New Medium Blog Post on Evaluating Security Capabilities for Edge AI

[stevenhoenisch]

Edge AI applications demand security capabilities tailored to meet their workload-specific requirements with minimum overhead and complexity. The underlying processors must maximize both the performance and security of the workload -- a balance that Intel processors address with built-in security. ⚖️

A critical question quickly arises, though: How can you benchmark the performance of an Edge AI workload’s underlying processors as part of your efforts to identify a solution that cost-effectively meets your performance requirements while satisfying your use case’s security needs? 🔐

With the opt-in security model of Edge Microvisor Toolkit, you can typically select which security features to enable for your validation or benchmarking effort so you can tune your deployment to balance the trade-offs between mitigating risks and minimizing overhead.

These security options protect computers, hypervisors, apps, and data with such capabilities as the following:

• Unified Kernel Image
• dm-verity
• Trusted compute
• Secure Boot and Trusted Platform Module (TPM) support for hardware-verified integrity
• Full-disk encryption

To find out more about these security capabilities of Edge Microvisor Toolkit, check our recent blog post on Medium titled Evaluating Security Capabilities for Edge AI Workloads on Intel Platforms with Edge Microvisor Toolkit. 💨

What security capabilities are important for your edge AI deployments? Let us know in the comments below or in the responses for the blog post on Medium.