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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11Yes—but the dependable control point is before Tempo, not inside Tempo after ingestion. Put Grafana Alloy or an OpenTelemetry Collector in front of Tempo, remove, replace, or hash sensitive span attributes there, and export only the transformed spans. Tempo is documented as a trace-ingestion and object-storage backend; the collector pipeline is where Grafana documents attribute and trace mutation.
This distinction matters for personally identifiable information (PII), credentials, tokens, and business identifiers. Once an unredacted span has been exported and persisted, deleting the field in a later pipeline does not undo copies already written to storage, indexed for search, or retained in backups.
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What the redaction architecture looks like
A typical deployment has four functional stages:
- Receive telemetry. Alloy or the OpenTelemetry Collector accepts OTLP, Jaeger, or Zipkin data from instrumented services and agents.
- Mutate spans. Processors delete, replace, or hash attributes and, when needed, change other span fields.
- Produce derived telemetry. Metrics connectors, service-graph generation, and similar components consume the already-processed spans.
- Decide retention and export. Tail sampling evaluates the transformed data, and the exporter sends the spans that remain to Tempo.
OpenTelemetry describes processors as components that transform, filter, and enrich telemetry as it flows through a pipeline. Grafana documents the Transform Processor as modifying incoming trace spans before they are exported to other components. Because Tempo stores trace data in object storage, this upstream boundary is the practical place to enforce a “never persist this value” rule.
Why the boundary is before export
A collector can prevent a value from leaving the telemetry network at all. A backend-side cleanup job cannot guarantee that the original value was never written, replicated, indexed, or captured in a backup. Treat the collector or Alloy as a data-loss-prevention boundary, not merely as a convenience for formatting spans.
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Which component should perform the mutation?
| Location | Best use | Rule expressiveness | Effect on downstream metrics and sampling | Operational ownership |
|---|---|---|---|---|
| Instrumentation SDK | Preventing collection of a value at its source | Application-specific logic | All consumers receive the reduced data | Every instrumented service team |
| Alloy or OpenTelemetry Collector | Centralized deletion, replacement, hashing, and conditional policy | attributes for direct key operations; transform with OTTL for conditional or coordinated changes |
Metrics connectors and tail sampling can use the sanitized fields | Telemetry-platform team |
| Tempo backend | Ingestion and storage of the data it receives | No documented general-purpose native rewrite setting for arbitrary sensitive span values | Too late to guarantee pre-storage removal | Tempo operators |
| Managed tail sampling | Choosing which traces to retain | Sampling policy, not field transformation | Changes volume, not the contents of a retained span | Service provider and customer, depending on the offering |
Use more than one layer when the risk justifies it. Removing a password in the SDK reduces accidental collection, while a collector rule catches fields emitted by libraries or services that the application team did not control. The collector remains the central enforcement point because every route to Tempo can pass through the same policy.
Using the Attributes Processor for straightforward rules
The attributes processor is appropriate when you know the exact attribute keys and need a direct operation: delete a key, replace its value, update it, or hash it. A representative collector configuration is:
processors:
attributes/redact:
actions:
- key: user.email
action: delete
- key: http.request.header.authorization
action: delete
- key: customer.id
action: hash
- key: deployment.environment
action: update
value: productionThe action names and available fields should be checked against the Alloy or Collector build you deploy. Keep the policy explicit: list every sensitive key, including nested HTTP, database, messaging, and custom application attributes. Deleting a known key is safer than trying to identify PII solely from its value.
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Deletion, replacement, and hashing are different controls
- Delete removes the attribute entirely. Use it for secrets and values that provide no diagnostic benefit.
- Replace or update preserves a stable field while substituting a constant such as
[redacted]. This is useful when dashboards or queries expect the key to exist. - Hash keeps a deterministic relationship between equal inputs. That can help correlate events, but a low-entropy identifier may still be reversible through guessing or dictionary attacks. Hashing is not the same as anonymization.
Do not hash credentials, session tokens, or other values whose presence alone is dangerous. Delete them instead. For identifiers that must remain joinable, document the algorithm, salt or key management, and access controls separately from the trace configuration.
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Using the Transform Processor and OTTL
The transform processor is the better fit for conditional logic, pattern-based scrubbing, or coordinated changes across several span fields. OTTL statements can inspect span names, attributes, status, and other fields, then mutate matching data before export.
processors:
transform/redact:
trace_statements:
- context: span
statements:
- delete_key(attributes, "user.email")
- replace_pattern(attributes["http.url"], "([?&]token=)[^&]+", "$1[redacted]")The exact OTTL functions and escaping rules vary with the Collector or Alloy version, so validate this style of statement in a staging pipeline before production. Pattern rules should be narrowly scoped. A broad regular expression can miss encoded values, redact too little, or damage useful diagnostics.
Typical OTTL decisions
- Delete an attribute only when the span belongs to an authentication or payment operation.
- Replace a query parameter while preserving the rest of a URL for troubleshooting.
- Remove database statement text for selected systems while retaining a statement class or operation name.
- Copy a non-sensitive category into a derived attribute, then delete the original value.
When a rule depends on a field that another processor changes, keep the dependency order obvious and test both matching and non-matching spans. A transform that runs after a key has been deleted cannot use that key in its condition.
