--- name: data-masking-7dd7665bcd04 description: >- Mask, redact, and anonymize sensitive data (PII, PCI, PHI) in databases, logs, and APIs. Use when protecting PII in dev/staging environments, redacting sensitive data from logs, anonymizing data for analytics, or applying k-anonymity and differential privacy for GDPR-compliant data sharing. license: Apache-2.0 compatibility: "Python 3.10+, Node.js 18+. Libraries: faker, presidio-analyzer, presidio-anonymizer, spaCy en_core_web_lg." metadata: author: terminal-skills version: "1.1.0" category: development repository: https://github.com/microsoft/presidio tags: "data-masking, pii, privacy, anonymization, redaction" use-cases: "Mask production PII before copying to dev/staging environment, Scrub sensitive data from application logs, Redact PII from API responses for analytics" agents: "claude-code, openai-codex, gemini-cli, cursor" --- # Data Masking ## Overview Data masking replaces real sensitive data with realistic but fake data, preserving format and structure. Essential for: - **Dev/staging environments**: Use masked production data without exposing real PII - **Log sanitization**: Prevent PII from appearing in log aggregation systems - **Analytics**: Analyze behavioral patterns without raw PII - **Testing**: Realistic test data that won't trigger real consequences ## Instructions 1. Inventory where PII lives (tables, log fields, API payloads, exports) and classify it as direct identifiers or quasi-identifiers. 2. Pick the technique per use case from the table below; prefer irreversible masking for dev/staging and tokenization only where the real value must be recovered. 3. Mask at the boundary (export job, log formatter, API serializer), never in the consuming app. 4. Verify: scan the masked output with the patterns or Presidio and fail the pipeline on any hit. ## Masking Techniques | Technique | How | When to Use | |-----------|-----|-------------| | **Static masking** | Replace data at rest permanently | Dev DB copy | | **Dynamic masking** | Mask on-read, original preserved | Role-based views | | **Tokenization** | Replace with token that maps to real value | Payment cards | | **Format-preserving** | Keep format, change values (e.g., real-looking SSN) | Testing | | **Redaction** | Replace with placeholder (`[REDACTED]`) | Logs | | **Generalization** | Replace specific value with range (age 34 → 30-40) | Analytics | ## PII Pattern Library ```python import re PII_PATTERNS = { "email": r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Za-z]{2,}\b', "phone_us": r'\b(?:\+1[-.]?)?\(?[0-9]{3}\)?[-.\s]?[0-9]{3}[-.\s]?[0-9]{4}\b', "ssn": r'\b(?!000|666|9\d{2})\d{3}-(?!00)\d{2}-(?!0000)\d{4}\b', "credit_card": r'\b(?:4[0-9]{12}(?:[0-9]{3})?|5[1-5][0-9]{14}|3[47][0-9]{13}|6(?:011|5[0-9]{2})[0-9]{12})\b', "ip_address": r'\b(?:(?:25[0-5]|2[0-4]\d|[01]?\d\d?)\.){3}(?:25[0-5]|2[0-4]\d|[01]?\d\d?)\b', "date_of_birth": r'\b(?:0[1-9]|1[0-2])[\/\-](?:0[1-9]|[12]\d|3[01])[\/\-](?:19|20)\d{2}\b', "passport": r'\b[A-Z]{1,2}[0-9]{6,9}\b', "zip_code": r'\b\d{5}(?:-\d{4})?\b', } ``` These regexes are a fast first pass and over-match: `passport` and `zip_code` hit ordinary numbers and product codes, `phone_us` hits any 10-digit run. Use them for logs; for free text or unknown columns use Presidio below. Validate card candidates with a Luhn check before redacting. ## Email and Credit Card Maskers ```python import re from faker import Faker fake = Faker() Faker.seed(4471) # same seed -> same fake values on every run (stable fixtures) def mask_email(email: str) -> str: """Mask email preserving domain structure.""" local, domain = email.split('@') masked_local = local[0] + '*' * (len(local) - 2) + local[-1] if len(local) > 2 else '***' return f"{masked_local}@{domain}" def mask_credit_card(card_number: str) -> str: """Mask credit card — show only last 4 digits.""" cleaned = re.sub(r'[\s-]', '', card_number) return '*' * (len(cleaned) - 4) + cleaned[-4:] def mask_ssn(ssn: str) -> str: """Mask SSN — show only last 4.""" cleaned = ssn.replace('-', '').replace(' ', '') return f"***-**-{cleaned[-4:]}" def mask_phone(phone: str) -> str: """Mask phone — show only last 4 digits.""" digits = re.sub(r'\D', '', phone) return f"***-***-{digits[-4:]}" ``` ## Log Sanitizer Middleware ```javascript // Node.js log scrubbing (winston) const PII_PATTERNS = { email: /\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Za-z]{2,}\b/g, creditCard: /\b(?:4[0-9]{12}(?:[0-9]{3})?