Abstract
This chapter delves into the essential security practices for deploying Tiny Machine Learning (TinyML) applications on microcontrollers within Internet of Things (IoT) devices, focusing on minimizing vulnerabilities from inception to deployment. It begins by outlining common security threats specific to TinyML environments, such as model theft and data poisoning, with real-world case studies illustrating their impact. The chapter then discusses robust security architectures, emphasizing the need for lightweight, yet effective, cryptographic techniques tailored for constrained environments. A comprehensive analysis of current methodologies for secure bootstrapping, secure key storage, and firmware updates is presented, illustrating their significance in maintaining the integrity and confidentiality of TinyML applications. Practical implementation guidelines and code samples are provided to enable readers to apply these strategies in their own TinyML deployments. This discussion not only highlights practical implementations but also sets the stage for adopting emerging standards in secure TinyML deployment.