Insider computing trends of 2023 from a UC Berkeley PhD
This is part of Solutions Review’s Premium Content Series, a collection of columns written by industry experts on maturing software categories. In this presentation, opaque systems Co-founder and CEO Rishabh Poddar offers his 2023 insider computing trends driving the need for secure data analytics.
Organizations are facing increasing pressure to address the conflicts and inherent trade-offs around data security, data privacy, and data analytics. As the cost of data breaches continues to rise, organizations need better data protection. For example, a single data breach could cost an organization in the US. $9 million average. In recent years, hundreds of new federal, state, and cross-border policies and regulations have emerged that introduce increased compliance scrutiny and potential penalties. The ability to protect data and secure personal information is increasingly difficult due to the large volumes of personal and sensitive data being shared across mobile devices and apps, SaaS applications, and online commerce.
However, increased data protection through encryption techniques alone is not the answer. The most critical need is to enable multiple parties within and between organizations to securely share sensitive data and perform analytics and artificial intelligence, gaining time-critical information without violating regulatory policies or data privacy. Examples of business cases where multiple parties need to collaborate on sensitive data are financial fraud, money laundering, drug investigation, or monetization and ad targeting. 2023 will be the year we see major technological breakthroughs that remove these inherent challenges and conflicts associated with multi-stakeholder artificial intelligence and sensitive data analytics. Organizations will realize that:
Confidential Computing Trends for 2023
Traditional data encryption techniques are no longer enough
As organizations look to protect data throughout its lifecycle
With the increase in data breaches, hackers and regulatory policies, it is necessary to protect sensitive data throughout its lifecycle, from data at rest to data in use and during analysis. Traditional encryption techniques now become 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 safe and secure.
In 2023, we will see organizations move away from traditional encryption techniques and accelerate the 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.
Faster adoption of Trusted Execution Environments (TEEs)
Because sensitive data enables programmatic trust instead of relying solely on institutional trust
Today, data is everywhere and growing exponentially. Almost all organizations are embracing the cloud to accelerate their digital transformation initiatives and process more cloud-based data. As cloud data grows, cloud infrastructure alone is still 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” through solutions that provide complete end-to-end protection. This, in essence, is the adoption of confidential computing and
in particular, TEEs that provide programmatic confidence by encrypting and protecting the data in use. By 2023, we will see most large organizations use TEE for sensitive workloads, and with that, confidential computing will gain ground and reach a $54 billion market opportunity by 2026.
Organizations Will Switch to Confidential Computing Analytics Platforms
That do not compromise data security
The optimal way to perform analytics and machine learning on sensitive data is to enable analytics on encrypted data so that all sensitive data is protected end-to-end; at rest, in transit, and encrypted during analysis and processing. The massive organizational need to protect data throughout its lifecycle will lead to the rapid adoption of sensitive AI and analytics platforms that allow data analysts and machine learning professionals to safely analyze data without having to than expose them unencrypted during processing. Adoption will be driven by increasing business use cases that require sensitive analysis of sensitive data and the high costs associated with a data breach or breach of data privacy regulations and compliance policies.
The rise of artificial intelligence and confidential analysis
It will focus on multi-party data sharing and collaborative analysis
Organizations are quickly realizing that some of the most important use cases for confidential computing require collaborative analytics from multiple parties and AI. Multi-party confidential AI and analytics platforms make sensitive data usable by enabling secure and scalable analytics and machine learning directly on encrypted data within enclaves.
Organizations will adopt these solutions to help speed the transition of sensitive workloads to enclaves in sensitive cloud computing environments, and analyze encrypted data while ensuring it is never exposed unencrypted during compute. Multiple data teams within and across organizations will adopt these platforms to perform collaborative analysis or machine learning on their collective data while ensuring that each party only has access to the data and insights they are authorized to see.