Course Outline
Introduction to Edge AI Security
- Overview of security challenges in Edge AI
- Threat landscape: cyberattacks targeting edge devices
- Regulatory compliance and established security frameworks
Encryption and Authentication for Edge AI
- Data encryption methods for securing AI models
- Hardware-based security: TPM and secure enclaves
- Implementing robust authentication and access control mechanisms
Secure AI Model Deployment and Protection
- Mitigating adversarial attacks on AI models
- Techniques for model obfuscation and protection
- Ensuring model integrity and trustworthiness
Resilience Strategies for Edge AI Systems
- Developing fault-tolerant Edge AI architectures
- Utilizing AI-driven anomaly detection for identifying security breaches
- Automated threat response mechanisms
Secure Edge-to-Cloud Communication
- Implementing secure communication protocols
- Data privacy and federated learning in Edge AI contexts
- Ensuring compliance with industry security standards
Future Trends and Best Practices in Edge AI Security
- AI-powered cybersecurity solutions for edge computing
- Emerging threats and evolving security strategies
- Ethical considerations in AI security
Summary and Next Steps
Requirements
- Advanced comprehension of AI and machine learning principles
- Proficiency in cybersecurity fundamentals and encryption techniques
- Familiarity with IoT and Edge computing infrastructures
Target Audience
- Cybersecurity professionals
- AI engineers
- IoT developers
Testimonials (3)
Experience sharing, it's teacher's know-how and valuable.
Carey Fan - Logitech
Course - C/C++ Secure Coding
get to understand more about the product and some key differences between RHDS and open source OpenLDAP.
Jackie Xie - Westpac Banking Corporation
Course - 389 Directory Server for Administrators
the knowledge of the trainer was very high - he knew what he was talking about, and knew the answers to our questions