Building Microsoft Sentinel Connectors in Minutes with the Sentinel Connector Builder Agent
Overview We previously announced the public preview of the Microsoft Sentinel connector builder agent via VS code extension, that helps developers build Microsoft Sentinel codeless connectors faster with low-code and AI-assisted prompts. This post walks through a hands-on lab using a mock Network Log API to demonstrate how the Sentinel connector builder agent simplifies building Codeless Connector Framework (CCF) pull connectors. Instead of manually creating ingestion infrastructure and configuration files, you’ll use a guided, conversational workflow in VS Code to generate connector artifacts, test them against a live API, and deploy them into Microsoft Sentinel. The lab focuses on the end-to-end experience ranging from API setup to validated connector deployment so you can see how quickly a working integration can be produced. For additional guidance beyond this lab, refer to our MS Learn documentation. The Lab Environment This lab is built around a mock Network Log API hosted as an Azure Function App. The purpose of the lab environment is to give us a live API that we can use to build, validate, and test the Sentinel CCF connector builder agent against end to end. The API exposes 50 synthetic network activity records that look and behave like a real product data source, including web traffic, DNS requests, blocked remote access attempts, malware command-and-control blocks, VPN activity, and other common network events. That makes it a useful stand-in for the type of telemetry many teams want to onboard into Microsoft Sentinel. The API is intentionally shaped like the kind of source a customer might expose for telemetry retrieval. It uses API key authentication through the X-API-Key header, returns paginated results through a nextLink model, and provides a predictable response structure that the builder agent can map into a pull connector configuration. The repo contains everything needed for the walkthrough. There is an ARM template to deploy the Function App, reference documentation for the API, and a sample connector package showing the generated polling config, table schema, DCR, and connector definition. The end goal of the lab is straightforward: use the builder agent to generate a CCF pull connector that ingests this API into the custom NetworkLogAPIGetNetworkLogs_CL table in Sentinel. Prerequisites Before starting, make sure you have the following: Azure subscription -- with Contributor access on a resource group (for deploying the Function App) and Microsoft Sentinel Contributor access on a Sentinel-enabled workspace (for deploying the connector) Microsoft Sentinel workspace -- an existing Log Analytics workspace with Sentinel enabled. See Onboard Microsoft Sentinel to a Log Analytics workspace for more information. Azure CLI -- See How to install the Azure CLI for more information. VS Code with the Microsoft Sentinel for Visual Studio Code extension installed. GitHub Copilot -- with access to premium models. The connector builder agent requires Claude Sonnet 4.5 or 4.6, which uses Copilot premium model credits. Lab Repository -- Once the aforementioned prerequisites are met, you can access the lab repository here: Azure-Sentinel/Tools/CCF-Connector-Builder-Agent-Accelerator at master · Azure/Azure-Sentinel Deploying the Mock API The full CLI commands for this section are available in the repo. For a simpler option, you can use GitHub Copilot to handle the deployment. Enter this prompt: Follow the deployment instructions in Sentinel-CCF-Pull-Connector-Builder-Agent-Accelerator/agent-instructions.md. Let’s deploy the Network Log API and build a CCF pull connector. At a high level, the setup is four steps: clone the repo, create a resource group, ensure you have a Sentinel-enabled workspace, and deploy the Function App using the included ARM template. The template takes two parameters: an ApiKey of your choice (the secret the CCF connector will use to authenticate) and your Log Analytics workspace resource ID for Application Insights. Deployment takes about two to three minutes and outputs the FunctionAppName and endpoint URLs you will need later. Once deployed, verify the API is live: curl -s -H "X-API-Key: <your-api-key>" \ "https://<functionappname>.azurewebsites.net/api/GetNetworkLogs?page=1&pageSize=3" </functionappname></your-api-key> You should see a response like this: The API also exposes an /api/RefreshData endpoint that regenerates the 50 sample records with fresh timestamps. This is useful later in the walkthrough when you want to produce new events and trigger an immediate ingestion cycle without waiting for the next polling interval: curl -s -X POST -H "X-API-Key: <your-api-key>" \ "https://<functionappname>.azurewebsites.net/api/RefreshData" </functionappname></your-api-key> Building the Connector with the Sentinel Connector Builder Agent With the Microsoft Sentinel extension installed and GitHub Copilot running in agent mode, open a Copilot chat and enter a single prompt pointing at the API documentation file: That is the entire invocation. The agent takes it from there. It works through a structured seven-step sequence: preparation, polling config, table schema, DCR, connector definition, package validation, and summary. The agent produces four files in a sentinel-connectors/NetworkLogAPI_CCF/ output folder: NetworkLogAPI_PollingConfig.json – This is the API poller configuration. The agent reads the documentation and correctly identifies the GET /api/GetNetworkLogs endpoint, configures API Key authentication via the X-API-Key header, sets up NextPageUrl pagination using $.metadata.nextLink with a $.metadata.hasNextPage stop condition, and wires up the since query parameter for incremental delta pulls using the timestamp field. The RefreshData endpoint is correctly excluded, which the agent recognizes as a maintenance operation, not a security data stream. NetworkLogAPI_Table.json – This is the custom Log Analytics table schema for NetworkLogAPIGetNetworkLogs_CL . All 20 fields from the API response are mapped to the correct column types, with timestamp promoted to TimeGenerated as the standard Sentinel time column. NetworkLogAPI_DCR.json – This is the Data Collection Rule. This defines the stream declaration, the workspace destination, and the KQL transform that maps the raw snake_case API fields ( sourceIp , destinationIp , threatIndicator , etc.) to their PascalCase table columns. NetworkLogAPI_ConnectorDefinition.json – This is the connector UI configuration. This drives what the connector page looks like in Microsoft Sentinel: the title, description, prerequisite instructions, the BaseUrl and ApiKey input fields, sample KQL queries, and the connectivity status logic. The only point where the agent paused for input was to propose a connector description and ask for confirmation before writing it to the file. Everything else such as endpoint selection, auth type, pagination pattern, schema mapping, KQL transform, cross-file consistency was selected autonomously. To put that in perspective: without the agent, a developer building this connector from scratch would need to manually author four JSON files, understand the CCF schema for polling configs, DCRs, and connector definitions, write the KQL transform by hand, and validate that every cross-file reference lines up correctly. The agent compresses that work, typically hours of reading documentation, trial-and-error, and portal debugging, into a single prompt. Testing the Connector Before deploying anything to a Sentinel workspace, the Microsoft Sentinel connector builder agent lets you validate the generated polling config against the live API directly from your editor. Right-click the sentinel-connectors/NetworkLogAPI_CCF folder, select Microsoft Sentinel → Test Connector (Preview), and a Configuration Variables panel opens asking for the two template variables from the polling config: BaseUrl and apiKey . For other API patterns, there may be additional and different inputs. For example, apiKey input could be swapped with clientID and secret if the API supports OAUTH. Enter the Function App base URL and your API key, and the test runner connects immediately. The panel shows a live polling session. Poll #1 returns HTTP 200 with 50 events, and a countdown timer shows when the next poll will fire. Switching to the Events tab displays the ingested records in a tabular view with columns for timestamp , severity , action , bytesIn , bytesOut , category , and the rest of the mapped fields fresh from the API. Additionally, there are tabs for Headers, Payload, and Response, which can be useful for verifying that your pollerconfig.json configuration provides the expected request to your api with a working response. Data Extracted: The Test Connector feature can be used to visualize the response data in a table format to verify that data will land in a Sentinel table based on your configuration. Request from Poller: The Test Connector feature can be used to validate the request and response headers that will go out to the API based on the generated poller configuration. Request Response: The Test Connector feature shows you the live response from the API with respect to the request going to the API based on the poller configuration. This is a meaningful pre-flight check. It confirms that auth is working, the $.data events path resolves correctly, pagination is functional, and the polling interval fires as configured all before a single file is deployed to Azure. The most common connector configuration issues (wrong base URL, incorrect header name, mismatched JSON path) surface here in seconds rather than after a failed deployment and a 20-minute wait for Sentinel to attempt its first ingestion cycle. It is also the fastest way to troubleshoot if something goes wrong after deployment, far quicker than pushing changes to Azure and waiting for the connector to poll again. Deploying and Enabling the Connector With the connector tested and passing, deployment is the same right-click menu: right-click the sentinel-connectors/NetworkLogAPI_CCF folder, select Microsoft Sentinel → Deploy Connector (Preview). If you are not already signed in to Azure, the extension will prompt you to authenticate. The agent will also provide a clickbox in the chat window to invoke a connector deployment. Right Click Deploy Connector: UI Prompt Based Deploy Method: Once signed in, a workspace picker lists all available Log Analytics workspaces across your subscriptions. Select the one with Sentinel enabled