Detects and replaces personal data in documents and conversations, so the rest of the process works on already-clean information.
Plenty of projects stall because nobody wants to be the one moving personal data without need, even when the processing is perfectly lawful. Anonymising at intake removes that blocker and shrinks the scope of what has to be protected downstream.
The categories that apply to your case, beyond names and identity numbers.
Data is swapped for consistent identifiers, so the document stays useful.
The mapping stays under your control, to re-identify only when there is a basis for it.
The sectors where this one comes up most, and what it covers in each.
It depends how you configure it. Keeping the mapping makes it pseudonymization, which is the usual choice because it allows re-identification when there are grounds. Without it, there is no way back.
It helps shrink the scope of processing, which is one part. Judging whether the output is still personal data is your data protection officer's call.
No detector is perfect. That is why it is paired with sampled review and the level is tuned to how critical the process is.
In the demo we go through your case: which models you need, where they run and what it takes to meet the regulation that applies to you.
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