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Course Outline

Introduction to Edge AI Security

  • Overview of security challenges specific to Edge AI
  • Threat landscape: analyzing cyberattacks on edge devices
  • Regulatory compliance and applicable security frameworks

Encryption and Authentication in Edge AI

  • Data encryption methods for securing AI models
  • Hardware-level security: TPM and secure enclaves
  • Establishing strong authentication and access control

Secure AI Model Deployment and Protection

  • Mitigating adversarial attacks on AI models
  • Methods for model obfuscation and protection
  • Guaranteeing model integrity and trustworthiness

Resilience Strategies for Edge AI Systems

  • Engineering fault-tolerant Edge AI architectures
  • 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
  • Adhering to 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 knowledge of AI and machine learning concepts
  • Practical experience with cybersecurity principles and encryption techniques
  • Familiarity with IoT and Edge computing environments

Audience

  • Cybersecurity professionals
  • AI engineers
  • IoT developers
 21 Hours

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