Listen to this Post
Introduction:
The rapid, decentralized adoption of Artificial Intelligence (AI) tools within enterprises has created a new and critical attack surface. While business units leverage these applications for productivity, security teams are often left in the dark, facing a proliferation of “Shadow AI”—unsanctioned tools operating outside IT governance. This lack of visibility into data traffic, risk levels, and usage patterns directly undermines compliance frameworks (like GDPR or HIPAA) and exposes organizations to data leaks, making inventory and risk assessment the new frontline of cybersecurity defense.
Learning Objectives:
- Identify and categorize sanctioned versus unsanctioned (Shadow AI) applications within your environment.
- Apply risk-scoring frameworks to AI tools based on data handling, permissions, and compliance.
- Utilize Microsoft’s security stack (Defender, Sentinel, Purview) to monitor and govern AI application usage.
You Should Know:
1. Discovery: Identifying AI Applications in Your Environment
Before you can secure AI usage, you must first see it. Most Shadow AI operates over the web, meaning your primary discovery tools are your web gateways and cloud access security brokers (CASB). In a Microsoft-centric environment, this starts with Microsoft Defender for Cloud Apps.
What this does: It analyzes traffic logs from your firewall and proxies to identify cloud and AI app usage, even from unmanaged devices.
Step‑by‑step guide to Discovery:
- Log in to the Microsoft Defender Portal (security.microsoft.com).
- Navigate to Cloud Apps > Cloud app catalog.
- Use the filters to search for categories like “AI,” “Machine Learning,” or “Large Language Models.”
- To see what your organization is actually using, go to Cloud Apps > Activity log or Discover > Discovered apps.
– Note: If the data is sparse, ensure your firewalls and proxies are forwarding logs to Defender for Cloud Apps.
5. Linux Command (Proxy Log Analysis): If using a Squid proxy, you can grep for AI domains to get a quick manual view.
Check Squid access logs for common AI tool domains
sudo grep -E "openai.com|anthropic.com|perplexity.ai|huggingface.co" /var/log/squid/access.log | awk '{print $7}' | sort | uniq -c | sort -nr
6. Windows Command (DNS Cache): On a local workstation, you can check recent DNS queries to see if AI tools have been contacted.
View DNS cache entries related to AI services (Run as Administrator) ipconfig /displaydns | findstr /i "openai azure.ai huggingface"
2. Risk Assessment: Categorizing and Scoring AI Apps
Once discovered, not all AI apps are created equal. A high-risk app might request extensive permissions (access to email, drives) or have a weak security posture regarding data residency. Defender for Cloud Apps provides a risk score based on over 80 attributes.
Step‑by‑step guide to Risk Scoring:
- In Defender for Cloud Apps, navigate to the Discovered apps page.
- Click on a specific app (e.g., a new note-taking AI tool).
- Review the Risk Score breakdown. Check factors like:
– General: Data center location (GDPR compliance).
– Security: Multi-Factor Authentication (MFA) support, encryption at rest.
– Compliance: GDPR, ISO 27001 certifications.
4. Tag the App: Create custom tags to mark the app as “Sanctioned,” “Unsanctioned,” or “Monitoring Required.”
5. PowerShell (Graph API): For programmatic risk assessment, you can use the Graph API to pull discovered app data.
Install Module (if needed)
Install-Module -Name MSAL.PS
Connect to Defender for Cloud Apps (Example Concept)
This requires an App Registration with appropriate permissions.
$discoveredApps = Invoke-RestMethod -Uri "https://your-tenant.portal.cloudappsecurity.com/api/discovered_apps/" -Headers $authHeader
$discoveredApps | Where-Object {$<em>.riskScore -eq "High" -and $</em>.category -like "AI"} | Select-Object name, riskScore, instances
3. Governing Data Traffic and Controlling Shadow AI
Discovery without action is just a report. The goal is to move from visibility to governance. You must create policies that block high-risk Shadow AI or control how data is uploaded to sanctioned tools.
Step‑by‑step guide to Creating Governance Policies:
- Access Control: In Defender for Cloud Apps, go to Policies > Policy management.
2. Create an App Discovery Policy:
- Name: Block High-Risk Shadow AI
- Category: Cloud Discovery
- Filter: Select `Risk Score` equals `High` AND `App tag` equals `Unsanctioned` (or
Not sanctioned). - Governance Action: Select Block (this pushes a rule to your integrated firewall/proxy to block the app).
- Data Loss Prevention (DLP) for AI: To prevent sensitive data from being pasted into ChatGPT (a sanctioned tool), you need to integrate Microsoft Purview.
