Course Outline
Introduction to Edge AI Security
- Overview of Edge AI security challenges.
- Threat landscape: cyberattacks on edge devices.
- Regulatory compliance and security frameworks.
Encryption and Authentication for Edge AI
- Data encryption techniques for securing AI models.
- Hardware-based security: TPM and secure enclaves.
- Implementing strong 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 security breaches.
- Automated threat response mechanisms.
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 knowledge of AI and machine learning concepts.
- Experience with cybersecurity principles and encryption techniques.
- Familiarity with IoT and Edge computing environments.
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