In an era where machine learning permeates critical domains such as finance and healthcare, safeguarding these systems against vulnerabilities is paramount. This paper explores the imperative of securing machine learning systems and advocates for a holistic strategy merging hardware and software co-design. Our proposed framework integrates hardware-level security mechanisms with software-based techniques, presenting a robust approach to fortifying machine learning algorithms and models against potential threats.