Secure LLM data flows with Polyaxon

An LLM workflow moves data through more places than its model endpoint. A retrieval step reads documents, a tool receives arguments, generated code opens files, and an evaluation job saves results. Securing the workflow means deciding what each step may see and where its outputs may go.

Polyaxon gives these steps a shared project and run context. Use that context to organize execution, credentials, and evidence, while keeping data-access decisions in trusted application code and infrastructure policy.

Start with a data-flow inventory

Consider an assistant that investigates failed training runs. It needs error categories, package versions, and selected metrics. It usually does not need raw customer records, the training service's credentials, or unrestricted access to every artifact.

Define a contract for each boundary:

Record the bundle revision and collection policy with the run. A reproducible investigation should explain which data was available, not merely which model was called.

Keep credentials with the component that needs them

Use Polyaxon connections for configured external systems. Attaching a connection makes its resolved resources available to the workload; it is not a per-tool authorization decision.

For generated-code analysis, let a trusted collector fetch the permitted subset. Upload that subset to a sandbox-enabled service through the filesystem interface. Keep the model provider key in the controller if the execution service never calls the model.

The following component fragment disables two automatic injections:

plugins:
  sandbox: true
  auth: false
  mountArtifactsStore: false

Here, auth: false disables the automatic Polyaxon authentication context inside the workload. It does not remove separately attached secrets, cloud identity, or Kubernetes service-account access. Review those independently. Disabling the artifact-store mount also means that a trusted process must retrieve and persist approved outputs.

Treat output as another data boundary

A process can leak sensitive content through an exception, a filename, a chart label, or ordinary stdout. Limiting its input is therefore more effective than relying only on log cleanup.

Have the controller reject unexpected output types and sizes, redact sensitive fields, and store only the files needed for review. Do not place access tokens in prompts, command arguments, run names, tags, or metrics. Metadata is useful precisely because many teammates can inspect it.

Use artifact tracking for approved evidence. Writing into the synced outputs directory persists a file through the configured artifact workflow; logging an artifact reference alone does not copy an arbitrary scratch file.

Review retention and recovery together

Assign separate retention policies to temporary workspace files, evaluation reports, provider logs, and durable source datasets. Stopping a sandbox does not delete every copy of information that passed through it.

For an incident, preserve the run UUID, component revision, data-bundle identifier, and sanitized command receipt. Revoke affected credentials and follow the storage system's deletion or retention process where needed.

This approach makes data security part of the normal Polyaxon workflow: narrowly scoped inputs, explicit execution responsibilities, and reviewable outputs. Start with one investigation path, document its boundaries, and expand access only when a concrete use case requires it.