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Yes—but the redaction should happen before a span is exported to Tempo. Put Grafana Alloy or the OpenTelemetry Collector in front of Tempo, then use the attributes processor for deleting, replacing, updating, or hashing known attributes and the transform processor for conditional OTTL rules. Tempo then receives only the transformed telemetry that the exporter sends.
Tempo is an ingestion and storage backend: it accepts OTLP, Jaeger, and Zipkin traces and stores trace data in object storage. Treat the collector or Alloy pipeline as the privacy boundary rather than expecting Tempo to rewrite arbitrary values after ingestion.
What “redact before storage” means in a Tempo deployment
A typical trace travels through several components before it becomes searchable in Tempo. An application or SDK creates spans, a collector receives them, processors mutate or filter them, and an exporter sends the resulting spans to Tempo. The storage backend persists what arrives at its ingestion endpoint; it is not normally the place where sensitive values are removed.
- Receive telemetry. Alloy or the OpenTelemetry Collector accepts OTLP, Jaeger, or Zipkin data.
- Mutate sensitive fields. Attribute deletion, replacement, hashing, and conditional OTTL statements run on the received telemetry.
- Generate derived data. Metrics connectors, service-graph components, or other processors use the already-transformed spans when they are placed after the redaction step.
- Evaluate retention policies. Head or tail sampling decides which traces continue, but it does not scrub values from spans.
- Export to Tempo. The exporter sends the sanitized result, which Tempo stores in object storage.
This ordering matters because a value can leak through a derived metric, a sampling decision, or an exporter log even if a later processor eventually removes it from the span. Make the first irreversible privacy action occur before any downstream component that can copy, label, log, or retain the data.
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Which component should perform the redaction?
| Location | Rule expressiveness | Effect on metrics and sampling | Operational ownership | Important limitation |
|---|---|---|---|---|
| Application SDK or instrumentation | Can prevent a value from being emitted at source; logic is specific to each application and language | Every downstream consumer sees the reduced data | Owned by application teams; changes require coordinated releases | Coverage is difficult to guarantee across all services, libraries, and protocol versions |
| Grafana Alloy or OpenTelemetry Collector | attributes handles direct key operations; transform and OTTL handle conditions and coordinated mutations |
Transforms can run before metrics connectors, service graphs, and tail sampling | Central platform or observability team can enforce a shared policy | Anything emitted before the processor, or through a bypass pipeline, is outside the policy |
| Tempo backend | Tempo documentation describes ingestion and storage, not a general arbitrary-field rewrite stage | Too late to reliably protect values already accepted or copied by upstream components | Backend operators control storage and retention | Do not assume a native Tempo setting can redact every span field before persistence |
Many organizations use both source controls and a collector policy: instrumentation avoids obvious secrets, while the collector provides a final, centrally managed safeguard for every route to Tempo.
Choose between the attributes and transform processors
Use attributes for known keys
The attributes processor is designed for straightforward operations on span attributes. It can delete a key, replace its value, update an existing value, or hash an attribute. This is usually the clearest option for stable fields such as an email address, an authorization header, a customer identifier, or a database statement attribute that should never reach storage.
Hashing is not the same as anonymization in every threat model. A low-entropy identifier can sometimes be guessed and hashed again, so hash only when a consistent pseudonym is genuinely required and the resulting value is acceptable for your privacy policy. Delete or replace the field when correlation is not needed.
Use transform and OTTL for conditions
The transform processor applies OpenTelemetry Transformation Language (OTTL) statements to incoming spans. It is appropriate when a rule depends on whether an attribute exists, matches a pattern, belongs to a particular service, or must be changed together with other span fields. It can also support coordinated changes that are awkward to express as a list of independent attribute actions.
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For example, a conditional rule can replace db.statement only when that attribute is present, or remove a header value when its name matches a sensitive pattern. Keep the condition explicit so that a missing attribute is not treated as an error. OTTL syntax and supported functions depend on the collector or Alloy version you deploy, so validate statements against that distribution before rollout.
A collector pipeline that removes common PII
The following YAML illustrates the placement and intent of a pipeline. It receives OTLP, applies direct attribute actions, applies an OTTL transform, and exports the result to a Tempo endpoint. Adapt receiver, exporter, authentication, and queue settings to your deployment; the essential privacy property is that the processors appear before the exporter.
receivers:
otlp:
protocols:
grpc:
http:
processors:
attributes/redact:
actions:
- key: user.email
action: delete
- key: user.id
action: hash
- key: http.request.header.authorization
action: delete
- key: enduser.id
action: update
value: REDACTED
transform/redact:
trace_statements:
- context: span
statements:
- set(attributes["db.statement"], "[REDACTED]") where attributes["db.statement"] != nil
service:
pipelines:
traces:
receivers: [otlp]
processors: [attributes/redact, transform/redact]
exporters: [otlp/tempo]The example deliberately uses different actions: email is deleted, an identifier is hashed for correlation, an authorization header is deleted, and another identifier is replaced with a constant. A real policy should use your actual attribute names and include every path by which the value can arrive. Attribute naming is not universal: an HTTP library may use one semantic-convention key while a database instrumentor uses another.
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Alloy exposes the same OpenTelemetry processing concepts through its own configuration syntax. Do not paste collector YAML into an Alloy configuration file; translate the receiver, processor, pipeline, and exporter into the Alloy components supported by your installed release. The data-flow rule remains unchanged: receive, transform, then export.
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How to build a redaction policy step by step
- Inventory data at the source. List span attributes, resource attributes, event attributes, and exception fields that can contain names, email addresses, tokens, session identifiers, payment data, query text, or free-form user input. Include values generated by libraries rather than only fields your application sets directly.
