> ## Documentation Index > Fetch the complete documentation index at: https://docs.siftstack.com/llms.txt > Use this file to discover all available pages before exploring further. # Step 4: Capture and detect an event in the data with a Rule export const SiftIcon = ({className}) => ; ## Overview Now that you have explored the dataset and visualized key signals, this step shows you how to formalize your observation by creating a [Rule](/documentation/reference/rule-settings). In Sift, Rules are reusable logic expressions that automatically detect patterns or thresholds in telemetry data. When you notice an unusual behavior such as a sudden temperature drop, you can turn that insight into a Rule so that future data is automatically monitored for similar conditions. In the dataset, we can see a sharp and sudden decline in both **max\_air\_temp(C)** and **max\_ground\_temp(C)**. The image below shows that on October 6, 2021, at 17:00:00, both signals dropped well below their normal range. This event may reflect an unexpected environmental condition, like a cold front, or possibly a brief telemetry issue. By capturing this behavior in a Rule, you ensure that future datasets are automatically checked for similar anomalies and brought to your attention for review. ## Create a Rule To monitor this anomaly in future data, we will define it as a Rule that Sift can automatically detect and highlight during ongoing telemetry analysis. 1. In **Sift**, click . 2. Click the **Rules** tab. 3. Click **New Rule**. 4. In the **Rule name** box, enter the following: ``` fl_low_max_temp_anomaly ``` 5. Replace `fl` with your own initials. 6. In the **Asset name**, select your Asset (for example, **fl\_mars\_env\_monitoring\_station**). 7. In the **Input Channels** list, select the following Channels: * **max\_air\_temp(C)** * **max\_ground\_temp(C)** 8. In the **Enter an expression** box, enter: ``` $1 < -50 && $2 < -60 ``` 9. Click **Save**.