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SQL Anonymize

Secure Clean Room

Create secure, anonymized copies of production data for development and testing. Realistic data that bears no resemblance to the source while preserving referential integrity.

The Clean Room Approach

Data flows one way into the clean room. No production data escapes.

┌─────────────────┐     ┌──────────────────┐     ┌─────────────────┐
│   PRODUCTION    │     │    CLEAN ROOM    │     │   DEV/TEST      │
│   DATABASE      │ ──► │   PROCESSING     │ ──► │   DATABASE      │
│                 │     │                  │     │                 │
│ • Real names    │     │ • K-anonymity    │     │ • Fake names    │
│ • Real SSNs     │     │ • Data masking   │     │ • Fake SSNs     │
│ • Real emails   │     │ • FK preservation│     │ • Fake emails   │
│ • Real addresses│     │ • Distribution   │     │ • Fake addresses│
└─────────────────┘     └──────────────────┘     └─────────────────┘
                               ▲
                               │
                        No data flows back
                        (Audit enforced)

Anonymization Techniques

K-Anonymity

Ensure each record is indistinguishable from at least k-1 others

Data Masking

Replace sensitive values with realistic but fake data

Tokenization

Replace values with tokens that preserve format

Generalization

Replace specific values with ranges or categories

Perturbation

Add noise to numeric values while preserving distributions

Shuffling

Randomize column values while maintaining statistics

Column-Level Configuration

# sql2ai-anonymize.yaml
tables:
  Customers:
    columns:
      FirstName:
        method: faker
        type: first_name
        locale: en_US

      LastName:
        method: faker
        type: last_name

      Email:
        method: mask
        pattern: "****@{domain}"
        preserve_domain: true

      SSN:
        method: tokenize
        format: "###-##-####"

      DateOfBirth:
        method: perturb
        range: 365  # +/- 1 year

      Salary:
        method: generalize
        buckets: [50000, 75000, 100000, 150000]

  Orders:
    preserve_referential_integrity: true
    sample_percentage: 10  # Only copy 10% of orders

Before & After

Production Data

NameEmailSSN
John Smithjohn@acme.com123-45-6789
Jane Doejane@corp.com987-65-4321
Bob Wilsonbob@tech.io456-78-9012

Anonymized Data

NameEmailSSN
Michael Brown****@acme.comXXX-XX-7834
Sarah Johnson****@corp.comXXX-XX-2156
David Miller****@tech.ioXXX-XX-4589

Compliance Ready

Meet data privacy requirements while enabling development

GDPR
HIPAA
PCI-DSS
CCPA
SOC 2

Protect Your Production Data

Create realistic dev/test environments without exposing sensitive information.

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