Microsoft Sentinel data lake - Security Data Lake

What is Microsoft Sentinel data lake?

Microsoft Sentinel data lake is a purpose-built, cloud-native security data lake. It ingests, stores, and analyzes large volumes of diverse security data in an open, extensible format. By centralizing security data on a single platform, Microsoft states it provides deep visibility, long-term retention, and advanced analytics.

The service addresses a common problem with traditional SIEM approaches: the cost and complexity of storing and querying long-term security data force organizations to choose between coverage and cost. Microsoft Sentinel data lake is fully managed, so you don’t deploy or maintain data infrastructure yourself. It stores a single copy of security data across assets, activity logs, and threat intelligence and makes it available to multiple analytics engines.

Technically, the data lake uses open Parquet files for interoperability and extensibility, separates storage from compute, and integrates natively with Microsoft Sentinel SIEM and its security operations workflows.

Core Features

• Two storage tiers: analytics tier for hunting, alerting, and incident management; data lake tier for cost-effective long-term retention of up to 12 years
• Unified data across Microsoft Defender XDR, third-party sources, assets, activity logs, and threat intelligence
• KQL query editor with IntelliSense, autocomplete, and the full range of KQL capabilities including machine learning functions
• Jupyter notebooks with Python libraries for machine learning, advanced analytics, and visualization
• Jobs for one-time or scheduled promotion of data from the lake tier to the analytics tier
• Activity auditing for data access, query events, and job management, enabled by default
• Compatibility with all existing Microsoft Sentinel data connectors

Typical Use Cases

Long-term retention of security telemetry
Regulated organizations must retain security data verifiably for years. The data lake tier is designed for retention periods of up to 12 years without putting that data out of reach for analysis.

Forensics and incident response
Historical context is decisive when working through a security incident. KQL queries and notebooks let you analyze past time ranges and promote relevant data into the analytics tier on demand.

Anomaly detection with your own models
Security data science teams use Jupyter notebooks with Python libraries to run their own machine learning models against raw data and refresh results on a schedule.

Consolidating heterogeneous security sources
Beyond Microsoft sources, firewall, proxy, DNS, EDR, and identity logs can be brought together through the existing Sentinel connectors and analyzed jointly.

Benefits

• Cost control through tiered storage, on-demand data promotion, and a single copy of the data
• Fully managed: no data infrastructure of your own to run
• Open Parquet format and separation of storage and compute for interoperability and flexibility
• Multiple analytics engines on the same data: KQL for queries, Python notebooks for deeper analysis
• Native integration with Microsoft Sentinel SIEM and existing security operations workflows
• Traceability through auditing enabled by default

Integration with innFactory

As a Microsoft Solutions Partner, innFactory supports your adoption of Microsoft Sentinel data lake: assessing your existing data sources and retention requirements, splitting data across the analytics and data lake tiers, building KQL queries and notebook-based analyses, and embedding all of it into your security operations processes.

We also advise on the architecture of your Azure security and data stack, for example in combination with Microsoft Defender, Microsoft Entra ID, and Azure Monitor. Contact us for a no-obligation consultation.