Read about how Microsoft Azure’s Confidential Computing Platform and Opaque Systems are combining together to ease analytics and AI on sensitive data.

Confidential Computing’s hurdles have inhibited organizations from achieving faster value from data secured in Trusted Execution Environments (TEE’s), inclusive of enclaves and confidential VMs. Although every public cloud provider now provides Confidential Computing Clouds, that securely store data in encrypted form in enclaves, the hurdles to analyzing and performing machine learning on this data has prevented obtaining fast insights.

With Confidential Analytics and AI, those challenges are no longer. Now, every analyst or data scientist can leverage their existing skills to analyze highly confidential data.

Read the blog to understand how to:

  • Protect and process data in Azure’s enclave-enabled VMs.
  • Securely setup enclave clusters – including secure key distribution and securing inter-enclave communication.
  • Move data into enclaves, encrypt it at rest, in transit, and during processing.
  • Eliminate the need for highly specialized in-house skills to run analytics and ML on data in enclaves
  • Perform collaborative analytics across multiple parties on encrypted data, while adhering to regulatory compliance policies.