How to Use AI in a Privacy-Compliant Way

Before using AI, classify your data: public, internal, sensitive, personal. Public content can go into general-purpose tools; sensitive data should go through private deployment, de-identification, or local models, and keep audit logs.

Classify and de-identify data

List sensitive items such as customer lists, contracts, financial data, and medical records. Remove names, phone numbers, ID numbers, and account numbers before uploading. If you can summarize, don't send the original; if you can run locally, don't upload to the cloud.

Choose compliant tools

Check the privacy policy, data retention, whether data is used for training, server locations, and subprocessors. Enterprise plans usually let you disable training, set permissions, and export logs. If there's no clear commitment, don't share confidential information.

Policies and training

Create an allowlist and a blocklist, and require approval for account access. Periodically audit conversations and exported logs. Making team members aware of the consequences of violations is more effective than just buying tools.

FAQ

Is turning off training enough to be safe?

No. You also need to check retention periods, access controls, encryption, and server locations. For the most sensitive data, consider de-identification or private deployment.

What should individual users watch out for?

Don't upload ID cards, bank cards, medical records, or unpublished work. Use temporary chats, delete history regularly, and review privacy settings.