Comparison page
Privacy gateway vs DLP: why prompts need a different control layer
Traditional DLP protects documents, email, and endpoints. AI prompts need a layer that understands prompt structure, model routing, and the context that should stay hidden.
DLP helps with broad prevention, but it was not built for prompt-time decisions
AI workflows are interactive and context-sensitive. The same prompt may need partial masking, full blocking, or a safe route depending on the model, the team, and the data class.
Use DLP for baseline prevention; use a gateway for AI-specific control
Sintetiko sits in front of the model, classifies the prompt, applies policy, and only forwards the safe version when it should continue.
Decision guide
How to tell whether DLP or a privacy gateway fits the job
This checklist is useful for security, legal, and platform teams planning an AI rollout.
Review the data shape
Prompts can contain free text, files, IDs, tool outputs, or structured JSON, which changes how protection should work.
Decide the intervention point
A gateway acts at request time, while classic DLP often acts at document, email, or endpoint level.
Define the action
Choose whether to mask, tokenize, block, or route to a safer model based on the prompt content.
Keep the trace
Make the decision visible to security without broadly exposing the original data.
Comparison dimensions
Content surface
DLP is broad; a privacy gateway is built for prompts, responses, and model-adjacent workflows.
Inspection depth
Gateways inspect prompt structure and the specific request context before anything leaves the app.
Sensitive data handling
Gateways can redact, mask, or reroute values instead of only blocking the whole payload.
Policy control
Both enforce rules, but gateways are better for AI-specific policies and audit trails.
Best-fit scenarios
Related pages
If DLP is not enough for AI, use a gateway built for prompts
We can help you define the policy boundary that protects sensitive data without breaking the workflow.