Risk assessments for foods, ingredients and additives have evolved over the past few decades to incorporate new techniques such as benchmark dose modeling, and to further develop the utility of tools such as the threshold of toxicological concern, and even to explore advancements in carcinogen risk assessment. However, exposure assessments still tend to rely on deterministic approaches utilizing conservative inputs. Deterministic assessments are easy to conduct and easy to interpret; however, as point estimates of exposure, they do not account for variability in the level and/or likelihood of occurrence of a chemical or population differences in exposure inputs. Probabilistic assessments can further refine deterministic assessments using distributions for various exposure inputs and, as such, better account for exposure uncertainty. This project will survey the current state of adoption of probabilistic exposure modeling by regulatory agencies to inform risk management decisions and regulations for food contaminants, develop criteria for determining high quality distributions for input variables, and highlight the value of further integration of probabilistic exposure assessments into regulatory frameworks, including a discussion of factors that hinder widespread adoption.