and click Deploy. The extension deploys all four files to the workspace in the correct order: table schema first, then DCR, polling config, and connector definition. Once deployed, navigate to your Sentinel workspace via https://security.microsoft.com, go to Data Connectors, and find the Network Log API connector. The connector page shows the description, prerequisite notes, and the two credential fields generated by the agent: API Base URL and API Key. Enter your Function App base URL and API key and click Connect. The status updates to show the connector is connected and the deployment succeeded. Note: Data will appear in the workspace within 5 to 30 minutes depending on the polling interval. Run this query in Log Analytics to confirm ingestion. Note that the agent derives the table name from the vendor name and endpoint, so yours may differ slightly from the example below. Check the agent's summary output or the NetworkLogAPI_Table.json file for the exact name: NetworkLogAPIGetNetworkLogs_CL | sort by TimeGenerated desc | take 10 If you want to generate a fresh batch of events immediately rather than waiting for the next polling cycle, use the RefreshData endpoint to reset the sample records with new timestamps: curl -s -X POST -H "X-API-Key: " \ "https://.azurewebsites.net/api/RefreshData" Next Steps If you want to go further: Try it with your own API. The lab repo includes documentation on adapting the polling config, schema, and KQL transform to a real data source. Review the CCF connector schema documentation to understand the full range of supported configurations: pagination patterns, auth types, incremental pull strategies, and delta filter expressions. Explore the Microsoft Sentinel content hub to see how published connectors are structured and what the certification requirements look like for production submissions. Conclusion Following these steps, you saw how a working Sentinel connector can be generated, tested, and deployed in minutes rather than requiring days of manual configuration and infrastructure setup. If you are an ISV building a Sentinel integration and want hands-on support, Microsoft’s App Assure program is available to help. We partner with ISVs on connector development, validation, and deployment and provide guidance through implementation, testing, and readiness for production. You can get started by reaching out through our intake form. See our other Sentinel connector feature’s hands-on labs Building a CCF Nested API Pull Connector: A Technical Lab WalkthroughMicrosoft Sentinel data lake FAQ
Microsoft Sentinel data lake (generally available) is a purpose‑built, cloud‑native security data lake. It centralizes all security data in an open format, serving as the foundation for agentic defense, enhanced security insights, and graph-based enrichment. It offers cost‑effective ingestion, long‑term retention, and advanced analytics. In this blog we offer answers to many of the questions we’ve heard from our customers and partners. General questions What is the Microsoft Sentinel data lake? Microsoft has expanded its industry-leading SIEM solution, Microsoft Sentinel, to include a unified, security data lake, designed to help optimize costs, simplify data management, and accelerate the adoption of AI in security operations. This modern data lake serves as the foundation for the Microsoft Sentinel platform. It has a cloud-native architecture and is purpose-built for security—bringing together all security data for greater visibility, deeper security analysis, contextual awareness and agentic defense. It provides affordable, long-term retention, allowing organizations to maintain robust security while effectively managing budgetary requirements. What are the benefits of Sentinel data lake? Microsoft Sentinel data lake is purpose built for security offering flexible analytics, cost management, and deeper security insights. Sentinel data lake: Centralizes security data delta parquet and open format for easy access. This unified data foundation accelerates threat detection, investigation, and response across hybrid and multi-cloud environments. Enables data federation by allowing customers to access data in external sources like Microsoft Fabric, ADLS and Databricks from the data lake. Federated data appears alongside native Sentinel data, enabling correlated hunting, investigation, and custom graph analysis across a broader digital estate. Offers a disaggregated storage and compute pricing model, allowing customers to store massive volumes of security data at a fraction of the cost compared to traditional SIEM solutions. Allows multiple analytics engines like Kusto, Spark, and ML to run on a single data copy, simplifying management, reducing costs, and supporting deeper security analysis. Integrates with GitHub Copilot and VS Code empowering SOC teams to automate enrichment, anomaly detection, and forensic analysis. Supports AI agents via the MCP server, allowing tools like GitHub Copilot to query and automate security tasks. The MCP Server layer brings intelligence to the data, offering Semantic Search, Query Tools, and Custom Analysis capabilities that make it easier to extract insights and automate workflows. Provides streamlined onboarding, intuitive table management, and scalable multi-tenant support, making it ideal for MSSPs and large enterprises. The Sentinel data lake is designed for security workloads, ensuring that processes from ingestion to analytics meet evolving cybersecurity requirements. Is Microsoft Sentinel SIEM going away? No. Microsoft is expanding Sentinel into an AI powered end-to-end security platform that includes SIEM and new platform capabilities - Security data lake, graph-powered analytics and MCP Server. SIEM remains a core component and will be actively developed and supported. Getting started What are the prerequisites for Sentinel data lake? To get started: Connect your Sentinel workspace to Microsoft Defender prior to onboarding to Sentinel data lake. Once in the Defender experience see data lake onboarding documentation for next steps. Note: Sentinel is moving to the Microsoft Defender portal and the Sentinel Azure portal will be retired by March 31, 2027. I am a Sentinel-only customer, and not a Defender customer. Can I use the Sentinel data lake? Yes. You must connect Sentinel to the Defender experience before onboarding to the Sentinel data lake. Microsoft Sentinel is generally available in the Microsoft Defender portal, with or without Microsoft Defender XDR or an E5 license. If you have created a log analytics workspace, enabled it for Sentinel and have the right Microsoft Entra roles (e.g. Global Administrator + Subscription Owner, Security Administrator + Sentinel Contributor), you can enable Sentinel in the Defender portal. For more details on how to connect Sentinel to Defender review these sources: Microsoft Sentinel in the Microsoft Defender portal In what regions is Sentinel data lake available? For supported regions see: Geographical availability and data residency in Microsoft Sentinel | Azure Docs. Is there an expected release date for Microsoft Sentinel data lake in GCC, GCC-H, and DoD? While the exact date is not yet finalized, we plan to expand Sentinel data lake to the US Government environments. . How will URBAC and Entra RBAC work together to manage the data lake given there is no centralized model? Entra RBAC will provide broad access to the data lake (URBAC maps the right permissions to specific Entra role holders: GA/SA/SO/GR/SR). URBAC will become a centralized pane for configuring non-global delegated access to the data lake. For today, you will use this for the “default data lake” workspace. In the future, this will be enabled for non-default Sentinel workspaces as well – meaning all workspaces in the data lake can be managed here for data lake RBAC requirements. Azure RBAC on the Log Analytics (LA) workspace in the data lake is respected through URBAC as well today. If you already hold a built-in role like log analytics reader, you will be able to run interactive queries over the tables in that workspace. Or, if you hold log analytics contributor, you can read and manage table data. For more details see: Roles and permissions in the Microsoft Sentinel platform | Microsoft Learn Data ingestion and storage How do I ingest data into the Sentinel data lake? To ingest data into the Sentinel data lake, you can use existing Sentinel data connectors or custom connectors to bring data from Microsoft and third-party sources. Data can be ingested into the analytics tier or the data lake tier. Data ingested into the analytics tier is automatically mirrored to the lake (at no additional cost). Alternatively, data that is not needed in the analytics tier can be ingested directly into the data lake. Data retention is configured directly in table management, for both analytics retention and data lake storage. Note: Certain tables do not support data lake-only ingestion via either API or data connector UI. See here for more information: Custom log tables. What is Microsoft’s guidance on when to use analytics tier vs. the data lake tier? Sentinel data lake offers flexible, built-in data tiering (analytics and data lake tiers) to effectively meet diverse business use cases and achieve cost optimization goals. Analytics tier: Is ideal for high-performance, real-time, end-to-end detections, enrichments, investigation and interactive dashboards. Typically, high-fidelity data from EDRs, email gateways, identity, SaaS and cloud logs, threat intelligence (TI) should be ingested into the analytics tier. Data in the analytics tier is best monitored proactively with scheduled alerts and scheduled analytics to enable security detections Data in this tier is retained at no cost for up to 90 days by default, extendable to 2 years. A copy of the data in this tier is automatically available in the data lake tier at no extra cost, ensuring a unified copy of security data for both tiers. Data lake tier: Is designed for cost-effective, long-term storage. High-volume logs like NetFlow logs, TLS/SSL certificate logs, firewall logs and proxy logs are best suited for data lake tier. Customers can use these logs for historical analysis, compliance and auditing, incident response (IR), forensics over historical data, build tenant baselines, TI matching and then promote resulting insights into the analytics tier. Customers can run full Kusto queries, Spark Notebooks and scheduled jobs over a single copy of their data in the data lake. Customers can also search, enrich and promote data from the data