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Introduction:
As organizations rapidly adopt artificial intelligence, the security posture around AI assets often lags behind, creating new attack surfaces. Microsoft has launched the Security Dashboard for AI (Preview), a unified, real‑time dashboard that aggregates AI security insights across Microsoft Entra, Defender, Purview, and Security Copilot. This tool enables security teams to discover shadow AI, prioritize risks, and drive remediation—all from a single pane of glass.
Learning Objectives:
- Understand the core capabilities of Microsoft Security Dashboard for AI.
- Learn how to access, navigate, and interpret the dashboard’s risk data.
- Implement practical remediation steps based on dashboard insights using Azure CLI, PowerShell, and Microsoft 365 Defender.
You Should Know:
- Getting Started: Accessing the Microsoft Security Dashboard for AI
The dashboard is available in preview at https://ai.security.microsoft.com. To access it, you need appropriate roles: Global Administrator, Security Administrator, or Security Reader in Azure Active Directory. Ensure your tenant has the required licenses (Microsoft 365 E5, or standalone Defender for Cloud Apps and Purview). Once signed in, you’ll see an overview of AI asset discovery, risk scores, and compliance status.
Step‑by‑step:
- Navigate to https://ai.security.microsoft.com and sign in with your work account.
- If prompted, accept the preview terms.
- Explore the main sections: “AI assets,” “Risk posture,” and “Remediation recommendations.”
- Use the “Add filter” option to narrow down by app type, risk level, or department.
2. Discovering AI Assets in Your Environment
The dashboard leverages Microsoft Defender for Cloud Apps and Microsoft Purview to automatically discover AI applications and data interactions. It detects sanctioned and unsanctioned AI tools (e.g., ChatGPT, Copilot instances) and maps their usage.
To manually verify AI app activity, run an advanced hunting query in Microsoft 365 Defender:
CloudAppEvents
| where Timestamp > ago(7d)
| where Application has_any ("AI", "ChatGPT", "Copilot", "OpenAI")
| project Timestamp, AccountDisplayName, Application, ActionType, IPAddress
| sort by Timestamp desc
For a broader view, use Azure CLI to list service principals associated with AI apps:
az ad sp list --query "[?contains(displayName, 'AI') || contains(displayName, 'Chat')].{Name:displayName, AppId:appId}" --output table
3. Assessing AI Risk Posture
The dashboard provides a risk score based on factors like data exposure, app permissions, and compliance violations. For example, it flags AI apps with over-privileged OAuth grants or those accessing sensitive data.
To check OAuth permissions for an AI app via PowerShell (Microsoft Graph):
Connect-MgGraph -Scopes "Application.Read.All"
$apps = Get-MgServicePrincipal -All | Where-Object {$_.DisplayName -like "AI"}
foreach ($app in $apps) {
Write-Host "App: $($app.DisplayName)"
Get-MgServicePrincipalOauth2PermissionGrant -ServicePrincipalId $app.Id | Format-List
}
4. Prioritizing Risks with MITRE ATT&CK Mapping
Microsoft maps detected AI risks to the MITRE ATLAS framework (Adversarial Threat Landscape for AI Systems). Common techniques include prompt injection, model theft, and data poisoning. The dashboard highlights these in the “Threat techniques” section.
You can generate a prioritized report using the Microsoft 365 Defender API. For example, to retrieve AI‑related incidents:
curl -X GET "https://api.security.microsoft.com/api/incidents" \
-H "Authorization: Bearer $token" \
-H "Content-Type: application/json" | jq '.value[] | select(.title | contains("AI"))'
5. Remediation Actions: From Dashboard to Fix
When the dashboard flags a risky AI app, you can take immediate action:
– Block the app via Defender for Cloud Apps: In the dashboard, select the app and choose “Block app.”
– Apply a Data Loss Prevention (DLP) policy in Microsoft Purview to prevent sensitive data from being shared with AI tools.
Example: Create a DLP policy using PowerShell:
Connect-IPPSSession New-DLPCompliancePolicy -Name "Block AI Data Exfiltration" -Mode Enable New-DLPComplianceRule -Name "No sensitive data to AI" -Policy "Block AI Data Exfiltration" -BlockAccess $true
6. Integrating with Microsoft Security Copilot
Security Copilot can assist in investigating AI risks. From the dashboard, you can pivot to Copilot and ask natural language questions:
– “Summarize the top three AI risks in my environment.”
– “Show me all incidents involving prompt injection.”
– “Recommend remediation steps for over‑privileged AI apps.”
To enable this, ensure Security Copilot is provisioned and integrated with your Microsoft 365 Defender workspace.
7. Continuous Monitoring and Compliance Reporting
Set up automated reports to track AI security posture over time. Use the Microsoft 365 Defender API to schedule data extraction and feed it into a SIEM or a Power BI dashboard.
Example: A Power Automate flow can trigger daily and send an email summary:
– Trigger: Recurrence (daily)
– Action: Send HTTP request to Microsoft 365 Defender API for AI asset count.
– Action: Compose email with risk score and top threats.
Alternatively, use the following PowerShell script to export a daily report:
$report = Invoke-RestMethod -Uri "https://api.security.microsoft.com/api/ai/risks" -Headers @{Authorization = "Bearer $token"}
$report | Export-Csv "AI_Risk_Report_$(Get-Date -Format 'yyyyMMdd').csv"
What Undercode Say:
- Key Takeaway 1: The Microsoft Security Dashboard for AI centralizes previously siloed AI risk data, enabling rapid discovery of shadow AI and misconfigurations.
- Key Takeaway 2: Proactive remediation—such as blocking over‑privileged apps and enforcing DLP—is now streamlined through dashboard‑driven workflows.
- Analysis: With AI adoption accelerating, security teams face new threats like prompt injection and model theft. Microsoft’s dashboard fills a critical gap by providing visibility and actionable intelligence. However, it’s only a first step; organizations must combine these insights with robust governance, employee training, and continuous monitoring. The preview phase suggests Microsoft will soon integrate more automated responses and adversarial ML detection, making AI security an integral part of daily SecOps.
Prediction:
Within the next 12–18 months, AI‑specific security dashboards will become as essential as traditional SIEMs. Expect to see deeper integration with threat intelligence feeds, real‑time model behavior analysis, and automated incident response tailored to AI workloads. As attackers increasingly target AI supply chains, these tools will evolve to detect data poisoning, model inversion, and other advanced adversarial techniques before they cause damage.
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