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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 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.
 21 Hours

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