Azure Confidential Computing: Key Benefits
Azure confidential computing comprises technologies that safeguard your data and models at every stage of the AI lifecycle, even when in use. This allows you to execute AI workloads with sensitive data without risking unauthorized access or tampering. In this blog post, we’ll explore what Azure confidential computing offers, its advantages, and how you can employ it to develop secure AI solutions.
What is Azure Confidential Computing?
Azure confidential computing is grounded in the concept of trusted execution environments (TEEs). TEEs are hardware-protected memory areas that isolate code and data from the rest of the system. They thwart access or modification by anyone, including cloud operators, malicious admins, or privileged software like the hypervisor. TEEs also offer cryptographic attestation, validating the integrity and identity of the code within.
It supports two TEE types: software-based and hardware-based. Software-based TEEs use techniques like encryption and sandboxing, creating isolated environments. Hardware-based TEEs utilize dedicated hardware features like secure enclaves or protected memory, ensuring more robust isolation. Azure provides both TEE types through various services and VM sizes.
Advantages Confidential Computing
It provides several advantages for AI developers and users:
- Protecting data and models in use: Run AI workloads with sensitive data (e.g., personal, financial, or health information) without exposing them to unauthorized access or tampering. Safeguard model architecture and weights from theft or reverse-engineering.
- Enabling new scenarios and collaborations: Unlock new possibilities for AI applications demanding high security and privacy. Enable multi-party training and federated learning without sharing data or models centrally.
- Increasing trust and compliance: Boost trust and transparency in your AI solutions by offering verifiable proof of data and model protection. Comply with regulations such as GDPR or HIPAA mandating data privacy and protection.
How to Utilize Azure Confidential Computing for AI?
The Confidential Computing offers multiple services and tools for building AI solutions with TEEs. Here are some examples:
- Azure Machine Learning: Train and deploy AI models using hardware-based TEEs (e.g., Intel SGX or AMD SEV). Orchestrate federated learning across edge devices or cloud nodes.
- Azure Cognitive Services: Access pre-built AI models for vision, speech, language, and decision-making using software-based TEEs (e.g., Open Enclave SDK or Intel SGX). Customize these models securely with your data.
- NVIDIA GPU VMs: Run GPU-accelerated AI workloads using hardware-based TEEs (e.g., NVIDIA A100 Tensor Core GPUs with Ampere Protected Memory). Ensure data and model confidentiality and integrity while harnessing GPU performance.
- Microsoft Research Confidential AI: Explore cutting-edge research projects and tools that delve into the confidential computing frontier for AI. Examples include CrypTFlow2 for secure multi-party computation on encrypted data and CryptoNets for encrypted model inference.
Azure confidential computing empowers you to safeguard your data and models throughout the AI lifecycle, even during use. With Azure confidential computing, you can create trustworthy AI solutions that deliver security, privacy, collaboration, and compliance benefits. To delve deeper into Azure confidential computing and get started, click here.