Your AI Knows Too Much: Confidential AI Is No Longer Optional

Generative models are transitioning from research labs to regular business processes — and a silent, structural weakness is growing right alongside them.

Take this one example: Microsoft Copilot accessed almost three million sensitive records per organization in the first half of 2025. Such numbers mean there’s a systemic issue with how AI is approached. Companies have built powerful engines for processing data, but they haven’t put in place enough protections for the data they use.

Large language models and assistant tools often compute documents, messages, and databases without any hardware-based protections. This gap, which reveals “data in use” during inference, now directly threatens businesses that prioritize privacy, compliance, and customer trust.

It’s here that confidential computing comes in. The process uses hardware-assisted trusted execution environments (TEEs) to keep code and data safe while they are being used. These TEEs are isolated memory areas that are protected from the host operating system and hypervisor.

That protection changes the security model, allowing secrets to be processed without being exposed to the rest of the stack. Microsoft and other cloud providers now offer private inference and virtual machines (VMs) that integrate inference within TEEs.

Why privacy is important for rules and governance

Conventional encryption usually safeguards data that is either at rest or in transit, with data that is being actively processed not enjoying the same protection, even though that is where generative AI interacts the most with private inputs.

Earlier in the year, a study by Cornell University researchers on Trusted Compute Units revealed how hardware-backed isolation and attestation can help reduce the need for trust between parties. Furthermore, last year, analysts estimated that the value of private computing infrastructure was worth upwards of $13 billion, with forecasts indicating even more rapid growth to $350.04 billion by 2032.

Simply put, more people are using the technology, leading to regulators and compliance teams showing more interest in AI systems than ever. As such, a framework that ensures sensitive data never leaves protected execution during model queries will make audit trails much easier to follow, reducing the risk of legal problems.

However, if runtime protections don’t become the norm, that difference could make it take longer for regulators to accept AI in fields that are tightly governed.

Real-life situations where privacy is a must

There are several enterprise scenarios that show why confidential AI is not a luxury but something that every organization must have.

For example, in finance, this could apply to institutions that need to run fraud detection and risk models against customer transaction histories without giving external model operators access to raw records.

In healthcare, there could be situations where diagnostic models must operate on protected clinical data while preserving patient privacy and meeting strict regulatory obligations. Furthermore, governments and defense agencies may require attested execution to run sensitive workloads on third-party clouds.

There are also research pipelines that combine datasets from several sources, notably federated health research that can only proceed if each participant is confident that their inputs will remain unreadable during computation.

There’s also academic and industry research that continues to document privacy-preserving ML approaches for medical workflows that pair well with confidential execution.

How confidential execution actually protects data-in-use

Confidential execution depends on two connected systems: hardware isolation and attestation. Hardware isolation prevents software outside the secure enclave from reading enclave memory.

Attestation is a verifiable way to check the code running inside the enclave and that the enclave itself is genuine. These features let a model owner show that a model will work with inputs in a verified, sealed environment and that outputs will only come out after the enclave’s rules are followed.

The result is a contract between data owners, model operators, and cloud providers that makes it less likely for them to need adversarial legal workarounds or resort to brittle policy gymnastics.

Realistic limits and obstacles

Of course, adopting confidential AI comes with its own challenges. Attestation and hardware that can work in enclaves make deployment more complicated, given that there is a gap in the ecosystem between traditional model-serving tooling and enclave-aware runtimes. Some enclave runtimes impose heavy overheads, costing up to ten times more to run, and there could also be trade-offs in performance depending on the amount of work.

Furthermore, costs are higher now than they were for plain cloud inference, which could make it challenging for small businesses to make sense of the numbers. There is also the issue of changing interoperability standards, which may leave some early adopters at risk of being stuck with their vendors.

However, these issues are merely transient engineering problems that are amenable to resolution. Cloud providers and chipmakers keep coming out with new products, and software projects are making middleware that links up current ML stacks and TEEs.

If anything, companies that think standard encryption and access controls are enough are at greater risk because they will be vulnerable when models process a lot of sensitive records.

A call for enterprise practice changes

Risk management teams need to change the rules for how to buy and use AI. Data classification and governance are still important, but they need to be backed up by technical guarantees when they are in use.

Contracts with third-party model providers should require them to show proof of attestation. Procurement teams should ask for performance baselines and audited options for confidential execution. Red-team exercises should include enclave-aware testing as part of their work. These steps shift responsibility from ad hoc commitments to controls that can be checked.

Also, public policy and industry standards groups need to set expectations. Auditors, compliance officers, and regulators should demand verifiable runtime safeguards for categories of workloads that process regulated data.

These rules will lower the number of breaches that happen later on and give the sector the freedom to innovate without worrying about legal problems. This will let finance, healthcare, and public-sector workflows use new models without any regulatory hurdles.

Confidentiality as the standard

Generative AI has become a useful tool for businesses that deal with very private information. That reality means that confidential execution is no longer just a niche option; it must be standard practice for any AI system that handles regulated, proprietary, or personal data.

There are already tools available, like TEEs, attestation services, and private VM offerings, and the economics are getting better very quickly. The other option is to keep sensitive assets open to inference-time leakage, which could have legal, financial, and reputational effects that businesses can no longer afford. For instance, according to IBM’s 2025 Cost of Data Breach report, the average global breach cost reached almost $5 million, with regulations such as the GDPR imposing fines as high as €20 million for serious breaches.

Companies that put privacy first in AI governance will have an advantage: they can run more thorough, compliant tests and use models in areas where outside innovation was once off-limits. In short, protecting data while models run is not a barrier to adoption; it is the basis for responsible, scalable AI in business and government.