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Confidential computing uses hardware-backed, attested trusted execution environments (TEEs) to reduce exposure of sensitive data while it is being processed. It addresses a gap left by encryption at rest and in transit—but it does not make a workload invulnerable or remove the need to trust its hardware, software, configuration, and operators.
What confidential computing protects
Data is commonly described in three states: stored on a device or server, moving across a network, and actively being processed. Encryption at rest and in transit protects the first two states. During computation, however, data generally has to be available to the software that processes it. That can expose plaintext to parts of the underlying system, including privileged software or administrators.
The Confidential Computing Consortium (CCC) defines the field as “the protection of data in use by performing computation in a hardware-based, attested Trusted Execution Environment.” NIST describes related hardware-enabled features as isolating and processing encrypted data in memory so it is less exposed to concurrent workloads or the underlying system and platform. In plain terms, a TEE aims to create a protected execution boundary around data and code while a workload runs.
That boundary is intended to support three assurances: confidentiality of data, integrity of data, and integrity of code. Confidentiality limits who can inspect information inside the environment; integrity aims to prevent unauthorized changes to protected data or the code processing it. These are design goals whose strength depends on the particular implementation and configuration.
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How the trust boundary changes
In conventional cloud computing, customers rely on the provider’s infrastructure and privileged software to handle workloads securely. A hardware-backed TEE can reduce the extent to which customers must trust the host operating system, hypervisor, administrators, or other tenants with plaintext during execution. The scope of that reduction varies by TEE and setup; it does not mean the provider has no role in the system or that every workload behavior is hidden.
Microsoft says that, when Azure confidential computing is properly configured, Microsoft cannot access unencrypted customer data in use. This is Microsoft’s description of its service and threat model, not a universal guarantee about all cloud providers, workloads, or confidential-computing technologies.
Confidential computing is not limited to public cloud or a single processor family. The CCC describes potential uses on on-premises servers, gateways, IoT and edge devices, and user devices, as well as public-cloud servers. Trusted processing can also involve components such as GPUs or network interface cards. The available protections and deployment choices depend on the specific platform.
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Attestation: checking what is running
Attestation provides evidence about a TEE’s identity, origin, or state, including information about its software. A relying party—such as a service that holds a secret—can evaluate that evidence against its own policy before releasing a key or accepting a result. The point is to make trust decisions based on evidence about a particular environment rather than simply assuming that any machine claiming to use a TEE is acceptable.
Attestation is a trust input, not a blanket certification that an application is safe. A policy that accepts the wrong measurements, a faulty workload delivery process, or an overly permissive key-release rule can undermine the protection. The party provisioning secrets still has to decide what evidence is acceptable and how to respond when the environment changes.
Where confidential computing can help
Sensitive workloads on shared infrastructure
Organizations may want to use cloud infrastructure without giving the host operator ordinary access to workload plaintext in memory. A TEE can narrow that exposure, potentially making some sensitive workloads more practical to run on shared infrastructure. It is a risk-reduction measure, not a substitute for deciding whether the provider, hardware, service configuration, and operational model fit the workload’s threat model.
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Keys and machine identities
Keys and machine identities need protection not only while stored, but also when software uses them. NIST’s hardware-enabled security work identifies protecting such secrets while in use as a motivation for confidential computing. The surrounding architecture still matters: key custody, identity, attestation policy, and secret provisioning must work together.
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NIST IR 8320E, Hardware-Enabled Security: Confidential Computing of Data in Cloud Workloads, describes a draft approach for protecting datasets acted on by AI workloads in cloud infrastructure. NIST listed it as an initial public draft dated May 29, 2026, and said the comment period ended July 13, 2026. It is an example of the technology’s relevance to AI, not evidence that every AI pipeline can be protected end to end. Data may enter or leave protected execution, and model, application, and output handling remain part of the security design.
Collaborative analysis
A TEE can provide a place to process sensitive data under a narrower trust boundary, which may help organizations analyze information without exposing it directly to the infrastructure operator. It does not settle what participants are authorized to do with the data or what results may be disclosed. Application design, governance, access policies, and output controls determine those boundaries.
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Payment processing: one vendor-reported example
Intel’s February 2024 solution brief describes a Microsoft payment-processing system using Azure confidential computing and Intel SGX enclaves to protect key operations. Intel’s brief says Microsoft moved $25 billion in annual credit-card transaction volume to Azure confidential computing and reports $2 million in hardware-security savings after moving from on-premises infrastructure. These are vendor-published case-study claims, not independently audited industry statistics or a forecast of savings for other deployments.
