Nowadays, many electronic systems store valuable Intellectual Property (IP) information inside Non-Volatile Memories (NVMs). Encryption mechanisms are widely used by designers in order to enhance the integrity of such IPs and protect them from any kind of unauthorized access or modification. At the same time, often such IPs are critical from a reliability standpoint. Thus, dedicated techniques are employed to detect possible reliability threats (e.g., transient faults affecting the NVM content). The weights of a neural network (NN) model (e.g., integrated into an object detection system for autonomous driving or robotics) are a typical example of precious IP from both security and reliability standpoints. Indeed, NN weights often constitute proprietary data, stemming from an extensive and costly training process; moreover, their correctness is key for the NN to work reliably. In this article, we explore the capability of encryption mechanisms to ensure protection from both security and reliability threats. In particular, we applied several encryption mechanisms to two neural network applications to secure their weights and we assessed, via extensive fault injection campaigns, the fault detection that they provide. Experimental results show that by cleverly choosing the proper encryption scheme it is possible to achieve very high fault detection rates (greater than 99%) with respect to Multiple Bit Upsets. The gathered results pave the way to the integration of reliability and security mechanisms to achieve better results with lower costs.