Your Red Hat OpenShift AI models are waiting at the door. Who’s knocking?

Your Red Hat OpenShift AI models are waiting at the door. Who’s knocking? The new front door is an API How Red Hat and F5 work together Build faster, stay protected Red Hat Product Security About the author Shane Heroux More like this Blog post Blog post Original podcast Original podcast Keep exploring Browse by channel Automation Artificial intelligence Open hybrid cloud Security Edge computing Infrastructure Applications Virtualization Share You’ve trained the model, packaged it on Red Hat OpenShift AI, and it’s ready to work. The next move is exposing it through an API so people and applications can use it. At that moment, your model stops being an internal experiment and becomes a front-door service. And like any front door, somebody is going to knock … sometimes it’s the right user, sometimes not. Your model is no longer just a project in a lab: it’s a production endpoint. And like any endpoint, it’s a target. How do you ensure that only the right applications and users are interacting with it? How do you protect the sensitive data it might be trained on or the proprietary logic it contains? Every API endpoint is a target. For AI models, the attack surface is bigger than a simple app service. Beyond simple denial-of-service traffic, models can be tricked into leaking data through prompt injection, or probed until sensitive training data shows up. Even when no data leaks, attackers may try to hijack compute cycles or scrape responses in bulk. As deployments spread across clouds, datacenters, and edge sites, these risks multiply.