Blind AI enables confidential AI model deployment by leveraging secure enclaves, protecting sensitive data and proprietary models from unauthorized access. It targets developers and organizations requiring robust privacy for AI workloads, offering a secure execution environment for inference.

How It Works

Blind AI utilizes Intel SGX (Software Guard Extensions) to create isolated, encrypted memory regions called enclaves. Models and data are loaded into these enclaves, where computations occur. The host system and even the cloud provider cannot access the model's weights or the inference data, ensuring end-to-end confidentiality. This approach provides a strong security guarantee against sophisticated attacks targeting the underlying infrastructure.

Quick Start & Requirements

• Install via pip install blindai.
• Requires Intel SGX-enabled hardware and a compatible Linux environment.
• Setup involves configuring SGX drivers and potentially a TEE (Trusted Execution Environment) SDK.
• Official documentation and examples are available at https://github.com/mithril-security/blindai.

Highlighted Details

• Supports popular ML frameworks like PyTorch and TensorFlow.
• Offers a Python SDK for easy integration into existing workflows.
• Enables secure multi-party computation for collaborative model training or inference.
• Provides a REST API for remote, confidential inference.

Maintenance & Community

• Actively maintained by Mithril Security.
• Community support channels are available via Discord.

Licensing & Compatibility

• Licensed under the Apache License 2.0.
• Permissive license allows for commercial use and integration into closed-source applications.

Limitations & Caveats

The reliance on Intel SGX means compatibility is limited to specific hardware and operating systems. SGX deployment can be complex, and performance overhead may be a consideration for highly latency-sensitive applications.