Introducing Safety Retention: A Clearer Choice for AI Privacy
OpenRouter ·
People building with AI should be able to access advanced models and understand what happens to their data. OpenRouter’s role is to make those boundaries clear across a diverse model ecosystem.
We’re introducing Safety Retention as a new data policy tier alongside Zero Data Retention (ZDR) and Retention and Training. It allows data to be stored exclusively for abuse prevention, without being used for model training.
One consistent framework makes data policies easier to compare across models and providers. It separates the question of whether data is stored from what that data can be used for.
Three data policy tiers
Retention means keeping a copy of what you send to a model or what it sends back after processing. Our framework distinguishes three tiers:
| Tier | What it means | Is your data trained on? |
|---|---|---|
| ZDR | No routine storage after processing, as described in the ZDR policy. | No |
| Safety Retention | OpenRouter or a provider may store any request, whether flagged or not, only to detect, investigate, and prevent abuse. | No |
| Retention and Training | Data may be stored and used for training under the applicable provider policy. | May be, under the provider’s policy. |
Safety Retention limits why data can be kept. It does not require a request to be flagged before it can be stored.
Why Safety Retention exists
Safety systems can look for malicious cyber activity and misuse involving chemical, biological, radiological, or nuclear materials. They can also help detect unauthorized attempts to copy a model’s capabilities. Some threats are visible in one request; others emerge as patterns across many requests.
Retaining requests can help safety systems detect abuse before any individual request is flagged. Retained content is accessed only to detect, investigate, and prevent abuse, and is not used for model training.
OpenRouter and our providers may begin retaining requests for abuse prevention under this tier.
What this means for privacy
Storing content is a meaningful change in how data is handled. Privacy depends both on whether data is kept and on the limits placed on its use. As more capable models bring additional safeguards, those distinctions become increasingly important.
We’re updating our documentation to reflect this framework.
Safeguards and access
Broad access to advanced AI depends on safeguards providers can trust and data policies users can understand. Effective abuse prevention can help providers serve more legitimate developers and businesses, including smaller teams without direct relationships to major AI labs.
Our role is to make these differences understandable across providers, so users can compare data policies without having to learn a different privacy vocabulary each time.
Our goal is to keep advanced AI broadly accessible while making the boundaries around customer data clear.