  Artificial intelligence (AI) models introduce privacy vulnerabilities to
systems. These vulnerabilities may impact model owners or system users; they
exist during model development, deployment, and inference phases, and threats
can be internal or external to the system. In this paper, we investigate
potential threats and propose the use of several privacy-enhancing technologies
(PETs) to defend AI-enabled systems. We then provide a framework for PETs
evaluation for a AI-enabled systems and discuss the impact PETs may have on
system-level variables.
