Summary: AI applications create new paths for sensitive data to move, persist, and be accessed, from prompts and tool calls to memories and transcripts. Learn why traditional approaches can fall short and how data-centric security helps protect sensitive information throughout AI workflows while preserving utility.

Somewhere in your company, a customer just used your new sales chatbot. Or support. Or professional services. They gave their account number, phone number, and maybe even their credit card number as they bought your product, diagnosed an issue, or updated their deployment. And that interaction probably went great, escalating to a human only when necessary. When it’s done, that copilot saved memories and transcripts to offer better services next time and, wait, did they save that sensitive data too?

AI applications, especially agents, generate memories and transcripts and send message data to tools, including the model itself. These messages can contain sensitive data and often require it depending on the problem the applications solve, but our tools and processes to regulate this data haven’t kept up with the technology.

If this was a traditional database, you’d have security reviews, strict access controls, technologies like data classification and tokenization (not to be confused with AI Tokens) to ensure privacy and security, and rich logs to prove who accessed what. With gen AI, reality can still look like the wild west. Open ended inputs, unfamiliarity with new technology, and pressure to go live all lead to security being neglected.

You can strip sensitive data coming in using technologies like DLP, but this often compromises utility. Humans handling escalation can’t see the account number. Tool calls can’t match on customer ID. Deletion requests don’t know what to delete.

To properly protect sensitive data in AI applications, you need to architect around it from the start. Classify data as it’s ingested and protect it before it hits your data stores. Prefer technologies such as tokenization (the security kind) and encryption that preserve access for the workflows that need it. Protect the data, not the prompt, to allow models to still understand user intent and maintain personalization.

If you’re worried about the data flowing into your AI application, or your rollout is blocked on securing AI inputs, get in touch. ALTR has everything you need to classify and protect sensitive data, including in your AI workflows.