Functions from the field of artificial intelligence (AI) and especially neural network (NN) inference are increasingly being found in resource-limited devices that cannot outsource complex calculations to external servers. This "Edge AI" paradigm creates new security challenges as attackers now have physical access to devices, in addition to known attacks, and can perform side channel and error injection attacks. At the same time, such systems often process sensitive data such as health-related measurements. In addition, the NN models themselves can have substantial economic value, so they must be protected against unauthorized extraction. For the reasons mentioned, there is a growing interest in dealing with specific security threats and protective measures for the NN inference hardware. Project SeMSiNN looks at the security of mixed-signal (MS) NN inference hardware, an approach that is particularly attractive for edge AI because it leads to radical energy savings compared to fully digital realizations.