-Tokens pro Aufruf : ≈ 871

Nur Eingabekosten, ohne die Antwort des Modells. Öffentliche Standardpreise der Anbieter (Mistral AI, DeepSeek, OpenAI, Anthropic) von USD in Euro umgerechnet zum EZB-Kurs vom 29. September 2026. Schätzung: 1 Token ≈ 3,6 Zeichen.

Kennung

contextetech--mask-us-personal-data

Typ

Schutzregeln

Datei

mask-us-personal-data.guardrail.json

Format

JSON (application/json)

Größe

1.928 Bytes (1,9 KB)

Kodierung

UTF-8

Geschätzte Tokens

≈ 534

Sprache

Englisch

Lizenz

MIT

Kommerzielle Nutzung

Erlaubt

Personenbezogene Daten

Fiktiv (erfunden)

Version

1.0

Veröffentlicht am

2. Oktober 2026 um 21:54

Aktualisiert am

2. Oktober 2026 um 21:54

Speicherung

in der Datenbank

Nutzungen

0

Gefällt mir

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"
    }
  ]
}

Text prüfen (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