Confidential computing is an essential part of operations for a company that handles sensitive data. The need for securing data is a no-brainer. How to secure it, however, is not.
What is confidential computing used for?
Addressing security concerns is a big part of today's data landscape. If it is not for protecting sensitive customer data, securing intellectual assets or managing cloud infrastructures, confidential computing offers an extra layer of security for new startups as well as more seasoned companies. Even though the integration and deployment of these systems may be technically challenging, confidential computing offers benefits for companies in a climate where data breaches and hacks are getting as common as afternoon tea for Britons. Companies are driven by data and lack of proper securing attracts for data exposure, unauthorised access and malicious use. In worst cases, heavy data breaches can lead to the full loss of customer trust, killing the company.
Blocking the FedEx guy from accessing sensitive data
Confidential computing is a technology that isolates sensitive data to a securely protected part of a computer's CPU. In simpler terms, confidential computing ensures that the sensitive data and processes of a company stay confidential even when handled by third-parties like cloud providers, supply chain partners, investors or other collaborators. For instance, a FedEx guy has access to certain order information to successfully deliver a package. However, the more sensitive data like a customer's bank account details are disclosed from the third-party employee but still securely stored in the system of the mother company. A customer ordering a dozen packages can sleep peacefully knowing that their (now empty) bank account details are not exposed to the FedEx employee. Confidential computing enables the effective cooperation between two parties, like the eCommerce store and the delivery company, to share information without compromising sensitive customer data.
Confidential computing protects your company from nosey third-parties
First off, it is important for any company to understand the basics and identify what fields of data need confidential computing technologies in the first place. These fields can be the company's intellectual properties like algoritms, sensitive user data like financial information or even processes with third-parties, like investors, that require sharing data in order to effectively collaborate. For an overexaggerated example, a research collaboration that lacks proper data protection could lead to an unfortunate case of sensitive data exposure, leading to a competitor gaining access to the data, enabling them to rapidly develop the rivaling product. The potential for market domination...stripped away.
Secondly, identifying the data that requires confidential computing integration, meeting data compliance and understanding the company's regulatory framework are huge parts of successful data protection. Machine learning can help startups optimize regulatory compliance. Not all data needs hardcore protection and not all businesses face risks for data exposures or requirements to strict compliance. However, it might be good for a tech startup to start prioritizing confidential computing integrations early rather than later. No founder wants to face class-action lawsuits from angry customers whose financial details are now selling on the internet for 1.99.