Preview of Azure Confidential Clean Rooms for secure multiparty data collaboration
Today, we are excited to announce the preview of Azure Confidential Clean Rooms, a cutting-edge solution designed for organizations that require secure multi-party data collaboration. With Confidential Clean Rooms, you can share privacy sensitive data such as personally identifiable information (PII), protected health information (PHI) and cryptographic secrets confidently, thanks to robust trust guarantees that help ensure that your data remains protected throughout its lifecycle from other collaborators and from Azure operators. This secure data sharing is powered by confidential computing, which helps protect data in-use by performing computations in hardware-based, attested Trusted Execution Environments (TEEs). These TEEs help prevent unauthorized access or modification of application code and data during use. Organizations across industries need to perform multi-party data collaboration with business partners, outside organizations, and even within company silos to improve business outcomes and bolster innovation. Confidential Clean Rooms help derive true value from such collaborations by enabling granular and private data to be shared while providing safeguards on data exfiltration hence protecting the intellectual property of the organization and the privacy of its customers and addressing concerns around regulatory compliance. Whether you’re a data scientist looking to securely fine-tune your ML model with sensitive data from other organizations, or a data analyst wanting to perform secure analytics on joint data with your partner organizations, Confidential Clean Rooms will help you achieve the desired results. You can sign up for the preview here Key Features Secure Collaboration and Governance: Allows collaborators to create tamper-resistant contracts that contain the constraints which will be enforced by the clean room. Governance verifies validity of those constraints before allowing data to be released into clean rooms and helps generate tamper-resistant audit trails. This is made possible with the help of an implementation of the Confidential Consortium Framework CCF). Enhanced Data Privacy: Provides a sandboxed execution environment which allows only authorized workloads to execute and prevents any unauthorized network or IO operations from within the clean room. This helps keep your data secure throughout the workload execution. This is possible with the help of deploying clean rooms in confidential containers on Azure Container Instances (ACI) which provides container group level integrity with runtime enforcement of the same. Verifiable trust at each step with the help of cryptographic remote attestation forms the cornerstone of Confidential Clean Rooms. Salient Use Cases Azure Confidential Clean Rooms caters to use cases spanning multiple industries. Healthcare: For fine-tuning and inferencing with predictive healthcare machine-learning (ML) models and for joint data analysis for advancing pharmaceutical research. This can help protect the privacy of patients and intellectual property of organizations while demonstrating regulatory compliance. Finance: For financial fraud detection through analysis of combined data across banks and other financial institutions and for providing personalized offers to customers through secure analysis of transaction data and purchase data in retail outlets Media and Advertising: For improving marketing campaign effectiveness by combining data across advertisers, ad-techs, publishers and measurement firms for audience targeting and attribution and measurement Retail: For enhanced personalized marketing and improved inventory and supply chain management Government and Public Sector Organizations: For analysis of high security data across multiple government and public sector organizations to streamline benefits for citizens Customer Testimonials We are already partnering with several organizations to accelerate their secure multi-party collaboration journey with confidential clean rooms. Confidential computing in healthcare allows secure data processing within isolated environments, called 'clean rooms', protecting sensitive patient data during AI model development, validation and deployment. Apollo Hospitals uses Azure Confidential Clean Rooms to enhance data privacy, encrypt data, and securely train AI models. The benefits include secure collaboration, anonymized patient privacy, intellectual property protection, and enhanced cybersecurity. Apollo’s pilot with Confidential Clean Rooms showed promising results, and future efforts aim to scale secure AI solutions, ensuring patient safety, privacy, and compliance as the healthcare industry advances technologically. - Dr. Sujoy Kar, Chief Medical Information Officer and Vice President, Apollo Hospitals Azure Confidential Clean Rooms is a game changer to make collaborations on sensitive data both seamless and secure. When combined with Sarus, any data processing job is automatically analyzed using the most advanced privacy technology. Once validated, they are processed securely in Confidential Clean Rooms protecting both the privacy of data and the confidentiality of the analysis itself. This eliminates administrative overheads and makes it very easy to build advanced data processing pipelines. With our partner EY, we're already leveraging it to help international banks improve AML practices without compromising privacy. - Maxime Agostini, CEO & Cofounder of Sarus Read here to learn more about how Sarus is using Confidential Clean Rooms. As co-leaders on this Data Consortium Pilot, we are thrilled to be working with industry partners, Sarus and Microsoft, to drive this initiative forward. By combining Sarus’ privacy preserving technologies