> For the complete documentation index, see [llms.txt](https://docs.didge.io/home/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.didge.io/home/foodcore-sensors/technical/audit-trail.md).

# Audit Trail

### Overview

The **Audit Trail** feature in the sensor management platform is a system-level traceability tool designed to maintain transparency and accountability across all administrative activities. It provides a clear, timestamped log of key actions taken on sensors or devices, allowing administrators and compliance teams to review changes, identify who made them, and understand the specific technical alterations involved.

***

### Key Functions

<figure><img src="https://2675140142-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FPnSdrHOYeIaTHbt8Z0Qc%2Fuploads%2FapiI6fksBJEoruq1hQ15%2FScreenshot%202025-04-19%20at%202.01.29%E2%80%AFpm.png?alt=media&amp;token=31741843-e772-4a13-9ee9-96bdf3f31a99" alt=""><figcaption></figcaption></figure>

#### 1. **Action Logging**

Each entry in the audit log captures the following information:

* **Time**: The exact date and time the action was performed.
* **User**: The name and email address of the person who initiated the action.
* **Action Type**: The nature of the action taken (e.g., `attachToDashboard`, `downlink`, `upsert`).
* **Raw Data (Optional)**: A technical representation of the action, useful for debugging or forensic analysis.

#### 2. **Traceability by Sensor or Device**

The system logs actions on a per-device basis. This means you can trace all relevant administrative interactions for a specific device, which is particularly useful for investigations, audits, and verifying configurations.

***

### Raw Data Panel

An optional **Raw Data** toggle reveals a JSON-formatted payload for each action. This raw view shows:

* Field-level changes (e.g., `upperThreshold`, `delay`, `label` values)
* Specific settings applied through a `downlink`
* Configuration context when attaching devices to dashboards

#### Example:

```json
{
  "label": "Drawer Chiller - Local MZP",
  "alerts": [
    {
      "capability": "temperatureC_SHT",
      "upperThreshold": 8,
      "upperCompliance": 4
    }
  ]
}
```

This provides clarity on what values were input or modified, and supports technical tracebacks if needed.

<figure><img src="https://2675140142-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FPnSdrHOYeIaTHbt8Z0Qc%2Fuploads%2F5xarxHwRpezB5JqNwCWr%2FScreenshot%202025-04-19%20at%202.01.50%E2%80%AFpm.png?alt=media&amp;token=2004d28c-3df4-4f01-b996-3fb5ccab6969" alt=""><figcaption></figcaption></figure>

***

### Use Cases

* **Change Tracking**: Identify who made specific adjustments and when.
* **Compliance Verification**: Ensure changes are aligned with food safety or operational protocols.
* **Issue Resolution**: Use raw data to diagnose configuration issues or troubleshoot sensor behavior.

***

### Benefits

* Supports **auditability** and **compliance** requirements
* Improves **team accountability**
* Enables **historical traceability** for all critical device interactions

***

### Summary

The **Audit Trail** is a crucial administrative tool for maintaining operational integrity. It not only logs what changes have occurred, but also provides visibility into the context, purpose, and users behind each action. Combined with its technical depth via the raw data option, it ensures a full audit capability for high-stakes environments such as food safety monitoring systems.


---

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