Correct processor ordering
Ordering determines whether privacy rules actually influence derived telemetry.
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- Receive spans. Configure the OTLP, Jaeger, or Zipkin receiver used by your applications.
- Redact or transform. Run the
attributesand/ortransformprocessors first for fields that must not reach any later component. - Generate metrics or service graphs. Metrics connectors and graph components now see sanitized values.
- Evaluate tail sampling. Sampling policies match the transformed attributes, names, and status fields.
- Export to Tempo. Only the resulting spans are sent to the Tempo exporter.
Grafana recommends placing a transform before metrics connectors and the tail-sampling processor when the changed fields affect metric labels or sampling rules. For example, if a policy samples customer.id but you delete that key first, the policy can no longer match it. Derive a non-sensitive category before deletion when a business rule genuinely needs one, and verify that the derived value cannot itself identify a person.
Does tail sampling hide sensitive values?
No. Tail sampling chooses whether a trace is retained; it does not scrub values from spans that continue through the pipeline. Head sampling makes an early decision, while tail sampling evaluates all or most spans before deciding. In either case, a retained trace can still contain every sensitive attribute present at the sampling point.
For privacy-sensitive deployments, mutate first and sample second. This also prevents sampling decisions from being based on raw identifiers. Sampling reduces the amount of data stored, but it is not a redaction mechanism and should not be presented as one in a compliance design.
Can Tempo redact data after ingestion?
The documented role of Tempo is to ingest traces and store trace data in object storage. The reviewed Tempo material documents redaction-capable processors in the upstream collector pipeline, not a general-purpose Tempo setting that rewrites arbitrary sensitive span values before persistence.
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Plan on Alloy or the OpenTelemetry Collector as the supported pre-storage control point unless the documentation for the exact Tempo version you operate introduces a specific backend transformation feature. If an unredacted trace has already reached object storage, investigate retention, deletion, and backup procedures separately; a new sanitized export does not automatically remove the earlier copy.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to build and verify a safe pipeline
1. Inventory data sources
List every service, library, receiver, and exporter that can add span attributes. Include HTTP headers, URLs, database statements, messaging metadata, user fields, and custom baggage promoted into spans. Security reviews often miss attributes added by automatic instrumentation.
2. Classify each field
- Always delete: passwords, access tokens, session cookies, authorization headers, and private keys.
- Delete or replace: email addresses, phone numbers, postal addresses, full names, and free-form request or error bodies.
- Hash only with a documented need: stable identifiers used for correlation and protected against guessing.
- Retain after review: low-risk operation names, status codes, durations, and coarse service metadata.
3. Apply rules in a non-production collector
Send representative traces containing both matching and non-matching examples. Inspect exported payloads, not only the collector logs. Confirm that nested attributes, repeated events, links, and baggage-derived fields do not retain the original value.
4. Test downstream behavior
Run the metrics connectors, service-graph generation, and tail-sampling policies with the transformed data. Check that dashboards still have the labels they require and that sampling rules still match the intended categories.
5. Monitor for policy failures
Alert on exporter errors, unexpected attribute keys, and configuration reload failures. A broken processor or an accidentally bypassed pipeline can restore raw data flow while the rest of the system appears healthy.
6. Control alternate routes
Ensure applications cannot send directly to Tempo around the collector. Apply network policy and authentication so the sanitized pipeline is the only permitted route for trace export.
Common failure modes and fixes
| Symptom | Likely cause | Fix |
|---|---|---|
| Raw email or token appears in Tempo | A service bypassed the collector, or the rule used the wrong key | Block direct exporters, inspect the actual attribute name, and add a test span for that source |
| Sampling no longer selects expected traces | The identifying field was renamed, hashed, or deleted before tail sampling | Move the sampling dependency to a non-sensitive derived field or update the policy |
| Metrics labels disappear | Redaction ran before a metrics connector that depended on the deleted key | Create a safe derived label first, or change the metric design |
| Some URLs are clean but others leak values | Only one encoding or URL location was covered by the pattern | Test query strings, paths, headers, and encoded forms separately; prefer deleting the entire field when feasible |
| Hashing still exposes identities | Inputs are guessable or the hash is shared broadly | Delete the field, or protect a keyed correlation design with restricted access and documented rotation |
What changes with Grafana Cloud Traces?
Grafana documents Adaptive Traces as managed tail sampling for Grafana Cloud Traces. A managed sampler can remove the need to operate your own sampling pipeline, but the cited documentation does not establish that Adaptive Traces performs arbitrary sensitive-field redaction. Verify the current hosted service’s data-processing, retention, and deletion controls before treating it as a compliance solution.
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Quick Recap
Practical design checklist
- Route every trace through Alloy or an OpenTelemetry Collector before Tempo.
- Use the
attributesprocessor for known-key deletion, replacement, updating, or hashing. - Use the
transformprocessor with OTTL for conditions, patterns, and multi-field logic. - Delete secrets rather than hashing them.
- Run transformations before metrics connectors and tail-sampling policies when affected fields influence those components.
- Test exported payloads with realistic, encoded, nested, and library-generated attributes.
- Prevent direct application-to-Tempo routes.
- Document which values are retained, replaced, hashed, or removed, and review the policy whenever instrumentation changes.
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