In this work, we have investigated ion transport in aqueous sodium chloride confined within nanoscale graphene slit pores. Our simulations of these confined electrolytes were carried out with first principles-level (revPBE-D3) accuracy with the help of a machine learning potential developed and validated in our previous work. We have established that the ionic conductivity in highly confined electrolytes is significantly lower than that in bulk electrolytes. By computing each of the Onsager transport coefficients, we demonstrate that this decrease in conductivity arises from changes in both ion self-diffusion (Nernst-Einstein transport) and ion-ion correlations. We find that the overall density of the solution is the major factor dictating the self-diffusion coefficients of all species in the electrolyte, and that the ions' diffusion mechanism shifts towards more vehicular motion as the degree of confinement increases. We further establish that non-ideal contributions to transport, particularly cation-anion correlations, become more significant in highly confined systems. Rationalizing this increase in cation-anion correlations requires a dynamic picture of ion pairing based on the average diffusion length of ion pairs, rather than a purely static quantification of the fraction of associated ions. Finally, we demonstrate that while water diffuses faster at the graphene interface than in the center of the pores, ion diffusion is largely unchanged across the height of the slit.