Enclaves and confidential virtual machines
Two common deployment patterns are application enclaves and confidential virtual machines (VMs). They create different isolation boundaries and should not be treated as interchangeable. The best fit depends on the application, platform, threat model, and operational requirements.
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|---|---|---|
| Isolation boundary | A selected application component and its protected data. Intel’s SGX payment case describes enclaves for selected code and data. | A VM or trust domain. AMD documents SEV confidential VMs; Azure documents VM offerings using AMD SEV-SNP and Intel TDX. |
| Workload compatibility | Depends on how much of the application is placed in the enclave, required code changes, and the platform’s supported features. | Depends on the supported VM, operating system, devices, and cloud configuration. |
| Attestation and key release | Policy should verify evidence for the enclave and its software before releasing secrets. | Policy should verify evidence for the VM and its software before releasing secrets. |
| Performance and scaling | Workload-specific; the CCC says TEE characteristics vary, and comparable independent benchmarks are not established here. | Workload-specific; the CCC says TEE characteristics vary, and comparable independent benchmarks are not established here. |
| Cost comparison | A neutral comparative cost is not stated in the cited sources. | A neutral comparative cost is not stated in the cited sources. |
Cloud availability is specific to supported instances and current service configuration. Microsoft Azure documents AMD SEV-SNP- and Intel TDX-based confidential VMs, with differences in configuration and attestation paths. AMD lists providers offering SEV-based confidential VMs, including AWS, Google Cloud, IBM, Microsoft Azure, and Oracle Cloud Infrastructure; exact product support and availability vary. Check the current provider documentation for the region, instance, and configuration you intend to use.
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What confidential computing does not protect by itself
The CCC’s technical analysis emphasizes that no system offers absolute security and that TEE protections depend on implementation and threat assumptions. Confidential computing can raise the bar for particular attacks, but it does not eliminate other ways to compromise a workload.
- Side channels: Timing, cache behavior, power use, and other observable signals may reveal information even when an attacker cannot directly read protected memory. Mitigations can require work from hardware providers, runtime and library vendors, and application developers.
- Faulty attestation or provisioning: A TEE cannot help if the verifier accepts unsuitable evidence, the workload is delivered incorrectly, or a key is released under an unsafe policy. Protocol and workload- or data-provisioning attacks remain relevant.
- Implementation differences and bugs: Protections such as rollback, replay, and integrity controls vary among implementations. Security claims need to be specific to the hardware and configuration in use.
- Threats outside the stated model: The CCC analysis identifies sophisticated invasive physical attacks, upstream hardware supply-chain attacks, and denial of service as generally outside current TEE threat models. A TEE does not ensure that a service will remain available.
- Application flaws and misuse: Memory isolation does not fix authorization bugs, insecure application logic, unsafe outputs, or inappropriate use of data. Sensitive code still needs secure design and, where relevant, side-channel-aware implementation.
Confidential computing also does not replace encryption at rest or in transit, key-management controls, identity systems, secure boot, patching, logging, or governance. Those controls protect different parts of the system and remain necessary around the TEE.
How to decide whether it fits
Start with the exposure you are trying to reduce. If the concern is that a host operator or privileged host software could inspect plaintext during processing, confidential computing may address a meaningful part of that risk. If the main risk is a vulnerable application, unauthorized user, unsafe output, or service outage, a TEE alone is not the answer.
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- Define the threat model. Identify who might access the workload, what data must remain confidential, and which attacks are in or out of scope—including side channels, physical access, supply chain, and denial of service.
- Choose the isolation boundary. Determine whether a selected code path belongs in an enclave or whether a VM-level trust domain better matches the workload. Check compatibility, required code changes, operating-system support, and device needs.
- Set attestation and secret-release policy. Specify which evidence and software measurements are acceptable, who verifies them, and under what conditions keys or other secrets may be provisioned.
- Plan for operations. Account for patching, measurement changes, incident response, key custody, logging, and governance. A deployment also needs a way to handle a workload that fails policy checks or whose attestation changes.
- Evaluate workload-specific costs and performance. Test the actual application and configuration. The cited sources do not establish a neutral, comparable performance benchmark or cost comparison across TEE types.
For organizations deploying their own infrastructure, AMD EPYC server processors support AMD SEV on eligible server CPU generations; confirm support for the exact processor and platform. For managed services, compare providers and configurations rather than assuming confidential VM offerings are interchangeable.
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