Summary

This chapter began by defining adversarial ML, which is always the result of some entity purposely attacking the software to elicit a specific result. Consequently, unlike other kinds of damage, the data may not have any damage at all, or the damage may be so subtle as to defy easy recognition. The first step in recognizing that there is a problem is to determine why an attack would take place – to get into the hacker’s mind and understand the underlying reason for the attack.

A second step in keeping hackers from attacking your software is to understand the security issues that face the ML system, which defies a one size fits all solution. A hospital doesn’t quite face the same security issues that a financial institution does (certainly they face different legal requirements). Consequently, analyzing the needs of your particular organization and then putting security measures in place that keep a hacker at bay is essential. One of the most potent ways...