How are organizations using Confidential Computing?
Prevent fraud in financial services
Detect or develop cure for diseases in the healthcare industry
Secure intellectual property across industriess
Data exists in three states: at rest, in use, and in transit. Data that is stored is "at rest", data that is being processed is "in use", and data that is traversing across the network is "in transit". Even if you encrypt data at rest and in transit across the network, the data they process are still vulnerable to unauthorized access and tampering at runtime. Protecting the data in use is critical to offer complete security across the data lifecycle. And in today’s data-driven world, it is best to rely on a method that focuses on the data itself.
Cryptography or encryption is now commonly used by organizations to protect data confidentiality (preventing unauthorized viewing) and data integrity (preventing unauthorized changes). There are now advanced data security platforms that enable applications to run within secure enclaves or trusted execution environments that offer encryption for the data and applications.
What is Confidential Computing?
Confidential computing is the protection of data in use using hardware-based Trusted Execution Environments (TEE). A Trusted Execution Environment is commonly defined as an environment that provides a level of assurance of data integrity, data confidentiality, and code integrity. A hardware-based TEE uses hardware-backed techniques to provide increased security guarantees for the execution of code and protection of data within that environment. (Confidential Computing Consortium)
Benefits of Using Confidential Computing
Protect data and applications in use.
Secure intellectual property.
Enable secure encryption across the data lifecycle.
Prevent insider attacks and unauthorized access to data.
Get complete control over cloud data and seamlessly migrate/move workloads to the cloud.
Enable secure collaboration with external entities like partners and customers.
Secure and anonymous analytics on multiple data sets
Securing Healthcare AI
Protecting data in use for ML models
Protect Function-as-a-Service data