Error telemetry helps developers diagnose failures, but messages, URLs, request objects and stack traces can contain sensitive or identifying values. Design a minimal error record and verify it before sending application data to any collector.
Define a small diagnostic record
Start with the exception type, a sanitized message, an application release, a route pattern and the stack information needed for diagnosis. Use a property allowlist and review whether each field is necessary. A route pattern such as /accounts/:id has different exposure from a full URL containing an account ID and token.
The OWASP Logging Cheat Sheet describes data to exclude or protect, including access tokens, passwords and sensitive personal data. It also covers validation, sanitization, access and disposal. Do not turn that guidance into a claim that every logging purpose prohibits every identifier in all circumstances.
Inspect both the browser and collector
Generate a synthetic error containing recognizable fake sensitive values and confirm they are removed or transformed as intended before persistence. Inspect network payloads as well as the final event. Include nested properties, query parameters, headers and exception messages in the test.
Client filtering can reduce collection, while collector controls provide another enforcement point. Verify the controls your system actually offers rather than assuming every SDK has the same sanitization API. A browser collection failure should not make the underlying application fail.
Review identifiers and IP handling independently
Hashing, truncation and rotation can reduce linkability, but do not automatically establish anonymisation or eliminate all data-protection requirements. The EDPB distinguishes these concepts. Another persistent identifier or custom property can still link events even when one field rotates.
For Databuddy, inspect the documented collection model and your own error properties. Avoid a blanket promise that no raw IP or identifying value can ever appear anywhere in an application's infrastructure, logs or supplied payloads.
Test grouping without collecting unnecessary context
Trigger the same synthetic exception twice and a materially different one once. Check whether grouping is useful for diagnosis and whether it depends on a field you should remove. An error group, a visitor count and a user profile represent different concepts.
Use the Databuddy error workflow to evaluate investigation alongside website events and performance. Optional identification and richer context should be deliberate decisions, not accidental consequences of serializing a whole request.
Choose and verify the lifecycle controls
Set retention according to the diagnostic purpose, applicable obligations and the organization's review. This guide does not prescribe a universally lawful 30-, 90- or 365-day period. Include exports, backups and downstream destinations in the procedure.
Test access with a restricted synthetic teammate, verify the supported deletion mechanism and confirm what happens when collection or storage is unavailable. Handle the relevant collection choices and opt-out signals under the policy established for the deployment; a minimal payload does not make every signal a no-op.
Keep a repeatable acceptance record
- Record allowed fields and recognizable synthetic values that must be removed.
- Inspect sent, received and stored representations.
- Verify grouping and useful diagnosis with the minimized record.
- Test access, retention/deletion and relevant collection decisions.
- Repeat the checks when optional features, sources or destinations change.
The privacy review worksheet helps document the processing arrangement. The AI-assisted analytics guide covers the additional review when connected assistants can access diagnostic data.