lake tier to the analytics tier for full analytics. For more details see documentation. What does it mean that a copy of all new analytics tier data will be available in the data lake? When Sentinel data lake is enabled, a copy of all new data ingested into the analytics tier is automatically duplicated into the data lake tier. This means customers don’t need to manually configure or manage this process, every new log or telemetry added to the analytics tier becomes instantly available in the data lake. This allows security teams to run advanced analytics, historical investigations, and machine learning models on a single, unified copy of data in the lake, while still using the analytics tier for real-time SOC workflows. It’s a seamless way to support both operational and long-term use cases—without duplicating effort or cost. What is the guidance for customers using data federation capability in Sentinel data lake? Starting April 1, 2026, federate data from Microsoft Fabric, ADLS, and Azure Databricks into Sentinel data lake. Use data federation when data is exploratory, infrequently accessed, or must remain at source due to governance, compliance, sovereignty, or contractual requirements. Ingest data directly into Sentinel to unlock full SIEM capabilities, always-on detections, advanced automation, and AI‑driven defense at scale. This approach lets security teams start where their data already lives — preserving governance, then progressively ingest data into Sentinel for full security value. Is there any cost for retention in the analytics tier? Analytics ingestion includes 90 days of interactive retention, at no additional cost. Simply set analytics retention to 90 days or less. Analytics retention beyond 90 days will incur a retention cost. Data can be retained longer within the data lake by using the “total retention” setting. This allows you to extend retention within the data lake for up to 12 years. While data is retained within the analytics tier, there is no charge for the mirrored data within the lake. Retaining data in the lake beyond the analytics retention period incurs additional storage costs. See documentation for more details: Manage data tiers and retention in Microsoft Sentinel | Microsoft Learn What is the guidance for Microsoft Sentinel Basic and Auxiliary Logs customers? If you previously enabled Basic or Auxiliary Logs plan in Sentinel: You can view Basic Logs in the Defender portal but manage it from the Log Analytics workspace. To manage it in the Defender portal, you must change the plan from Basic to Analytics. Once the table is transitioned to the analytics tier, if desired, it can then be transitioned to the data lake. Existing Auxiliary Log tables will be available in the data lake tier for use once the Sentinel data lake is enabled. Billing for these tables will automatically switch to the Sentinel data lake meters. Microsoft Sentinel customers are recommended to start planning their data management strategy with the data lake. While Basic and Auxiliary Logs are still available, they are not being enhanced further. Sentinel data lake offers more capabilities at a lower price point. Please plan on onboarding your security data to the Sentinel data lake. Azure Monitor customers can continue to use Basic and Auxiliary Logs for observability scenarios. What happens to customers that already have Archive logs enabled? If a customer has already configured tables for Archive retention, existing retention settings will not change and will be automatically inherited by the Sentinel data lake. All data, including existing data in archive retention will be billed using the data lake storage meter, benefiting from 6x data compression. However, the data itself will not move. Existing data in archive will continue to be accessible through Sentinel search and restore experiences: o Data will not be backfilled into the data lake. o Data will be billed using the data lake storage meter. New data ingested after enabling the data lake: o Will be automatically mirrored to the data lake and accessible through data lake explorer. o Data will be billed using the data lake storage meter. Example: If a customer has 12 months of total retention enabled on a table, 2 months after enabling ingestion into the Sentinel data lake, the customer will still have access to 10 months of archived data (through Sentinel search and restore experiences), but access to only 2 months of data in the data lake (since the data lake was enabled). Key considerations for customers that currently have Archive logs enabled: The existing archive will remain, with new data ingested into the data lake going forward; previously stored archive data will not be backfilled into the lake. Archive logs will continue to be accessible via the Search and Restore tab under Sentinel. If analytics and data lake mode are enabled on table, which is the default setting for analytics tables when Sentinel data lake is enabled, all new data will be ingested into the Sentinel data lake. There will only be one storage meter (which is data lake storage) going forward. Archive will continue to be accessible via Search and Restore. If Sentinel data lake-only mode is enabled on table, new data will be ingested only into the data lake; any data that’s not already in the Sentinel data lake won’t be migrated/backfilled. Only data that was previously ingested under the archive plan will be accessible via Search and Restore. What is the guidance for customers using Azure Data Explorer (ADX) alongside Microsoft Sentinel? Some customers might have set up ADX cluster for their DIY lake setup. Customers can choose to continue using that setup and gradually migrate to Sentinel data lake for new data that they want to manage. The lake explorer will support federation with ADX to enable the customers to migrate gradually and simplify their deployment. What happens to the Defender XDR data after enabling Sentinel data lake? By default, Defender XDR tables are available for querying in advanced hunting, with 30 days of analytics tier retention included with the XDR license. To retain data beyond this period, an explicit change to the retention setting is required, either by extending the analytics tier retention or the total retention period. You can extend the retention period of supported Defender XDR tables beyond 30 days and ingest the data into the analytics tier. For more information see Manage XDR data in Microsoft Sentinel. You can also ingest XDR data directly into the data lake tier. See here for more information. A list of XDR advanced hunting tables supported by Sentinel are documented here: Connect Microsoft Defender XDR data to Microsoft Sentinel | Microsoft Learn. KQL queries and jobs Is KQL and Notebook supported over the Sentinel data lake? Yes, via the data lake KQL query experience along with a fully managed Notebook experience which enables spark-based big data analytics over a single copy of all your security data. Customers can run queries across any time range of data in their Sentinel data lake. In the future, this will be extended to enable SQL query over lake as well. Note: Triggering a KQL job directly via an API or Logic App is not yet supported but is on the roadmap. Why are there two different places to run KQL queries in Sentinel experience? Advanced hunting queries both XDR and analytics tables, with compute cost included. Data lake explorer only queries data in the lake and incurs a separate compute cost. Consolidating advanced hunting and KQL explorer user interfaces is on the roadmap. This will provide security analysts a unified query experience across both analytics and data lake tiers. Where is the output from KQL jobs stored? KQL jobs are written into existing or new custom tables in the analytics tier. Is it possible to run KQL queries on multiple data lake tables? Yes, you can run KQL interactive queries and jobs using operators like join or union. Can KQL queries (either interactive or via KQL jobs) join data across multiple workspaces? Security teams can run multi-workspace KQL queries for broader threat correlation Pricing and billing How does a customer pay for Sentinel data lake? Billing is automatically enabled at the time of onboarding based on Azure Subscription and Resource Group selections. Customers are then charged based on the volume of data ingested, retained, and analyzed (e.g. KQL Queries and Jobs). See Sentinel pricing page for more details. 2. What are the pricing components for Sentinel data lake? Sentinel data lake offers a flexible pricing model designed to optimize security coverage and costs. At a high level, pricing is based on the volume of data ingested/processed, the volume of data retained, and the volume of data processed. For specific meter definitions, see documentation. 3. How does the business model for Sentinel SIEM change with the introduction of the data lake? There is no change to existing Sentinel analytics tier ingestion business model. Sentinel data lake has separate meters for ingestion, storage and analytics. 