In this talk we will consider probability constraints acting on nonlinear systems involving an elliptically symmetrically distributed random vector. The nonlinear system is assumed to be convex in the decision vector. Under these assumptions we can provide a series of tools that allow us to establish novel convexity results for upper level sets of the probability function provided a sufficiently large threshold is taken. This latter threshold can be computed analytically.