Hardware attacks on resource-constrained IoT devices are evolving rapidly. These threats have become a significant concern due to the increase of IoT devices used in applications such as human health, public transportation, autonomous vehicles, defense, and environmental monitoring. Recent studies show the potential of using deep learning to steal user data by monitoring hardware features and side-channel information. Additionally, machine learning (ML) approaches have recently been widely adopted in IoT applications. Advanced platforms demand novel circuits and architectures that can yield several orders of magnitude improvements in energy consumption in ML applications while maintaining consistent accuracy. Neuromorphic computing leveraging digital, mixed-signal, and analog processing has been shown to be a promising candidate due to energy, wire count, and area efficiency. Thus, an effective cutting-edge hardware approach for neuromorphic computing to perform rapid, energy-efficient, and secure supervised and unsupervised learning at the IoT edge is sought. Here we discuss the challenges and potential benefits of using neuromorphic computing modules for security at the IoT edge.