4. What happens to the existing Sentinel SIEM and related Azure Monitor billing meters when a customer onboards to Sentinel data lake? When a customer onboards to the Sentinel data lake, nothing changes with analytic ingestion or retention. Customers using data archive and Auxiliary Logs will automatically transition to the new data lake meters. How does data lake storage affect cost efficiency for high volume data retention? Sentinel data lake offers cost-effective, long-term storage with uniform data compression of 6:1 across all data sources, applicable only to data lake storage. Example: For 600GB of data stored, you are only billed for 100GB compressed data. This approach allows organizations to retain greater volumes of security data over extended periods cost-effectively, thereby reducing security risks without compromising their overall security posture. here How “Data Processing” billed? To support the ingestion and standardization of diverse data sources, the Data Processing feature applies a $0.10 per GB (US East) charge for all data ingested into the data lake. This feature enables a broad array of transformations like redaction, splitting, filtering and normalization. The data processing charge is applied per GB of uncompressed data Note: For regional pricing, please refer to the “Data processing” meter within the Microsoft Sentinel Pricing official documentation. Does “Data processing” meter apply to analytics tier data mirrored in the data lake? No. Data processing charge will not be applied to mirrored data. Data mirrored from the analytic tier is not subject to either data ingestion or processing charges. How is retention billed for tables that use data lake-only ingestion & retention? Sentinel data lake decouples ingestion, storage, and analytics meters. Customers have the flexibility to pay based on how data is retained and used. For tables that use data lake‑only ingestion, there is no included free retention—unlike the analytics tier, which includes 90 days of analytics retention. Retention charges begin immediately once data is stored in the data lake. Data lake storage billing is based on compressed data size rather than raw ingested volume, which significantly reduces storage costs and delivers lower overall retention spend for customers. Does data federation incur charges? Data federation does not generate any ingestion or storage fees in Sentinel data lake. Customers are billed only when they run analytics or queries on federated data, with charges based on Sentinel data lake compute and analytics meters. This means customers pay solely for actual data usage, not mere connectivity. How do I understand Sentinel data lake costs? Sentinel data lake costs driven by three primary factors: how much data is ingested, how long that data is retained, and how the data is used. Customers can flexibly choose to ingest data into the analytics tier or data lake tier, and these architectural choices directly impact cost. For example, data can be ingested into the analytics tier—where commitment tiers help optimize costs for high data volumes—or ingested data directly into the Sentinel data lake for lower‑cost ingestion, storage, and on‑demand analysis. Customers are encouraged to work with their Microsoft account team to obtain an accurate cost estimate tailored to their environment. See Sentinel pricing page to understand Sentinel pricing. How do I manage Sentinel data lake costs? Built-in cost management experiences help customers with cost predictability, billing transparency, and operational efficiency. Reports provide customers with insights into usage trends over time, enabling them to identify cost drivers and optimize data retention and processing strategies. Set usage-based alerts on specific meters to monitor and control costs. For example, receive alerts when query or notebook usage passes set limits, helping avoid unexpected expenses and manage budgets. See our Sentinel cost management documentation to learn more. If I’m an Auxiliary Logs customer, how will onboarding to the Sentinel data lake affect my billing? Once a workspace is onboarded to Sentinel data lake, all Auxiliary Logs meters will be replaced by new data lake meters. Do we charge for data lake ingestion and storage for graph experiences? Microsoft Sentinel graph-based experiences are included as part of the existing Defender and Purview licenses. However, Sentinel graph requires Sentinel data lake and specific data sources to build the underlying graph. Enabling these data sources will incur ingestion and data lake storage costs. Note: For Sentinel SIEM customers, most required data sources are free for analytics ingestion. Non-entitled sources such as Microsoft Entra ID logs will incur ingestion and data lake storage costs. How is Entra asset data and ARG data billed? Data lake ingestion charges of $0.05 per GB (US EAST) will apply to Entra asset data and ARG data. Note: This was previously not billed during public preview and is billed since data lake GA. To learn more, see: https://learn.microsoft.com/azure/sentinel/datalake/enable-data-connectors When a customer activates Sentinel data lake, what happens to tables with archive logs enabled? To simplify billing, once the data lake is enabled, all archive data will be billed using the data lake storage meter. This provides consistent long-term retention billing and includes automatic 6x data compression. For most customers, this change results in lower long‑term retention costs. However, customers who previously had discounted archive retention pricing will not automatically receive the same discounts on the new data lake storage meters. In these cases, customers should engage their Microsoft account team to review pricing implications before enabling the Sentinel data lake. Thank you Thank you to our customers and partners for your continued trust and collaboration. Your feedback drives our innovation, and we’re excited to keep evolving Microsoft Sentinel to meet your security needs. If you have any questions, please don’t hesitate to reach out—we’re here to support you every step of the way. Learn more: Get started with Sentinel data lake today: https://aka.ms/Get_started/Sentinel_datalake Microsoft Sentinel AI-ready platform: https://aka.ms/Microsoft_Sentinel Sentinel data lake videos: https://aka.ms/Sentineldatalake_videos Latest innovations and updates on Sentinel: https://aka.ms/msftsentinelblog Sentinel pricing page: https://aka.ms/MicrosoftSentinel_Pricing
Agent 365 connector: Monitor, hunt, and investigate AI agent activity in Microsoft Sentinel
As enterprises scale the use of AI agents, SOC teams need visibility into AI agent behavior. The Agent 365 connector, now in public preview, streams rich agent telemetry from Agent 365 into Microsoft Sentinel data lake. Agent activity, such as agent data exposure or access drift, is surfaced alongside other security data, giving SOC teams a unified view across digital environments. AI Agent actions are correlated with agent identity, endpoint, and cloud signals, enabling analysts to run end‑to‑end investigations using KQL, graph, and MCP-powered workflows. Why this matters for organizations By centralizing security and AI agent telemetry in Sentinel data lake, organizations establish a unified control plane for securing AI agents. This enables security teams to analyze agent activity in context with broader signals and investigate using familiar Sentinel tools. This unlocks the ability for SOCs to detect risky or anomalous agent behavior early, understand impact quickly, and respond with speed and confidence. As AI agents take on real operational responsibility, this level of visibility is critical to prevent blind spots, reduce risk, and ensure agents operate safely at enterprise scale. End‑to‑end visibility into AI agent behavior: A centralized view of AI agent behavior allows AI agents to be treated as first-class entities alongside users, identities, endpoints, and workloads. Advanced hunting with KQL: Hunt using KQL to proactively uncover unusual AI agent execution patterns, sensitive actions, or activity without clear human context. These hunts help surface potential risk early using the same workflows already used for other security data. Analyzing blast radius and impact with Sentinel graph: Security teams can correlate AI agent activity with identities, endpoints, and cloud resources to understand blast radius and potential impact during an investigation. By pivoting across related entities in Sentinel, analysts can assess how agent actions connect to the broader environment and support deeper, end‑to‑end investigations. Querying agent data through MCP: Use MCP to surface agent observability data through AI assistants, letting analysts pull agent telemetry into investigation workflows alongside other Sentinel data. Agent 365 connector key capabilities Install the Agent 365 connector with a single click using Sentinel Content Hub in the Defender portal. Once enabled, two capabilities come online automatically: Unified agent telemetry across Agent 365 agent experiences: Rich Agent 365 agent telemetry streams into Sentinel data lake, ready to analyze alongside identity, endpoint, and cloud signals using familiar SOC workflows. ASIM unified schema for AI agent observability: Agent 365 agent observability data is normalized into an ASIM-aligned schema so it is consistent, queryable, and ready for analytics and detections. With the connector in place, Sentinel data lake becomes the system of record and the control plane for Agent 365 agent security—turning agent behavior into first-class security signals across SecOps workflows like hunting, investigation, detection engineering, and response. Use cases Prevent sensitive data exposure from misconfigured agents When an AI agent is granted broader access than intended, a crafted prompt could override safeguards and expose confidential data. With agent telemetry, security teams can trace the full execution path—from prompt to tools to data access—to quickly identify the root cause and contain the exposure. Detect and control agent access drift over time As agents take on new tasks, their permissions can expand beyond the original scope, often without clear visibility. Agent telemetry enables continuous behavioral baselining, making it easier to spot abnormal access patterns early and prevent privilege misuse before it escalates. Uncover hidden lateral movement across agent workflows Agents often collaborate and delegate tasks across systems, creating complex chains of execution that are difficult to track. Agent telemetry provides visibility into these interactions, mapping delegation paths and helping teams understand and limit the potential blast radius. Defend against prompt injection and manipulation attacks Attackers can craft prompts to override agent instructions and manipulate behavior. By capturing prompts and reasoning flows, agent telemetry enables detection of these attacks and provides the context needed to investigate and remediate quickly. Accelerate SOC investigations with end-to-end visibility When an agent is involved in a security alert, understanding its actions can be challenging. Agent telemetry correlates prompts, identities, tools, and data access into a unified timeline, giving SOC teams the clarity needed to investigate faster and respond with confidence. Strengthen governance and compliance for AI agents Organizations need visibility into what agents exist and what data they can access. Agent telemetry provides a comprehensive audit trail of agent activity and access patterns, supporting compliance reporting and policy enforcement. Enable proactive threat hunting on agent behavior Security teams need to stay ahead of emerging risks as agent usage grows. Agent telemetry enables advanced hunting across agent activity, helping detect anomalies, uncover patterns, and identify threats before they impact the organization. Get started with Agent 365 connector Getting started is straightforward. In the Microsoft Defender portal, navigate to Microsoft Sentinel Open Content hub and search for Agent 365 Install the Agent 365 Connector (if not already installed) Open the connector page and select Connect to begin ingestion Once connected, AI agent telemetry starts flowing into Sentinel, ready for hunting, investigation, and response. Data ingestion and analytics are billed using existing Sentinel meters. Learn more Find the Agent 365 data connector | Microsoft Learn Discover and manage Sentinel out-of-the-box content | Microsoft Learn Connect data sources to Sentinel by using data connectors | Microsoft Learn Sample KQL queries for Sentinel data lake | Microsoft Learn Watch the Sentinel data lake video playlist | Microsoft Security Get started with Sentinel data lake | Microsoft LearnWhat’s new in Microsoft Sentinel: RSAC 2026
