Securing Edge AI and Embedded Intelligence

• An understanding of embedded systems or edge AI deployment environments
• Experience with Python and ML frameworks (e.g., TensorFlow Lite, PyTorch Mobile)
• Basic familiarity with cybersecurity or IoT threat models

Audience

• Embedded AI developers
• IoT security specialists
• Engineers deploying ML models on edge or constrained devices

Securing Edge AI and Embedded Intelligence is the practice of protecting machine learning models and data in constrained, often physically exposed environments such as drones, sensors, smart cameras, and autonomous systems.

This instructor-led, live training (online or onsite) is aimed at intermediate-level engineers and security professionals who wish to secure AI models deployed at the edge against threats such as tampering, data leakage, adversarial inputs, and physical attacks.

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

• Identify and assess security risks in edge AI deployments.
• Apply tamper resistance and encrypted inference techniques.
• Harden edge-deployed models and secure data pipelines.
• Implement threat mitigation strategies specific to embedded and constrained systems.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Introduction to Edge AI and Embedded Systems

• What is Edge AI? Use cases and constraints
• Edge hardware platforms and software stacks
• Security challenges in embedded and decentralized environments

Threat Landscape for Edge AI

• Physical access and tampering risks
• Adversarial examples and model manipulation
• Data leakage and model inversion threats

Securing the Model

• Model hardening and quantization strategies
• Watermarking and fingerprinting models
• Defensive distillation and pruning

Encrypted Inference and Secure Execution

• Trusted execution environments (TEEs) for AI
• Secure enclaves and confidential computing
• Encrypted inference using homomorphic encryption or SMPC

Tamper Detection and Device-Level Controls

• Secure boot and firmware integrity checks
• Sensor validation and anomaly detection
• Remote attestation and device health monitoring

Edge-to-Cloud Security Integration

• Secure data transmission and key management
• End-to-end encryption and data lifecycle protection
• Cloud AI orchestration with edge security constraints

Best Practices and Risk Mitigation Strategy

• Threat modeling for edge AI systems
• Security design principles for embedded intelligence
• Incident response and firmware update management

Summary and Next Steps