What this category of data looks like
Data for this category primarily comes from WAF, IDS, firewalls, and threat intelligence platforms. Update frequency is daily or when a protection event triggers. Daily associated documents include rule change tickets, block logs, false positive analysis reports, and policy rollback records. Block logs include fields such as request source IP, triggered rule ID, block time, and business interface identifier. False positive analysis reports must mark false positive trigger scenarios, rule matching thresholds, and business impact scope. Policy rollback records must log rollback time, pre-rollback rule version, and post-rollback status. This category directly maintains protection policies and rule sets, differing from security monitoring and SOC on-duty categories which only handle alert monitoring. This document is written for FastGPT v4.17.0.
What constraints these characteristics impose on model access and configuration
Data sources are scattered, and updates have no fixed batch window. This requires model access to support multi-data source incremental synchronization, to avoid excessive system resource usage from full pull operations. Document fields vary widely. This requires configuring models to support custom field mapping, to adapt to log formats from different protection devices. False positive analysis and rule gray release require frequent model calls. This requires configuring interface timeout thresholds to fit real-time interaction scenarios, and supporting task splitting by protection event granularity, to avoid timeouts caused by excessive single-task data volume. Additionally, policy rollback records must be associated with historical model inference results. This requires properly setting the maxContext parameter in configurations, to ensure complete context association information for subsequent audits and retrospectives.
How to configure settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the multi-field concatenated length of block logs and false positive analysis reports, to avoid context overflow |
temperature | 0.1–0.3 | Used for rule false positive analysis and policy optimization scenarios, requires consistency in inference results to reduce randomness |
vision | false | This category primarily processes structured logs and text reports, no visual model capability is needed |
AIPROXY_API_ENDPOINT | Fill in according to the root address of the AI Proxy service in the deployment environment, format example: http://aiproxy:3000, no additional /v1 or model service path required | Environment variable configured for App/Admin services, ensures stable model call links |
maxTokens | 2000–3000 token | Meets the analysis output length for a single block log or rule change ticket |
toolChoice | auto | Automatically selects appropriate tools to handle tasks such as log parsing and rule matching verification |
Three common misconfigurations
- Symptom: The selectable model list in the interface is complete, but only a small number of models appear in the tool call pop-up. Cause: Full matching for
toolChoicewas not enabled in model access configurations, only a small number of preset model tags were bound. - Symptom: Call errors occur after local deployment, even though
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare configured and the corresponding TCP port is accessible. Troubleshooting items: Variables were not configured as App/Admin server environment variables,AIPROXY_API_TOKENdoes not match the AI Proxy server configuration, the AI Proxy service is not running normally, or network policies block access from the App/Admin service to the AI Proxy service. - Symptom: False positive analysis results cannot match the triggered rule ID from the original block log. Cause: The
maxContextcontext retention parameter was not configured, causing loss of field association information for original logs during inference.
How to confirm configurations are correct
- Check that the App/Admin service startup verification passes, confirm that
AIPROXY_API_ENDPOINT(AI Proxy service root address, format example: http://aiproxy:3000, no additional path required) andAIPROXY_API_TOKEN(AI Proxy administrator Token, consistent with server configuration and non-empty) are correctly configured as server environment variables. - Upload a single typical block log sample, trigger a model inference task, check that the output includes expected content such as rule matching verification and false positive possibility analysis.
- View the tool call configuration panel, confirm that
toolChoiceis set toauto, and all connected models are bound to the corresponding task scenarios. - Adjust the
temperatureparameter to 0.2, trigger inference for the same log three times repeatedly, confirm that result differences are within a reasonable range.
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.