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Encryption at rest protects stored data; encryption in transit protects data moving between endpoints; confidential computing is designed to protect data while it is actively processed. For AI inference, the distinction matters because a prompt can be encrypted on its way to a service and still be plaintext inside the environment that runs the model. Confidential inference adds a hardware-based, attested execution boundary intended to reduce the cloud host’s ability to inspect prompts, model weights, and intermediate data in memory. It complements TLS and storage encryption; it does not replace them.
What is the difference between encryption at rest, in transit, and in use?
The difference is the state of the data each control is meant to protect. A prompt or model can be stored, sent over a network, and processed in memory at different moments, so protecting one state does not automatically protect the others. Google Cloud describes the lifecycle succinctly: encryption at rest protects stored data, encryption in transit protects data moving between two points, and encryption in use protects data while it is processed.
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| Protection layer | Data state | Typical control | What remains exposed for inference |
|---|---|---|---|
| At rest | Stored weights, prompts, logs, checkpoints, databases, or disks | Disk, object, or database encryption and key management | Data must be made available for execution; once loaded for processing, storage encryption alone is not the protection for that plaintext. |
| In transit | Prompts, responses, model data, or key traffic moving among a client, gateway, and service | TLS, mutual TLS, or another authenticated encrypted channel | Plaintext is available at service endpoints and may be present in process memory during inference. |
| In use | Inputs, outputs, model weights, and intermediate state being processed | A hardware-based Trusted Execution Environment (TEE), memory encryption, isolation, and attestation | TEE implementation limits, side channels, endpoint compromise, metadata, and workload compatibility still require assessment. |
These are complementary protections, not competing choices. Disk encryption does not secure a network connection, and a secure network connection does not keep a service’s working data confidential from every component at the destination. Confidential computing addresses the processing stage, while the other layers cover storage and movement.
Why does encryption in transit not hide a prompt during inference?
TLS protects data as it travels between authenticated endpoints. The service endpoint must receive and handle the request to run inference, so the fact that traffic was encrypted on the way does not by itself establish that the host or software running that service cannot access the prompt.
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For example, a client may send a prompt through an encrypted connection to a model-serving endpoint. The connection protects the traffic in transit, but at the receiving service the application has to work with the request. If the request is logged, copied to telemetry, passed to components outside a protected execution boundary, or processed on a host an organization does not fully trust, TLS alone does not address those exposures. The same reasoning applies to model weights and responses: encrypted network transport is valuable, but it is not an in-use confidentiality guarantee.
Authentication and integrity also matter. An encrypted channel should help the client establish that it is communicating with the intended endpoint and that traffic has not been altered in transit. Those properties answer a different question from whether privileged cloud software can inspect plaintext after delivery. A design that needs both properties should preserve authenticated transport and separately assess the execution environment.
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Confidential computing aims to let code process data inside a hardware-based, attested TEE, using protections such as memory encryption and isolation. The Confidential Computing Consortium definition quoted in Microsoft Learn describes it as protecting data in use by performing computation in a hardware-based, attested Trusted Execution Environment.
The practical shift is that an organization need not rely solely on the cloud operator’s promise that privileged infrastructure will not inspect working data. Azure’s documentation says its confidential-computing services are designed to help prevent unauthorized access to data in use, including access by the cloud operator, by processing data in an attested TEE. Its product information describes memory encryption and processing only after the environment has been verified as trusted. These are claims about the documented service and configuration, not a guarantee that every cloud workload or TEE has identical properties.
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“Confidential” should not be read as “the entire system is private.” The client device, application code outside the TEE, model-serving stack, logging and telemetry, access policies, and traffic metadata remain part of the system and need their own controls. The protection boundary is only as useful as the components actually included in it and the way the workload uses them.
How does attestation affect keys and inference inputs?
Attestation provides evidence about a TEE so a policy can decide whether to trust it. In a suitable design, an attestation service or policy engine checks signed measurements describing the relevant hardware, firmware, virtual machine, or workload before allowing a key or sensitive input to be released.
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When evaluating a design, ask what exactly is measured, who verifies the evidence, which policy determines acceptance, and what happens when verification fails or the environment changes. The relevant evidence and policy should correspond to the workload being trusted; checking an environment without tying the result to key release does not itself prevent a key from being sent elsewhere.
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How do confidential VMs apply to AI inference?
A confidential VM is one way to run a workload inside a confidential-computing boundary. Google documents Confidential VM as keeping sensitive code and data encrypted in memory during processing and using it alongside encryption at rest and in transit. Microsoft also documents confidential VMs and other confidential-computing services based on attested TEEs.
For AI, the useful question is not simply whether a provider offers a confidential VM. It is whether the specific model-serving workload can run on the supported configuration while keeping the relevant data and execution inside the intended boundary. Hardware and accelerator support matter: Google’s cited AI documentation lists confidential-computing configurations including A3 with NVIDIA H100 and G4 with NVIDIA RTX PRO 6000. Configuration names and availability can change, so verify current support, regions, and service details with the provider before committing to an architecture.
