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Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →To keep prompts, completions, personal data, and credentials out of OpenTelemetry GenAI traces, prevent content capture in the instrumentation first, then apply a deliberate sanitization policy in the Collector before export. Finally, inspect the traces that leave both the application and Collector: a rule that misses the payload’s actual format is not protection.
What sensitive data can appear in a GenAI trace?
OpenTelemetry’s GenAI span conventions identify model instructions, user messages, and model outputs as potentially sensitive and often large. The conventions state: “OpenTelemetry instrumentations SHOULD NOT capture them by default, but SHOULD provide an option for users to opt in.” See the GenAI span semantic conventions.
If content capture is enabled, review these fields and related payloads:
gen_ai.input.messages,gen_ai.output.messages, andgen_ai.system_instructions.- Prompt-variable values and agent or tool fields such as
gen_ai.tool.call.argumentsandgen_ai.tool.call.result. - Retrieval queries, retrieved documents, and memory content.
- Custom span attributes, events, exception messages, URLs, and baggage added by your application or instrumentation.
Do not assume that a field is safe just because it is metadata or is not named as a prompt. Its sensitivity depends on what your application puts there.
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Prevent capture at the source
Start by inventorying the instrumentation libraries, SDKs, application hooks, span attributes, events, and linked logs that produce or accompany your traces. Inspect actual emitted telemetry rather than relying only on configuration names. OpenTelemetry’s handling sensitive data guidance puts responsibility on implementers to review what their libraries expose.
Where possible, leave prompt and completion capture disabled. If a specific debugging workflow genuinely needs the content, make capture an explicit opt-in and account for access controls, retention, and applicable organizational and regulatory requirements. One option described in the GenAI conventions is to store content separately and put a reference in the span, allowing the content store to have its own access controls.
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Truncating a prompt or output can reduce volume, but does not ensure safety: sensitive data may occur at the beginning. For structured payloads, preserve useful structure while removing or replacing sensitive values when possible.
Choose a Collector control that fits the policy
Collector processors act on telemetry before it is exported, but they solve different problems. The OpenTelemetry sensitive-data guidance describes attribute, filter, and transform processors; the redaction processor documentation describes allowlists and pattern-based masking.
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| Control | Best suited to | Trade-off |
|---|---|---|
| Attribute processor | Deleting, modifying, or hashing named attributes, such as user.email or user.full_name. |
You need to know the attribute keys your instrumentation emits. |
| Filter processor | Dropping a whole span or record when it should not be retained. | You lose all details in the dropped record. |
| Redaction processor | Keeping selected attribute keys and masking values that match configured patterns. | Incorrect rules can remove useful fields or allow sensitive values through. |
| Transform processor | Targeted rewrites or deletions, including transformations based on patterns. | Rules still need to cover the payload shapes actually emitted. |
Use an allowlist when you want to keep only known-safe fields
The redaction processor’s allowed_keys setting names the attribute keys to retain. An empty allowed_keys list removes all attributes; allow_all_keys: true disables the allowlist. Build the list deliberately around the fields needed for diagnosis, and check for sensitive content in nested or alternate attributes rather than assuming one allowlist catches every representation.
Review masking patterns and exceptions
The redaction processor evaluates blocked patterns in configured order. Allowed-value patterns take precedence over blocked-value patterns, so an overly broad exception can let a matching value through. Ignored keys bypass redaction checks; use them only for fields whose values are known to be safe.
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Do not treat a hash as automatic anonymization
Hashing an identifier can preserve correlation, but predictable or low-entropy values may be guessed or reversed in practice. OpenTelemetry’s sensitive-data guidance cautions against assuming a simple hash provides sufficient anonymization; the Collector documentation recommends HMAC rather than a simple hash when hashing low-entropy data. Protect the key material and choose the approach for your threat model.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Configure the pipeline and match the payload representation
GenAI input and output content has defined JSON schemas, but its representation on spans can vary with language and signal support: it may be structured attributes or JSON-serialized strings. A rule that targets nested fields may not match a serialized blob; a string regular expression may miss an alternate encoding or unexpected payload shape. Check the language, instrumentation, and deployed versions you use, then inspect the emitted form.
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Place sanitization before telemetry reaches systems with broader access or longer retention. Confirm that the processors are attached to the active Collector pipeline and execute in the intended order; defining a processor does not by itself make it run. The redaction processor’s documentation follows the current main branch, so check its configuration against the Collector release you deploy.
OpenTelemetry provides processor capabilities, not a universal detector that guarantees removal of every possible secret or personal detail from arbitrary model content. Your rules need to reflect your application’s data and the formats it emits.
Test what actually leaves the pipeline
- Create controlled test traces with representative prompt, completion, tool, retrieval, exception, and URL values. Use synthetic sensitive-looking values rather than real personal data or credentials.
- Inspect span attributes and events, and any linked log records, after instrumentation and again after the Collector.
- Check that each test value is absent, removed, or masked as intended at each boundary. Include alternate and serialized payload forms that your application can emit.
- Assess whether the replacement or masking affects debugging, correlation, or attribute cardinality.
- Where useful, review the redaction processor’s audit summaries for counts and keys removed or masked. Keep sensitive values out of the audit channel.
- Repeat the checks when instrumentation libraries, semantic conventions, Collector versions, or application payloads change.
Check the rest of the trace, not only GenAI spans
Other spans in the same trace can expose sensitive data. OpenTelemetry’s HTTP span conventions recommend scrubbing sensitive URL query values when instrumentation can identify them; examples include sig and cloud-provider signature or credential parameters. Its database client span conventions recommend collecting query text by default only when sanitization excludes sensitive information, and replacing literals with placeholders.
Include URLs, database statements, exception details, custom attributes, and related logs in the same review. A GenAI-specific policy cannot protect data recorded elsewhere in the trace.
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