From Domain Lists to Decisions: Scaling WHOIS Threat Intelligence with MDTI and Microsoft Graph
The real problem isn't WHOIS. It's what WHOIS costs you at scale. Ask any SOC analyst whether WHOIS data is useful and they'll say yes without hesitation. Registrant emails, creation dates, registrars, and nameservers are among the most durable pivots in domain-based threat intelligence — the threads that tie a single phishing domain back to an entire adversary campaign. Now ask them how often they actually pull WHOIS for every suspicious domain in an alert. The honest answer is: not always. And that gap is the real problem. Here's why. When a campaign surfaces two hundred lookalike domains, manual WHOIS becomes a losing game: It's slow. One-domain-at-a-time portal lookups don't finish before the incident clock runs out. It's inconsistent. Different analysts capture different fields, so the data can't be compared or correlated. It's un-auditable. Screenshots and copy-paste don't hold up in an investigation review months later. It loses the pattern. By the time you've checked twenty domains by hand, the shared registrant email that connected them has scrolled off the screen. The outcome of all this friction is the part that should worry a security leader: analysts quietly stop enriching, or enrich only a sample. The pivot that would have collapsed twenty alerts into one campaign never happens. The newly registered domain that should have been escalated gets triaged as routine. The intelligence exists — the process just can't reach it fast enough. The outcome we're actually after Reframe the goal. This isn't a project to "run WHOIS." It's a project to produce a specific set of outcomes: Every suspicious domain gets enriched — not a hand-picked sample. Enrichment is consistent and structured, so patterns across domains become visible instead of buried. The output is audit-ready — defensible in an investigation and reusable in a report. The whole thing is fast enough to happen inside the incident, not the day after. Get-MDTIWhois.ps1 exists to deliver exactly those outcomes. It reads a list of domains, calls Microsoft Defender Threat Intelligence (MDTI) through Microsoft Graph, and returns both a normalized CSV for immediate triage and the raw JSON for deep investigation — in one pass, read-only, with nothing changed in your environment. What changes for the analyst The clearest way to see the value is the before-and-after. Before: An alert fires on a suspected phishing campaign with two hundred candidate domains. The analyst opens a portal, checks a handful of domains, eyeballs a few registrant fields, and makes a judgment call on incomplete data. Correlation is mental and fragile. Twenty related alerts stay twenty separate alerts. After: The analyst drops the domains into a text file, runs one command, and gets a spreadsheet where all two hundred are enriched with the same fields. Sorting by registrant email instantly reveals that forty of them share one address, one budget registrar, and creation dates in the same week. Twenty alerts become one campaign. One judgment call becomes one defensible conclusion. That's the outcome: not "we ran WHOIS," but "we saw the campaign the manual process would have missed." Where the value shows up This translates into concrete wins across several SOC workflows: Faster campaign triage. Shared registrant emails and nameservers collapse dozens of alerts into a single adversary-infrastructure story — directly reducing mean-time-to-respond. Better newly-registered-domain hunting. Enrich domains from DNS or proxy logs, sort by registration date, and surface the freshly registered domains that are disproportionately malicious — before they're clicked. Enrichment that feeds the platform. The normalized CSV drops straight into a Sentinel watchlist, or the same Graph call can be wrapped in a Logic App / Azure Function playbook to auto-enrich domain entities the moment an incident is created. Reusable intelligence. Registrant and nameserver pivots become custom indicators and hunting queries — value that compounds over time instead of being thrown away after each investigation. Why this approach, specifically There are other ways to get WHOIS. A few reasons this approach earns its place in a modern, Defender-portal-centric SOC: It uses first-party intelligence. MDTI is Microsoft's own threat-intelligence graph, queried natively through Microsoft Graph — no third-party data broker, no separate contract, no data leaving your Microsoft trust boundary. It's read-only by design. The script cannot modify Sentinel, Defender, tenant settings, or domain records. Enrichment tooling should never be able to change the environment it observes — and this one can't. It's built to survive real-world APIs. Throttling (HTTP 429) and transient 5xx errors are retried with backoff; permanent misses (404) aren't retried needlessly; a single failed domain never aborts the run. You always get a complete, dependable result set. It handles secrets the way you'd want. Client secrets come from an environment variable or a secure prompt — never hard-coded, never committed to source control. The engineering exists to protect the outcome: a complete, consistent, trustworthy enrichment set every single time, even when the input list is messy