As more businesses connect AI tools with live databases, documents, APIs, and other enterprise data sources, I think an important question is where privacy protection should happen.
If a retrieved record contains names, email addresses, financial information, customer details, credentials, or other confidential information, should that data be identified and anonymized before it reaches the LLM?
Access controls can determine who or what is allowed to access a data source, but they may not prevent sensitive information from being included in the actual context sent to an AI model. An additional layer that detects and anonymizes sensitive information could potentially reduce that exposure while allowing the AI workflow to continue.
The challenge is maintaining enough useful context. If too much information is removed, the AI response may become less accurate or useful.
I’m interested in how other teams are approaching this when connecting enterprise data to AI applications.
What is the best way to detect PII before sending enterprise data to an LLM?
Can data anonymization protect sensitive information without reducing the usefulness of AI responses?
