# Why conditional independence assumption in GP models is usually valid? Or it isn't?

I'm interested in the ground for making conditional independence assumption (e.g. that different target dimensions do not covary for given input $$x$$) when we are modelling some multidimensional signal with Gaussian Process? Why it is natural to assume this? Or it is just our way to manage with the curse of dimensionality?