This article is part of The Sentinel data lake Practitioner Series. Part 1 of the series focuses on operationalizing the Sentinel data lake and our strategic vision for the customers. This series is evolving based on inputs and feedback from the community as well as various components of turning raw security data and workflows into operational security engine.
Why This Series?
Microsoft recently announced Sentinel data lake unlocking massive potential for security teams. Security data lakes are the foundation of modern detection and investigation.
This blog series is designed to empower you to fully leverage your Sentinel data lake investment – providing practical tools, actionable workflows, and analyst-ready templates that simplify querying datalake-tier data and enable SOC teams to turn raw logs into meaningful security insights. With the right guidance, you can maximize the value you get from your Sentinel data lake.
Microsoft Security research team has worked extensively on modular Jupyter notebooks, Python-based data analysis, enrichment, and visualization libraries, and security-driven analysis workflows at scale. We believe the key to adoption lies in researcher-driven operationalization—bringing these methods directly to practitioners in ways they can use immediately.
Strategic Vision for Operationalization of Sentinel data lake
Our approach centers on researcher-led enablement with ready-to-use workflows and customer community activation.
The above infographic outlines four building blocks that brings a security data lake to life:
- Research curated and Community-Powered Content Hub
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- Researcher-curated GitHub repository.
- Shared notebooks, detection templates, and models.
- Continuous contributions from the security researchers and community.
- Notebook & Model Templates
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- Jupyter & VS Code notebooks tailored for analyst use.
- ML/GenAI models tailored for security data enrichment and anomaly detection.
- Modular queries for detections and investigations.
- Historical Data Enablement
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- Analytics to data lake tier automation for cost-efficient historical queries.
- Dynamic baselining over months/years of logs to tune detections.
- Unlocking long-tail investigation scenarios otherwise left dormant.
- Practical real world Use Cases
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- Historical threat hunting on network, identity, and cloud logs.
- Dynamic detection tuning at scale.
- GenAI-powered investigations.
- Post-incident deep dives to uncover the full blast radius.
Getting Started Notebook: Building Familiarity with the Data Lake Framework
Before diving into advanced workflows, we’ve published a Getting Started Notebook designed to help practitioners quickly onboard to the Sentinel Data Lake environment.
This notebook introduces foundational concepts that will be used across subsequent examples and pipelines.
What it covers:
- Connecting to the Data Lake:
Learn how to establish authenticated Spark sessions and securely read data from the Sentinel Data Lake workspace. - Exploring Data with Apache Spark:
A short hands-on tour using PySpark to inspect schema, preview records, and perform lightweight data transformations at scale. - Writing Back to the Lake:
Understand the pattern of persisting processed or enriched datasets back to data lake tier for reuse in analytic notebooks and downstream detection pipelines via elevating them to analytics tier. - Running Modular Pipelines:
Step through a simple example of how pipeline jobs ingest raw security logs (e.g., SigninLogs), apply filters and enrichments, and output ready-to-use tables for later detection development.
This foundational notebook ensures analysts and engineers are comfortable with the basic Spark + Sentinel data lake interaction model — the same model used in the advanced operational notebooks (for example, Password Spray Detection or Anomaly Detection workflows) later in this series.
Our Commitment
This new blog series will serve as a practitioner’s guide for operationalizing security data lakes. In the following weeks, we’ll gradually deliver:
- Modular Notebook templates to accelerate hunting, baselining, and investigations.
- End-to-end workflows connecting datalake-tier → analytics-tier → Sentinel detections.
- Enrichment and Gen AI-driven tools to reduce repetitive manual work and investigation friction.
- Reusable examples and walkthroughs based on real-world high-volume data sources
Our goal is to make Sentinel data lake practical for customers by delivering actionable notebooks, workflows, and enablement.
Expected Outcomes for Customers
By operationalizing the Sentinel data lake in this way, enterprise customers can expect:
- Reduced Time-to-Value – Analysts can move from raw logs to actionable detections in days, not months.
- Improved Detection Quality – Long-term baselining and historical analysis reduce false positives and increase fidelity in your detections.
- Operational Efficiency – Automated enrichment and packaged workflows minimize manual investigation effort.
- Cost Optimization – analytics tier -to-data lake tier data workflows avoid expensive, ad-hoc queries and make historical data practical to use.
Join the Journey
This series is built by practitioners, for practitioners. Alongside blogs, we’ll also share:
- GitHub repository with reusable notebooks and model templates.
- Webinars and demos to walk through the workflows.
Together, we’ll move beyond storage and make the security data lake truly operational, analyst-friendly, and impactful.
Upcoming articles will demonstrate how notebooks and templates can turn research into workflows that are ready for analysts, featuring practical notebook examples available on GitHub.
What's next?
Join us at Microsoft Ignite in San Francisco on November 17–21, or online, November 18–20, for deep dives and practical labs to help you maximize your Microsoft Defender investments and to get more from the Microsoft capabilities you already use. Security is a core focus at Ignite this year, with the Security Forum on November 17th, deep dive technical sessions, theater talks, and hands-on labs designed for security leaders and practitioners
Featured sessions
- BRK237: Identity Under Siege: Modern ITDR from Microsoft
Join experts in Identity and Security to hear how Microsoft is streamlining collaboration across teams and helping customers better protect, detect, and respond to threats targeting your identity fabric. - BRK240 – Endpoint security in the AI era: What's new in Defender
Discover how Microsoft Defender’s AI-powered endpoint security empowers you to do more, better, faster. - BRK236 – Your SOC’s ally against cyber threats, Microsoft Defender Experts
See how Defender Experts detect, halt, and manage threats for you, with real-world outcomes and demos. - LAB541 – Defend against threats with Microsoft Defender
Get hands-on with Defender for Office 365 and Defender for Endpoint, from onboarding devices to advanced attack mitigation.
Explore and filter the full security catalog by topic, format, and role: aka.ms/SessionCatalogSecurity.
Why attend?
Ignite is the place to learn about the latest Defender capabilities, including new agentic AI integrations and unified threat protection. We will also share future-facing innovations in Defender, as part of our ongoing commitment to autonomous defense.
Security Forum—Make day 0 count (November 17)
Kick off with an immersive, in person preday focused on strategic security discussions and real-world guidance from Microsoft leaders and industry experts. Select Security Forum during registration.
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