Exploit SIEM Synergy: Supercharge Azure Sentinel Now

Introduction

In today’s sprawling security ecosystems, many organizations run parallel SIEMs. They use a legacy platform to handle traditional infrastructure. Alongside this, they use Microsoft Sentinel to watch cloud-native workloads. The challenge? Making these systems talk to each other effectively.

This post shows how to ingest alerts from third-party SIEM platforms into Microsoft Sentinel. More importantly, it demonstrates how to correlate those alerts with native Azure signals to strengthen your threat detection strategy.

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🧩 Why Integrate 3rd-Party SIEM Alerts into Sentinel?

• Unified Threat Visibility: Get a consolidated view by centralizing detection signals across hybrid environments.
• Enrich Detections: Combine legacy infrastructure alerts with Azure workload telemetry for deeper context.
• Boost Automation: Trigger Sentinel playbooks based on events from 3rd-party systems.
• Compliance + Governance: Prove you have a centralized view of threat activity across all platforms.

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🚛 Step 1: Ingest 3rd-Party SIEM Alerts via API, Syslog, or Logic Apps

Depending on the SIEM platform (Splunk, QRadar, ArcSight, etc.), alerts can be forwarded using one of these methods:

Option A – Syslog Forwarding to Sentinel (via Linux Agent or AMA):

1. Set up a Linux Syslog server VM or Azure Monitor Agent (AMA) data collection rule.
2. Configure your SIEM (e.g., Splunk Heavy Forwarder) to send alerts to a specific port (514/UDP or TCP).
3. Use custom log parsers in Sentinel to normalize alert data.

Option B – Azure Logic Apps with API Push:

1. Configure a Logic App in Sentinel to receive webhooks from your 3rd-party SIEM.
2. Use HTTP trigger and parse JSON schema from the alert payload.
3. Output to a custom log table in Log Analytics.

Option C – Azure Event Hub Ingestion:

1. Push alert logs from your SIEM to Azure Event Hub.
2. Use Sentinel’s Event Hub data connector to ingest data.
3. Set up Kusto queries to access alert content.

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🧠 Step 2: Normalize and Tag Incoming Alerts

To correlate effectively, all alert data must include:

• Timestamps
• Alert name / ID
• Source and destination IP or user
• Device hostname / system involved

Use a custom KQL transformation to standardize fields using Sentinel’s parser function or Data Collection Transformation (DCR).

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🔍 Step 3: Correlate Alerts with Azure Internal Data

Here’s where it gets powerful—let’s correlate third-party alerts with signals from:

• Azure AD Sign-ins
• Microsoft Defender for Endpoint alerts
• Cloud App logins
• Firewall or NSG Flow Logs

Example: Correlating QRadar Alerts with Azure AD Sign-In Data

kqlCopyEditlet thirdPartyAlerts = CustomSIEMAlerts_CL
| where TimeGenerated > ago(1h)
| where AlertSeverity_s == "High";

thirdPartyAlerts
| join kind=inner (
    SigninLogs
    | where TimeGenerated > ago(1h)
    | where ResultType == 50074 // Conditional Access failure
) on $left.SourceIP_s == $right.IPAddress

This example detects users who triggered high-severity alerts from QRadar and also had failed Conditional Access attempts, indicating a possible lateral movement attempt.

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⚡ Bonus: Trigger Sentinel Automation from 3rd Party Alerts

You can connect Logic App playbooks to alert events using:

• Azure Monitor Alerts (based on KQL queries)
• Watchlist triggers
• Custom Logic App event-based automation

Example: If a critical alert is received from Splunk about privilege escalation, auto-disable the affected Azure AD user.

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🌐 Real-World Use Case

Scenario: A Splunk alert for a detected PowerShell reverse shell is received.

• Sentinel ingests it via Syslog.
• A query joins this with Azure Defender alerts showing suspicious process creation.
• Sentinel raises a new incident combining both insights.
• Playbook disables the VM and alerts SecOps.

This cross-SIEM and cloud telemetry integration helps stop multi-stage attacks that would otherwise be siloed.

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💡 Final Thoughts

Integrating your 3rd-party SIEM platform into Sentinel isn’t just about visibility—it’s about intelligent correlation and automated response. This hybrid model is ideal for organizations migrating to the cloud but still managing legacy infrastructure.

With thoughtful normalization and strategic KQL joins, you can unlock the true XDR capabilities that Microsoft Sentinel was built for.