|5[1-5][0-9]{14}|3[47][0-9]{13})\b/g, ssn: /\b(?!000|666|9\d{2})\d{3}-(?!00)\d{2}-(?!0000)\d{4}\b/g, phone: /\b(?:\+1[-.]?)?\(?[0-9]{3}\)?[-.\s]?[0-9]{3}[-.\s]?[0-9]{4}\b/g, password: /"password"\s*:\s*"[^"]*"/g, token: /"(?:token|api_key|secret|authorization)"\s*:\s*"[^"]*"/gi, }; function sanitizeLog(data) { let sanitized = typeof data === 'string' ? data : JSON.stringify(data); sanitized = sanitized.replace(PII_PATTERNS.email, '[EMAIL]'); sanitized = sanitized.replace(PII_PATTERNS.creditCard, '[CREDIT_CARD]'); sanitized = sanitized.replace(PII_PATTERNS.ssn, '[SSN]'); sanitized = sanitized.replace(PII_PATTERNS.phone, '[PHONE]'); sanitized = sanitized.replace(PII_PATTERNS.password, '"password":"[REDACTED]"'); sanitized = sanitized.replace(PII_PATTERNS.token, (match) => { const key = match.split(':')[0]; return `${key}:"[REDACTED]"`; }); return sanitized; } // Wrap Winston logger to auto-sanitize const winston = require('winston'); const logger = winston.createLogger({ transports: [new winston.transports.Console()], format: winston.format.combine( winston.format.printf(({ level, message, ...meta }) => { return JSON.stringify({ level, message: sanitizeLog(message), ...JSON.parse(sanitizeLog(JSON.stringify(meta))) }); }) ) }); ``` ## Database Masking (PostgreSQL) ```sql -- Create masked view for dev access CREATE OR REPLACE VIEW users_masked AS SELECT id, -- Mask name: keep first letter + *** LEFT(first_name, 1) || '***' AS first_name, LEFT(last_name, 1) || '***' AS last_name, -- Mask email: preserve domain REGEXP_REPLACE(email, '^([^@])([^@]*)(@.+)$', '\1***\3') AS email, -- Mask phone: show only last 4 '***-***-' || RIGHT(phone, 4) AS phone, -- Mask SSN: show only last 4 '***-**-' || RIGHT(ssn, 4) AS ssn, -- Keep non-sensitive fields as-is created_at, status, country FROM users; -- Grant dev team access to masked view only (not base table) GRANT SELECT ON users_masked TO dev_team; REVOKE SELECT ON users FROM dev_team; -- Dynamic, role-aware masking: use a SECURITY DEFINER function or the -- PostgreSQL Anonymizer extension (anon.start_dynamic_masking) instead of ad-hoc views. ``` ## Microsoft Presidio — Auto-Detection ```python # pip install presidio-analyzer presidio-anonymizer && python -m spacy download en_core_web_lg # (Python 3.10+; the default NLP engine needs the spaCy model) import os from presidio_analyzer import AnalyzerEngine from presidio_anonymizer import AnonymizerEngine from presidio_anonymizer.entities import OperatorConfig analyzer = AnalyzerEngine() anonymizer = AnonymizerEngine() def mask_text_presidio(text: str, masking_style: str = "replace") -> str: """Auto-detect and mask PII using Presidio NLP.""" results = analyzer.analyze(text=text, language="en") if masking_style == "replace": # Replace with type label: [EMAIL_ADDRESS] operators = { "DEFAULT": OperatorConfig("replace", {"new_value": "[REDACTED]"}), "EMAIL_ADDRESS": OperatorConfig("replace", {"new_value": "[EMAIL]"}), "PHONE_NUMBER": OperatorConfig("replace", {"new_value": "[PHONE]"}), "PERSON": OperatorConfig("replace", {"new_value": "[NAME]"}), "US_SSN": OperatorConfig("replace", {"new_value": "[SSN]"}), } elif masking_style == "hash": # Since 2.2.361 hashes use a random salt; pass a fixed salt (from a secret) # if the same input must map to the same output across rows operators = {"DEFAULT": OperatorConfig("hash", {"hash_type": "sha256", "salt": os.environ["MASKING_SALT"]})} anonymized = anonymizer.anonymize( text=text, analyzer_results=results, operators=operators ) return anonymized.text # Example text = "Contact Dana Whitfield at dana.whitfield@northwind-mail.org or 555-123-4567" print(mask_text_presidio(text)) # → "Contact [NAME] at [EMAIL] or [PHONE]" (exact labels depend on detected entities) ``` ## Production DB → Dev DB Pipeline ```bash #!