Uncovering Insider Risks with Full Summary in Uncoder AI: A Microsoft Defender for Endpoint Case

Identifying unauthorized access to sensitive data—especially passwords—remains a critical concern for cybersecurity teams. When such access happens through legitimate tools like Notepad, visibility becomes a challenge. But with Uncoder AI’s Full Summary feature, security analysts can immediately understand the logic behind detection rules targeting exactly that type of threat.

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In a recent case, a Microsoft Defender for Endpoint (MDE) query was used to monitor whether sensitive files (such as password*.txt or password*.xls ) were opened using Notepad, triggered via Windows Explorer (explorer.exe). This behavior, while not inherently malicious, can signal data leakage, insider misuse, or unintentional exposure.

Full Summary: From Raw Query to Real Insight

Rather than spend valuable time dissecting the query’s components, analysts using Full Summary were presented with a structured explanation. The AI broke the rule down into three core elements:

1. Explorer.exe as the initiator – ensuring the file open event came from typical user interaction.
   
   
2. Notepad.exe as the tool used – a benign app, often leveraged for quick file viewing.
   
   
3. Password-related filenames – specifically .txt , .csv , .doc , .xls extensions containing the keyword “password”.

Input we used (click to show the text)

DeviceProcessEvents | where (InitiatingProcessFolderPath endswith @'\explorer.exe' and FolderPath endswith @'\notepad.exe' and (ProcessCommandLine endswith @'password*.txt' or ProcessCommandLine endswith @'password*.csv' or ProcessCommandLine endswith @'password*.doc' or ProcessCommandLine endswith @'password*.xls'))

Why This Matters

By exposing attempts to open files likely containing passwords using Notepad, the detection rule uncovers subtle signals of:

• Insider threat activity
  
• Data exfiltration via native apps
  
• Non-compliance with sensitive data handling policies
  

Uncoder AI’s Full Summary helped bridge the gap between raw KQL-like syntax and investigative action. It gave threat hunters immediate clarity on the behavior being flagged and reduced the margin for misinterpretation.

Faster Response. Deeper Confidence.

What previously required a manual rule breakdown and internal documentation lookup is now handled by AI in seconds. Analysts can instantly understand if a detection targets potential data leakage, inappropriate access, or regulatory violations.

In this use case, the team didn’t just gain time—they gained certainty. And when you’re dealing with sensitive data, that certainty can be the difference between stopping a breach and writing a report after the fact.

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