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Sujit Mahakhud

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Microsoft Sentinel (2026) — Technical Deep Dive into Architecture, Data Lake, Analytics & Cost Engineering

Microsoft Sentinel has evolved far beyond a traditional cloud-native SIEM. In 2026, it is shaping into a unified SecOps data and analytics platform, deeply integrated with Microsoft Defender, powered by a new Sentinel Data Lake, enhanced ingestion controls, and platform-level correlation.

This article focuses purely on the technical engineering side of Sentinel — architecture, ingestion design, analytics behavior, data lake strategy, automation, and cost control.

📌 Why this matters

If you are:

  • Designing a new Sentinel deployment
  • Migrating from another SIEM
  • Optimizing an existing workspace

Then success in 2026 depends less on “connecting logs” and more on:

  • Data lake architecture
  • DCR-based ingestion filtering
  • Normalization (ASIM)
  • Defender correlation behavior
  • Cost engineering

📑 Table of contents

  1. Sentinel architecture in 2026
  2. Sentinel Data Lake — design & retention
  3. Ingestion architecture & Data Collection Rules
  4. Normalization & ASIM
  5. Analytics rules, Fusion & Defender correlation
  6. Automation & SOAR engineering
  7. Cost engineering strategies
  8. Advanced KQL examples
  9. Implementation checklist
  10. Microsoft reference links

1️⃣ Sentinel Architecture in 2026

The most fundamental change is Sentinel’s evolution into a Defender-native SIEM layer with split analytics and data lake storage.

🔧 High-level architecture diagram

Architectural flow

Telemetry → DCR filtering → Analytics tier (hot) + Data lake (cold) → Defender correlation → Unified incidents & SOAR

This is no longer a pure SIEM pipeline — it is now a security data platform.

2️⃣ Sentinel Data Lake — design & retention strategy

Microsoft introduced a separate Sentinel Data Lake tier to decouple storage from analytics.

Why this is critical

  • Long retention without SIEM query cost explosion
  • Large-scale historical hunting
  • Compliance and forensic readiness
  • Defender telemetry can land directly in the lake

Engineering model

TierPurpose
Analytics tierDetections, near-real-time correlation
Data Lake tierLong-term, cold, large-volume security data

Example retention strategy

  • SigninLogs: 180 days analytics → 2 years lake
  • EDR telemetry: 90 days analytics → 2 years lake
  • DNS / proxy: 30 days analytics → lake-only
  • Compliance logs: lake-only

Microsoft docs

3️⃣ Ingestion architecture & Data Collection Rules (DCRs)

In 2026, DCRs are not optional. They are the foundation of cost control and data quality.

What DCRs enable

  • Event-level filtering
  • Field-level transformations
  • ASIM alignment
  • Routing to analytics vs lake tier

Engineering principle

❌ Send everything → filter later → pay more
✅ Filter early → normalize → store what matters

Microsoft docs

4️⃣ Normalization & ASIM (non-negotiable)

Microsoft’s detection and UEBA engines increasingly rely on ASIM-normalized schemas.

Why this matters

  • Cross-source detections work
  • UEBA baselines stabilize
  • Fusion correlation improves
  • Hunting queries become reusable

Microsoft docs

5️⃣ Analytics rules, Fusion & Defender correlation

Sentinel analytics no longer work in isolation.

Microsoft is consolidating SIEM and XDR correlation inside the Defender portal.

Technical impact

  • XDR alerts and SIEM detections merge
  • Fusion logic operates at platform level
  • Incident shapes and grouping may change

Migration guidance

https://learn.microsoft.com/en-us/azure/sentinel/move-to-defender

Engineering recommendation

After migration, always re-validate:

  • Incident grouping
  • Automation triggers
  • Alert suppression
  • Severity logic

6️⃣ Automation & SOAR engineering

Sentinel SOAR in 2026 should follow decision-first automation.

High-value automation use cases

  • Threat intelligence enrichment
  • Identity risk scoring
  • Auto-closure of known false positives
  • Conditional containment

Design principle

Automate context before containment.
Never auto-block without validation logic.

7️⃣ Cost engineering (most teams fail here)

Sentinel cost is architecture-driven.

Major cost drivers

  • Ingested GB/day
  • Retention duration
  • Analytics tier volume
  • Query frequency

Cost visibility KQL

Usage 
| where TimeGenerated > ago(32d)
| where StartTime >= startofday(ago(31d)) and EndTime < startofday(now())
| where IsBillable == true
| summarize BillableDataGB = sum(Quantity) / 1000 by Solution, DataType
| sort by Solution asc, DataType asc

Microsoft pricing

https://www.microsoft.com/en-in/security/pricing/microsoft-sentinel

8️⃣ Advanced KQL examples

Ingestion-aware failed logon detection

let ingestion_delay = 5m;
SigninLogs
| where TimeGenerated > ago(1h + ingestion_delay)
| where ResultType != "0"
| summarize Failed = count() by UserPrincipalName, bin(TimeGenerated, 5m)
| where Failed > 10

Identify noisy data sources

Heartbeat
| summarize Events = count() by Computer, bin(TimeGenerated, 1d)
| top 10 by Events

Cost trend visibility

Usage
| where Entity == "Ingestion"
| summarize GB = sum(Quantity) by bin(TimeGenerated, 1d)

9️⃣ Implementation checklist

  •  Inventory connectors and parsers
  •  Design analytics vs lake retention
  •  Deploy DCRs as code
  •  Enforce ASIM mapping
  •  Validate Defender correlation behavior
  •  Optimize expensive KQL rules
  •  Implement automation rules
  •  Review ingestion cost weekly

🔗 Microsoft reference links

🎯 Final note

Microsoft Sentinel in 2026 is not “log management.”

It is:

  • A security data platform
  • A correlation engine
  • A SOAR orchestrator
  • A cost-engineering challenge

Real Sentinel expertise now lies in architecture, data modeling, and detection engineering.

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