Adversarial Machine Learning (AML) is emerging as a major eld aimed at the protection of automated ML systems against security threats. The majority of work in this area has built upon a game-theoretic framework by modelling a conict between an attacker and a defender. After reviewing game-theoretic approaches to AML, we discuss the benets that a Bayesian Adversarial Risk Analysis perspective brings when defending ML based systems. A research agenda is included.