- Classify each field. Decide whether it should be deleted, replaced with a constant, or hashed. Document why a field must remain and who may access the resulting traces.
- Choose the simplest processor. Use an attributes action for a fixed key and a transform statement for conditional, pattern-based, or multi-field logic.
- Place processors before consumers. Put redaction before metrics connectors, service-graph generation, and tail-sampling evaluation whenever those components use an affected attribute.
- Close bypass paths. Check that every application exports through the protected collector or Alloy pipeline. A second exporter, debug pipeline, or direct-to-Tempo endpoint can bypass the policy.
- Test with synthetic secrets. Send spans containing unmistakable test email addresses, tokens, identifiers, and query text. Inspect collector output and the stored trace to verify that the original values are absent.
- Monitor the policy. Alert on processor errors, configuration reload failures, unexpected attribute volume, and new instrumentation fields. Treat a failed redaction processor as a privacy incident, not merely an observability outage.
Ordering with metrics, service graphs, and sampling
Transformation is not an isolated formatting step. Other components may copy attributes into labels, derive relationships, or make retention decisions.
Metrics connectors and service graphs
If a sensitive attribute is used as a metric label or service-graph dimension, generating those outputs before redaction can preserve the sensitive value in another telemetry stream. Run the transform first when the changed field participates in those calculations. If you need a non-sensitive grouping, create a deliberately coarse replacement or derived value and verify that it cannot be reversed into the original.
Tail sampling
Tail sampling evaluates all or most spans of a trace before deciding whether to retain it. If a policy keeps traces based on an attribute that you later rename or delete, sampling may stop matching. Conversely, if sampling evaluates the raw value first, that value has already entered a component that may buffer, log, or expose it. Redact before tail-sampling policy evaluation whenever privacy requires the value to be absent at that point.
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Head sampling
Head sampling makes an early decision, often before the complete trace is known. It reduces the amount of telemetry processed, but it does not sanitize a trace that is selected. A retained span still carries every sensitive field present when the sampling decision and subsequent export occur.
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Why sampling is not redaction
Sampling answers “which traces should we keep?” Redaction answers “which values may exist in the retained telemetry?” These controls solve different problems. A one-percent sampling rate still sends sensitive data from every trace that wins the sampling decision, and tail sampling can inspect raw values before choosing a trace. Apply mutation independently of the sampling rate whenever a field must not be stored.
Can Tempo remove data after it has been ingested?
The documented Tempo role is to ingest traces and store them in object storage. The reviewed Tempo material documents redaction-capable processors in the upstream collector pipeline, but not a general-purpose native Tempo setting that rewrites arbitrary sensitive span values before persistence. Design for pre-export scrubbing rather than relying on a backend rewrite.
Changing a collector rule does not retroactively sanitize objects that were already written. If an incident has placed prohibited data in storage, follow your organization’s retention and deletion procedures for the affected traces and object-storage data, and record the time range and pipelines involved.
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Grafana documents Adaptive Traces as managed tail sampling for Grafana Cloud Traces. It can reduce the need to operate your own sampling pipeline, but the cited documentation does not establish that Adaptive Traces performs arbitrary sensitive-field redaction. Confirm the current hosted service’s data-processing and deletion controls before treating managed sampling as a compliance mechanism. If a field must never leave your environment, scrub it in your own SDK, Alloy instance, or OpenTelemetry Collector before export.
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Validate that the policy actually protects storage
Check the collector boundary
- Enable only the minimum diagnostic logging needed during a test; debug logs can themselves expose payloads.
- Confirm that the active traces pipeline names the redaction processors in the expected order.
- Verify that configuration reloads succeed and that the running process has loaded the intended file or Alloy component graph.
Check every representation of the value
- Search span attributes, resource attributes, span events, and exception data for the original test value.
- Inspect derived metrics and service-graph telemetry for labels copied from the sensitive field.
- Review exporter and retry-queue diagnostics for accidental payload logging.
Check the stored result
Query the test trace in the Tempo consumer used by your team and verify that the original value cannot be found. Test both traces that should be retained and traces that should be dropped by sampling, because a dropped trace can still reveal a problem through intermediate logs or metrics.
Common failure modes and fixes
The processor is configured but never runs
A processor definition alone does nothing until it is listed in the active traces pipeline. Check the pipeline’s processor list, configuration file selection, and successful reload logs.
The rule targets the wrong key
Semantic-convention names differ across instrumentors and versions. Capture representative telemetry in a controlled environment, identify the exact key and scope, and add tests for each instrumentation library you operate.
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Move the mutation processors earlier. Any component that evaluates, labels, copies, buffers, or logs the raw value must run after the privacy transformation.
Hashing creates an unintended identifier
A stable hash can enable cross-trace correlation and may be reversible for predictable inputs. Replace or delete the value when correlation is unnecessary, protect any mapping material, and obtain privacy approval for the remaining use.
A direct exporter bypasses the policy
Search application and sidecar configurations for exporters that send directly to Tempo or another trace backend. Route them through the governed collector or apply equivalent source-side controls.
Only the obvious field is removed
Free-form exception messages, SQL text, URLs, baggage-derived attributes, and resource metadata can contain the same person or token under a different key. Extend the inventory and test data generated by every major library, not just the fields in one sample trace.
Quick Recap
Practical decision checklist
- Use the
attributesprocessor for a fixed key that needs deletion, replacement, updating, or hashing. - Use
transformwith OTTL for conditions, patterns, or coordinated edits across fields. - Transform before metrics connectors, service-graph generation, and tail-sampling rules that use affected attributes.
- Do not count head or tail sampling as privacy protection.
- Assume Tempo stores what the exporter sends and design the collector boundary accordingly.
- Test the running pipeline and the stored trace, then repeat tests after instrumentation or collector upgrades.
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