or the API is having a bad day. The output is built for decisions, not just data Two files, two jobs: The normalized CSV is the decision surface. One row per domain, every registrant/registrar/date/nameserver field an analyst pivots on — sortable, filterable, watchlist-ready. This is where twenty alerts become one campaign. The raw JSON is the safety net. Full API fidelity per domain, preserved for the audit trail and for the registrar-specific edge cases the normalized view can't anticipate. Together they answer both questions a SOC actually asks: "What do I do right now?" (CSV) and "Can I stand behind it later?" (JSON). What it takes to get there The barrier to entry is low: An Entra ID app registration with the Graph application permission ThreatIntelligence.Read.All (admin-consented), on a tenant with MDTI API access. A text file of domains, one per line. One command: $env:MDTI_CLIENT_SECRET = "<client-secret>" .\Get-MDTIWhois.ps1 ` -TenantId "<tenant-id>" ` -ClientId "<client-id>" ` -InputTxt ".\domains.txt" Under the covers it authenticates with a client-credentials grant, normalizes and de-duplicates the input, calls GET /security/threatIntelligence/hosts/{domain}/whois per domain, and writes both outputs. Minutes of setup; a repeatable capability afterward. Use it responsibly The project is provided as-is, for educational and demonstration purposes, and is not intended for production use without independent review, validation, and monitoring. Keep in mind: A 404 means MDTI has no current WHOIS record for that host — not that the domain is safe. WHOIS fields vary by registrar — always retain the raw JSON for anything you'll act on. WHOIS can contain personal data — validate your storage and sharing obligations before distributing enrichment output. Never commit secrets — use the environment-variable or secure-prompt path. The bottom line The value here isn't that WHOIS is difficult. It's that the friction of doing WHOIS well, for everything, quietly erodes detection quality — analysts under time pressure enrich less, correlate by memory, and miss the campaign hiding in plain sight. Get-MDTIWhois.ps1 removes that friction. It turns a flat list of domains into structured, audit-ready intelligence in a single read-only pass, so the pivot that reveals the adversary's infrastructure actually happens — inside the incident, not after it. That's the outcome worth automating. Disclaimer This article and the referenced tooling are provided “as-is,” for educational and demonstration purposes only, without warranties of any kind, express or implied. They are not intended for production, safety-critical, or regulated use without independent review, testing, validation, and appropriate safeguards. The views expressed are the author’s own and do not necessarily represent those of any employer or of Microsoft. You are solely responsible for how you deploy, modify, or adapt this tooling, and for ensuring compliance with all applicable laws, regulations, licensing terms, privacy obligations, and organizational security policies. Source: MDTI-WHOIS in the Microsoft Security Operations Toolkit. Provided as-is for educational purposes; test in a controlled environment before production use.
Creating Custom Intune Reports with Microsoft Graph API
Systems administrators often need to be able to report on data that is not available in the native reports in the Intune console. In many cases this data is available to them through Microsoft Graph. However, in some instances administrators may need to pull data from other sources or store it for tracking trends over time. For example, generating a custom dashboard to track Windows 365 license costs requires pulling data from Microsoft Graph and combining it with licensing details that are not available in Graph, but may be stored in another location (an IT Asset Management Tool for example). The Windows 365 Cost Dashboard is an example of how you can combine Intune data from Microsoft Graph with information pulled from another source. This guide provides step-by-step instructions to pull data from Microsoft Graph API, ingest it to Azure Log Analytics, and connect to your workspace with Power Bi. This solution demonstrates how to gather and store Graph API data externally for richer reporting and integrate it with data from an additional data source to produce a dashboard tailored to your unique needs. By using this dashboard as an example, administrators can unlock deeper insights while leveraging Intune's powerful foundation. The solution: This dashboard and the accompanying PowerShell script are meant to demonstrate an end-to-end example of gathering data from Microsoft Graph and ultimately being able to visualize it in a Power Bi dashboard. While it does create the Azure Infrastructure needed to complete the scenario in the demonstration, it can be extended to gather and report additional information. What does this do? This example consists of two separate pieces – the Power Bi dashboard and a PowerShell script that creates all the Azure resources needed to gather data from Microsoft Graph and ingest it into a Log Analytics workbook. This post will discuss all of the infrastructure elements that are created and the steps to get your data from Log Analytics into the Power Bi dashboard, but I want