Security is entering a new era, one defined by explosive data growth, increasingly sophisticated threats, and the rise of AI-enabled operations. To keep pace, security teams need an AI-powered approach to collect, reason over, and act on security data at scale. At RSA Conference 2026 (RSAC), we’re unveiling the next wave of Sentinel innovations designed to help organizations move faster, see deeper, and defend smarter with AI-ready tools. These updates include AI-driven playbooks that accelerate SOC automation, Granular Delegated Admin Privileges (GDAP) and granular role-based access controls (RBAC) that let you scale your SOC, accelerated data onboarding through new connectors, and data federation that enables analysis in place without duplication. Together, they give teams greater clarity, control, and speed. Come see us at RSAC to view these innovations in action. Hear from Sentinel leaders during our exclusive Microsoft Pre-Day, then visit Microsoft booth #5744 for demos, theater sessions, and conversations with Sentinel experts. Read on to explore what’s new. See you at RSAC! Sentinel feature innovations: Sentinel SIEM Sentinel data lake Sentinel graph Sentinel MCP Threat Intelligence Microsoft Security Store Sentinel promotions Sentinel SIEM Playbook generator [Now in public preview] The Sentinel playbook generator delivers a new era of automation capabilities. You can vibe code complex automations, integrate with different tools to ensure timely and compliant workflows throughout your SOC and feel confident in the results with built in testing and documentation. Customers and partners are already seeing benefit from this innovation. “The playbook generator gives security engineers the flexibility and speed of AI-assisted coding while delivering the deterministic outcomes that enterprise security operations require. It's the best of both worlds, and it lives natively in Defender where the engineers already work.” – Jaime Guimera Coll | Security and AI Architect | BlueVoyant Learn more about playbook generator. SIEM migration experience [General availability now] The Sentinel SIEM migration experience helps you plan and execute SIEM migrations through a guided, in-product workflow. You can upload Splunk or QRadar exports to generate recommendations for best‑fit Sentinel analytics rules and required data connectors, then assess migration scope, validate detection coverage, and migrate from Splunk or QRadar to Sentinel in phases while tracking progress. “The tool helps turn a Splunk to Sentinel migration into a practical decision process. It gives clear visibility into which detections are relevant, how they align to real security use cases, and where it makes sense to enable or prioritize coverage—especially with cost and data sources in mind.” – Deniz Mutlu | Director | Swiss Post Cybersecurity Ltd Learn more about SIEM migration experience. GDAP, unified RBAC, and row-level RBAC for Sentinel [Public preview, April 1] As Sentinel environments grow for enterprises, MSSPs, hyperscalers, and partners operating across shared or multiple environments, the challenge becomes managing access control efficiently and consistently at scale. Sentinel’s expanded permissions and access capabilities are designed to meet these needs. Granular Delegated Admin Privileges (GDAP) lets you streamline management across multiple governed tenants using your primary account, based on existing GDAP relationships. Unified RBAC allows you to opt in to managing permissions for Sentinel workspaces through a single pane of glass, configuring and enforcing access across Sentinel experiences in the analytics tier and data lake in the Defender portal. This simplifies administration and improves operational efficiency by reducing the number of permission models you need to manage. Row-level RBAC scoping within tables enables precise, scoped access to data in the Sentinel data lake. Multiple SOC teams can operate independently within a shared Sentinel environment, querying only the data they are authorized to see, without separating workspaces or introducing complex data flow changes. Consistent, reusable scope definitions ensure permissions are applied uniformly across tables and experiences, while maintaining strong security boundaries. To learn more, read our technical deep dives on RBAC and GDAP. Sentinel data lake Sentinel data federation [Public preview, April 1] Sentinel data federation lets you analyze security data in place without copying or duplicating your data. Powered by Microsoft Fabric, you can now federate data from Fabric, Azure Data Lake Storage (ADLS), and Azure Databricks into Sentinel data lake. Federated data appears alongside native Sentinel data, so you can use familiar tools like KQL hunting, notebooks, and custom graphs to correlate signals and investigate across your entire digital estate, all while preserving governance and compliance. You can start analyzing data in place and progressively ingest data into Sentinel for deeper security insights, advanced automation, and AI-powered defense at scale. You are billed only when you run analytics on federated data using existing Sentinel data lake query and advanced insights meters. les for unified investigation and hunting Sentinel cost estimation tool [Public Preview, April 9] The new Sentinel cost estimation tool offers all Microsoft customers and partners a guided, meter-level cost estimation experience that makes pricing transparent and predictable. A built-in three-year cost projection lets you model data growth and ramp-up over time, anticipate spend, and avoid surprises. Get transparent estimates into spend as you scale your security operations. All other customers can continue to use the Azure calculator for Sentinel pricing estimates. See the Sentinel pricing page for more information. Sentinel data connectors A365 connector [Public preview, May 5] Bring AI agent telemetry into the Sentinel data lake to investigate agent behavior, tool usage, prompts, reasoning and execution using hunting, graph, and MCP workflows. GitHub audit log connector using API polling [General availability, March 6] Ingest GitHub enterprise audit logs into Sentinel to monitor user and administrator activity, detect risky changes, and investigate security events across your development environment. Google Kubernetes Engine (GKE) connector [General availability, March 6] Collect Google Kubernetes Engine (GKE) audit and workload logs in Sentinel to monitor cluster activity, analyze workload behavior, and detect security threats across Kubernetes environments. Microsoft Entra and Azure Resource Graph (ARG) connector enhancements [Public preview, April 15] Enable new Entra assets (EntraDevices, EntraOrgContacts) and ARG assets (ARGRoleDefinitions) in existing asset connectors, expanding inventory coverage and powering richer, built‑in graph experiences for greater visibility. With over 350 Sentinel data connectors, customers achieve broad visibility into complex digital environments and can expand their security operations effectively. “Microsoft Sentinel data lake forms the core of our agentic SOC. By unifying large volumes of Microsoft and third-party data, enabling graph-based analysis, and supporting MCP-driven workflows, it allows us to investigate faster, at lower cost, and with greater confidence.” – Øyvind Bergerud | Head of Security Operations | Storebrand Learn more about Sentinel data connectors. Sentinel connector builder agent using Sentinel Visual Studio Code extension [Public preview, March 31] Build Sentinel data connectors in minutes instead of weeks using the AI‑assisted Connector Builder agent in Visual Studio Code. This low‑code experience guides developers and ISVs end-to-end, automatically generating schemas, deployment assets, connector UI, secure secret handling, and polling logic. Built‑in validation surfaces issues early, so you can validate event logs before deployment and ingestion. Example prompt in GitHub Copilot Chat: @sentinel-connector-builder Create a new connector for OpenAI audit logs using https://api.openai.com/v1/organization/audit_logs Get started with custom connectors and learn more in our blog. Data filtering and splitting [Public preview, March 30] As security teams ingest more data, the challenge shifts from scale to relevance. With filtering and splitting now built into the Defender portal, teams can shape data before it lands in Sentinel, without switching tools or managing custom JSON files. Define simple KQL‑based transformations directly in the UI to filter low‑value events and intelligently route data, making ingestion optimization faster, more intuitive, and easier to manage at scale. Filtering at ingest time allows you to remove low-value or benign events to reduce noise, cut unnecessary processing, and ensure that high-signal data drives detections and investigations. Splitting enables intelligent routing of data between the analytics tier and the data lake tier based on relevance and usage. Together, these two capabilities help you balance cost and performance while scaling data ingestion sustainably as your digital estate grows. Create workbook reports directly from the data lake [Public preview, April 1] Sentinel workbooks can now directly run on the data lake using KQL, enabling you to visualize and monitor security data straight from the data lake. By selecting the data lake as the workbook data source, you can now create trend analysis and executive reporting. Sentinel graph Custom graphs [Public preview, April 1] Custom graphs let you build tailored security graphs tuned to your unique security scenarios using data from Sentinel data lake as well as non-Microsoft sources. With custom graph, powered by Fabric, you can build, query, and visualize connected data, uncover hidden patterns and attack paths, and help surface