What the Data for This Category Looks Like
The typical data subjects for this category are cybersecurity grade protection assessment institutions, security consulting firms, and enterprise security leads. Core daily documents include four types: classification determination reports, gap analysis reports, remediation plans, and assessment reports. Data sources include raw data exported from assessment tools, on-site interview records, asset system inventories, and security device operation logs. Data updates follow individual assessment project cycles: all documents are iterated within the project cycle, then archived after the project concludes. Core data fields include control item number, remediation status, associated asset ID, and report version number. Units use standardized identifiers such as project numbers, control item sequence numbers, and document version numbers.
Constraints Imposed on Conversation Logs and Auditing
Data sources for this category are dispersed and linked to multiple compliance document types. Conversation logs and auditing must accurately associate exclusive data for each assessment project. The project-based data update rhythm requires logs to bind project identifiers via chatId to avoid mixing logs across different projects. Core documents include multiple structured reports. Audit logs must record verification and remediation steps for each control item, so responseChatItemId must be associated to locate specific interaction nodes. Errors directly relate to regulatory inspection risks, so audit logs must retain full interaction details without omitting critical operational steps. Additionally, differences between this category and general security baseline checks require logs to mark control items corresponding to cybersecurity grade protection standards, ensuring audit content aligns with industry specifications.
Configuration Settings
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Log storage isolation rule | Divide project-exclusive storage directories by chatId | Matches the project-based data update characteristic of this category, avoiding log mixing across different assessment projects |
| Log detail toggle | Set the detail parameter to record only critical interactions | Reduces redundant log storage usage while retaining key information required for compliance auditing |
| Concurrent log write threshold | 2-3 calls per second, adapted to 1000 MB single-project log storage space | Matches concurrent assessment consulting scenarios for this category, preventing log write overflow |
| Session ID binding rule | Force association of responseChatItemId with assessment report ID | Ensures audit logs can directly locate the remediation steps for corresponding control items |
| Log archiving cycle | Archive 30 days after project conclusion | Aligns with the project-level data update rhythm of this category, meeting compliance retention requirements |
Three Common Misconfigurations
- Phenomenon: The tool call module continuously outputs redundant runtime records. Cause: The
detailparameter is not configured to record only critical interactions, resulting in full logs being retained synchronously. - Phenomenon: Empty results are returned when concurrently calling MCP workflows. Cause: Log storage for different sessions is not isolated by
chatId, leading to log data from multiple concurrent requests overwriting each other, making it impossible to match return results for the corresponding session. - Phenomenon: Feishu webhook sending fails. Cause: Text spliced by loop nodes exceeds the storage limit of log fields, causing log write exceptions, which in turn triggers pre-check failures for webhook calls.
How to Verify Correct Configuration
- Trigger the interaction flow for a single assessment project. Review the log list to confirm all logs carry exclusive
chatIdidentifiers, fully isolated from logs of other projects. - Call the control item gap verification link once. Check if the log associates
responseChatItemIdwith the control item number of the corresponding assessment report, ensuring interaction nodes can be accurately located. - Simulate 2-3 concurrent calls per second. View the log storage directory to confirm no data overwriting or write failure error messages appear.
- Trigger the Feishu webhook test flow. Check if the spliced loop text is fully recorded in the log, with no field truncation or abnormal errors.
The values provided are common starting points and should be measured against the reader's own samples.
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.