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Before adding a document to an AI workflow, establish what information it contains, who is permitted to use it and which systems will receive it. An apparently ordinary support transcript can contain identifying or confidential details.
The customer-support workflow is a useful place to practise data minimisation. Compare the actual controls and maintenance work when choosing between local AI and hosted APIs.
Map the data journey
List where the data is collected, processed, stored, logged and deleted. Include retrieval databases, evaluation exports, analytics tools and error reports. The model provider is only one part of that journey.
Use public or fictional data for initial prototypes. If real data is needed, minimise it and check the applicable agreement and organisational requirements. Removing a name does not necessarily anonymise the remaining record.
Questions for a project review
- Is the complete document necessary, or would a relevant extract suffice?
- Can another user retrieve these passages through search?
- Are logs retaining material the application no longer needs?
- Can deletion propagate to indexes, backups and evaluation copies?
- Is a third-party integration receiving more information than intended?
Provider policies and legal obligations vary. Check the actual current terms and get appropriate specialist advice for regulated or sensitive use cases. Do not assume that “local”, “encrypted” or “enterprise” settles every question.
Discussion: Which secondary data copy is easiest to overlook when adding AI to an existing application?
Technical background: MCP security best practices.
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