As AI projects move from experimentation to production, I’ve started wondering whether API governance needs to be handled differently from traditional web APIs.
With a few endpoints, access control is fairly straightforward.
But once you’re dealing with multiple models, private repositories, inference endpoints, service accounts, and different teams, things become more complicated.
Some of the questions I’m interested in are:
I’m particularly interested in whether teams use a dedicated API governance tool or build these controls themselves around their existing infrastructure.
For example, an API governance workflow could potentially combine: Access control → security checks → API compliance → documentation checks → audit logs
For teams building AI applications or inference services: Which governance controls have actually been useful in production?
And at what point did you feel that basic access permissions were no longer enough and you needed a more structured API governance approach?