risks that are hard to detect when data is analyzed in isolation. These graphs provide the knowledge context that enables AI-powered agent experiences to work more effectively, speeding investigations, revealing blast radius, and helping you move from noisy, disconnected alerts to confident decisions at scale. In the words of our preview customers: “We ingested our Databricks management-plane telemetry into the Sentinel data lake and built a custom security graph. Without writing a single detection rule, the graph surfaced unusual patterns of activity and overprivileged access that we escalated for investigation. We didn't know what we were looking for, the graph surfaced the risk for us by revealing anomalous activity patterns and unusual access combinations driven by relationships, not alerts.” – SVP, Security Solutions | Financial Services organization Custom graph API usage for creating graph and querying graph will be billed starting April 1, 2026, according to the Sentinel graph meter. Creating custom graph Using the Sentinel VS Code extension, you can generate graphs to validate hunting hypotheses, such as understanding attack paths and blast radius of a phishing campaign, reconstructing multi‑step attack chains, and identifying structurally unusual or high‑risk behavior, making it accessible to your team and AI agents. Once persisted via a schedule job, you can access these custom graphs from the ready-to-use section in the graph experience in the Defender portal. Graphs experience in the Microsoft Defender portal After creating your custom graphs, you can access them in the graphs section of the Defender portal under Sentinel. From there, you’ll be able to perform interactive graph-based investigations, such as using a graph built for phishing analysis to help you quickly evaluate the impact of a recent incident, profile the attacker, and trace its paths across Microsoft telemetry and third-party data. The new graph experience lets you run Graph Query Language (GQL) queries, view the graph schema, visualize the graph, view graph results in tabular format, and interactively travers the graph to the next hop with a simple click. Sentinel MCP Sentinel MCP entity analyzer [General availability, April 1] Entity analyzer provides reasoned, out-of-the-box risk assessments that help you quickly understand whether a URL or user identity represents potential malicious activity. The capability analyzes data across modalities including threat intelligence, prevalence, and organizational context to generate clear, explainable verdicts you can trust. Entity analyzer integrates easily with your agents through Sentinel MCP server connections to first-party and third-party AI runtime platforms, or with your SOAR workflows through Logic Apps. The entity analyzer is also a trusted foundation for the Defender Triage Agent and delivers more accurate alert classifications and deeper investigative reasoning. This removes the need to manually engineer evaluation logic and creates trust for analysts and AI agents to act with higher accuracy and confidence. Learn more about entity analyzer and in our blog here. Entity analyzer will be billed starting April 1, 2026, based on Security Compute Units (SCU) consumption. Learn more about MCP billing. Sentinel MCP graph tool collection [Public preview, May 20] Graph tool collection helps you visualize and explore relationships between identities and device assets, threats and activities signals ingested by data connectors and alerted by analytic rules. The tool provides a clear graph view that highlights dependencies and configuration gaps, which makes it easier to understand how content interacts across your environment. This helps security teams assess coverage, optimize content deployment, and identify areas that may need tuning or additional data sources, all from a single, interactive workspace. Executing graph queries via the MCP tools will trigger the graph meter. Claude MCP connector [Public preview, April 1] Anthropic Claude can connect to Sentinel through a custom MCP connector, giving you AI-assisted analysis across your Sentinel environment. Microsoft provides step-by-step guidance for configuring a custom connector in Claude that securely connects to a Sentinel MCP server. With this connection you can summarize incidents, investigate alerts, and reason over security signals while keeping data inside Microsoft's security boundary. Access to large language models (LLMs) is managed through Microsoft authentication and role-based controls, supporting faster triage and investigation workflows while maintaining compliance and visibility. Threat Intelligence CVEs of interest in the Threat Intelligence Briefing Agent [Public preview in April] The Threat Intelligence Briefing Agent delivers curated intelligence based on your organization’s configuration, preferences, and unique industry and geographic needs. CVEs of interest which highlights vulnerabilities actively discussed across the security landscape and assesses their potential impact on your environment, delivering more timely threat intelligence insights. The agent automatically incorporates internet exposure data powered by the Sentinel platform to surface threats targeting technologies exposed in your organization. Together, these enhancements help you focus faster on the threats that matter most, without manual investigation. Microsoft Security Store Security Store embedded in Entra [General availability, March 23] As identity environments grow more complex, teams need to move faster and extend Entra with trusted third‑party capabilities that address operational, compliance, and risk challenges. The Security Store embedded directly into Entra lets you discover and adopt Entra‑ready agents and solutions in your workflow. You can extend Entra with identity‑focused agents that surface privileged access risk, identity posture gaps, network access insights, and overall identity health, turning identity data into clear recommendations and reports teams can use immediately. You can also enhance Entra with Verified ID and External ID integrations that strengthen identity verification, streamline account recovery, and reduce fraud across workforce, consumer, and external identities. Security Store embedded in Microsoft Purview [General availability, March 31] Extending data security across the digital estate requires visibility and enforcement into new data sources and risk surfaces, often requiring a partnered approach. The Security Store embedded directly into Purview lets you discover and evaluate integrated solutions inside your data security workflows. Relevant partner capabilities surface alongside context, making it easier to strengthen data protection, address regulatory requirements, and respond to risk without disrupting existing processes. You can quickly assess which solutions align to data security scenarios, especially with respect to securing AI use, and how they can leverage established classifiers, policies, and investigation workflows in Purview. Keeping integration discovery in‑flow and purchases centralized through the Security Store means you move faster from evaluation to deployment, reducing friction and maintaining a secure, consistent transaction experience. Security Store Advisor [General availability, March 23] Security teams today face growing complexity and choice. Teams often know the security outcome they need, whether that's strengthening identity protection, improving ransomware resilience, or reducing insider risk, but lack a clear, efficient way to determine which solutions will help them get there. Security Store Advisor provides a guided, natural-language discovery experience that shifts security evaluation from product‑centric browsing to outcome‑driven decision‑making. You can describe your goal in plain language, and the Advisor surfaces the most relevant Microsoft and partner agents, solutions, and services available in the Security Store, without requiring deep product knowledge. This approach simplifies discovery, reduces time spent navigating catalogs and documentation, and helps you understand how individual capabilities fit together to deliver meaningful security outcomes. Sentinel promotions Extending signups for promotional 50 GB commitment tier [Through June 2026] The Sentinel promotional 50 GB commitment tier offers small and mid-sized organizations a cost-effective entry point into Sentinel. Sign up for the 50 GB commitment tier until June 30, 2026, and maintain the promotional rate until March 31, 2027. This promotion is available globally with regional variations in pricing and accessible through EA, CSP, and Direct channels. Visit the Sentinel pricing page for details and to get started. Sentinel RSAC 2026 sessions All week – Sentinel product demos, Microsoft Booth #5744 Mon Mar 23, 3:55 PM – RSAC 2026 main stage Keynote with CVP Vasu Jakkal [KEY-M10W] Ambient and autonomous security: Building trust in the agentic AI era Tue Mar 24, 10:30 AM – Live Q&A session, Microsoft booth #5744 and online Ask me anything with Microsoft Security SMEs and real practitioners Tue Mar 24, 11 AM – Sentinel data lake theater session, Microsoft booth #5744 From signals to insights: How Microsoft Sentinel data lake powers modern security operations Tue Mar 24, 2 PM – Sentinel SIEM theater session, Microsoft booth #5744 Vibe-coding SecOps automations with the Sentinel playbook generator Wed Mar 25, 12 PM – Executive event at Palace Hotel with Threat Protection GM Scott Woodgate The AI risk equation: Visibility, control, and threat acceleration Wed Mar 25, 1:30 PM – Sentinel graph theater session, Microsoft booth #5744 Bringing knowledge-driven context to security with Microsoft Sentinel graph Wed Mar 25, 5 PM – MISA theater session, Microsoft booth #5744 Cut SIEM costs without reducing protection: A Sentinel data lake case study Thu Mar 26, 1 PM – Security Store theater session, Microsoft booth #5744 What's next for Security Store: Expanding in portal and smarter discovery All week – 1:1 meetings with Microsoft security experts Meet with Microsoft Defender and Sentinel SIEM and Defender Security Operations Additional resources Sentinel data lake video playlist Explore the full capabilities of Sentinel data lake as a unified, AI-ready security platform that is deeply integrated into the Defender portal Sentinel data lake FAQ blog Get answers to many of the questions we’ve heard from our customers and partners on Sentinel data lake and billing AI‑powered SIEM migration experience ninja training Walk through the SIEM migration experience, see how it maps detections, surfaces connector requirements, and supports phased migration decisions SIEM migration experience documentation Learn how the SIEM migration experience analyzes your exports, maps detections and connectors, and recommends prioritized coverage Accenture collaborates with Microsoft to bring agentic security and business resilience to the front lines of cyber defense Stay connected Check back each month for the latest innovations, updates, and events to ensure you’re getting the most out of Sentinel. We’ll see you in the next edition!Extending Sentinel Data Integration: Azure Blob Storage Support for CCF Connectors