/bin/bash # mask-db-for-dev.sh — Safe production → dev data pipeline set -e PROD_DB="${PROD_DATABASE_URL:?set PROD_DATABASE_URL}" # read-only replica credentials DEV_DB="${DEV_DATABASE_URL:?set DEV_DATABASE_URL}" echo "Dumping production schema..." pg_dump --schema-only "$PROD_DB" > schema.sql echo "Applying schema to dev..." psql "$DEV_DB" < schema.sql echo "Copying and masking data..." psql "$PROD_DB" -c "\COPY ( SELECT id, LEFT(first_name, 1) || 'XXXX' AS first_name, 'User' AS last_name, 'user_' || id || '@masked.invalid' AS email, '555-000-' || LPAD((ROW_NUMBER() OVER())::TEXT, 4, '0') AS phone, created_at, status FROM users ) TO STDOUT WITH CSV" | psql "$DEV_DB" -c "\COPY users FROM STDIN WITH CSV" echo "Done. Dev database ready with masked data." ``` ## Statistical Anonymization (GDPR) **Anonymization vs Pseudonymization (GDPR Article 4):** - **Anonymization**: Irreversible -- data can never be linked to an individual. Falls outside GDPR scope. - **Pseudonymization**: Reversible -- data can be re-linked with additional info. Still personal data under GDPR. **Key techniques for true anonymization:** - **k-Anonymity**: Each record is indistinguishable from at least k-1 others on quasi-identifiers (age, ZIP, gender). Generalize values into ranges and suppress groups smaller than k. - **l-Diversity**: Each equivalence class has at least l distinct sensitive attribute values, preventing attribute disclosure. - **Differential Privacy**: Mathematical privacy guarantee controlled by epsilon -- add calibrated noise to query results. Use `diffprivlib` (Python) or Google DP libraries. k-anonymity alone is often insufficient for GDPR -- combine with l-diversity and/or differential privacy. ## Examples ### Example 1: Safe staging copy **User request:** "Refresh staging from production but nobody on the dev team may see real emails or phone numbers." Run the pipeline script above with `PROD_DATABASE_URL` and `DEV_DATABASE_URL` set, then verify: ```bash psql "$DEV_DATABASE_URL" -tAc "SELECT count(*) FROM users WHERE email NOT LIKE '%@masked.invalid'" ``` Expected output: `0`. Any other number means unmasked rows leaked and the refresh must be discarded. ### Example 2: Scrub PII from application logs **User request:** "Our request logs contain customer emails and card numbers; clean them before they reach Datadog." Wrap the logger with `sanitizeLog` from the middleware section. A line such as `{"msg":"refund for dana.whitfield@northwind-mail.org card 4111111111111111"}` is emitted as `{"msg":"refund for [EMAIL] card [CREDIT_CARD]"}`. Add a unit test that feeds sample PII through the logger and asserts none survives. ## Guidelines - Mask irreversibly by default; a reversible mapping or key stored beside the data is pseudonymization, still personal data under GDPR. - Regex finds formats, not names or addresses; use Presidio (and review its misses) for free text. - Salts and encryption keys come from a secret manager or environment variable, never from the repository. - Keep referential integrity: the same real value must map to the same fake value in every table (seeded Faker or salted hash). - Test on a copy first, and scan the output before anyone gets access. - Do not treat k-anonymity alone as GDPR-grade anonymization. --- Source attribution: terminal-skills Original project: https://github.com/TerminalSkills/skills/blob/8a9852d489f274538be9bf1c9121aeb403750b1c/skills/data-masking/SKILL.md License: Apache-2.0 License information: https://www.apache.org/licenses/LICENSE-2.0 Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. 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See the License for the specific language governing permissions and limitations under the License. # Mega AI Hub resource metadata Creator / publisher: terminal-skills Original project: https://github.com/TerminalSkills/skills/blob/8a9852d489f274538be9bf1c9121aeb403750b1c/skills/data-masking/SKILL.md Attribution basis: Source repository record Fields of work: Data & Analytics, Development & Engineering, Legal Roles: Data Analyst, ML / AI Engineer, Backend Developer, Privacy / Data Protection, Compliance Officer Specialties: API, Backend Tools: Faker Country applicability: Country not specified Classification basis: Source category, role and specialization labels Instruction SHA-256: aab51250298226cc337f11bdc9a5b2f51a5c31f0210ff2215cd5ec7f99cf22e8