Data handling & privacy
How customer data is treated at a conceptual level: minimisation, purpose limitation, and operational practices that reduce accidental exposure — without publishing internal architecture diagrams.
Data handling is described in operator terms: what to collect, how long to keep it, and how to reduce exposure — without publishing internal architecture.
Minimisation
Collect what you need
Smaller datasets reduce risk and simplify subject access workflows.
Practical checks
- Remove duplicate uploads across threads.
- Avoid exporting full workspaces “just in case”.
- Prefer structured fields over giant attachments.
Retention mindset
Customer requests
Access / correction / deletion
- Route through internal privacy first.
- Include workspace identifiers and scope.
- Expect identity verification steps.
Support
→Coordinate sensitive requests with Harold support.
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