– In Microsoft Purview compliance portal, go to Solutions > Data Loss Prevention > Policies.
– Create a policy for Teams chats and channel messages or Endpoint devices.
– Define conditions: Content contains `U.S. Social Security Number (SSN)` AND the activity is Pasting to browser.
– Action: Block the paste and show a policy tip to the user.
4. Monitoring User Behavior with Microsoft Sentinel
To detect anomalies like an employee exfiltrating a massive amount of data to an AI tool, you need a SIEM (Security Information and Event Management) like Microsoft Sentinel.
Step‑by‑step guide to KQL Detection:
1. Open Microsoft Sentinel > Logs.
- Connect the CloudAppEvents table (which ingests data from Defender for Cloud Apps).
- Run a KQL query to find large uploads to AI tools:
CloudAppEvents | where TimeGenerated > ago(24h) | where ActionType == "FileUploaded" | where Application has_any ("chat.openai.com", "bard.google.com", "claude.ai") | summarize TotalUploadedBytes = sum(TotalBytes) by AccountDisplayName, Application, bin(TimeGenerated, 1h) | where TotalUploadedBytes > 5000000 // Files larger than 5MB | project AccountDisplayName, Application, TotalUploadedBytes, TimeGenerated - Create an Analytics Rule: Turn this query into an alert. If an employee uploads multiple files or very large files to an AI tool in a short window, have Sentinel create an incident.
5. Hardening the Environment: Zero Trust for AI
The ultimate mitigation is to apply a Zero Trust model: never trust, always verify. For AI, this means treating every API call to an AI service as a potential threat.
Step‑by‑step guide to Conditional Access for AI Apps:
- In Microsoft Entra Admin Center (Azure AD), go to Protection > Conditional Access.
2. Create a new policy:
- Name: Require Compliant Device for AI Access
- Users: Select specific groups (e.g., “Marketing – AI Users”).
- Target Resources: Select Cloud apps > Include > Select apps. Search for and add apps like “ChatGPT,” “Copilot,” etc.
- Conditions: (Optional) Set location policies to block access from non-corporate networks.
- Grant: Select Grant access > Require device to be marked as compliant AND Require Multifactor Authentication.
6. Securing API Keys and Service Principals
Many developers are bypassing web interfaces and using AI directly via APIs. These API keys, if hardcoded in code or exposed in repositories, are a massive security risk.
Linux Command (Scan for Secrets in Code):
Use tools like `truffleHog` or `git-secrets` to scan repositories for leaked AI API keys.
Example using truffleHog (ensure it's installed: pip install truffleHog) Scan a local git repo for high-entropy strings (potential keys) trufflehog --regex --entropy=True /path/to/your/repository
Azure Key Vault Integration:
Instead of hardcoding keys, developers should store them in Azure Key Vault.
Store an OpenAI key in Key Vault az keyvault secret set --vault-name "YourVaultName" --name "OpenAI-APIKey" --value "sk-xxxxxxxxxxxxxxxxxxxx" Retrieve it from an Azure Function or App Service using Managed Identity (Code example depends on language, but principle is: never touch the raw key)
What Undercode Say:
- Shadow AI is the new Shadow IT: Just as unmanaged cloud storage (Dropbox, Google Drive) once plagued security teams, unmanaged AI tools represent a data exfiltration risk of unprecedented scale. The ease of pasting data into a browser makes traditional DLP endpoints difficult to enforce.
- Visibility requires a multi-layered approach: You cannot rely on a single tool. Combining CASB (Defender for Cloud Apps) for network visibility, SIEM (Sentinel) for behavioral analytics, and Endpoint DLP (Purview) for client-side controls provides the necessary depth to govern AI usage effectively.
- Governance must be balanced with enablement: A pure “block-all” approach will drive users further underground, creating more Shadow AI. The strategy must shift to identifying low-risk tools that can be swiftly sanctioned and secured, while aggressively monitoring or blocking high-risk, non-compliant applications that put corporate data at risk.
Prediction:
Within the next 18 months, regulatory bodies will begin issuing specific fines for data leaks originating from unsanctioned AI usage, similar to GDPR violations today. This will force the rapid evolution of “AI Security Posture Management” (AI-SPM) as a distinct market category, merging CASB capabilities with model-specific vulnerability scanning to automatically discover, assess, and remediate risks in both custom and SaaS-based AI models.
▶️ Related Video (76% Match):
🎯Let’s Practice For Free:
IT/Security Reporter URL:
Reported By: Raj Bharath – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
