Picture this: You're running AI models on patient records, financial data, or proprietary algorithms in the cloud. Everything's encrypted, right? Well—until it’s not. The moment that data starts being processed, it’s suddenly exposed. That’s the gap we hear about in nearly every security conversation: "What happens when sensitive information is in use?"
Confidential computing is how cloud providers are finally closing that gap—creating secure enclaves that protect data even while it’s being crunched, queried, or analyzed. And clearly, it’s not just a nice-to-have anymore. As of 2025, the confidential computing market is growing at breakneck speed. It jumped from $7.15 billion in 2023 to $13.33 billion in 2024—and is on track to hit a whopping $350 billion by 2032. This blog post breaks down how cloud providers are using confidential computing to protect sensitive information during processing.
What is confidential computing?
Confidential computing flips the usual script on data protection. Instead of only encrypting data at rest or in transit, it keeps data secure while it’s actively being used — that critical moment when it’s most vulnerable. It does this using secure enclaves, also known as Trusted Execution Environments (TEEs), which isolate sensitive workloads so that not even cloud providers can peek inside.
Let’s say you’re running AI models on personal data. Normally, the data has to be decrypted to be processed, which opens a window for exposure. Confidential computing ensures that the data is protected from the time it enters the system until it exits. For those who are serious about cloud security, it's a significant advancement.
How cloud providers integrate confidential computing
Cloud service providers such as Amazon Web Services (AWS), Google Cloud, and Microsoft Azure are at the forefront of incorporating these technologies into their offerings. Let's examine several instances.
Microsoft Azure’s confidential computing
Azure has been pushing the boundaries with Azure Confidential VMs.These virtual machines are specifically designed to protect data during processing by utilizing hardware-based TEEs such as Intel® Software Guard Extensions (SGX) and AMD Secure Encrypted Virtualization (SEV). They ensure that sensitive data cannot be accessed by unauthorized parties by limiting the use of isolated environments to only authorized code.
The interesting part is that NVIDIA and Microsoft have teamed up to offer GPUs in secure enclaves. This creates new opportunities, particularly for AI applications that require a lot of computing power. Whether you're training a new machine learning model or conducting extensive inference activities, Azure is making sure your data is always protected.
Google Cloud’s confidential computing
Additionally, Google Cloud is advancing its Confidential Virtual Machines (VMs), which are fueled by AMD Secure Encrypted Virtualization (SEV) technology. Google has proven that it can give users total control over encryption keys, often managed through Cloud External Key Manager (EKM), while protecting data in the secure enclave.
Users from industries like healthcare and finance especially appreciate this functionality since it allows them to manage their encryption keys without worrying about exposure during computation.
AWS Nitro Enclaves
When talking about AWS, Nitro Enclaves must be brought up. This solution isolates sensitive data, even in multi-tenant scenarios and containerized environments, which is an essential feature for cloud-native applications. Clients can use Nitro to handle the most sensitive tasks, such as intellectual property and financial transactions, in a secure environment without requiring a network connection or permanent storage. It is easy to integrate AWS's scalable and flexible solution into existing cloud infrastructure.
Why confidential computing matters for AI and other workloads
AI-related workloads are growing, which raises a whole new range of privacy and data security concerns. Take a financial organization, for instance, that wants to employ AI to identify fraud. Private transaction data must be analyzed, but they cannot risk disclosing it, not even to their cloud provider. That data might be processed within secure enclaves using secret computing, guaranteeing its protection at all times. The result? They can utilize AI models with confidence and not be concerned about security breaches or unauthorized access.
Recent advancements: The technology is evolving fast
What’s exciting about confidential computing today is how fast it’s evolving. These secure enclaves are becoming even more potent and accessible due to recent advancements. Cloud providers are changing the game for AI and ML applications by incorporating support for GPU workloads within secure enclaves.
Here's an example: The NVIDIA H100 Tensor Core GPUs have enabled Google Cloud and Microsoft Cloud to provide highly scalable, high-performance environments in the secure enclaves. This is huge, especially for businesses doing compute-intensive operations like building large AI models.
We're also witnessing advancements in the capacity of remote attestation. With the use of this technology, the code operating within the enclave is confirmed to be unaltered. Because it ensures that only the intended task is carried out within a secure environment and that no malicious code can penetrate, it is especially important when managing sensitive data.
Real-world applications of confidential computing
Confidential computing is more than a buzzword; it is revolutionizing industries. Here are some real-world use cases:
Healthcare: Analyzing private patient data without ever sharing it with unauthorized personnel.
Financial services: Using secure enclaves to protect real-time transaction data while running fraud detection models.
AI training: Making it possible for multiple organizations to work together on model training without disclosing private information.
In all these cases, the data remains encrypted during the computation process, providing organizations full confidence that sensitive information stays protected.
Challenges and considerations
Of course, there are challenges, just like with any new technology.
- One typical concern is the performance overhead associated with operating workloads in secure enclaves. Even while these overheads are gradually decreasing as technology advances, some applications may still need to account for them, particularly when efficiency and speed are crucial.
- Another challenge we’ve faced is the compatibility of legacy systems with confidential computing infrastructure. Integrating confidential computing into existing architectures often requires custom development, and that’s something teams need to be prepared for.
What's next for confidential computing?
Looking ahead, it's exciting to see wider adoption of confidential computing in cloud-native environments. With AI and machine learning becoming integral parts of business operations, the necessity to protect data-in-use will only grow.
As cloud providers keep incorporating technologies related to private computing, we could see even more creative applications and secure-by-default environments across industries. Also, innovations like confidential containers and secure orchestration tools such as Kubernetes Confidential Pods are pushing this tech into mainstream adoption.
Ready or not, confidential computing is here to stay
Confidential computing is eventually paving the way for a more secure cloud future. It is no longer optional to ensure that your data is protected during computation, whether you work in AI, finance, education, healthcare, or any other data-sensitive sector.
It's time to begin exploring confidential computing if you aren't already. As cloud providers continue to improve, we can expect this technology to become an essential component of any business’s security strategy.
