The objective of the proposed research is to solve the safety verification problem for autonomous systems. Runtime assurance (RTA) is a solution approach where a safety filter module is added downstream in the control hierarchy that can verify the safety of the system. The control barrier function (CBF) framework is the prevalent control-theoretic implementation of the safety filter. In the context of robotics applications, the safety constraint represents the volume in the workspace occupied by the environment. A certified CBF must be synthesized for the safety constraint to employ the CBF-based safety filter. However, the safety constraint always changes for unseen environments, and online re-synthesis of the associated CBF is not feasible. The root cause of such a challenge is the environment-centric nature of the CBF framework. The system state is evaluated against the CBF associated with the safety constraint of the environment, i.e., the system should be forward invariant with respect to the environment-centric safety constraint and never enter the volume of the environment. Thus, a new CBF must be synthesized whenever the safety constraint changes. However, the safety constraint can be described ego-centrically, i.e., the environment should never enter the volume of the robot. The key difference is that the ego-safety constraint, i.e., the volume of the robot, is fixed. Consequently, such an innate property of the problem is leveraged to develop control invariant set theory, similar to that of CBF, that seeks to make the environment forward invariant with respect to the ego-centric safety constraint. Such a control theory and its associated function will be called ego-centric control barrier function (egoCBF). The key advantage of the egoCBF framework is that it needs to be synthesized only once, and it will be certified for any unseen environment. Also, the certified synthesis method will be presented.