What the data for this use case looks like
Typical users of this use case include security operations engineers, WAF or IDS product administrators, and SOC teams. Data primarily comes from WAF, IDS, firewalls, and threat intelligence platforms. Update cadence is daily or triggered by protection events. Daily produced documents include rule change tickets, block logs, false positive analysis reports, and policy rollback records. Document fields include rule unique identifier, change operation type, associated block event ID, false positive trigger frequency, and similar items. Common units are event counts, character counts, and timestamp formats. Data flows through the full lifecycle of protection policies, covering rule creation, gray release, and rollback. It provides core basis for multi-turn analysis in rule operations.
Constraints for Multi-turn Dialogue and Prompts
Dispersed data sources and high update frequency require multi-turn dialogue to support association of logs and change records from different systems. It avoids context fragmentation. Daily documents include multiple specialized fields. Prompts must explicitly specify extraction of specific fields such as rule ID and false positive frequency. This avoids confusion with generic data fields. False positive analysis and rule review require access to historical samples. Dialogue must retain complete context links. This supports cross-turn sample comparison and policy tuning. At the same time, protection policy changes affect subsequent analysis in real time. Dialogue context must automatically include the latest rule and log data. This ensures real-time accuracy of analysis basis.
How to Configure Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Context retention duration | 1200–1800 seconds | Covers the full multi-turn dialogue cycle for a single rule review or false positive analysis |
| Historical session binding | Bind to current chatId | Ensures multi-turn dialogue associates rule and log data for the same protection scenario |
| Variable injection | Inject rule ID, block event count | Aligns with specialized fields of the use case data, improves prompt accuracy |
| Input and output token counting | Enable separate counting | Meets actual needs for viewing token usage |
| Historical message limit | First 8 historical conversations | Avoids context overload interfering with rule analysis logic |
This configuration guidance applies to FastGPT v4.17.0.
Three Common Configuration Errors
- Phenomenon: Cannot directly call historical block logs or rule change records in multi-turn dialogue. Associated fields are empty. Cause: Did not bind the current session
chatId, or set context retention duration too short. Historical data fails to load. - Phenomenon: Tool call return content includes unredacted sensitive information. Sensitive content output in AI dialogue cannot be disabled. Cause: Did not configure variable filtering rules, and did not exclude sensitive fields such as customer IP and policy details from dialogue context. Sensitive information is returned with dialogue content.
- Phenomenon: Prompts use markdown syntax to optimize rule descriptions. Returned results are raw text with syntax symbols, not rendered as rich text. Cause: Did not explicitly require content to be rendered as markdown format in the prompt, or did not enable content formatting related configurations. Syntax symbols are not processed.
How to Verify Successful Configuration
- Initiate a dialogue containing a known rule ID. Check if corresponding block logs and change records can be associated. Confirm context binding is effective.
- After configuring variable filtering rules, initiate a dialogue containing customer IP. Check if returned content is redacted. Confirm sensitive content control is effective.
- Input a prompt containing markdown-formatted rule descriptions. Check if returned results are rendered as rich text. Confirm formatting configuration is effective.
- View the dialogue statistics panel. Confirm input and output tokens display separate values. Confirm token counting configuration is effective.
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.