Confidential computing uses a hardware-based, attested Trusted Execution Environment (TEE) 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 secure its software, keys, and operations.
What confidential computing protects
Data has three broad states: it can be stored, moving over a network, or actively being processed. Encryption at rest and in transit protects the first two states. During computation, however, software generally needs access to usable data. A TEE creates a hardware-backed boundary intended to limit exposure in that third state.
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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’s glossary describes hardware-enabled features that isolate and process encrypted data in memory, reducing its exposure to concurrent workloads and the underlying system or platform.
A TEE aims to protect confidentiality, integrity, and code integrity: it seeks to limit who can inspect data during execution, detect unauthorized changes to protected data, and help establish that the expected code is running. These are bounded assurances tied to a particular implementation and configuration, not a promise that every part of an application is safe.
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How a TEE changes the trust boundary
In a conventional cloud deployment, customers rely on the cloud infrastructure and privileged software to handle workloads securely. A hardware-backed TEE is designed to reduce how much the customer must trust the host operating system, hypervisor, administrators, or other tenants with plaintext during execution. What is isolated, and from which threats, depends on the technology and its configuration.
Attestation provides evidence about a TEE’s identity, origin, or state, including relevant software measurements. A relying party can check that evidence against its own policy before releasing secrets or accepting a result. Attestation is therefore an input to a trust decision—not a blanket certification that the application is secure or behaves appropriately.
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For a practical deployment, the trust decision typically has this shape:
- Establish the workload. The software and its configuration must be delivered through a process the organization trusts.
- Verify the evidence. A verifier checks the attestation against an explicit policy, including which hardware and software measurements are acceptable.
- Release secrets conditionally. Keys or other sensitive inputs should be provisioned only if the evidence passes the policy checks.
- Control outputs and operations. The application still needs appropriate authorization, output handling, monitoring, patching, and incident response.
Microsoft describes Azure confidential computing as a way to protect data in use from access by the infrastructure operator. Microsoft says that, when Azure confidential computing is properly configured, it cannot access unencrypted customer data in use. That is Microsoft’s description of its service and configuration—not a universal guarantee for every cloud provider, workload, or TEE.
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Where confidential computing can be useful
- Sensitive cloud workloads: Organizations can use shared infrastructure while reducing the need to trust its host operator with data in memory.
- Secrets and machine identities: NIST’s hardware-enabled security work identifies protection of keys and machine identities while in use as a motivation.
- AI workloads: NIST IR 8320E, an initial public draft dated May 29, 2026, describes an approach for protecting datasets used by AI workloads in cloud infrastructure. Its comment period ended July 13, 2026. It is an example of relevance to AI, not evidence that every stage of an AI pipeline can be protected end to end.
- Collaborative analysis: A TEE can provide a more constrained place for organizations to process sensitive data without exposing it to the infrastructure operator. Application design, governance, access rules, and output controls still determine what is ultimately disclosed.
Confidential computing is not limited to public cloud servers. The CCC’s technical analysis describes potential use on on-premises servers, gateways, IoT devices, edge systems, and user devices, as well as in components such as GPUs and network interface cards.
Enclaves and confidential virtual machines are different approaches
Two common patterns illustrate why “confidential computing” is not a single interchangeable product feature. An application enclave protects selected code and data; a confidential virtual machine (VM) protects a broader VM trust domain. The right boundary depends on the workload, required compatibility, and threat model.
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| Approach | What is isolated | Workload implications | Examples in the cited sources |
|---|---|---|---|
| Application enclave | A selected application component and its data. | Teams may need to place sensitive operations inside the enclave and adapt code or its surrounding software. Compatibility depends on the implementation. | Intel SGX enclaves, described in Intel’s Microsoft payment-processing case study. |
| Confidential VM | A VM or defined VM trust domain, rather than only a selected application component. | Can suit workloads intended to run as a VM, but supported operating systems, instances, configuration, and attestation paths vary by provider and hardware. | AMD SEV-based VMs, including SEV-SNP; Azure also documents confidential VMs using Intel TDX. |
AMD lists cloud providers offering SEV-based confidential VMs, including AWS, Google Cloud, IBM, Microsoft Azure, and Oracle Cloud Infrastructure. Availability and exact product support vary. Azure documentation likewise describes configuration and attestation differences between supported offerings, so confirm the current instance, region, and service requirements before deployment.
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Neither pattern is automatically superior. Compare the isolation boundary, code and operating-system changes, supported devices, who verifies attestation, which measurements are checked, how keys are released, and how the design addresses side channels and operational risks. Performance, memory constraints, scale, and cost are workload- and service-specific; the cited material does not establish a neutral, current benchmark or cost comparison.
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What the payment-processing example shows—and does not show
Intel’s February 2024 solution brief describes Microsoft moving payment processing to Azure confidential computing using Intel SGX enclaves to protect key operations. Intel reports that the deployment handles $25 billion in credit-card transactions per year and that Microsoft saved $2 million in hardware-security costs after moving from on-premises infrastructure. These are vendor-published case-study claims, not independently audited industry statistics or a forecast of what another organization would save.
What confidential computing does not solve
The CCC’s technical analysis cautions that no TEE provides absolute security. Protection depends on the implementation, configuration, and threats it is designed to address.
- Side channels: Timing, cache activity, power use, and other observable behavior may leak information even when an attacker cannot directly read protected memory. Mitigations may require work from hardware, runtime and library providers, and application developers.
- Faulty attestation or provisioning: Isolation is not enough if the verifier accepts the wrong measurements, workload delivery is compromised, or a key-release policy is misconfigured.
- Implementation differences and bugs: Protections against behaviors such as rollback, replay, and integrity attacks vary across technologies and configurations.
- Out-of-scope threats: The CCC analysis generally places sophisticated invasive physical attacks, upstream hardware supply-chain attacks, and denial of service outside current TEE threat models.
- Application flaws and misuse: A TEE does not fix authorization bugs, unsafe outputs, insecure application logic, or poor data governance.
Confidential computing belongs within a broader security architecture. Encryption at rest and in transit, sound identity and key-management controls, secure boot, patching, logging, and governance remain important. A TEE narrows one part of the trust boundary; it does not eliminate trust or automatically establish regulatory compliance.
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Start with the data and threat you are trying to protect, then test whether the proposed TEE meaningfully reduces that exposure without creating unmanageable operational complexity.
Quick Recap
- Name the adversary and protected state. Specify whether the concern is a host administrator, hypervisor, other tenant, or another threat—and whether the sensitive data is exposed during execution.
- Choose the boundary deliberately. Decide whether selected code needs an enclave or whether a VM-level boundary better fits the workload.
- Make attestation policy concrete. Identify who verifies evidence, what measurements and versions are acceptable, and what happens when verification fails.
- Design key release and recovery. Decide where secrets are held, what conditions permit their release, and how rotation, revocation, and incidents will be handled.
- Test the whole application. Review side-channel exposure, permissions, inputs and outputs, logging, update procedures, and failure behavior—not just whether the TEE launches.
- Validate deployment-specific constraints. Confirm supported hardware, region, operating system, devices, provider configuration, and pricing for the exact service being considered.
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