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PlatformPrivacy SQL

Protect and unprotect data with the SQL you already write.

Privacy SQL takes the complexity out of protecting data. Developers and users see only what they are allowed to see, and cannot get around your protections.

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  • Your systems
  • Sensitive identity
  • Protected data
  • Anonomatic protection

  1. Protect or unprotect data with standard SQL.

    When protecting data means learning a new tool, adoption lags, and protection lags with it.

    With Privacy SQL, data users treat Anonomatic Schemas like database views and get fully transformed data back. Teams adopt standard security and privacy rules faster, and more data ends up protected.

    • Standard SQL
    • No-code Privacy ETL Schemas
    • Available through Anonomatic JDBC driver
    Privacy SQL: one standard query, split and recombined A standard SQL query (select name and balance from accounts where balance is over 10,000 and last name is Smith) goes to Anonomatic, which splits it: the balance test (balance > 10000) runs against the customer database and the last-name test (last_name = Smith) against PII Vault. The two answers come back together as one result: protected account data, with names restored only for an authorised user. Standard SQL SELECT name, balance FROM accounts WHERE balance > 10000 AND last_name = 'Smith' Privacy SQL: one standard query, split and recombined A standard SQL query (select name and balance from accounts where balance is over 10,000 and last name is Smith) goes to Anonomatic, which splits it: the balance test (balance > 10000) runs against the customer database and the last-name test (last_name = Smith) against PII Vault. The two answers come back together as one result: protected account data, with names restored only for an authorised user. Standard SQL SELECT name, balance FROM accounts WHERE balance > 10000 AND last_name = 'Smith'

  2. Different users, different access, different results.

    The same query should not return the same thing to everyone. What a person sees ought to depend on what they are allowed to see.

    Your data is kept fully de-identified, yet your teams keep querying it the way they always have. Different users, with different access, get different results.

    • Data kept fully de-identified
    • Results follow each user’s access
    Isometric illustration. Three navy source databases feed the Anonomatic mark. Beyond it, three analysts at three desks see three different results on their screens: protected teal rows on one, teal rows with gold rows among them on the next, grey rows on the third.

  3. No retraining, no rewrites.

    Years of reports, dashboards and SQL skills are an investment. A protection layer that breaks them is a cost nobody budgeted for.

    Privacy SQL leaves them alone. Existing reports, dashboards and SQL skills keep working.

    • Existing reports
    • Existing dashboards
    • Existing SQL skills
    Isometric illustration. Two colleagues, one holding a mug and one holding a laptop, stand before a large dashboard of teal bar and line charts with a check mark. A short stack of servers sits beside the screen.

Your data stays in your environment. Anonomatic never receives or sees it.

  • Runs in your public cloud, private cloud or on‑premises
  • Deploys as a single container
  • Fits into your existing systems, no rip‑and‑replace
Explore PII Vault

Related

Keep exploring.

  1. Privacy ETL

    Apply your privacy and security rules automatically while data travels between systems.

  2. Protection Methods

    Every protection method from one vendor, under one set of policies.

Use your sensitive data without giving up privacy.

Tell us what you need to do with your data. We’ll show you how Anonomatic can help protect it.

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