Summary

This chapter has helped you understand various kinds of ML applications and how those applications are affected by various security threats. It has also emphasized the limitations of ML and pointed out some of the misconceptions that people have about ML – and possibly computers in general. Finally, you have discovered the ways in which humans inadvertently introduce security issues into ML applications by making invalid assumptions and by corrupting data in ways that humans understand, but computers don’t.

Knowing about the various forces at work to corrupt your ML model and data may be frightening at first, but there are certain things you can do to mitigate the threat, such as ensuring users are trained not to unintentionally introduce bias into the dataset. ML security measures can help you achieve these goals in an efficient manner. Of course, constant diligence is also a requirement.

The dataset end of things takes focus in the next chapter. It’s not just users who can ruin your day by introducing a security problem; using the wrong dataset source or any number of other issues can also be a problem. This next chapter will help you understand these issues so that you can consider the solutions presented in light of your organization’s needs.