ai governance gdpr

AI governance & GDPR: from pilot to operating model

Many AI projects fail not on the models but in operations: unclear ownership, missing evidence, no standard approvals. A clear governance model keeps teams fast and auditable.

What a sound setup contains

RACI for AI decisions: who approves, who operates, who is liable.
Data flow documentation: inputs, processing, storage locations, deletion concepts.
Technical control points: prompt and output logging, guardrails, monitoring, escalation.
Release process: risk-based rather than ad hoc, including a rollback path.

Trust signal: The result is an operating model you can put to work, including a responsibility matrix, not just a PDF.

Get in touch

What an enquiry sets off

The reply comes from the engineer who would do the work, not from a sales layer that passes it on.
A few sentences are enough for a first read. System access, architecture diagrams and an NDA come later, and only if this goes anywhere.
No is a possible outcome. If the problem is solvable in-house, or there is no slot free right now, the reply says so.

Next steps: the AI Act checklist, understanding the risk classes, and then a thirty-minute assessment.