Microsoft Sentinel Multi-Tier Strategy: From Auxiliary Logs to Data Lake Mastery

The Reality Check: Why Your Sentinel Costs Are Spiraling

Imagine a client spending £45,000 monthly on Sentinel—90% of it on data they access maybe twice a year for compliance. Sound familiar? With Microsoft's Data Lake preview rolling out and auxiliary logs automatically migrating, it's time to get strategic about data tier management.

After managing a couple of Sentinel environments and seeing the Data Lake in action, I'm sharing what I've discovered about making the most of Microsoft's evolving data tier strategy.

Understanding the Four Tiers

1. Analytics Tier (The Premium Option)

This is your hot storage—full KQL support, real-time analytics rules, and sub-second query performance. Perfect for:

• Security events requiring real-time detection
• Data you query multiple times daily
• Incident investigation primary sources

Cost: Highest tier, but essential for active threat detection.

2. Basic Logs (The Middle Ground)

Reduced functionality but significant cost savings. Good for:

• Verbose logs you might need occasionally
• Compliance data with infrequent access
• Backup investigation sources

Limitations: 8-day retention, limited KQL functions, no scheduled rules.

3. Auxiliary Logs (Being Deprecated)

If you're using auxiliary logs, pay attention—they're automatically migrating to Data Lake. This includes:

• StorageBlobLogs
• AzureMetrics
• ContainerLogV2

Action Required: Review your auxiliary log usage before migration completes.

4. Data Lake (The New Frontier)

Microsoft's latest addition offers:

• 12-year retention capability
• ~90% cost reduction compared to Analytics tier
• Full KQL support through summary rules
• Seamless integration with Sentinel hunting

Strategic Tier Assignment

Here's my recommended approach for common log types:

Implementation Steps

Step 1: Audit Current Usage

// Find your most queried tables
Usage
| where TimeGenerated > ago(30d)
| summarize QueryCount = count(), DataGB = sum(Quantity)/1024 by DataType
| order by DataGB desc

Step 2: Identify Migration Candidates

Look for tables with:

• High ingestion volume but low query frequency
• Compliance-only retention requirements
• No active analytics rules

Step 3: Configure Data Lake

Enable Data Lake through the Sentinel workspace settings and configure your retention policies. Remember:

• Data Lake supports up to 12 years retention
• Use summary rules to aggregate data for faster queries
• Set up lifecycle policies for automatic tiering

Cost Savings Example

For a typical enterprise environment:

• Before optimization: £45,000/month
• After strategic tiering: £12,000/month
• Savings: 73% reduction

What's Next?

I've shared what I've discovered so far, but there's definitely more to explore—especially with the VS Code extension and Jupyter notebooks for Data Lake queries.

Resources & Further Reading

• Microsoft Sentinel Data Lake Overview - Official documentation
• Introducing Microsoft Sentinel Data Lake - Microsoft Community Hub announcement
• Set up Connectors for Data Lake - Configuration guide
• Cybrush Attack Range - Test your detections across tiers
• Cybrush Medium Publication - More Sentinel insights