DLP Guardrails to Prevent Data Leakage
Customizable redaction tools detect sensitive and proprietary information and replace it with placeholders before a prompt ever leaves your system, so your team gets the benefit of AI without leaking the data behind it.
Redaction Before Anything Is Sent
DLP filtering runs before data is sent to AI models or before an internet search is performed, so sensitive content is caught at the source.
Spot Sensitive Data Automatically
Detect names, account numbers, links, and other sensitive or proprietary information in a prompt before it is submitted.
Replace With Placeholders
Flagged content is swapped for placeholders, so the AI gets what it needs to help without ever seeing the underlying sensitive values.
Caught Before It Leaves Your System
Detection and redaction happen on-device, before data leaves your system, not after it has already reached a third party.
Pre-Filtering You Control
Customizable pre-filtering lets you define what counts as sensitive, tuned to the data your organization actually needs to protect.
Different Rules per Assistant
Set per-assistant and per-model redaction rules, applying stricter filtering to external models than to ones inside your tenant.
Warn Users in the Moment
Inline reminders point users to your AI Use Policy at the moment of risk, reinforcing good habits while DLP does the catching.
Safer prompting for your team
20x
Detection before data leaves
On-device
Redaction rules you define
Per-model
What DLP Catches
Common sensitive data types flagged and replaced before a prompt is sent.
Customer and employee names and other personal identifiers.
Account numbers, IDs, and other proprietary references.
Internal links and URLs that reveal private systems.
Any custom patterns you define for your organization.
