Design data-residency controls for AI execution
Map AI execution data paths to Polyaxon compute placement, connections, artifacts, external providers, and documented residency controls.
Keeping an AI workload in an approved region does not establish where all of its data goes. Inputs may come from one store, model requests may leave the cluster, and logs or artifacts may be retained elsewhere.
Design residency controls around the complete execution path. Polyaxon provides workload placement and data-connection context; the deployment, storage systems, and external services determine the actual boundaries.
Inventory every data destination
For an agent that analyzes private documents, map the following:
| Data path | Questions to resolve |
|---|---|
| Input collection | Which source store and replicas contain the documents? |
| Execution | Which cluster, namespace, and node pool run the workload? |
| Model request | Which provider endpoint processes the selected content? |
| Logs and metadata | Where are records stored and who can inspect them? |
| Artifacts and backups | Which storage locations retain outputs and copies? |
| Support and debugging | Can files be exported to another environment? |
Include temporary files and error output. A residency review that omits logs can miss the very records most often copied during troubleshooting.
This is an engineering inventory, not a certification or legal determination. Have the responsible compliance and security teams define the applicable requirements.
Constrain compute placement
A self-managed Polyaxon deployment can run work on the Kubernetes infrastructure your organization operates. Where licensed, compute agents and queues route operations to configured namespaces or clusters.
Restrict which queues a project can use when that capability is available. Use environment configuration for the workload's node-selection requirements and enforce mandatory placement through infrastructure policy.
A friendly queue name such as “regional” is not evidence. Document the actual cluster location, associated storage, and administrative ownership. Review failover behavior so a capacity shortage does not trigger an unapproved destination.
Review connections and external calls
Configure connections for approved data stores and attach only those needed by the workload. Review provider permissions, replication, and retention outside Polyaxon as part of the same design.
Keep model-provider access in a trusted controller when generated code does not need it. Minimize the data sent to the model and select an endpoint consistent with the approved processing policy.
Outbound restrictions belong in the cluster or network layer, as described in sandbox network access. An authenticated inbound proxy does not constrain outgoing requests.
Align artifact and diagnostic policies
Use artifact tracking to preserve approved outputs in the configured store. Define what may be logged as ordinary metadata and what requires restricted artifact access.
Record dataset identifiers, policy revisions, and execution run UUIDs instead of copying private content into tags or run names. Establish retention and deletion procedures for the artifact store, backups, provider records, and any exported reports.
Stopping the workload does not delete these other copies. Cleanup must follow each system's actual lifecycle.
Keep a reviewable placement record
For each approved workload profile, retain the compute destination, permitted connections, model endpoint policy, artifact store, and exceptions. Revisit the record when an image, integration, or failover design changes.
Polyaxon makes the execution context visible and repeatable. A complete residency design connects that context to the surrounding systems, with evidence for every destination rather than an assumption based on where the Pod ran.