Course Outline
Fundamentals of Edge AI Security
- Examining the key challenges in Edge AI security
- Analyzing the threat landscape: cyber-attacks targeting edge devices
- Navigating regulatory compliance and established security frameworks
Encryption and Authentication in Edge AI
- Applying data encryption methods to protect AI models
- Leveraging hardware-based security features like TPMs and secure enclaves
- Establishing robust authentication and access control mechanisms
Securing AI Model Deployment and Integrity
- Mitigating adversarial attacks on AI models
- Implementing model obfuscation and protection techniques
- Verifying model integrity and trustworthiness
Enhancing Resilience in Edge AI Systems
- Creating fault-tolerant Edge AI architectures
- Utilizing AI-driven anomaly detection for breach identification
- Deploying automated threat response protocols
Secure Edge-to-Cloud Connectivity
- Adopting secure communication protocols
- Managing data privacy and federated learning in Edge AI
- Maintaining adherence to industry security standards
Emerging Trends and Best Practices in Edge AI Security
- Integrating AI-powered cybersecurity for edge computing
- Preparing for emerging threats and evolving security strategies
- Addressing ethical considerations in AI security
Recap and Future Directions
Requirements
- Advanced proficiency in AI and machine learning principles
- Practical experience with cybersecurity fundamentals and encryption protocols
- Working knowledge of IoT and Edge computing landscapes
Target Audience
- Cybersecurity experts
- 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