Course Outline
Introduction to Edge AI Security
- Overview of challenges in Edge AI security
- Threat landscape: cyberattacks targeting edge devices
- Regulatory compliance and security frameworks
Encryption and Authentication for Edge AI
- Data encryption techniques for securing AI models
- Hardware-based security: TPMs and secure enclaves
- Implementing robust authentication and access control
Secure AI Model Deployment and Protection
- Preventing adversarial attacks on AI models
- Techniques for model obfuscation and protection
- Ensuring model integrity and trustworthiness
Resilience Strategies for Edge AI Systems
- Designing fault-tolerant Edge AI architectures
- AI-driven anomaly detection for identifying security breaches
- Automated mechanisms for threat response
Secure Edge-to-Cloud Communication
- Implementing secure communication protocols
- Data privacy and federated learning in Edge AI
- Ensuring compliance with industry security standards
Future Trends and Best Practices in Edge AI Security
- AI-powered cybersecurity 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 concepts
- Practical experience with cybersecurity principles and encryption techniques
- Familiarity with IoT and Edge computing ecosystems
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