The rapid adoption of AI in healthcare has made the clinical functionality more convenient at the expense of exposing multifaceted cybersecurity risks which are difficult to detect using the traditional perimeter- based approach to security. Although explainable AI is fully deployed with intent to achieve transparency and regulation compliance, the prospects of explainable AI as a native security control have not been pursued. The chapter is a presentation of Explainability- as- a- Defense (XaaD) a framework which transforms the results of AI explanations into continuous behavioral fingerprints to perform proactive cybersecurity monitoring. A XaaD architecture that takes advantage of a layered architecture with explainability generation, drift detection, and human- in- the- loop validation is proposed, which is shown in the example of a clinical decision support, medical chatbots, IoMT analytics, and federated learning. XaaD re- imagines explainability as a post- hoc interpretability condition into an active form of defensive health care AI performance of trust.