3 Questions Answered About Confidential Computing

Confidential computing is the ability to safeguard data and applications by running them in secure enclaves, could be the next major technology industry buzzword. Unfortunately, that's roughly half true. In reality, the concept of confidential computing is already at forefront of several groundbreaking use cases. That said, the concept isn't as well-known due to a lack of knowledge around what it is, what it does and how it works.

Companies require a fresh strategy in the current environment, with increasing security risks and high-visibility threats collide with the "go faster" move to cloud and DevOps. Enter AWS Nitro, where security makes business faster and makes work feasible that was previously impossible. It could empower security teams with the ability to resolve issues that the company didn't think could be solved.

What is Confidential Computing?

The best way to protect it in the increasingly data-driven world is to rely on an approach that is focused on the data. At a fundamental level, data can exist in three different states. It can exist in three states: when it's stored, it's "at at rest", while it's being processed it "in use" while when it travels across networks, it's "in transit." Security best practices today use encryption to safeguard data, whether it's in transit or in a state of rest. However, this data is still susceptible to unauthorized access or tampering during processing or running time. It is crucial to protect the data throughout its use to ensure security throughout its whole existence.

Confidential computing protects data and the applications that process that data by running it within secure enclaves which isolate data and code to prevent unauthorised access, even when the infrastructure for computing has been compromised. Hardware-backed hardware-backed methods are employed to offer greater security and protection for code execution , as also data protection within Confidential Computing environments (TEE).

What do I have to do with Confidential Computing?

Confidential computing has already shown its capabilities in a variety of innovative use cases. With privacy and security concerns abound, these cannot facilitate sharing critical data in real-time , while still meeting strict compliance regulations. Technology is already helping speed up the release of new medicines at a lower cost.

Meanwhile, Consilient uses the technology to fight financial fraud with machine learning and an Azure confidential computing model that enables AI training, without centralizing the data. This means that financial institutions and government agencies can more accurately predict malicious activities, which reduces false-positive rates, and increases the efficiency of risk management for legitimate businesses.

The UC San Francisco Center for Digital Health Innovation is a collaborative effort to accelerate the development and validation of algorithms for clinical use. In the field of healthcare, getting the approval of regulators for clinical artificial intelligence (AI) algorithms requires diverse and detailed clinical data - it's the only method to build, optimize and validate unbiased algorithms.

Businesses can run sensitive software and data on untrusted infrastructures like public clouds or other hosted environments that use hardware-level encryption. This increases security and privacy and protects systems from being hacked. Let's be blunt: organizations should encrypt their information and maintain their keys or they will be hacked by someone else.

When should I start using Confidential Computing?

As the example above from UCSF illustrates, the quick answer to this question is "now." However apart from using it to safeguard AI for healthcare there are many other applications. It is a good idea to protect in-use data for machine learning models, as well as securing blockchain and providing safe and confidential analytics across a variety of data sets.

Every company is determined to address a macro trend that is the utilization of the data it has accumulated. For most, siloed data isn't complete and can be valuable when paired with information from other organizations. But, a lot of data are confidential and must be secured.