What this category of data looks like
Data for this category comes primarily from business databases, data dictionaries, masking execution tools, and access approval systems. It updates per batch project or single task. Supporting documents include masking plans, field mapping tables, masking verification reports, and approval records. Core fields include sensitive data identifier, masking rule code, execution timestamp, verification result, and approval status. There are no fixed standard units. Settings adjust based on project configuration.
What Constraints These Characteristics Impose on Conversation Logs and Auditing
Data sources for this category are scattered and updated per project. Conversation log auditing must link multi-source records from business databases, masking tools, and approval systems to form a complete compliance chain. Field mapping tables must be synchronized to audit logs. Without this, original sensitive field identifiers and masking rule matching cannot be traced. Sensitive field auditing relies on the detail field to record masking execution details. Missing this field makes compliance verification impossible. Audit cycles must align with project update rhythms. This avoids cross-project data confusion and inaccurate audit results. FastGPT v4.17.0 and later versions optimized multi-source log association logic. Configuration rules for this version must be adapted.
How to Configure Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Log retention period | 90–180 days | Matches conventional retention requirements for compliance audits, covers archiving cycles for most projects |
chatId association rule | Bind project unique identifier | Ensures conversation logs and masking records for the same project correspond one-to-one, avoids cross-project data mixing |
responseChatItemId recording scope | Full conversation nodes | Fully retains content comparison basis before and after masking, supports field-by-field audit verification |
detail field collection switch | Enabled | Records specific masking execution rules and verification results, meets detail requirements for compliance reviews |
| Sensitive field audit frequency | After each task execution | Aligns with data update rhythm, detects abnormal masking operations promptly |
| Failure alert threshold | Masking failure rate ≥5% | Notifies abnormal scenarios promptly, avoids compliance risks |
Three Common Mistakes
- Phenomenon: After deploying the local version, conversation logs are automatically deleted on a fixed cycle. Historical masking operations cannot be traced. Cause: The log retention period configuration was not adjusted. The platform’s default short-term retention rule was used.
- Phenomenon: Specified code running result conversation logs cannot be deserialized on the client. They display empty or throw errors. Cause: Full collection of the
detailfield was not enabled. Necessary serialized metadata is missing from the logs. - Phenomenon: Unable to view question-and-answer masking audit records initiated via login-free links. Cause: The association rule between login-free sessions and project ID was not configured. Audit logs fail to link to masking operations for the corresponding session.
How to Confirm the Configuration Is Correct
- View the system log configuration page. Confirm that the log retention period setting matches the project cycle.
- Export the audit log for a single conversation. Check that it includes the
chatId,responseChatItemId, anddetailfields. - Initiate a masking test task. Confirm that audit logs are generated synchronously and linked to the corresponding project.
- Simulate an abnormal masking operation. Verify that the failure alert triggers normally.
The values provided are common starting points and should be evaluated against relevant sample data.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-10-19.