and Microsoft’s Azure Confidential Clean Rooms, not only does this project push the edge of technology innovation, but it strives to address a pivotal issue that affects us as Canadians. Through this work, we aim to help financial services organizations and regulators navigate the complexities of private and personal data sharing, without compromising the integrity of the data, and adhering to all relevant privacy regulations. For the purposes of this pilot, we are focusing our efforts on how this technology can play a pivotal role in helping better detect cases of human trafficking, however, we recognize that it can be used to help organizations for multiple other use cases, and cross industries, including health care and government & public sector. - Jessica Hansen, Privacy Partner EY Canada, and Dana Ohab, AI & Data Partner EY Canada Retrieval-Augmented Generation (RAG) applications accessing Large Language Models (LLMs) are common in private AI workflows, but managing secure access to sensitive data can be complex. SafeLiShare’s integration of its LLM Secure Data Proxy (SDP) with Azure Confidential Clean Rooms (ACCR) simplifies access control and token management. The joint solution helps ensure runtime security through advanced Public Key Infrastructure (PKI) and centralized policy management in Trusted Execution Environments (TEEs), enforcing strict access policies and admission controls to guarantee authorized access to sensitive data. This integration establishes trust bindings between the Identity Provider (IDP), applications, and data, safeguarding each layer without compromise. It also enables secure creation, sharing, and management of applications and data assets, ensuring compliance in high-performance AI environments. - Cynthia Hsieh, VP of Marketing, SafeLiShare Read here to learn more about how SafeLiShare is using Confidential Clean Rooms. Learn More Signup for the preview of Azure Confidential Clean Rooms Confidential Consortium Framework (CCF) Confidential containers on Azure Container Instances (ACI)
Try Phone Calling in Preview portal failing
Hi, I'm trying out the https://learn.microsoft.com/en-us/azure/communication-services/concepts/telephony/try-phone-calling on ACS in the Preview Azure Portal, and I get an error for even the most basic thing. I set up an ACS resource, and added a phone number for outbound and inbound calls. When I try to make a call, I get an error Call failed SIP code 603 Decline subcode 560603 I must be overlooking something really obvious. Can anybody help ? Stephane
Protecting Azure customers with the power of Azure confidential ledger
The Azure confidential ledger Basic SKU preview will allow select customers using other Azure products to uplevel integrity protection by storing periodic data, blobs, and application signatures in Azure confidential ledger as a point-in-time source of truth. The Basic SKU will have limited transactions per second compared to the existing Standard SKU. It is ideal for cases where periodic hash digests are sent to the Azure confidential ledger for advanced integrity protection of your main data source. The Basic SKU will be free of charge for the duration of the gated preview.Try new Azure confidential ledger features, including an Azure Blob Storage Marketplace application
To support customers in regulated industries and compliance scenarios who asked about higher integrity protection of storage blobs, the Azure confidential ledger team has launched a preview of a managed Marketplace application that will further protect data: Blob Storage Digests Backed by Confidential Ledger (Preview)..... The Azure confidential ledger team has also launched new features to enhance product and auditing experience: The Azure confidential ledger Portal experience has been improved with a new Ledger Explorer feature that allows observing transactions and validating the cryptographic proofs of ledger transactions...Confidential Computing is Child's Play with ACI
In this fun example we’ll be using a containerised version of the Minecraft game server to demonstrate how easy it is to take an existing container and deploy it unmodified using Azure Confidential Containers on Azure Container Instances to give you the tools you need to try this with ‘real’ workloads in your environment.Aligning with Kata Confidential Containers to achieve zero trust operator deployments with AKS
Confidential containers on Azure Kubernetes Service (AKS) leveraging https://github.com/confidential-containers/confidential containers open-source project are coming soon to Azure. If you would like to be part of the preview, please express your interest here https://aka.ms/cocoakspreviewMicrosoft introduces preview of Azure Managed Confidential Consortium Framework
Today we are pleased to announce the preview of Azure Managed Confidential Consortium Framework, a hosted version of the Confidential Consortium Framework (CCF) which leverages the isolation and attestation capabilities of Trusted Execution Environments provided by Azure confidential computing. The framework design decouples node provisioning and operation from network and application governance, making it possible for the solution provider to maintain the set of nodes executing the transactions, without having any access to their contents.Confidential VM node pool with AMD SEV-SNP protection available on AKS in public preview
AKS node pools now support the generally available confidential VM sizes (DCav5/ECav5). Confidential VMs with AMD SEV-SNP support bring a new set of security features to protect date-in-use with full VM memory encryption. This enables confidential VM node pools to target the migration of highly sensitive container workloads to AKS without any code refactoring while benefiting from the full AKS feature support.
