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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 secure 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 understanding of AI and machine learning concepts
- Experience with cybersecurity principles and encryption techniques
- Familiarity with IoT and Edge computing environments
Audience
- Cybersecurity professionals
- AI engineers
- IoT developers
21 Hours
Testimonials (4)
I learned a lot and gained knowledge can use at my work!
Artur - Akademia Łomżyńska
Course - Active Directory for Admins
General course information
Paulo Gouveia - EID
Course - C/C++ Secure Coding
Nothing it was perfect.
Zola Madolo - Vodacom
Course - Android Security
I genuinely enjoyed the great information and content.