  This paper considers a Cram\'er-Lundberg risk setting, where the components
of the underlying model change over time. These components could be thought of
as the claim arrival rate, the claim-size distribution, and the premium rate,
but we allow the more general setting of the cumulative claim process being
modelled as a spectrally positive L\'evy process. We provide an intuitively
appealing mechanism to create such parameter uncertainty: at Poisson epochs we
resample the model components from a finite number of $d$ settings. It results
in a setup that is particularly suited to describe situations in which the risk
reserve dynamics are affected by external processes (such as the state of the
economy, political developments, weather or climate conditions, and policy
regulations). We extend the classical Cram\'er-Lundberg approximation
(asymptotically characterizing the all-time ruin probability in a light-tailed
setting) to this more general setup. In addition, for the situation that the
driving L\'evy processes are sums of Brownian motions and compound Poisson
processes, we find an explicit uniform bound on the ruin probability, which can
be viewed as an extension of Lundberg's inequality; importantly, here it is not
required that the L\'evy processes be spectrally one-sided. In passing we
propose an importance-sampling algorithm facilitating efficient estimation, and
prove it has bounded relative error. In a series of numerical experiments we
assess the accuracy of the asymptotics and bounds, and illustrate that
neglecting the resampling can lead to substantial underestimation of the risk.
