Prompt and Trace Telemetry Governance: Pre-Ingestion Redaction, Tiered Retention, and Verifiable Deletion

A single AI Trace may carry a user's question, system Prompt, retrieval excerpts, response, tool arguments, and authentication headers, and may be copied repeatedly by SDKs, Collectors, queues, backends, backups, and debug exports. Nightly redaction is already too late; shortening the Trace retention period does not automatically delete old backups.

AI Application Data Privacy and Compliance explains the overall boundaries. This article focuses only on the telemetry plane: what may be collected, where it must be sanitized, how long it is retained, who may view it, and how deletion can be proven. Use metadata to locate issues by default; raw content should be accessed only as authorized, short-lived evidence.

Start with Field-Level Data Classification

Divide signals into four levels. L0 is publicly shareable operational metadata, such as service, region, model family, latency, token counts, and limited error codes. L1 covers internal identifiers, such as Prompt versions, tool names, document IDs, and tenant groups. L2 covers user input, model output, retrieval summaries, and tool results that may contain personal or business information. L3 covers credentials, authentication headers, passwords, private keys, complete payment information, and other content prohibited from entering telemetry.

Every span schema must declare its classification, purpose, owner, backend, and retention period. Unregistered attributes are discarded as L2 by default. Low-entropy email addresses and phone numbers can be enumerated, while stable hashes still create tracking identifiers. When correlation is strictly necessary, use an HMAC with a rotating key and isolate the key; otherwise, delete the value outright.

Complete the First Redaction Pass Before Emitting Events

The business layer is the most reliable location. Logging APIs should accept only a structured allowlist and must never serialize an entire request. Model spans should record the operation, model, token counts, latency, and outcome category, but not the content. The tool layer should record only the tool name, schema version, status, and byte count. Under the principles in Secrets Management, Authorization, Cookie, API Key, and connection strings must never enter telemetry.

When raw-content debugging is truly necessary, use a separate capture switch scoped to an event ID, an authorized tenant, and no more than one hour. Redact the content first, then encrypt it and write it to an isolated store. Ordinary Traces should contain only an opaque random reference that does not reveal the storage path. The switch must require two-person approval, expire automatically, and be audited.

The Collector Is a Leak-Prevention Boundary, Not a Postprocessing Cleanup Step

Every Trace and Log egress path should pass through a versioned Collector transform. Use an explicit allowlist; regular expressions cannot fully identify names, code, and secrets in free text. Content fields such as GenAI messages, system instructions, Prompt variables, and tool definitions are excluded from the allowlist and are therefore not forwarded by default.

processors:
  transform/telemetry_redaction:
    error_mode: propagate
    trace_statements:
      - context: span
        statements:
          - keep_keys(attributes, ["gen_ai.operation.name", "gen_ai.provider.name", "gen_ai.request.model", "gen_ai.usage.input_tokens", "gen_ai.usage.output_tokens", "gen_ai.prompt.version", "gen_ai.tool.name", "error.type", "ai.outcome"])
          - set(name, "telemetry.span")
          - set(status.message, "")
      - context: spanevent
        statements:
          - keep_keys(attributes, ["exception.type", "error.type", "ai.outcome", "gen_ai.tool.name"])
          - set(name, "telemetry.event") where name != "exception"
    log_statements:
      - context: log
        statements:
          - keep_keys(attributes, ["gen_ai.operation.name", "gen_ai.provider.name", "gen_ai.request.model", "gen_ai.prompt.version", "gen_ai.tool.name", "error.type", "ai.outcome"])
          - set(body, "")

service:
  pipelines:
    traces:
      processors: [memory_limiter, transform/telemetry_redaction, batch]
    logs:
      processors: [memory_limiter, transform/telemetry_redaction, batch]

The example fixes the span name and all non-exception event names, clears the status message and log body, and independently allowlists the three attribute classes. The metadata-only pipeline therefore has no egress path for content. Exceptions retain only their type, not the message or stacktrace. Externally controlled resource attributes also require a separate allowlist. error_mode: propagate ensures that rule errors block egress. Tool calls can replace payloads with the summaries and fingerprints described in MCP Audit Logging Design.

Before launch, send a canary containing a fake email address, token, Cookie, GenAI content attributes, and a secret log body through the complete pipeline, then search for its markers in every backend. Release only when raw content returns zero matches and metadata remains queryable. Tests must also verify that every pipeline references the processor.

The Retention Matrix Must Cover Every Copy

Define retention by purpose: L0 aggregate metrics may be retained for thirteen months, L1 Traces for thirty days, authorized L2 evidence for no more than seven days, and L3 for zero days. Use the same classification in every environment. RAG document IDs may be retained, but retrieved content must still follow Access Control During RAG Retrieval.

The asset inventory must cover indexes, object storage, queues, caches, snapshots, backups, exports, and vendors. Using expires_at, deletion jobs should record per-backend counts, failures, and completion times. When backups cannot support item-by-item deletion, require cryptographic erasure or a maximum lifetime, and rerun deletion after restoration. 202 Accepted does not mean deletion is complete; spot-check that objects are inaccessible.

Routine on-call work should expose only L0 and L1. Access to L2 requires an incident ID, stated purpose, approval, and short-lived authorization. Searches, exports, and sharing must all be audited. If a vendor SaaS product cannot disable training, secondary analysis, or cross-region replication, do not send it L2 data.

Cut Off the Copy Chain First During a Leak

When a Prompt or credential is found in a backend, immediately stop the affected telemetry egress, disable debug capture, revoke the credential, and freeze exports. Starting from the earliest occurrence, search Collectors, queues, indexes, object storage, and backups. Record the field, tenant, time window, viewers, and copies. Deleting only the first Trace discovered is not sufficient.

Roll back to the metadata-only configuration and replay the canary into an isolated backend. After sensitive fields return zero matches, restore 5% of traffic. Increase traffic only after two consecutive scan cycles pass. Do not set error_mode: ignore, and do not reopen egress before leaked credentials have been rotated.

After the incident, add the leak pattern to schema tests and the end-to-end canary, and ensure that deletion evidence covers every copy. Default denial, expiration timestamps, and queryable deletion evidence are what keep a Trace from turning an application failure into a long-term data risk.

Further Reading

• Data Privacy and Compliance for AI Applications
• MCP Audit Logging Design
• Access Control During RAG Retrieval
• Secrets Management