A control point in front of your models
Cloudflare AI Gateway can sit between an application and one or more model providers, giving teams a central place for analytics, routing, retries, rate limits and logging.
That is useful because scattered model calls are hard to observe. But centralising them also creates a place where sensitive prompts and outputs may be recorded.
Cloudflare says request and response payload logging is enabled by default, while customers can turn logging off or keep metadata without storing payload bodies.
Securedby.ai view
Before celebrating the visibility, decide what you actually need to retain. For many applications, usage metadata is valuable while raw prompt and response storage deserves a much stricter justification.
That trade-off connects directly to our guide on how AI can leak data without a traditional hack
Five checks worth making
- Decide whether raw payloads need to be logged. Metadata may be enough for many operational questions.
- Set a retention approach. Stored prompts can become a new sensitive dataset of their own.
- Restrict dashboard access. Observability data may contain more business context than ordinary infrastructure logs.
- Use DLP deliberately. Blocking or flagging rules should reflect real data-handling requirements, not a generic checkbox exercise.
- Document the extra processor in your architecture. A gateway changes the path data takes between your application and model provider.
Useful resources
Good places to learn more, check current product details and go deeper. We favour first-party documentation, then add genuinely useful independent references where they earn their place.