As organizations scale their security operations, the ability to ingest, process, and analyze high volumes of data reliably becomes increasingly critical. Microsoft Sentinel continues to expand its ecosystem through the Codeless Connector Framework (CCF), enabling ISVs to build and deliver integrations with Sentinel faster while simplifying deployment for customers. Today, CCF extends even further with support for Azure Blob Storage, introducing a new pattern for how data can be delivered into Sentinel. Expanding Connector Patterns with Azure Blob Storage CCF has traditionally enabled connectors that integrate directly with partner APIs and data sources. With this latest enhancement, ISVs can now build connectors that read data from Azure Blob Storage—unlocking new flexibility in how security data is collected and delivered. In this model, an ISV writes data to an Azure Blob Storage account. The Sentinel connector then reads from that storage layer, using Azure-native components such as Event Grid and storage queues to process events and forward them through data collection rules (DCR) into Log Analytics workspace. This approach introduces a durable data layer between the data source and Sentinel, enabling more resilient and scalable ingestion scenarios. Why a durable data layer matters By leveraging Azure Blob Storage as part of the ingestion pipeline, CCF connectors gain important operational advantages. This architecture allows data to be buffered and processed asynchronously, helping manage fluctuations in data volume and ensuring consistent delivery. Key benefits include: Resilience: Buffers spikes and handles backpressure to maintain steady ingestion Improved Compatibility: Supports widely adopted Azure Blob-based log streaming, enabling seamless integration with partners that already use Azure for audit data delivery Data protection: Reduces risk of data loss during outages or throttling Scalability: Supports high-volume ingestion scenarios across tenants Flexibility: Enables architectures that can support multiple SIEMs or data consumers Together, these capabilities make CCF Azure Blob Storage based connectors a strong fit for partners managing large, variable, or distributed data pipelines. Partner adoption Early partners are already taking advantage of this capability to modernize their integrations and support evolving customer needs. Cloudflare Cloudflare integrates with Microsoft Sentinel using the Codeless Connector Framework (CCF) to bring Cloudflare log data into centralized security operations workflows. The connector ingests Cloudflare logs—delivered via Logpush to Azure Blob Storage—into Sentinel for analysis, enabling security teams to correlate web, network, and application activity with other security signals. By combining Cloudflare’s global threat visibility with Sentinel analytics and automation, this integration supports more effective threat detection, investigation, and incident response across Cloudflare‑protected environments. Netskope Web Transaction Events Netskope integrates with Microsoft Sentinel to provide detailed visibility into web and cloud activity across users, applications, and SaaS services. The connector ingests Netskope web transaction logs into Sentinel—leveraging Azure Blob Storage as a staging layer for log streaming and ingestion—to enable near real‑time analysis of user behavior, policy violations, and potential threats. By combining Netskope’s inline web inspection with Sentinel’s analytics and correlation capabilities, this integration helps security teams detect risky activity, investigate incidents, and strengthen monitoring across modern cloud environments. These integrations demonstrate how Azure Blob Storage support can simplify ingestion architectures while improving reliability and scalability for customers. Here is what our partners say about the functionality. Cloudflare: Netskope: Get started Developers can begin building CCF Azure Blob Storage -enabled connectors today using the guidance on Microsoft Learn. This documentation provides step-by-step instructions for configuring storage, processing events, and connecting data to Sentinel. In the unlikely event that you encounter any issues in building or updating your connector, App Assure is here to help. We are an engineering-backed team committed to supporting customers and software development companies throughout their journey with Sentinel to streamline integration and accelerate time to market. Reach out to us via our intake form for assistance.Introducing the New Microsoft Sentinel Logstash Output Plugin (Public Preview!)
Many organizations rely on Logstash as a flexible, trusted data pipeline for collecting, transforming, and forwarding logs from on-premises and hybrid environments. Microsoft Sentinel has long supported a Logstash output plugin, enabling customers to send data directly into Sentinel as part of their existing pipelines. The original plugin was implemented in Ruby, and while it has served its purpose, it no longer meets Microsoft’s Secure Future Initiative (SFI) standards and has limited engineering support. To address both security and sustainability, we have rebuilt the plugin from the ground up in Java, a language that is more secure, better supported across Microsoft, and aligned with long-term platform investments. To ensure a seamless transition, the new implementation is still packaged and distributed as a standard Logstash Ruby gem. This means the installation and usage experience remains unchanged for customers, while benefiting from a more secure and maintainable foundation. What's New in This Version Java‑based and SFI‑compliant Same Logstash plugin experience, now rebuilt on a stronger foundation. The new implementation is fully Java‑based, aligning with Microsoft’s Secure Future Initiative (SFI) and providing improved security, supportability, and long-term maintainability. Modern, DCR‑based ingestion The plugin now uses the Azure Monitor Logs Ingestion API with Data Collection Rules (DCRs), replacing the legacy HTTP Data Collection API (For more info, see Migrate from the HTTP Data Collector API to the Log Ingestion API - Azure Monitor | Microsoft Learn). This gives customers full schema control, enables custom log tables, and supports ingestion into standard Microsoft Sentinel tables as well as Microsoft Sentinel data lake. Flexible authentication options Authentication is automatically determined based on your configuration, with support for: Client secret (App registration / service principal) Managed identity, eliminating the need to store credentials in configuration files Sovereign cloud support: The plugin supports Azure sovereign clouds, including Azure US Government, Azure China, and Azure Germany. Standard Logstash distribution model The plugin is published on RubyGems.org, the standard distribution channel for Logstash plugins, and can be installed directly using the Logstash plugin manager, no change to your existing installation workflow. What the Plugin Does Logstash plugin operates as a three-stage data pipeline: Input → Filter → Output. Input: You control how data enters the pipeline, using sources such as syslog, filebeat, Kafka, Event Hubs, databases (via JDBC), files, and more. Filter: You enrich and transform events using Logstash’s powerful filtering ecosystem, including plugins like grok, mutate, and Json, shaping data to match your security and operational needs. Output: This is where Microsoft comes in. The Microsoft Sentinel Logstash Output Plugin securely sends your processed events to an Azure Monitor Data Collection Endpoint, where they are ingested into Sentinel via a Data Collection Rule (DCR). With this model, you retain full control over your Logstash pipeline and data processing logic, while the Sentinel plugin provides a secure, reliable path to ingest data into Microsoft Sentinel. Getting Started Prerequisites Logstash installed and running An Azure Monitor Data Collection Endpoint (DCE) and Data Collection Rule (DCR) in your subscription Contributor role on your Log Analytics workspace Who Is This For? Organizations that already have Logstash pipelines, need to collect from on-premises or legacy systems, and operate in distributed/hybrid environments including air-gapped networks. To learn more, see: microsoft-sentinel-log-analytics-logstash-output-plugin | RubyGems.org | your community gem hostAccelerate Agent Development: Hacks for Building with Microsoft Sentinel data lake
