Data privacy and secure multiparty data analytics
Increase data privacy and security surrounding business and consumer data by protecting data in use. Discover new opportunities by enabling multiparty data analytics and machine learning that combine datasets, keeping data private among participants. Take advantage of the broad range of confidential compute offerings Azure provides, including hardware, services, SDKs, and deployment tools.
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, helping prevent data access by cloud providers, administrators, and users. Build on top of secure hardware using familiar tools, software, and cloud infrastructure, or migrate your existing workloads and applications.
AI and machine learning require enormous datasets to provide value, but organisations are disincentivised from sharing their data for competitive or regulatory reasons. Azure confidential computing allows organisations to combine datasets confidentially—without exposing data to each contributing organisation—enabling you to share AI and machine learning insights. Upload encrypted data to a secure enclave in a virtual machine 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. Customers retain control over their protected information so they can meet government regulations and compliance needs. Customise your confidential computing path using tools and solutions built in Azure, in open-source frameworks, and by independent software vendor partners.
Explore Azure confidential computing solution architectures
Confidential computing applies to various use cases for protecting data in use across industries such as government, financial services, and healthcare.
Create enclaves that protect data while processing in the CPU by keeping it encrypted and isolated in memory, thus protecting data from the operating system, hypervisors with escalated privileges, and Azure operators.
Easy way to deploy confidential workloads without requiring changes to existing applications or code
Deploy and manage containerized applications more easily with a fully managed Kubernetes service
Tamperproof, unstructured data store hosted in trusted execution environments (TEEs) and backed by cryptographically verifiable evidence
Remotely verify the trustworthiness of a platform and the integrity of the binaries running inside it
Safeguard cryptographic keys and other secrets used by cloud apps and services
Blogs and documentation
Explore technical resources
Azure confidential computing news and resources
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.
Customers are doing great things with Azure confidential computing
Protecting data for millions of customers
Jim O'Leary, VP of Engineering, Signal
"We utilize Azure confidential computing to provide scalable, secure environments for our services. Signal puts users first, and Azure helps us stay at the forefront of data protection with confidential computing."
Personalising offers while protecting privacy
Eddy Ortiz, Vice President of Innovation and Solution Acceleration, Royal Bank of Canada
"With Azure confidential computing, we can personalize offerings and protect privacy at the same time, creating exceptional digital experiences that clients can trust."
Accelerating transactions while protecting data
Joshua Goldbard, CEO, MobileCoin
"With hardware from Intel and Azure confidential computing, we've created a cryptocurrency that is fast, simple, and mobile-first; it just works."
Using privacy-preserving analytics in healthcare
Michael Blum, MD, Associate Vice Chancellor for Informatics, UCSF
"With this new technology, we expect to markedly reduce the time and cost, while also addressing data security concerns."
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, helping 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 organisations 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 it.
Prevention of fraud and waste, anti-corruption, anti-terrorism, records and evidence management, intelligence analysis, global weapons systems and logistics management, vulnerable population protection (including child exploitation, human trafficking, etc.), anti-money laundering, digital currencies, blockchain, transaction processing, customer analytics, proprietary analytics/algorithm, disease diagnostics, drug development and contact tracing.
Blockchain nodes are run and maintained by operators or validators who wish to ensure integrity and reach consensus on the state of the network. The nodes themselves are replicas and are used to track blockchain transactions. Each node has a full copy of the transaction history, ensuring integrity and availability in a distributed network. Blockchain technologies built on top of confidential computing can use hardware-based privacy to enable data confidentiality and secure computations.
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