to strip away all of the “extra” elements and talk about the most important part of the process first. Prerequisites The scripts shared in this blog post assume that you already have an Azure subscription and a resource group configured. You need to have an account with the role of “Owner” on the resource group (or equivalent permissions) to create resources and assign roles. The account will also need to have the “Application Developer” role in Entra Active Directory to create an App Registration. To run the resource creation script, you will need to have several modules available in PowerShell. To see the full list please review the script on GitHub. From Microsoft Graph API to Log Analytics: How we get there Microsoft Graph API can give us a picture of what our environment looks like right now. Reporting on data over time requires gathering data from Graph and storing it in another repository. This example uses a PowerShell script running in Azure Automation, but there are several different ways to accomplish this task. Let’s explore the underlying process first, and then we can review the overall scope of the script used in the example. The Azure Automation runbook [CloudPCDataCollection] calls Graph API to return details about each Windows 365 Cloud PC. It does this by making GET requests to the following endpoints: https://graph.microsoft.com/beta/deviceManagement/virtualEndpoint/cloudPCs https://graph.microsoft.com/v1.0/users/<userPrincipalName> As a best practice, we should only return the properties from an API endpoint that we need. To do that, we can append a select query to the end of the URI. Queries allow us to customize requests that are made to Microsoft Graph. You can learn more about Select (and other query operators) here. The example dashboard allows you to report on Windows 365 cost over time based on properties of the device (the provisioning policy, for example), or the primary user (department). We will request the Cloud PCs id, display name, primary user’s UPN, the service plan name and id (needed to cross reference our pricing table in Power Bi), the Provisioning Policy name, and the type (Enterprise, Frontline dedicated, or Frontline Shared). The complete URI to return a list of Cloud PCs is: https://graph.microsoft.com/beta/deviceManagement/virtualEndpoint/cloudPCs?$select=id,displayName,userPrincipalName,servicePlanName,servicePlanId,ProvisioningPolicyName,ProvisioningType Once we have a list of Cloud PCs, we need to find the primary user for each device. We can return a specific user by replacing the <userPrincipalName> value in the users URI above with the primary user UPN for a specific Cloud PC. Since we only need the department, we will minimize the results by only selecting the userPrincipalName (for troubleshooting), and department. The complete URI is: https://graph.microsoft.com/v1.0/users/<userPrincipalName>?$select=userPrincipalName,department Data sent to a data collection endpoint needs to be formatted correctly. Requests that don’t match the required format will fail. In this case, we need to create a JSON payload. The properties in the payload need to match the order of the properties in the data collection rule (explained later) and the property names are case sensitive. The automation script handles the creation of the JSON object, including matching the case and order requirements as shown here: # Get Cloud PCs from Graph try { $payload = @() $cloudPCs = Invoke-RestMethod -Uri 'https://graph.microsoft.com/beta/deviceManagement/virtualEndpoint/cloudPCs?$select=id,displayName,userPrincipalName,servicePlanName,servicePlanId,ProvisioningPolicyName,ProvisioningType' -Headers @{Authorization="Bearer $($graphBearerToken.access_token)"} $CloudPCArray= @() $CloudPCs.value | ForEach-Object { $CloudPCArray += [PSCustomObject]@{ Id = $_.id DisplayName = $_.displayName UserPrincipalName = $_.userPrincipalName ServicePlanName = $_.servicePlanName ServicePlanId = $_.servicePlanId ProvisioningPolicyName = $_.ProvisioningPolicyName ProvisioningType = $_.ProvisioningType } } # Prepare payload foreach ($CloudPC in $CloudPCArray) { If($null -ne $CloudPC.UserPrincipalName){ try { $UPN = $CloudPc.userPrincipalName $URI = "https://graph.microsoft.com/v1.0/users/$UPN" + '?$select=userPrincipalName,department' $userObj = Invoke-RestMethod -Method GET -Uri $URI -Headers @{Authorization="Bearer $($graphBearerToken.access_token)"} $userDepartment = $UserObj.Department } catch { $userDepartment = "[User department not found]" } } else { $userDepartment = "[Shared - Not Applicable]" } $CloudPC | Add-Member -MemberType NoteProperty -Name Department -Value $userDepartment $CloudPC | Add-Member -MemberType NoteProperty -Name TimeGenerated -Value (Get-Date).ToUniversalTime().ToString("o") $payload += $CloudPC } } catch { throw "Error retrieving Cloud PCs or user department: $_" } After the payload has been generated, the script sends it to a data collection endpoint using a URI that is generated by the setup script. # Send data to Log Analytics try { $ingestionUri = "$logIngestionUrl/dataCollectionRules/$dcrImmutableId/streams/$streamDeclarationName`?api-version=2023-01-01" $ingestionToken = (Get-AzAccessToken -ResourceUrl 'https://monitor.azure.com//.default').Token Invoke-RestMethod -Uri $ingestionUri -Method Post -Headers @{Authorization="Bearer $ingestionToken"} -Body ($payload | ConvertTo-Json -Depth 10) -ContentType 'application/json' Write-Output "Data sent to Log Analytics." } catch { throw "Error sending data to Log Analytics: $_" } Getting access tokens with a managed identity Security should be top of mind for any Systems Administrator. When making API calls to Microsoft Graph, Azure, and other resources you may need to provide an access token in the request. Access to resources controlled with an App Registration in Entra. In the past, this required using either a certificate or client secret. Both options create management overhead, and client secrets that are hard coded in scripts present a considerable security risk. Managed identities are managed entirely by Entra. There is no requirement for an administrator to manage certificates or client secrets, and credentials are never exposed. Entra recently introduced the ability to assign a User-assigned managed identity as a federated credential on an App Registration. This means that a managed identity can now be used to generate an access token for Microsoft Graph and other azure resources. You can read more about adding the managed identity as a federated credential here. Requesting an access token via federated credentials happens in two steps. First, the script uses the managed identity to request a special token scoped for the endpoint ‘api://AzureADTokenExchange'. #region Step 2 - Authenticate as the user assigned identity #This is designed to run in Azure Automation; $env:IDENTITY_header and $env:IDENTITY_ENDPOINT are set by the Azure Automation service. try { $accessToken = Invoke-RestMethod $env:IDENTITY_ENDPOINT -Method 'POST' -Headers @{ 'Metadata' = 'true' 'X-IDENTITY-HEADER' = $env:IDENTITY_HEADER } -ContentType 'application/x-www-form-urlencoded' -Body @{ 'resource' = 'api://AzureADTokenExchange' 'client_id' = $UAIClientId } if(-not $accessToken.access_token) { throw "Failed to acquire access token" } else { Write-Output "Successfully acquired access token for user assigned identity" } } catch { throw "Error acquiring access token: $_" } #endregion That token is then exchanged in a second request to the authentication endpoint in the Entra tenant for a token that is scoped to access 'https://graph.microsoft.com/.default' in the context of the App Registration. #region Step 3 - Exchange the access token from step 2 for a token in the target tenant using the app registration try { $graphBearerToken = Invoke-RestMethod "https://login.microsoftonline.com/$TenantId/oauth2/v2.0/token" -Method 'POST' -Body @{ client_id = $appClientId scope = 'https://graph.microsoft.com/.default' grant_type = "client_credentials" client_assertion_type = "urn:ietf:params:oauth:client-assertion-type:jwt-bearer" client_assertion = $accessToken.access_token } if(-not $graphBearerToken.access_token) { throw "Failed to acquire Bearer token for Microsoft Graph API" } else { Write-Output "Successfully acquired Bearer token for Microsoft Graph API" } } catch { throw "Error acquiring Microsoft Graph API token: $_" } #endregion Azure Resource Creation Script The PowerShell script included in this example will complete the following tasks: Creates a Log Analytics Workspace Define a custom table in the newly created workspace to store Cloud PC data Configure a data collection endpoint and data collection rule to ingest data into the custom table Create an Azure Automation account and runbook to retrieve data from Microsoft Graph and send it to the data collection endpoint Establish a User Assigned Managed Identity to run the data collection script from Azure Automation Register an App and assign a service principal with required Microsoft Graph permissions Add the Managed Identity as a federated credential within the App Registration Assign workbook operator and Monitoring Metrics Publisher roles to the Managed Identity Steps to Implement: 1. Download the script and Power BI Dashboard: Download the Power Bi dashboard and PowerShell script from GitHub: Windows 365 Custom Report Dashboard 2. Update Variables: Modify the PowerShell script to include your Tenant ID, Resource Group Name, and location Adjust other variables to fit your specific use case while adhering to Azure naming conventions 3. Run the PowerShell Script: Execute the script to create the necessary Azure resources and configurations. 4. Verify Resource Creation: Log into the Azure Portal. Navigate to Log Analytics and confirm the creation of the W365CustomReporting workspace. Click on Settings > Tables and confirm the W365_CloudPCs_CL table was created Search for Automation Accounts and locate AzAut-CustomReporting. 5. Run the Runbook and Pull Data into Log Analytics: Open the CloudPCDataCollection runbook, select Edit > Edit in portal and the click on Test Pane. Click start to test the CloudPCDataCollection runbook and ensure data ingestion into Log Analytics. The runbook may take several minutes to run. You should see a “Completed” status message and the output should include, “Data sent to Log Analytics.” Return to the Log Analytics workspace and select “Logs.” Click on the table icon in the upper left corner of the query window. Select Custom Logs > W365_CloudPCs_CL and click on “Run.” (Please note: initial data ingestion may take several minutes to complete. If the table is not available, please check later.) The table Logs should populate with data from the last 24 hours by default. Click on Share > Export to Power BI (as an M query)Export the data to Power BI using an M query. The file should download. Open the file to view the completed query. Select the contents of the file and copy it to the clipboard. 