Sensitive data is information that can cause harm to individuals or organizations if it is exposed.

You have a legal and moral obligation to protect any sensitive information you store or process, particularly if your organization falls under regulations such as GDPR, HIPAA, or PCI DSS.

Examples of sensitive data can include:

• Personally Identifiable Information (PII)
• Protected Health Information (PHI)
• Trade secrets
• Intellectual property
• Payroll and customer financial records
• Legal data
• Biometric data

With teams handling sensitive data every day, it gets hard to keep track of where it goes and who sees it. Sensitive data discovery is the practice of finding that data across the places your work actually happens, then deciding what to do about it.

In SaaS, that meant scanning apps like Slack, Google Drive, and Notion and flagging the files that held it. With AI in the mix, the same question now points somewhere else: which AI tools your people use, what those tools do with your data, and what your agents pull on your behalf.

Discovery tools inventory the data across the locations you have scanned and tell you what exists, where it sits, and how much of it there is. The good ones add context.

Metomic did that work for years, then took the same classification engine to where AI runs. It weighs the data your agents request and the data your people put into AI tools, tells you which of your AI tools are in real use rather than tried once and dropped, and shows what each tool's own terms say about training on your data.

Then it gives you the lever: coach, allow, block, or hold for a person to approve.

Every organization generates data faster than it can track it, and AI has made that worse. Your people paste into chat prompts. Your agents pull records through connectors. Discovery is how that sprawl becomes something you can manage.

You get more than a list. You get the shape of the problem: which data types you hold, where the risk concentrates, which teams and which tools are driving it, and which everyday processes keep creating the worst cases. That is the input to a decision, and it is what turns a written AI policy into something with controls behind it.

For example, your Customer Success team may be pasting KYC documents into a chat prompt because the approved path is slower. You will not fix that by writing another rule. You fix it by seeing the pattern, then putting the control in the path the work already takes.

Most tools do one half of this. They classify data sitting at rest, or they watch what an agent is doing. Metomic does both, which is why the classification informs the decision.

You see the AI tools your people use and each request your agents make, with the vendor detail attached: security posture, terms, and what the tool says it does with your data. Then you set the rules that hold.

Rules run where the work happens: coach a person before they paste, redact sensitive fields out of a response before an agent sees them, block a tool you have not approved, or hold a risky request while a person decides.

Each action lands in an audit record and in your SIEM, so when the regulator asks how your AI behaves, you have the answer already written down.