Industry executives and experts share their predictions for 2023. Read them in this 15th annual VMblog.com series exclusive.
Compromise No More: A New Era of Data Privacy
By Rishabh Poddar, CEO & Co-Founder Opaque Systems
Today, a single data breach in the US costs an average of $9M. As a result, enterprises are facing an exuberant amount of pressure to address the inherent tradeoffs surrounding data privacy and analytics that often lead to security breaches. This is coupled with the growing need to enable multiple parties within and across organizations to safely share confidential data and perform analytics and AI – deriving time-critical insights while not violating regulatory policies or data privacy. With the ability to protect data only becoming harder due to higher compliance standards, large volumes of personal data, and confidential data becoming more vulnerable technology that eliminates the challenge seamlessly, without friction, will become vital in the new year.
I predict that 2023 will be the year where we see major technology advancements that eliminate the inherent challenges and conflicts associated with multi-party AI and analytics on confidential data become adopted more frequently as organizations will realize that:
1. Current data encryption techniques are not strong enough to protect data throughout its entire lifecycle.
With increasing data breaches, hackers, and regulatory policies it’s become necessary to protect sensitive data throughout its lifecycle – from data at rest to data in use and during analysis. Traditional encryption techniques are now rendered insufficient as they only protect data at rest and in transit. To protect sensitive data throughout its entire lifecycle, data must be processed in an environment where it can be secure and protected when in use.
In 2023, we’ll see organizations shift away from traditional encryption techniques and accelerate their adoption of solutions that provide end-to-end confidentiality. Solutions that provide advanced AI and analytics along with ease of use and scalability to support the broad set of use cases will show the fastest adoption rates.
2. Trusted Execution Environments (TEEs) will enable programmatic trust, eliminating sole reliance on institutional trust to protect data.
Today, nearly all organizations are adopting the cloud to accelerate their digital transformation initiatives and process more cloud-based data. As a result, data is growing exponentially in the cloud and the cloud infrastructure alone remains insufficient to secure and protect sensitive data. To account for this, organizations will move from “institutional trust” where exposure includes internal bad actors, to “programmatic trust” via solutions that provide complete end-to-end protection. In 2023 we’ll see the majority of large organizations make use of TEEs for sensitive workloads and with that, confidential computing will gain faster traction reaching a $54B market opportunity by 2026.
3. Confidential computing analytic platform adoption is the answer to eliminate the need to compromise data security.
The massive organizational need to protect data throughout its lifecycle will lead to the rapid adoption of confidential AI and analytics platforms that enable data analysts and machine learning practitioners to securely analyze data without ever having to expose it unencrypted during processing. The adoption will be driven by the rise of business use cases that mandate confidential analytics on sensitive data and the hefty costs associated with a data breach or failure to meet data privacy regulations and compliance policies.
4. Multi-party data sharing and collaborative analytics will be the main focus of confidential AI and analytics adoption.
Organizations will adopt multi-party confidential AI and analytics platforms to help accelerate the transition of sensitive workloads to enclaves in confidential computing cloud environments, and analyze the encrypted data while ensuring it’s never exposed unencrypted during computation. Multiple data teams within and across organizations will adopt these platforms to perform collaborative analytics or machine learning on their collective data while ensuring that each party is only privy to the data and insights that they are authorized to see.
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ABOUT THE AUTHOR
Rishabh Poddar is the CEO and co-founder of Opaque Systems, a startup out of UC Berkeley tackling the seemingly untouchable challenge of multi-party collaboration on analytics and AI on encrypted data. While at UC Berkeley, Rishabh and the team pioneered confidential computing for collaborative analytics and AI in the RISELab through their MC2 open-source project, which became the basis for Opaque (after 5 years of extensive research).
