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Increase data privacy by protecting data in use
Overview
Protecting data privacy and security on your most sensitive workloads
- Increase data privacy and security surrounding business and consumer data by protecting data in use. Discover new opportunities with multiparty data analytics and machine learning that combine datasets to keep data private among participants. Take advantage of the broad range of confidential compute offerings Azure provides, including hardware, services, SDKs, and deployment tools.
- Help secure your sensitive and regulated data while it's being processed in the cloud. Azure confidential computing encrypts data in memory in hardware-based trusted execution environments and processes it only after the cloud environment is verified. This helps prevent data access by cloud providers, administrators, and users. Build on top of highly secure hardware using familiar tools, software, and cloud infrastructure, or migrate your existing workloads and applications.
- Combine datasets confidentially, without exposing your data to other contributing organizations. Benefit from confidential computing and valuable AI and machine learning insights. Upload encrypted data to a secure enclave in a virtual machine (VM) and perform algorithms on datasets from multiple sources.
- Migrate to the cloud and keep full control of your data in a trusted execution environment. Specify the hardware and software that have access to your data and code, and verifiably enforce this guarantee. You’ll retain control over your protected information to meet government regulations and compliance needs. Customize your confidential computing path using tools and solutions built in Azure, in open-source frameworks, and by independent software vendor partners.
Related products
Explore these related products
VMs with Application Enclaves
Create enclaves that protect data while processing in the CPU by keeping it encrypted and isolated in memory. This protects data from the operating system, hypervisors with escalated privileges, and Azure operators.
Confidential VMs
Easily deploy confidential workloads without requiring changes to existing applications or code.
GPU-enabled confidential VMs
Get the ease of migration to a confidential VM combined with the added compute power of a confidential computing GPU.
Confidential containers
Deploy and manage containerized applications more easily on Azure Kubernetes Service (AKS) or on Azure Container Instances.
Azure SQL Always Encrypted
Expand confidential computing capabilities of Always Encrypted with in-place encryption and richer confidential queries.
Trusted launch
Improve the security of generation 2 VMs with trusted launch, protecting against advanced and persistent attack techniques.
Azure confidential ledger
Tamperproof, unstructured data store hosted in trusted execution environments (TEEs) and backed by cryptographically verifiable evidence.
Microsoft Azure Attestation
Remotely verify the trustworthiness of a platform and the integrity of the binaries running inside it.
Azure Key Vault Managed HSM
Safeguard cryptographic keys and other secrets used by cloud apps and services.
Azure Databricks
Run your sensitive analytics workloads on confidential VM node clusters on Azure Databricks.
Azure Data Explorer
Run your sensitive analytics workloads on Azure ECasv5 and ECadsv5-series memory optimized confidential VM node clusters on Azure Data Explorer.
Azure Virtual Desktop
Run your sensitive Windows 11 workloads on confidential VMs on Azure Virtual Desktop.
Use cases
Explore Azure confidential computing solution architectures
Investment
Commitment to confidentiality
Microsoft is committed to transforming the cloud into the confidential cloud. As part of these efforts, Microsoft co-founded the Confidential Computing Consortium to democratize confidential computing and is accelerating innovation with a $20 billion investment.
Resources
Learn more about Azure confidential computing
Frequently asked questions
- Confidential computing protects data in use. Confidential computing encrypts data in memory and processes it only after the cloud environment is verified to be a trusted execution environment. This helps prevent data access by cloud operators, malicious admins, and privileged software.
- As datasets grow because of AI and cloud scalability, scrutiny on how data can be exposed is increasing—from both a consumer data privacy perspective and a compliance and regulation perspective. In parallel, the complexity of cyberthreats and ongoing attacks means organizations must proactively ensure that data is protected throughout its lifecycle.
- Confidential computing is expanding from regulated industries such as healthcare and financial services to the retail, manufacturing, and energy sectors. Every industry can benefit from confidential computing.
- Common use cases include reducing fraud and waste, anticorruption, antiterrorism, records and evidence management, intelligence analysis, global weapons systems and logistics management, vulnerable population protection (such as child exploitation and human trafficking), fighting money laundering, digital currencies, blockchain, transaction processing, customer analytics, proprietary analytics and algorithms, disease diagnostics, drug development, and contact tracing.
- Blockchain technologies built on Azure confidential computing use hardware-based privacy to support data confidentiality and secure computations.
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