Provider documentation should be read at the level of the named service, hardware, and configuration. The existence of one supported confidential GPU configuration does not establish that every accelerator, framework, model size, or deployment pattern is supported. Nor should a service description for one provider be generalized to all TEEs.
How should you compare confidential inference options?
Compare the complete workload and trust boundary, not just the label “confidential.” These questions help expose differences that can affect security, feasibility, and operations:
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- Data state: Which artifacts need protection while stored, sent, and processed—prompts, responses, model weights, intermediate state, logs, or keys?
- Trusted boundary: Which disks, network endpoints, host components, and execution components are inside the protection design? Which remain outside it?
- Operator and host resistance: What does the documented configuration protect against, including access by privileged cloud software, and what is outside its stated scope?
- Attestation and key release: Can a client or key policy verify measurements before sensitive inputs or decryption keys are released? What is checked and what happens on a failed check?
- AI compatibility: Are the required CPU or GPU, model size, framework, batching behavior, and network design supported on the chosen configuration?
- Performance and cost: Does the exact model meet latency, throughput, and budget requirements on the actual target hardware?
- Operations: How will monitoring, patching, incident response, logging, and recovery work without undermining the confidentiality goal?
There is no universal performance penalty figure established for confidential AI inference. The impact depends on the exact workload and hardware, so benchmark the model and serving configuration you intend to use rather than applying a generic percentage. Likewise, provider pricing and regional availability are configuration-specific and should be checked for the deployment under consideration.
What should a practical confidential-inference design include?
A practical design layers controls across the data lifecycle and makes verification part of the data path. The following sequence is a useful planning framework; exact provider interfaces and deployment steps vary.
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- Classify the data. Identify which prompts, responses, weights, checkpoints, logs, and intermediate artifacts are sensitive, and where each is stored, transmitted, and processed.
- Choose the execution boundary. Select a documented TEE or confidential VM configuration that supports the intended AI workload and hardware. Confirm the needed regions and software compatibility directly with the provider.
- Define the attestation policy. Specify which measurements and configuration properties make an environment acceptable. Decide which service verifies evidence and how the workload responds if verification fails.
- Gate sensitive material. Configure key release or input delivery so it depends on successful verification of the approved environment rather than solely on a provider endpoint name or administrative assertion.
- Retain transport and storage controls. Use authenticated encrypted channels for traffic and encryption with appropriate key management for stored artifacts. Treat these as protection for separate lifecycle states, not alternatives to the TEE.
- Map data outside the TEE. Review application logic, logging, telemetry, access policies, and other components that touch requests or outputs. Minimize what leaves the protected boundary and control what is retained.
- Test the deployed workload. Measure performance, verify failure behavior, and validate that the deployed model-serving configuration matches the attested policy and the provider documentation.
- Plan operations and recovery. Establish how updates, monitoring, incident response, and restoration will preserve the intended boundary and how keys will be handled when a workload or environment must be replaced.
What are the limitations and common mistakes?
Confidential computing can reduce reliance on the cloud host for protecting data during execution, but it does not remove the need to reason about the rest of the system. Common mistakes include assuming that a TEE automatically protects every application component, treating attestation as an optional badge, or selecting a configuration based on its name without confirming workload support.
- Stopping at TLS: A prompt can be protected on the network and still be available to software at the service endpoint. Keep in-use protection in scope when the host is part of the threat model.
- Stopping at disk encryption: Stored model files or logs are not protected by their storage control once they are being used by an unprotected process.
- Assuming all TEEs are equivalent: Hardware, measured components, provider guarantees, accelerator support, regions, and APIs differ. Evaluate the particular service and configuration.
- Ignoring surrounding software: Application logic outside the TEE, logs, telemetry, endpoint security, access policy, and traffic metadata may still expose information.
- Skipping workload validation: Compatibility and performance depend on the model, hardware, framework, and serving pattern. Validate the exact workload and do not infer a universal overhead.
- Confusing attestation with confidentiality by itself: Evidence only becomes an enforceable gate when a policy uses it to control access to keys or sensitive inputs.
Frequently Asked Questions
Can confidential inference protect a prompt from the AI provider?
It is designed to reduce the provider host’s ability to inspect data being processed when the workload is inside an appropriate attested TEE. It does not establish that every application component, endpoint, or provider service is unable to access the prompt.
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Does confidential computing protect model outputs too?
Outputs can be among the data handled within the protected computation, but confidentiality after they leave that boundary depends on how they are transmitted, displayed, logged, and stored. Treat the response path as a separate part of the design.
Can attestation prove that a model is safe or accurate?
Attestation is evidence about measured hardware, firmware, VM, or workload properties used to decide whether an environment matches policy. It is not a general assessment of model quality, correctness, or safety.
Should I use a confidential VM for every AI workload?
Not necessarily. The decision depends on whether the host is in your threat model, whether the needed model and hardware are supported, and whether the workload meets its operating and cost requirements on that configuration.
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