What This Category of Data Looks Like
Data for this category comes from four primary sources: data asset inventory, database metadata, business system descriptions, and regulatory documents. Daily produced documents include data catalogs, classification and grading rules, important data identification tables, and review minutes. Data is updated on a quarterly cycle or whenever a business system change occurs. Document structures primarily use fielded entries, including fields such as asset ID, sensitivity level, affiliated business domain, and update time. There are no uniform fixed unit requirements. Only the identification rules of the corresponding asset must be matched.
What Constraints These Characteristics Impose on Conversation Logs and Auditing
Multiple data sources require conversation logs to link to identifiers from asset inventory, metadata, business systems, and other sources. This prevents incomplete data traceability during audits. Quarterly or change-triggered update cycles require audit logs to record session records for data version changes. This ensures compliance reviews can match rule versions from the corresponding period. The fielded document structure requires logs to accurately bind fields such as asset ID and sensitivity level. This facilitates subsequent compliance checks. The time-consuming process of sensitive field identification and grading rule reviews requires logs to fully record execution steps. This helps troubleshoot faulty nodes that cause delays. The coverage of non-personal information assets requires audit logs to distinguish recording rules for personal information and other sensitive data. This avoids compliance confusion.
How to Configure Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
detail field enable status | Enabled | Must fully record detailed execution information for sensitive field identification and grading rule validation |
chatId binding rule | Link to the corresponding data asset ID | Each conversation log must be bound to a specific data classification and grading asset entry |
responseChatItemId configuration | Link to important data identification table entries | Audit logs must be associated with specific sensitive data identification entries |
| Conversation log retention period | 90 days | Matches general retention period requirements for compliance audits |
| Log query timeout | 60 seconds | Prevents query failures due to excessive data volume |
| Single log export entry limit | First 1000 entries | Meets bulk export requirements for compliance audits |
Three Common Configuration Mistakes
- Symptom: Conversation logs show the operation succeeded, but no corresponding classification and grading records appear in audit logs. Cause: The
detailfield is not enabled, andchatIdis not bound to the data asset ID. This causes key information to not be fully retained in logs. - Symptom: The classification and grading API call returns a 500 error, but no specific error content appears in system logs. Cause: The
detailfield is not configured to collect detailed execution information. This makes it impossible to capture internal exception details of the API. - Symptom: Configured grading rule parameters do not match actual execution results, and no parameter change records appear in system logs. Cause:
responseChatItemIdis not bound to the corresponding important data identification table entry. This causes logs to not record actual executed configuration parameters.
How to Confirm Configuration Is Complete
- Navigate to the FastGPT v4.17.0 conversation logs page, view any session linked to a data asset, and confirm the
detailfield includes information related to sensitive field identification and grading rule validation. - Manually trigger a classification and grading fault scenario, check that corresponding exception logs are generated, and confirm the log retention period matches the preset configuration requirements.
- Export a batch of log files, confirm the format matches preset requirements, and the number of entries does not exceed the single export limit.
- Simulate an abnormal response trigger, check that corresponding audit alerts are generated, and confirm alert configuration is active.
The values listed here are common starting points. Always validate against your own 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.