Unlocking Developer Innovation with Microsoft Sentinel data lake

Eleanor_Falla

Microsoft

Nov 05, 2025

Introduction

Microsoft Sentinel is evolving rapidly, transforming to be both an industry-leading SIEM and an AI-ready platform that empowers agentic defense across the security ecosystem. In our recent webinar: Introduction to Sentinel data lake for Developers, we explored how developers can leverage Sentinel’s unified data lake, extensible architecture, and integrated tools to build innovative security solutions. This post summarizes the key takeaways and actionable insights for developers looking to harness the full power of Sentinel.

Figure 1: Microsoft Sentinel journey

The Sentinel Platform: A Foundation for Agentic Security

Unified Data and Context

Sentinel centralizes security data cost-effectively, supporting massive volumes and diverse data types. This unified approach enables advanced analytics, graph-enabled context, and AI-ready data access—all essential for modern security operations. Developers can visualize relationships across assets, activities, and threats, mapping incidents and hunting scenarios with unprecedented clarity.

Extensible and Open Platform

Sentinel’s open architecture simplifies onboarding and data integration. Out-of-the-box connectors and codeless connector creation make it easy to bring in third-party data. Developers can quickly package and publish agents that leverage the centralized data lake and MCP server, distributing solutions through Microsoft Security Store for maximum reach.

The Microsoft Security Store is a storefront for security professionals to discover, buy, and deploy vetted security SaaS solutions and AI agents from our ecosystem partners. These offerings integrate natively with Microsoft Security products—including the Sentinel platform, Defender, and Entra, to deliver end‑to‑end protection. By combining curated, deploy‑ready solutions with intelligent, AI‑assisted workflows, the Store reduces integration friction and speeds time‑to‑value for critical tasks like triage, threat hunting, and access management.

Figure 2: Microsoft Sentinel product breakdown

Advanced Analytics and AI Integration

With support for KQL, Spark, and ML tools, Sentinel separates storage and compute, enabling scalable analytics and semantic search. Jupyter Notebooks hosted in on-demand Spark environments allow for rich data engineering and machine learning directly on the data lake. Security Copilot agents, seamlessly integrated with Sentinel, deliver autonomous and adaptive automation, enhancing both security and IT operations.

Figure 3: Microsoft Security partner ecosystem

Developer Scenarios: Unlocking New Possibilities

The webinar showcased several developer scenarios enabled by Sentinel’s platform components:

Threat Investigations Over Extended Timelines: Query historical data to uncover slow-moving attacks and persistent threats.
Behavioral Baselining: Model normal behavior using months of sign-in logs to detect anomalies.
Alert Enrichment: Correlate alerts with firewall and NetFlow data to improve accuracy and reduce false positives.
Retrospective Threat Hunting: React to new indicators of compromise by running historical queries across the data lake.
ML-Powered Insights: Build machine learning models for anomaly detection, alert enrichment, and predictive analytics.

These scenarios demonstrate how developers can leverage Sentinel’s data lake, graph capabilities, and integrated analytics to deliver powerful security solutions.

End-to-End Developer Journey

The following steps outline a potential workflow for developers to ingest and analyze their data within the Sentinel platform.

Figure 4: End-to-end developer journey

Data Sources: Identify high-value data sources from your environment to integrate with Microsoft Security data. The journey begins with your unique view of the customer’s digital estate. This is data you have in your platform today. Bringing this data into Sentinel helps customers make sense of their entire security landscape at once.
Data Ingestion: Import third-party data into the Sentinel data lake for secure, scalable analytics. As customer data flows from various platforms into Sentinel, it is centralized and normalized, providing a unified foundation for advanced analysis and threat detection across the customer’s digital environment.
Sentinel data lake and Graph: Run Jupyter Notebook jobs for deep insights, combining contributed and first-party data. Once data resides in the Sentinel data lake, developers can leverage its graph capabilities to model relationships and uncover patterns, empowering customers with comprehensive insights into security events and trends.
Agent Creation: Build Security Copilot agents that interact with Sentinel data using natural language prompts. These agents make the customer’s ingested data actionable, allowing users to ask questions or automate tasks, and helping teams quickly respond to threats or investigate incidents using their own enterprise data.
Solution Packaging: Package and distribute solutions via the Microsoft Security Store, reaching customers at scale. By packaging these solutions, developers enable customers to seamlessly deploy advanced analytics and automation tools that harness their data journey— from ingestion to actionable insights—across their entire security estate.

Conclusion

Microsoft Sentinel’s data lake and platform capabilities open new horizons for developers. By centralizing data, enabling advanced analytics, and providing extensible tools, Sentinel empowers you to build solutions that address today’s security challenges and anticipate tomorrow’s threats. Explore the resources below, join the community, and start innovating with Sentinel today!

App Assure: For assistance with developing a Sentinel Codeless Connector Framework (CCF) connector, you can contact AzureSentinelPartner@microsoft.com.

Microsoft Security Community: aka.ms/communitychoice