Getting Started with Azure Sentinel: Microsoft’s Cloud-Native SIEM

Introduction

In today’s threat-filled digital world, security teams need robust, intelligent, and scalable tools to detect and respond to threats in real time. Azure Sentinel, Microsoft’s cloud-native SIEM (Security Information and Event Management) and SOAR (Security Orchestration Automated Response), provides exactly that powered by AI, automation, and the scalability of Azure.

In this blog, we’ll explore what Azure Sentinel is, how it works, its key features, and how to get started.

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What is Azure Sentinel?

Azure Sentinel is Microsoft’s next-generation SIEM and SOAR solution, built on the Azure platform. It collects data at cloud scale across users, devices, applications, and infrastructure both on-premises and in multiple clouds and uses built-in AI to help analyze large volumes of data quickly.

✅ Sentinel = SIEM + SOAR + Cloud-Native + AI-Powered

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Why Azure Sentinel?

• Cloud-native: No infrastructure to manage. Scale on demand.

• AI & Machine Learning: Detect unknown threats and reduce false positives.

• Automation: Automate incident response and investigation using playbooks.

• Integration: Works with Microsoft 365 Defender, Azure, AWS, third-party tools.

• Cost-effective: Pay for what you use (Ingestion-based pricing).

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Key Components of Azure Sentinel

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How Azure Sentinel Works: The Flow

1. Connect your data sources (e.g., Office 365, Azure AD, firewalls, endpoints).

2. Collect & Ingest logs and telemetry into the Log Analytics workspace.

3. Analyze using built-in detection rules, UEBA (User & Entity Behavior Analytics), and custom KQL queries.

4. Investigate and group related alerts into incidents.

5. Respond & Automate actions through playbooks, ticketing integrations, or manual workflows.

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How to Get Started

Step 1: Enable Azure Sentinel

• Go to Azure Portal → Search "Sentinel"

• Create a new Log Analytics Workspace

• Add Azure Sentinel to that workspace

Step 2: Connect Data Sources

• Use Data Connectors (e.g., Azure AD, Microsoft Defender, AWS CloudTrail, Syslog)

• Some connectors are plug-and-play with just a few clicks

Step 3: Set Up Analytics Rules

• Use built-in rule templates or create your own with KQL

• Configure thresholds and response actions

Step 4: Create Workbooks

• Visualize insights with pre-built or custom dashboards

• Monitor trends and detect anomalies

Step 5: Automate Responses

• Create playbooks with Logic Apps

• Example: Automatically disable a user account when suspicious login is detected

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Real-World Use Cases

• Insider Threat Detection: Detect anomalous behavior from internal users

• Cloud Security Monitoring: Monitor multi-cloud environments (Azure, AWS, GCP)

• Threat Hunting: Query across all logs to identify hidden threats

• SOC Automation: Automate triage, enrichment, and remediation tasks

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Pricing Overview

Azure Sentinel pricing is based on:

• Data Ingestion (per GB)

• Log Analytics Retention

• Automation and Logic Apps usage

💡 Tip: Use Capacity Reservations or Commitment Tiers to reduce cost.

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Best Practices

• Enable UEBA for advanced behavior analytics

• Regularly review and tune Analytics Rules

• Set up Watchlists for tracking high-risk entities

• Integrate with Microsoft Defender XDR for deeper protection

• Use Notebooks (Jupyter in Azure) for advanced hunting

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Conclusion

Azure Sentinel empowers security teams to move faster and smarter in the face of modern threats. Its native integration with Microsoft and third-party solutions, paired with powerful AI, makes it an essential platform for any modern security operations center (SOC).

Whether you're new to SIEM/SOAR or looking to upgrade from legacy systems, Azure Sentinel offers unmatched capabilities, scalability, and intelligence.

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Next Steps

• 🔗 Azure Sentinel Documentation

• 📚 Follow my blog for upcoming hands-on labs, KQL queries, and playbook samples!