In Chapter 6, Unsupervised Machine Learning Algorithms, we looked at the CRISP-DM (Cross-Industry Standard Process for Data Mining) life cycle, which specifies the different phases of training and deploying a machine learning model. Once a model is trained and evaluated, the final phase is deployment. If it is a critical machine learning model, then we want to make sure that all of its security goals are met.

Let's analyze the common challenges faced in deploying a model such as this and how we can address those challenges using the concepts discussed in this chapter. We will discuss strategies to protect our trained model against the following three challenges:

• Man-in-the-Middle (MITM) attacks
• Masquerading
• Data tempering

Let's look at them one by one.