Building Secure and Resilient Edge AI Systems Training Course
Edge AI security is crucial for protecting AI-driven applications running on decentralized edge devices from cyber threats and vulnerabilities.
This instructor-led, live training (online or onsite) is aimed at advanced-level cybersecurity professionals, AI engineers, and IoT developers who wish to implement robust security measures and resilience strategies for Edge AI systems.
By the end of this training, participants will be able to:
- Understand security risks and vulnerabilities in Edge AI deployments.
- Implement encryption and authentication techniques for data protection.
- Design resilient Edge AI architectures that can withstand cyber threats.
- Apply secure AI model deployment strategies in edge environments.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
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
Open Training Courses require 5+ participants.
Building Secure and Resilient Edge AI Systems Training Course - Booking
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Testimonials (5)
I learned a lot and gained knowledge can use at my work!
Artur - Akademia Lomzynska
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
It opens up a lot and gives lots of insight what security
Nolbabalo Tshotsho - Vodacom SA
Course - Advanced Java Security
I genuinely enjoyed the great information and content.
Dennis Barela
Course - OpenLDAP Workshop
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