Welcome to the Confidential AI Workshop! This repository provides a series of hands-on tutorials to help you build and deploy secure and private AI solutions using Azure Confidential Computing.
Caution
This repository is intended for educational purposes only. It is not recommended for production use without further security review and customization to meet your specific requirements.
Confidential AI is an approach to building AI systems that protects data and models not just at rest and in transit, but also while in use. This is achieved by processing data within a hardware-based Trusted Execution Environment (TEE), an encrypted and isolated portion of the CPU or GPU memory.
Inside a TEE, code and data are shielded from the host operating system, the cloud provider, and even system administrators. This enables new possibilities for handling sensitive data, such as:
- Protecting valuable AI model intellectual property (IP) from theft.
- Processing sensitive user data (like medical records or financial information) without exposing it in plaintext.
- Meeting strict regulatory and privacy requirements by providing cryptographic proof of data protection.
This repository is designed as a hands-on workshop with standalone tutorials and reusable configuration modules. The structure is as follows:
├───assets
├───modules
│ ├───attestation
│ ├───key-management
│ ├───key-origin
│ ├───model-sourcing
│ └───os-disk-encryption
└───tutorials
├───confidential-llm-inferencing
├───confidential-ml-training
└───confidential-whisper-inferencing
- Tutorials: Contains the main end-to-end walkthroughs. Each tutorial is self-contained and guides you through building a complete Confidential AI solution.
- Modules: Contains focused, reusable guides for specific architectural choices you'll encounter in the tutorials, such as selecting a key management service or configuring OS disk encryption. This modular approach allows you to customize the tutorials to fit your specific security and architecture needs.
Before you begin, you will need the following:
- An Azure Subscription. Note: Azure Confidential Computing is not available on free trial accounts.
- Azure CLI version 2.46.0 or later.
- PowerShell as your command line interface.
- Sufficient quota for the VM SKUs used in the tutorials (e.g., DCasv5-series or NCCadsH100_v5-series).
Get started by choosing one of the following tutorials. Each one is designed to be standalone.
| Tutorial | Description | Key Concepts |
|---|---|---|
| Confidential ML Training | Learn the fundamentals by deploying a CPU-based Confidential VM. You will train a machine learning model on an encrypted dataset that is only ever decrypted inside the TEE. |          |
| Confidential LLM Inferencing | Add GPU acceleration to deploy a Large Language Model (LLM) on a Confidential VM with an NVIDIA H100 GPU. This tutorial shows how to protect both the model's weights and user prompts. |          |
| Confidential Whisper Pipeline | Build a complete, end-to-end secure AI pipeline. This tutorial demonstrates how to securely transcribe sensitive audio using Azure's Confidential Whisper service and analyze the transcript with your confidential LLM. |      |
This project welcomes contributions and suggestions.
For the contributions, this project endorses the Microsoft Enterprise AI Services Code of Conduct, which defines the requirements that all customers of Microsoft AI Services (as defined in the Microsoft Product Terms) must adhere to in good faith, in terms of responsible AI requirements, usage restrictions, content requirements, as well as additional conduct requirements if any for specific Microsoft AI services being leveraged in the tutorials, modules, and other assets of this project.
Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit Contributor License Agreements.
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This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.
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Microsoft and any contributors grant you a license to the Microsoft documentation and other content in this project under the Creative Commons Attribution 4.0 International Public License, see the LICENSE file, and grant you a license to any code in the repository under the MIT License, see the LICENSE-CODE file.
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