What the data for this category looks like
Typical stakeholders for this category include security operation engineers, WAF or IDS product administrators, and SOC teams. Daily processed documents include rule change tickets, blocking logs, false positive analysis reports, and policy rollback records. Data sources are mainly WAF, IDS, firewall devices, and threat intelligence platforms. Update frequency is daily or triggered by security incidents. Document fields include rule ID, blocking threshold, false positive rate, policy effective time, and others. There are no unified fixed units, and matching must be done according to specific business scenarios.
What constraints these characteristics impose on the "workflow orchestration" link
Decentralized data sources and inconsistent update frequencies require workflows to support multi-source data access and dynamic triggering. There are no unified units for document fields, so workflow configuration parameter mapping capabilities are needed to standardize fields from different sources. The most time-consuming links of false positive sample analysis and rule gray release require workflows to include branch nodes such as approval, review, and rollback. Since errors directly affect business and security, failure alerts and automatic rollback mechanisms must be configured to avoid normal business being blocked or attacks being missed.
How to set configurations
This page’s configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
variables | Configured as an object containing knowledge base ID, rule ID, and false positive rate | Supports dynamic input of multiple types of business data to adapt to document drafting requirements for different scenarios |
| Node trigger condition | Set to trigger after rule change ticket upload or blocking log generation | Aligns with the business process of security product rule operations, automatically starts the document drafting workflow |
| Failure retry count | Set to 2 times | Prevents workflow interruption from single exceptions, while reducing resource consumption from repeated triggers |
| Timeout period | Set to 300 seconds | Adapts to the time required for batch blocking log analysis and false positive sample verification |
| Dynamic parameter mapping | Map fields from blocking logs to the variables parameter | Ensures workflow nodes can accurately call corresponding business data |
| Document upload limit | Set to single file no larger than 100 MB | Adapts to the conventional size of daily rule change tickets and false positive analysis reports |
Three common configuration errors
- Symptom: Workflow prompts missing parameters when calling the knowledge base. Cause: The knowledge base ID was not written to the
variablesparameter, and only configuring the knowledge base in global settings without completing node-level binding. - Symptom: Workflow analysis is not triggered after document upload. Cause: The trigger association between the document upload node and subsequent rule drafting nodes was not configured, causing the process to fail to start automatically.
- Symptom: AI-generated documents do not reference specified blocking log data. Cause: The log fields were not correctly bound to the
variablesparameter during dynamic parameter mapping, causing the search node to call the wrong data source.
How to verify correct configuration
- Upload a rule change ticket that meets the size limit, check whether the workflow management panel automatically generates a running instance.
- View the workflow running log to confirm that the
variablesparameter contains the correct knowledge base ID and business field mapping relationship. - Trigger a simulated blocking log generation event, check whether the workflow executes the parameter mapping and knowledge base search steps as configured.
- Manually trigger a workflow exception interruption, confirm that the system triggers the alert mechanism after reaching the retry count.
The values listed here are common starting points, and should be evaluated 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.