It's a question every enterprise faces: How comfortable do we feel moving sensitive data to the cloud, bearing in mind the associated security and privacy risks?
Though global cloud adoption is expanding rapidly, security and privacy remain top concerns. For example, the Cloud Security Alliance survey last year showed security and privacy to be the leading worry for 58% of 1,900 professionals involved in cloud deployments.
That is why inside many companies, tension continues to simmer between development shops that see the scalable, flexible public cloud as an ideal innovation engine and security gatekeepers whose risk intolerance has earned them a reputation as the Department of No.
But what if organizations could have greater assurance that their data is protected from hackers and other prying eyes? What if reluctance about moving sensitive data to the cloud was alleviated?
Such is the promise of an emerging shift in data security: confidential computing.
Confidential computing not only reassures enterprises about the safety of moving more of their workloads to the public cloud, it also can enhance data security in any type of deployment and offer a host of business benefits.
An explanation of what confidential computing is must start with a description of the three stages of the data life cycle.
Data in Motion
When users send data, it is encrypted while transmitting across networks. Whether the user is someone in a company sending data to the cloud or a consumer sharing their credit card information with an online retailer, that's true. Standardized encryption technologies such as TLS protect the data during its journey.
Data at Rest
This is the designation for passive data that is not being computed on and is sitting in storage — records in databases, files on hard disks, etc. Organizations use disk encryption and other security technologies to safeguard data at rest.
Data in Use
This is where data is computed-on in some way. To do that, data must first be moved from the hard disk into system memory — aka RAM — and become unencrypted. At this stage, data becomes vulnerable to compromise due to a potential vulnerability within the millions lines of code that comprise the cloud provider’s system software operating system, hypervisor, firmware, or a cloud administrator. This is a large attack surface, and this is what consumers who move their confidential data from an on-premises setup to a public cloud worry about.
Confidential computing is an extra layer of security that extends encryption protection to data during runtime. It achieves this by running workloads in isolated hardware-encrypted environments, or trusted execution environments, that prevent unauthorized access or modification of applications and data while in use.
This eases a set of potential security threats in moving data to the cloud, such as a hypervisor vulnerability allowing other virtual machines to leak private data, or a rogue cloud provider employee with access to a company's physical machine backdooring a workflow.
There's a lot of industry buzz about confidential computing for a few reasons. The first is obvious: The rising number of cyberattacks is spurring a need for better data security.
Second, technologies that enable confidential computing, such as security features in operating systems and on CPUs, have achieved industrial strength, as with AMD-SEV and Intel SGX.
Third, the major public cloud providers, in particular Google Cloud Platform and Microsoft Azure, have been beefing up their confidential computing capabilities.
A note on that third point: Though much of the discussion about confidential computing has centered on its usefulness in securing sensitive data as it moves to the public cloud, its advantages are equally compelling and relevant for protecting data in use in many more environments, namely edge and on-premises deployments.
The space keeps heating up. For example, in May, AMD announced new confidential computing virtual machines for Google Cloud.
Confidential computing also has the support of a project community at the Linux Foundation, with commitments from nearly 40 member organizations and contributions from several open source projects.
Confidential computing has significant real-world benefits. Take financial services, for example. In that industry, business processes such as anti-money laundering and fraud detection require financial institutions to share data with external parties.
With confidential computing, they can process data from multiple sources without exposing customers' personal data. They can run analytics on the combined data sets to, say, detect the movement of money by one user among multiple banks, without introducing security and privacy problems.
By unlocking data computing scenarios that weren't possible before, confidential computing should be an important security and privacy advance for years to come.