Graph Neural Networks (GNNs) are now a fundamental building block in many systems, but how can we trust them? We begin by discussing conformal prediction, a framework that equips unreliable black-box models with strong statistical guarantees and quantifies their uncertainty. We then extend these guarantees to worst-case adversarial settings, addressing robustness. Finally, we look inside the black box to identify how nodes shape the model and its predictions.