---
title: "Example of a Custom Report for Target Segments"
url: "https://dm0bkdes5q7a2ls.productfruits.help/en/article/edeb7b19-d8bc-4440-b5ef-b2216d113f47"
language: "en"
last_updated: "2026-04-30T21:22:29.552Z"
---

# Example of a Custom Report for Target Segments

A custom report for target segments is most effective when it is designed for a specific audience (like a Data Owner or an Auditor) and focuses on accountability.

Below is an example of a **"Monthly Data Risk & Remediation Report"** designed for a specific business unit (e.g., the **Finance Department**). This report assumes you have segmented your targets using the tag `Dept: Finance`.

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## Report Title: Monthly Sensitive Data Governance Summary

**Target Segment:** `Finance_Department_Assets` (All targets tagged `Dept: Finance`)  
**Reporting Period:** October 1, 2023 – October 31, 2023  
**Audience:** VP of Finance / Data Privacy Officer

### 1\. Coverage Summary (The "Are we looking?" section)

This section proves that the Finance department's assets are being monitored as promised.

-   **Total Assets in Segment:** 45 (12 Databases, 28 File Shares, 5 SharePoint Sites)
-   **Scan Success Rate:** 98% (44 of 45 targets successfully scanned this month)
-   **Last Scan Date:** All targets scanned within the last 7 days.
-   **Gaps Identified:** 1 File Share (`\\Fin-Server\Legacy_Archive`) returned an "Access Denied" error; ticket #12345 opened for permissions fix.

### 2\. Risk Findings (The "What did we find?" section)

A breakdown of the sensitive data discovered within this specific segment.

-   **Total Sensitive Matches:** 1,240
-   **Top Data Types Found:**
-   -   **Credit Card Numbers (PCI):** 850 matches (Primary location: `\\Fin-Server\Invoices`)
    -   **Social Security Numbers (PII):** 340 matches (Primary location: `\\Fin-Server\Payroll_Exports`)
    -   **Bank Account Numbers:** 50 matches
-   **Risk Trend:** **\-15% reduction** in total matches compared to last month.

### 3\. Remediation Activity (The "What did we do about it?" section)

This is the most important section for proving the value of the program. It shows the outcome of your **Playbooks**.

-   **Automated Actions Taken:**
-   -   **Shredded:** 400 files (Temporary exports older than 90 days).
    -   **Quarantined:** 150 files (Sensitive files found in "Public" folders moved to `\\Secure\Finance_Vault`).
    -   **Notified:** 25 Data Owners alerted via email regarding unencrypted spreadsheets.
-   **Manual Actions Pending:** 12 high-risk files in the `Payroll_Exports` folder require manual review by the Finance Manager.

### 4\. Top 5 "At-Risk" Locations

A targeted list to help the Finance team prioritize their manual cleanup efforts.

1.  `\\Fin-Server\Invoices\2022_Archive` (600 PCI matches)
2.  `SharePoint: Finance_Team_Site / General / Drafts` (120 PII matches)
3.  `SQL_Server: Finance_Prod / Table: Customer_Profiles` (50 Bank Account matches)
4.  ... (and so on)

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### How to Generate This Report in Spirion Sensitive Data Platform

To create this report, you would use the **Report Builder** with the following filters:

1.  **Filter by Target Tag:** Set `Target Tag` EQUALS `Dept: Finance`.
2.  **Filter by Date:** Set `Scan Date` to `Last 30 Days`.
3.  **Group By:** Group results by `Target Name` and `Data Type`.
4.  **Columns to Include:**
5.  -   Target Name
    -   Match Count
    -   Data Type
    -   Remediation Status (to show Shredded/Quarantined/Pending)
    -   Last Scan Date

### Tip: The "Zero-Risk" Goal

For highly regulated segments (like Finance), your goal is to eventually produce a report where the **"Risk Findings"** section is near zero.

If you see a spike in a custom report for a specific segment, it usually indicates a **Process Failure** (e.g., a new automated report is exporting unencrypted data to a public share). By having a custom report for that segment, you catch the spike immediately rather than losing it in the "noise" of a company-wide report.

## Summary

This custom report transforms raw technical data into a **Business Narrative**. It tells the VP of Finance: *"We are scanning your data, we found these specific risks, we have already automated the cleanup of 80% of them, and here is the small list of items your team needs to review."*