What This Category of Data Looks Like
This category of data primarily comes from business databases, data dictionaries, masking execution tools, and access approval systems. Updates are rolled out per single batch or single project. Daily produced documents include masking plans, field mapping tables, masking validation reports, and access approval records. Data fields cover sensitive field identifiers, masking rule codes, execution batch numbers, approval statuses, and validation results. There are no unified fixed units. Values are matched flexibly based on the attributes of individual data entries.
Constraints Imposed on Conversation Logs and Auditing Workflows
Data is updated in batches and linked to projects. Conversation log auditing must isolate data by project dimension to prevent cross-project sensitive information leaks. Documents rely on field mapping tables. During audits, masking rules for corresponding fields must be linked to verify whether log information has completed compliant masking. Data sources cover multiple system integrations. Auditing logs must synchronize operation records from all systems, covering all stages such as masking tool execution and approval process triggering. Auditing for sensitive fields requires precise matching of identifiers. General log parsing rules cannot be relied on. Validation must be completed using field mapping tables.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chatId session isolation rule | Generated by project + user dimension | Matches the batch-based data update characteristics of this category, enabling cross-project conversation log isolation |
detail log collection granularity | 800-character truncation | Balances log readability and storage costs, covers core auditing information |
responseChatItemId response association identifier | Mandatorily bound to masking batch number | Facilitates linking auditing logs to masking execution records, enabling full-chain traceability |
| Log batch submission interval | 30 seconds | Balances auditing real-time performance and system load, adapts to synchronization requirements for multi-batch data |
| Log storage period | 90 days | Meets conventional retention requirements for compliance auditing, matches the retention period of approval records for this category |
| Sensitive field auditing trigger threshold | Calibrated via actual testing | Sensitive field definitions vary by project; parameters must be adjusted based on field mapping tables |
Three Common Misconfiguration Issues
- Symptom: Some sensitive fields in conversation logs are not masked. Cause: Audit validation rules are not linked to field mapping table configurations, so masking validation for corresponding fields is not triggered.
- Symptom: Conversation logs are not written to the database after the client closes the SSE connection. Cause: Requests are terminated before
responseChatItemIdbinding is completed, resulting in failed log persistence. - Symptom: Cross-project conversation logs are accessible to unauthorized users. Cause: Isolation rules are not configured by project dimension for
chatId, so session identifiers are not bound to project information.
How to Verify Configurations Are Correct
- Generate a test conversation. Check whether the complete context of the
detailfield is included in the auditing log, and verify that the collection granularity matches the configured requirements. - Check whether the
chatIdparameter is generated by project + user dimension. Attempt to access the corresponding session with an unauthorized account, and confirm that log content cannot be viewed. - Manually trigger an SSE connection, then close it quickly. Check whether log entries corresponding to
responseChatItemIdexist in the database, and verify that the binding logic is effective. - View the system log storage directory of FastGPT v4.17.0, and confirm that the log retention period matches the configured 90-day requirement.
All listed configuration values are common starting points. They should be evaluated against specific samples and use cases of the target deployment.
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.