Unlocking secure, private AI with confidential computing
Confidential computing use cases and benefits
GPU-accelerated confidential computing has far-reaching implications for AI in enterprise contexts. It also addresses privacy issues that apply to any analysis of sensitive data in the public cloud. This is of particular concern to organizations trying to gain insights from multiparty data while maintaining utmost privacy.
Another of the key advantages of Microsoft’s confidential computing offering is that it requires no code changes on the part of the customer, facilitating seamless adoption. “The confidential computing environment we’re building does not require customers to change a single line of code,” notes Bhatia. “They can redeploy from a non-confidential environment to a confidential environment. It’s as simple as choosing a particular VM size that supports confidential computing capabilities.”
Some industries and use cases that stand to benefit from confidential computing advancements include:
- Governments and sovereign entities dealing with sensitive data and intellectual property.
- Healthcare organizations using AI for drug discovery and doctor-patient confidentiality.
- Banks and financial firms using AI to detect fraud and money laundering through shared analysis without revealing sensitive customer information.
- Manufacturers optimizing supply chains by securely sharing data with partners.
Further, Bhatia says confidential computing helps facilitate data “clean rooms” for secure analysis in contexts like advertising. “We see a lot of sensitivity around use cases such as advertising and the way customers’ data is being handled and shared with third parties,” he says. “So, in these multiparty computation scenarios, or ‘data clean rooms,’ multiple parties can merge in their data sets, and no single party gets access to the combined data set. Only the code that is authorized will get access.”
The current state—and expected future—of confidential computing
Although large language models (LLMs) have captured attention in recent months, enterprises have found early success with a more scaled-down approach: small language models (SLMs), which are more efficient and less resource-intensive for many use cases. “We can see some targeted SLM models that can run in early confidential GPUs,” notes Bhatia.
This is just the start. Microsoft envisions a future that will support larger models and expanded AI scenarios—a progression that could see AI in the enterprise become less of a boardroom buzzword and more of an everyday reality driving business outcomes. “We’re starting with SLMs and adding in capabilities that allow larger models to run using multiple GPUs and multi-node communication. Over time, [the goal is eventually] for the largest models that the world might come up with could run in a confidential environment,” says Bhatia.
Bringing this to fruition will be a collaborative effort. Partnerships among major players like Microsoft and NVIDIA have already propelled significant advancements, and more are on the horizon. Organizations like the Confidential Computing Consortium will also be instrumental in advancing the underpinning technologies needed to make widespread and secure use of enterprise AI a reality.
“We’re seeing a lot of the critical pieces fall into place right now,” says Bhatia. “We don’t question today why something is HTTPS. That’s the world we’re moving toward [with confidential computing], but it’s not going to happen overnight. It’s certainly a journey, and one that NVIDIA and Microsoft are committed to.”
Microsoft Azure customers can start on this journey today with Azure confidential VMs with NVIDIA H100 GPUs. Learn more here.
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.
