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Ketch and Postgres

Find where personal data actually lives inside a Postgres database, and fulfill deletion requests against it directly, without a manual query written by hand for every request.

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Postgres

About Postgres

Databases like Postgres are where personal data ends up once it's left the application layer: customer records, event logs, anything an internal system persists. That makes a database one of the hardest places to answer two basic privacy questions with confidence: where does this person's data actually live across every schema and table, and can it be removed or updated on request without someone hand-writing a query and hoping they found everything.

Ketch connects to Postgres through the Ketch Transponder, a component built specifically to discover personal data inside a database and, where authorized, execute rights requests against it directly.

Capabilities

How Ketch works with Postgres

The Ketch Transponder connects to Postgres with a dedicated service account, and what it's authorized to do depends on the privileges granted to that account. Two functions are supported: Discovery and DSR Execution.DSR Execution goes further, and requires additional privileges: `UPDATE` and `DELETE` access, scoped to the specific databases, tables, views, and columns the organization authorizes. With that access granted, the Transponder can fulfill an access, deletion, or update request directly against the identified data, rather than only telling a team where the data is and leaving execution to someone else.

Discovery

Scans the information schema, tables, views, and columns Postgres exposes, using read-only `SELECT` access. This builds and maintains the data map: which tables and columns actually contain personal data, kept current as schemas change, rather than a one-time audit that goes stale.

With Ketch, teams can

  • Discover which Postgres tables and columns hold personal data on an ongoing basis, using read-only access that requires no write privileges at all
  • Grant DSR Execution privileges separately from Discovery, so a database administrator can authorize discovery immediately and decide separately, on its own timeline, whether to authorize direct deletion and update
  • Scope DSR Execution to specific databases, tables, or columns, rather than granting broad database-wide write access
  • Fold Postgres into the same Data Mapping and Data Subject Rights Automation workflows used for every other connected system, instead of treating the database as a manual, engineering-only process

The gap

The problem this integration solves

A database like Postgres doesn't organize itself around personal data. It organizes itself around whatever the application layer needed, and personal data ends up scattered across tables and columns with no inherent flag marking it as personal. That creates a consistent set of problems:

01. Nobody outside engineering can say with confidence which tables and columns actually hold personal data, and that confidence erodes further every time the schema changes

02. Fulfilling a deletion or access request against a database has historically meant someone hand-writing a query, which is slow, error-prone, and hard to prove was complete after the fact

03. Granting broad write access to any tool touching production data is a real risk on its own, independent of the privacy use case

04. Discovery and execution get treated as one all-or-nothing decision, when many organizations want visibility into where personal data lives well before they're ready to authorize automated deletion against it

Ketch resolves this by separating discovery from execution as two distinct privilege grants, so an organization can get an accurate, continuously updated data map immediately, and decide independently, on its own timeline, whether to authorize direct rights execution against the database.

Why Ketch

Why teams choose Ketch for Postgres privacy compliance

Permissioning infrastructure that governs Postgres the same way it governs every other system in your stack — not a one-off connector bolted onto a banner.

  • Discovery that stays current, not a one-time audit

    Because the Transponder connects continuously rather than running a point-in-time scan, the data map reflects schema changes as they happen, not as of whenever the last audit ran.

  • Backed by enforcement precedent

    Regulators increasingly expect businesses to prove technical enforcement, not just describe it on paper. The California Privacy Protection Agency's order against Tractor Supply required a full, current inventory of tracking technologies refreshed at least quarterly, the same underlying expectation that applies to knowing what personal data a database holds and being able to act on it.

Questions about the Postgres integration

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