TinyML Security and Privacy Training in Australia

The TinyML Security & Privacy Training focuses on securing machine learning models and data on ultra-constrained edge devices such as microcontrollers and embedded IoT systems.
Participants learn how to protect models, firmware, data, and device integrity, while ensuring privacy, compliance, and trust in TinyML deployments used in industrial, consumer, healthcare, and critical infrastructure environments.

This course bridges embedded security with ML-specific threats, helping teams build secure-by-design TinyML systems.

Welcome to the official TinyML Security and Privacy Training certification program. This comprehensive training is designed to elevate your professional skills and provide you with practical, industry-relevant knowledge in in Australia. As a globally recognized corporate training provider operating in 55+ countries, ProgNXT ensures that our curriculum meets the highest standards of excellence.

Whether you are looking to upskill your team or advance your personal career, our expert-led sessions will guide you through the core concepts of this domain. Upon successful completion of the 14 Hrs program, participants will receive a globally accepted certification, demonstrating their proficiency and readiness to tackle complex challenges in the field.

• Basic understanding of TinyML or embedded/IoT systems

• Familiarity with microcontrollers or edge devices

• Introductory knowledge of ML concepts (helpful)

• Basic awareness of cybersecurity principles is beneficial

• Embedded AI security fundamentals

• TinyML threat modeling

• Secure firmware and model protection

• Edge data privacy techniques

• Device identity and access control

• Secure update and lifecycle management

• Compliance and governance for embedded AI

By the end of this training, participants will be able to:

• Identify security and privacy risks in TinyML systems

• Protect TinyML models and firmware on edge devices

• Secure data collection and on-device inference

• Implement secure boot, updates, and access control

• Apply privacy-by-design principles to TinyML pipelines

• Design compliant and trustworthy embedded AI solutions