Solution · AI Training
Train on data — not on customers.
Trained models can memorise their training set — leaking customer details through inference, fine-tunes, or prompts. WorkLens generates training data with the same statistical shape as your real data, but where no real customer ever existed. Compliant by architecture, not by policy.
Built for: Heads of Data / ML / AI Engineering
The problem
- —LLMs and embedding models can leak training data through prompts and inference attacks
- —Anonymising real data still leaves it as personal data — regulators don't recognise pseudonymisation as anonymisation
- —Cloud synthetic-data SaaS requires you upload your real data first — that's the leak you're trying to prevent
- —Most synthetic-data tools sample from clean distributions and miss the rare-event tails where models fail
How WorkLens solves it
- ✓Recipe-driven generation: your file structure + statistical fingerprint defines the output, raw values never leave the device
- ✓True anonymisation (not pseudonymisation): no real customer is the source of any synthetic row
- ✓Rare-event preservation: configurable k-anonymous tail-sampling keeps the edge cases models actually need
- ✓Audit-trail integration: every batch ships with a signed receipt — model cards can cite the dataset's provenance