Define trusted execution boundaries for AI workloads
Distinguish confidential computing from workload isolation, and define how Polyaxon scheduling and evidence fit a trusted execution design.
“Trusted execution” can describe several different requirements. A team may want to stop generated code from reaching neighboring workloads, protect sensitive data from infrastructure operators, or release a secret only to an approved application.
Those requirements need different controls. Polyaxon can organize workloads and record their execution context, but a sandbox-enabled service is not automatically a hardware-backed trusted execution environment.
Name the threat before choosing the boundary
A trusted execution environment, or TEE, protects code and data within a hardware-supported boundary. Confidential-computing designs can protect data in use from software outside that boundary; the exact guarantees depend on the technology and deployment. See Microsoft's TEE overview.
That is distinct from limiting what an untrusted program may access. Code legitimately admitted to a protected environment can still misuse the data available to it. Encrypting memory does not make a generated analysis program trustworthy.
Write down the protected asset and adversary. “Keep tenant code away from other tenants” is a workload-isolation requirement. “Do not release a decryption key to an unapproved runtime” adds an attestation and key-management requirement.
Assign each control to an owner
| Requirement | Responsible layer |
|---|---|
| Authorize who submits or inspects a run | Polyaxon access configuration |
| Route workloads to approved compute | Polyaxon scheduling and cluster configuration |
| Provide hardware-backed protection | Confidential-computing infrastructure |
| Verify attestation and release keys | Attestation verifier and key-management integration |
| Restrict generated code's behavior | Application policy and workload runtime controls |
| Preserve non-sensitive execution evidence | Tracking and evidence storage |
Polyaxon environment settings can express node selection and other workload configuration. A node label can guide placement; it is not cryptographic proof of a node's identity or runtime state.
Likewise, commercial queue routing can direct workloads to a selected compute agent. It does not itself supply attestation.
Design the secret-release path separately
For a confidential-data analysis workflow, keep key release outside the generated program's discretion. A trusted verifier should evaluate the infrastructure's evidence and the approved workload identity before the application receives protected material.
Decide what happens when verification is unavailable, evidence is stale, or the image changes. A fallback that quietly runs on ordinary compute can defeat the original requirement. These checks must be implemented and validated in the confidential-computing integration; this is not a built-in Polyaxon TEE workflow.
Review connections carefully. A broadly mounted credential may bypass the intended release path even when the main application follows it.
Record useful evidence without exposing the asset
A trusted controller can record the run UUID, approved image digest, placement policy revision, and a reference to the verifier's decision. Store detailed attestation material according to its sensitivity and retention requirements.
Use tracking metadata to connect that decision to the application result. Avoid placing decrypted inputs, keys, or unrestricted diagnostic dumps into ordinary logs and artifacts.
Before adopting the design, exercise denial and recovery cases on the actual infrastructure. Verify that an unauthorized image cannot obtain the protected input and that a replacement workload follows the same approval path.
The goal is a defensible chain of responsibility: Polyaxon manages the work, the infrastructure supplies the promised boundary, and the application controls how protected data is used.