  This work proposes a structured approach to baselining a model, identifying
attack vectors, and securing the machine learning models after deployment. This
method for securing each model post deployment is called the BAD (Build,
Attack, and Defend) Architecture. Two implementations of the BAD architecture
are evaluated to quantify the adversarial life cycle for a black box Sentiment
Analysis system. As a challenging diagnostic, the Jigsaw Toxic Bias dataset is
selected as the baseline in our performance tool. Each implementation of the
architecture will build a baseline performance report, attack a common
weakness, and defend the incoming attack. As an important note: each attack
surface demonstrated in this work is detectable and preventable. The goal is to
demonstrate a viable methodology for securing a machine learning model in a
production setting.
