The functions of an autonomous system can generally be partitioned into those concerned with perception and those concerned with action. Perception builds and maintains an internal model of the world (i.e., the system environment) that is used to plan and execute actions to accomplish a goal established by human supervisors. Accordingly, assurance decomposes into two parts: a) ensuring that the model is an accurate representation of the world as it changes through time and b) ensuring that the actions are safe (and effective), given the model. Both perception and action may employ AI, including machine learning (ML), and these present challenges to assurance. However, it is usually feasible to guard the actions with traditionally engineered and assured monitors, and thereby ensure safety, given the model. Thus, the model becomes the central focus for assurance.