Why Traditional DLP Fails for AI
Tools designed for files and email cannot protect AI conversation flows.
The Problem
Traditional DLP does not understand AI-specific data flows
The Consequence
Generic DLP tools miss prompts, responses, and AI conversation patterns that expose sensitive data
The Problem
Blocking AI entirely kills productivity
The Consequence
Teams find workarounds, creating shadow AI usage that is even harder to control
The Problem
Existing DLP creates too much friction
The Consequence
Users abandon secure workflows and revert to copying data directly into AI tools
AI-Native Data Protection
Purpose-built for AI workflows with unique capabilities traditional DLP cannot match.
AI-Native Detection
Purpose-built for AI conversation flows, not retrofitted from file or email DLP
Context-Preserving Protection
Mask data while maintaining semantic meaning for accurate AI responses
Reveal Technology
Unique de-obscuring capability restores masked values in AI responses
Granular Policies
Define rules by data type, user role, AI tool, and sensitivity level
Real-Time Enforcement
Policies apply instantly, protecting data before it leaves your environment
Complete Visibility
Every detection, action, and policy decision is tracked for compliance
Traditional DLP vs AI DLP
See how purpose-built AI protection differs from retrofitted solutions.
Traditional DLP
Designed for files, email, and network traffic
Secured AI
Purpose-built for AI prompts, responses, and conversations
Traditional DLP
Blocks or allows—binary decisions
Secured AI
Masks data while enabling the workflow to continue
Traditional DLP
Responses remain redacted and unusable
Secured AI
Reveal Technology restores context in AI responses
Traditional DLP
Creates friction that drives shadow AI usage
Secured AI
Enables AI adoption with built-in protection
Traditional DLP
Pattern matching on static content only
Secured AI
Contextual AI understanding of conversation flows
How It Works
Real-time protection without workflow disruption.
Intercept
Capture prompts before they reach external AI services
Detect
Identify PII, PHI, and sensitive data using ML models
Protect
Apply policy—mask, block, or allow with logging
Reveal
Restore masked values in AI responses for usable output