How Desktop Engineers Can Safely Use AI for Intune Detection Rules

Artificial Intelligence has fundamentally changed the way we write automation. You can now generate a 50-line PowerShell script for a custom Intune detection rule in the time it takes to sip your coffee.

But there’s a catch. AI is confident, fast, and occasionally completely wrong.

If you blindly copy-paste AI-generated code into your production Intune tenant, you aren’t an engineer—you’re a gambler. Here is how to harness the speed of AI while maintaining the rigor of a senior desktop engineer.

The Real-World Enterprise Failure Scenario

Let’s look at a disaster that actually happened when an engineer trusted an LLM too much.

The Goal: Deploy a critical VPN client update via Intune to 5,000 remote Windows devices. The Prompt: “Write a PowerShell detection script for Intune to check if the VPN client version is greater than 5.2.”

The AI confidently spit out a script that used Get-WmiObject to query the Win32_Product class, checked the version, and returned an exit code. The engineer copied it, pasted it into the Intune portal, and assigned it to the All Corporate Devices group.

The Failure:

Win32_Product is notoriously slow and triggers a Windows Installer reconfiguration for every MSI package on the system. This caused CPU spikes and massive performance degradation on all 5,000 machines.
The AI’s script logic returned Exit 0 (success) when the version was less than 5.2 because of a flipped comparison operator (-lt instead of -gt), but printed “Installed” to standard output. Intune reads both STDOUT and exit codes for PowerShell detection rules. The conflicting logic caused Intune to enter an endless retry loop.

The result? 5,000 machines grinding to a halt, a flooded helpdesk, and a very uncomfortable meeting with the CIO.

⚠️ The Hallucination Warning: Where AI Lies

When it comes to Intune detection rules, AI models consistently hallucinate in three specific areas:

Context Confusion (System vs. User): AI often forgets that Intune Win32 apps typically run in the SYSTEM context. It will happily give you a script that checks HKCU (HKEY_CURRENT_USER) or $env:APPDATA. Under the SYSTEM context, these point to the hidden system profile, not the logged-in user. Your detection will fail 100% of the time.
Fake Cmdlets and Registry Keys: LLMs will invent PowerShell cmdlets that sound completely real (e.g., Get-IntuneAppStatus) or guess registry paths based on standard naming conventions rather than reality.
Misunderstanding Intune’s Output Requirements: Intune requires a specific output format for PowerShell detection rules. The script must write a string to STDOUT (like “Installed”) AND exit with code 0. AI often writes scripts that just return a boolean ($true or $false) without the proper Write-Host and Exit 0 formatting Intune expects.

The Practical Operator Checklist

Before you upload any AI-generated detection script to Intune, run it through this mandatory checklist:

Verify the Execution Context: If the app installs as System, ensure the script is querying HKLM, C:\Program Files, or C:\ProgramData. No HKCU or $env:UserProfile.
Confirm Intune Output Requirements: Does the script explicitly write to standard output (Write-Host "Found") when the app is detected, and exit with 0? Does it exit silently with a non-zero code when not detected?
Check for Deprecated/Dangerous Cmdlets: Replace any instance of Get-WmiObject with Get-CimInstance. Absolutely NEVER use Win32_Product.
Test Locally as SYSTEM: Use psexec -s -i powershell.exe to open a command prompt as the SYSTEM account on a test machine. Run the AI-generated script there first. Does it work?
Review Comparison Operators: Double-check version comparisons. Ensure the script casts strings to [version] types before comparing (e.g., [version]$currentVersion -ge [version]"5.2.0"). AI often does simple string comparisons, which will evaluate “10.0” as less than “9.0”.

The Bottom Line

AI won’t replace desktop engineers, but desktop engineers who use AI effectively—and safely—will replace those who don’t. Use LLMs as your high-speed drafting tool, but never forget that you are the editor, the QA tester, and the gatekeeper to your production environment.