6. Import Data into Power BI Dashboard: Open the Power BI template. In the table view on the right side of the screen, right click on the CloudPCs table and select “Edit Query.” Click on “Advanced Editor” on the ribbon to edit the query. Paste the contents of the downloaded M Query file in the editor and click “Done.” A preview of your data should appear. We need to make sure the columns match the data in the template. Right click on the “Time Generated” column and select Transform > Date Only. Right click on the same column and select “Rename.” Rename the column to “Date” Click “Close and Apply” to apply your changes and update the dashboard. 7. Update the Pricing and Service Plan Details table (Optional) The Pricing and Service Plan Details table was created via manual data entry, which allows for it to be updated directly within Power BI. To update the dashboard with your pricing information, right click on PricingAndServicePlanDetails table and select edit query Click on the gear icon to the right of “Source” Find the SKU Id that matches the Windows 365 Enterprise or Frontline licenses in your tenant; update the price column to match your pricing 8. (Optional) Update the timespan on the imported M query to view data over a longer period When we initially viewed the logs in Log Analytics, we left the time period set with the default value, “Last 24 Hours.” That means that the query that was created will only show data from the last day, even if the runbook has been configured to run on a schedule. We can edit that behavior by updating the table query. Edit the Cloud PCs table as you did before. In the advanced editor find the “Timespan” property. The Timespan value uses ISO 8601 durations to select data over a specific period. For example, “P1D” will show data from the previous 1 day. The past year would be represented by “P1Y” or “P365D”. Learn more about ISO 8601 duration format here: ISO 8601 - Wikipedia Please note that this query can only return data that is stored in Log Analytics. If you set it to “P1Y,” but only have collected information from the past month, you will still only see 1 month worth of data. Parting thoughts This example demonstrates how a systems administrator can leverage Microsoft Graph, Azure Log Analytics, and Power Bi to create custom reports. The script provided creates all the required resources to create your own custom reports. You can leverage the concepts used in this example to add additional data sources and expand your Log Analytics workbooks (by adding additional columns or tables) to store other data pulled from Microsoft Graph. By following this example, Systems Administrators can build custom Intune reports that integrate data from Microsoft Graph and external sources. This solution provides comprehensive, historical reporting, helping organizations gain valuable insights into their IT environments. Additional Credit: The script to create resources was adapted from the process described by Harjit Singh here: Ingest Custom Data into Azure Log Analytics via API Using PowerShell. Please visit that post for additional information on creating the underlying resources. Limitations: This example is not intended to be ready for production use. While the script creates the underlying infrastructure, it does not automatically schedule the Azure Automation runbook, nor does it change the default retention period in Log Analytics beyond 30 days. The use of Log Analytics and Azure Automation can incur charges. You should follow your organization’s guidelines when scheduling runbooks or updating retention policies. The pricing details table was created based on the Windows 365 SKUs listed on the Product names and service plan identifiers for licensing and the corresponding retail prices for Windows 365 Enterprise and Frontline as of February 26, 2025. You may need to update the pricing details to match your license costs or connect to an outside data source where your license details are stored to accurately reflect your cost details. Disclaimer The sample scripts are not supported under any Microsoft standard support program or service. The sample scripts are provided AS IS without warranty of any kind. Microsoft further disclaims all implied warranties including, without limitation, any implied warranties of merchantability or of fitness for a particular purpose. The entire risk arising out of the use or performance of the sample scripts and documentation remains with you. In no event shall Microsoft, its authors, or anyone else involved in the creation, production, or delivery of the scripts be liable for any damages whatsoever (including, without limitation, damages for loss of business profits, business interruption, loss of business information, or other pecuniary loss) arising out of the use of or inability to use the sample scripts or documentation, even if Microsoft has been advised of the possibility of such damages.
