Protect customer data throughout an AI workflow
AI applications often handle sensitive business material even when the feature appears simple. Data protection depends on what is collected, where it travels, how long it remains available and whether administrators can understand or remove it.
Implementation method
Draw a data-flow map covering uploads, embeddings, prompts, generated outputs, backups and external providers. Minimize identifiers before processing and apply access checks at retrieval time. Establish a retention period and a clear process for removing customer material. Audit logs should demonstrate actions without unnecessarily reproducing entire private documents.
Illustrative scenario
If a customer uploads a supplier contract for comparison, the assistant may need the commercial terms but not the personal phone numbers or private signatures. Redact or restrict irrelevant content and test that another customer cannot retrieve the file by changing a document identifier.
Quality checklist
- Data inventory and retention policy
- Document-level access enforcement
- Tests for cross-user data isolation