Securing the future of gen AI with confidential computing

As generative AI becomes more pervasive in the enterprise, executives are increasingly focused on securing data and maintaining data privacy. With generative AI foundation models growing in sophistication and processing sensitive information, protecting this data from unauthorized access, tampering, and theft has become essential to ensuring trust and operational resilience.

Confidential computing shows great promise in this area. It has emerged as a critical technology, enabling organizations to safeguard sensitive data during processing. And the implications for generative AI are profound. We're at a stage in which the immense promise of generative AI to transform organizations is being recognized. But many organizations have yet to fully realize its transformative capabilities. That's because they still favor safe bets over strategic innovation due to concerns about new potential risks involved with generative AI, which they may not feel fully prepared for.

Confidential computing can help move the needle. It provides a secure foundation for scaling AI initiatives, ensuring that data remains protected throughout the entire lifecycle. By understanding the mechanisms and advantages of confidential computing, organizations can better position themselves to balance innovation with security—ultimately building a secure and successful future for AI.

What is confidential computing?

Confidential computing is a cloud technology that encrypts data in use—also known as enclaving—so that it's only accessible to authorized code. This is a game-changer because it means that data can be processed in the cloud without exposing it to the cloud provider, or anyone who might compromise the cloud provider.

In most business applications, data is already protected when it's stored and moved, but it's not protected during processing. This can create a vulnerability. Confidential computing protects data at this sensitive stage through the use of Trusted Execution Environments (TEEs), which establish a secure enclave within the CPU for processing data. They operate by encrypting data before it enters the TEE and only decrypting it within this secure space. Confidential computing also uses attestations to validate the integrity and security of these environments, assuring that data processing is indeed taking place in a trusted manner.

To imagine what this looks like in practice, consider a major financial institution that uses vast amounts of data to deliver personalized services, forecast trends and optimize operations. For this institution, data is a valuable asset and protecting it is of critical concern, especially in the era of sophisticated cyberattacks. Confidential computing acts like an impenetrable vault inside the organization. It safeguards data assets even during processing with robust and reliable security measures.

How confidential computing protects gen AI

Generative AI workloads, whether inferencing (using an existing model) or training new models, inherently involve processing sensitive data. This can create significant risks related to things like prompt injection, adversary attacks and IP exposure. Businesses that outsource AI processing to third parties can also be exposed. Untrusted environments can compromise data integrity, leading to breaches and intellectual property theft.

Confidential Computing mitigates these risks by ensuring that data is encrypted not only in transit and at rest, but also in memory. This comprehensive encryption protects both the prompts used to generate AI outputs and the data itself, significantly enhancing overall security.

There are several mechanisms confidential computing employs to secure generative AI:

These comprehensive security measures assure companies that their data and intellectual property remain safeguarded. This, in turn, builds trust with stakeholders and positions the organization for long-term success.

3 ways confidential computing enables next-level gen AI value

Confidential computing plays a critical role in creating a secure foundation for companies to confidentially scale their AI initiatives. Organizations that have waited to pursue higher-stake, higher-value gen AI initiatives can shake off their hesitation, assured that their data and intellectual property are properly safeguarded. Let's look at the three main ways that confidential computing powers gen AI initiatives:

1. Maintaining privacy in data sharing

Generative AI thrives on data, and multi-party data sharing is essential for innovating across industries. Confidential computing can enable organizations to collaborate and derive valuable insights from shared data, without compromising privacy or regulatory compliance. This is especially promising as data sovereignty regulations become more stringent. Confidential computing gives companies the control they need, keeping data within their jurisdiction and invisible to cloud providers.

Secure data sharing can generate incredible value and we're already seeing the value in certain industries. Like telecommunications, where companies can securely share threat intelligence to create gen A cybersecurity solutions. Or in retail, where companies can pool customer behavior data to improve personalization and customer service, while respecting privacy regulations and protecting each other’s private data.

2. Protecting intellectual property

As AI models grow in complexity and value, the threat of IP theft looms larger. Confidential computing meets this challenge head-on by shielding sensitive data and models, particularly in cloud environments or when shared with external partners. This allows companies to pursue monetization opportunities by securely deploying their AI models at client sites or in shared cloud environments. Industrial equipment manufacturers, for example, can safely license their AI models while protecting the underlying algorithms and trade secrets. Confidential computing also helps companies without the necessary on-prem capabilities to securely train their models in the cloud, using vast datasets and powerful infrastructure.

3. Secure inferencing

Real-time data processing and inferencing are essential for many generative AI applications but can expose sensitive data to risks. Confidential computing makes this possible by protecting data in the moment using technologies like secure enclaves and encrypted virtualization. This helps ensure that data remains protected even during the vulnerable phase computation phase. This level of protection is particularly vital in industries like healthcare and finance, where data privacy is non-negotiable.

By using confidential computing, organizations can easily deploy new AI models in these sensitive settings while ensuring that their data and models are safe. This not only makes AI systems more trustworthy, but also lets businesses grow their AI projects safely, helping them innovate and compete.

Looking forward: the benefits of being proactive

For companies using generative AI, the need for proactive measures is even more pressing. Generative AI security is a complex challenge, given the vast amounts of data these models handle and the potential for misuse. Confidential computing not only mitigates risks associated with data breaches but also opens new avenues for strategic advancement.

It enables organizations to stay ahead of industry standards, positioning them as leaders in data security and privacy. This proactive approach can also attract partners and investors who value strong security postures, further differentiating the organization.

In short, confidential computing is a game-changer. With it, organizations can mitigate risks, stimulate growth and lay the groundwork for a secure, future-ready AI ecosystem.