Does Datapace make us compliant with Law 25 or the GDPR?

No tool does. Datapace gives you the inventory those regimes assume you have: which columns hold personal data, confirmed by your experts, and which tables, exports, and dashboards inherit them, kept current. What you do with it is your program.

What does Datapace read to find PII?

Column names and types, sampled structures, and how the column is used, with the scoped access your team agrees to. Low-confidence proposals, such as free-text fields, are routed to a person instead of being flagged automatically.

How do the flags reach AI systems and copilots?

Through the same governed context they read from. A flagged column is masked in what an agent can see, and the policy your team sets decides what it may query; every access is in the audit ledger.

PII you can account for, from the column to everything that inherits it

Teams answer to Law 25 in Québec, the GDPR in Europe, and their own customers, and the first question is always the same: where is the personal data. Datapace infers it per column, with a confidence score, from names, sampled structures, and how the column is used; your experts confirm it; and because lineage is on the same graph, the exports, dashboards, and copies that inherit the column are flagged with it. Samples are masked, and what agents and AI systems may see follows the same flags.

Today. Personal data located by memory and spreadsheet, with no way to know which exports and dashboards carry it.

With Datapace. PII inferred per column with a confidence score, confirmed by your experts, and followed through lineage to every table, export, and dashboard that inherits it.

What Datapace does here

Inferred per column, with confidence: Names, sampled structures, and usage propose which columns hold personal data, each with a confidence score; free-text columns that may hold names go to review rather than being guessed.
Confirmed by your experts: A person accepts or rejects each flag, so the inventory is one your privacy lead can stand behind, and validated stays distinct from inferred.
Followed through lineage: The exports, reporting tables, and dashboards that read a flagged column inherit the flag, so the inventory covers copies as well as sources.
Masked where it is shown: Samples and profiles shown to people and to AI systems are masked on flagged columns, and what an agent may read follows the same flags.
An inventory that stays current: A new column or a new export shows up as a proposal, not as a gap found at the next audit, because the graph is kept current as the databases change.

Questions teams ask

Does Datapace make us compliant with Law 25 or the GDPR?: No tool does. Datapace gives you the inventory those regimes assume you have: which columns hold personal data, confirmed by your experts, and which tables, exports, and dashboards inherit them, kept current. What you do with it is your program.
What does Datapace read to find PII?: Column names and types, sampled structures, and how the column is used, with the scoped access your team agrees to. Low-confidence proposals, such as free-text fields, are routed to a person instead of being flagged automatically.
How do the flags reach AI systems and copilots?: Through the same governed context they read from. A flagged column is masked in what an agent can see, and the policy your team sets decides what it may query; every access is in the audit ledger.

Go further

See this on your own data

Bring a use case. We will show you what Datapace reads on your live database, what your experts would confirm, and what the agents would propose first.

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