As a Senior Product Manager | Developer Architect on the App Assure team working to bring Microsoft Sentinel and Security Copilot solutions to market, I interact with many ISVs building agents on Microsoft Sentinel data lake for the first time. I’ve written this article to walk you through one possible approach for agent development – the process I use when building sample agents internally at Microsoft. If you have questions about this, or other methods for building your agent, App Assure offers guidance through our Sentinel Advisory Service. Throughout this post, I include screenshots and examples from Gigamon’s Security Posture Insight Agent. This article assumes you have: An existing SaaS or security product with accessible telemetry. A small ISV team (2–3 engineers + 1 PM). Focus on a single high value scenario for the first agent. The Composite Application Model (What You Are Building) When I begin designing an agent, I think end-to-end, from data ingestion requirements through agentic logic, following the Composite application model. The Composite Application Model consists of five layers: Data Sources – Your product’s raw security, audit, or operational data. Ingestion – Getting that data into Microsoft Sentinel. Sentinel data lake & Microsoft Graph – Normalization, storage, and correlation. Agent – Reasoning logic that queries data and produces outcomes. End User – Security Copilot or SaaS experiences that invoke the agent. This separation allows for evolving data ingestion and agent logic simultaneously. It also helps avoid downstream surprises that require going back and rearchitecting the entire solution. Optional Prerequisite You are enrolled in the ISV Success Program, so you can earn Azure Credits to provision Security Compute Units (SCUs) for Security Copilot Agents. Phase 1: Data Ingestion Design & Implementation Choose Your Ingestion Strategy The first choice I face when designing an agent is how the data is going to flow into my Sentinel workspace. Below I document two primary methods for ingestion. Option A: Codeless Connector Framework (CCF) This is the best option for ISVs with REST APIs. To build a CCF solution, reference our documentation for getting started. Option B: CCF Push (Public Preview) In this instance, an ISV pushes events directly to Sentinel via a CCF Push connector. Our MS Learn documentation is a great place to get started using this method. Additional Note: In the event you find that CCF does not support your needs, reach out to App Assure so we can capture your requirements for future consideration. Azure Functions remains an option if you’ve documented your CCF feature needs. Phase 2: Onboard to Microsoft Sentinel data lake Once my data is flowing into Sentinel, I onboard a single Sentinel workspace to data lake. This is a one-time action and cannot be repeated for additional workspaces. Onboarding Steps Go to the Defender portal. Follow the Sentinel Data lake onboarding instructions. Validate that tables are visible in the lake. See Running KQL Queries in data lake for additional information. Phase 3: Build and Test the Agent in Microsoft Foundry Once my data is successfully ingested into data lake, I begin the agent development process. There are multiple ways to build agents depending on your needs and tooling preferences. For this example, I chose Microsoft Foundry because it fit my needs for real-time logging, cost efficiency, and greater control. 1. Create a Microsoft Foundry Instance Foundry is used as a tool for your development environment. Reference our QuickStart guide for setting up your Foundry instance. Required Permissions: Security Reader (Entra or Subscription) Azure AI Developer at the resource group After setup, click Create Agent. 2. Design the Agent A strong first agent: Solves one narrow security problem. Has deterministic outputs. Uses explicit instructions, not vague prompts. Example agent responsibilities: To query Sentinel data lake (Sentinel data exploration tool). To summarize recent incidents. To correlate ISVs specific signals with Sentinel alerts and other ISV tables (Sentinel data exploration tool). 3. Implement Agent Instructions Well-designed agent instructions should include: Role definition ("You are a security investigation agent…"). Data sources it can access. Step by step reasoning rules. Output format expectations. Sample Instructions can be found here: Agent Instructions 4. Configure the Microsoft Model Context Protocol (MCP) tooling for your agent For your agent to query, summarize and correlate all the data your connector has sent to data lake, take the following steps: Select Tools, and under Catalog, type Sentinel, and then select Microsoft Sentinel Data Exploration. For more information about the data exploration tool collection in MCP server, see our documentation. I always test repeatedly with real data until outputs are consistent. For more information on testing and validating the agent, please reference our documentation. Phase 4: Migrate the Agent to Security Copilot Once the agent works in Foundry, I migrate it to Security Copilot. To do this: Copy the full instruction set from Foundry Provision a SCU for your Security Copilot workspace. For instructions, please reference this documentation. Make note of this process as you will be charged per hour per SCU Once you are done testing you will need to deprovision the capacity to prevent additional charges Open Security Copilot and use Create From Scratch Agent Builder as outlined here. Add Sentinel data exploration MCP tools (these are the same instructions from the Foundry agent in the previous step). For more information on linking the Sentinel MCP tools, please refer to this article. Paste and adapt instructions. At this stage, I always validate the following: Agent Permissions – I have confirmed the agent has the necessary permissions to interact with the MCP tool and read data from your data lake instance. Agent Performance – I have confirmed a successful interaction with measured latency and benchmark results. This step intentionally avoids reimplementation. I am reusing proven logic. Phase 5: Execute, Validate, and Publish After setting up my agent, I navigate to the Agents tab to manually trigger the agent. For more information on testing an agent you can refer to this article. Now that the agent has been executed successfully, I download the agent Manifest file from the environment so that it can be packaged. Click View code on the Agent under the Build tab as outlined in this documentation. Publishing to the Microsoft Security Store If I were publishing my agent to the Microsoft Security Store, these are the steps I would follow: Finalize ingestion reliability. Document required permissions. Define supported scenarios clearly. Package agent instructions and guidance (by following these instructions). Summary Based on my experience developing Security Copilot agents on Microsoft Sentinel data lake, this playbook provides a practical, repeatable framework for ISVs to accelerate their agent development and delivery while maintaining high standards of quality. This foundation enables rapid iteration—future agents can often be built in days, not weeks, by reusing the same ingestion and data lake setup. When starting on your own agent development journey, keep the following in mind: To limit initial scope. To reuse Microsoft managed infrastructure. To separate ingestion from intelligence. What Success Looks Like At the end of this development process, you will have the following: A Microsoft Sentinel data connector live in Content Hub (or in process) that provides a data ingestion path. Data visible in data lake. A tested agent running in Security Copilot. Clear documentation for customers. A key success factor I look for is clarity over completeness. A focused agent is far more likely to be adopted. Need help? If you have any issues as you work to develop your agent, please reach out to the App Assure team for support via our Sentinel Advisory Service . Or if you have any other tips, please comment below, I’d love to hear your feedback.Extending App Assure’s Sentinel Promise through the Sentinel Advisory Service
At RSAC last year, we introduced the Microsoft Sentinel Promise with a straightforward commitment to our customers: that third-party data ingestion for Sentinel is reliable, predictable, and scalable without the need for complex custom coding and architecting. In other words, your connectors for Sentinel will just work. That promise has guided App Assure’s work ever since, enabling customers to bring data from across their various security solutions into Sentinel to drive clearer insights and stronger protection. Over the past year, that foundation has proven critical. As organizations move from legacy SIEM platforms to Sentinel, consistent access to high-quality third-party data remains essential, not only for detection and response, but increasingly for advanced analytics and AI-driven security experiences. With the introduction of Microsoft Sentinel data lake, customers and partners can now reason over security data cost-effectively and at greater scale. But as many teams are discovering, unlocking those outcomes requires more than simply getting data in the door. At App Assure, we’ve seen a clear pattern emerge. Software companies often revisit connector design and data modeling multiple times as they help our mutual customers move from ingestion to analytics, and then again as they begin building agentic AI solutions, whether through Security Copilot, MCP server integrations, or custom workflows. Each iteration brings new requirements and new questions, often upstream of where teams initially started. That’s why, as an extension of our Sentinel Promise, we’re excited to announce the Sentinel Advisory Service from App Assure. A Natural Evolution The Sentinel Advisory Service builds directly on the work we’ve been doing through the Sentinel Promise and our support for Sentinel data lake. Our commitment to helping customers bring third-party data into the platform remains unchanged. What this new service adds is an expert-guided approach focused on helping software companies design customer solutions and data strategies with downstream outcomes in mind. Rather than addressing ingestion challenges in isolation, the Sentinel Advisory Service is designed to help teams think end-to-end across the Sentinel platform: aligning connector design, data structure, and platform capabilities to support advanced scenarios such as AI agents, analytics jobs, and marketplace-ready solutions. The goal is fewer rebuild cycles, faster progress, and greater confidence as teams move from data ingestion to meaningful security outcomes. What Sentinel Advisory Service Offers The Sentinel Advisory Service is a no-cost program delivered by App Assure in close collaboration with Sentinel engineering to continually make it easier to build and maintain connectors that utilize data lake and facilitate building agentic AI solutions on top of it. Key areas of support include: Technical workshops covering best practices for Sentinel integrations, data lake usage, and agent development Advisory guidance on leveraging Sentinel platform features to support AI-driven security scenarios Code samples and design reviews to unblock development and improve solution quality Break/fix assistance and escalation paths to Microsoft engineers to assist with software development and provide product feedback Early Partner Momentum We’re already seeing strong momentum from software companies participating in early advisory engagements. Partners are working with App Assure to refine Sentinel integrations and explore new agentic AI scenarios built on a solid data foundation. Their work reflects a broader shift across the ecosystem: moving beyond connectivity alone, toward building differentiated, outcome-driven security solutions on Sentinel. Below are some of the partners we’ve already worked with and what they have to say about the experience: Srinivas Chakravarty, VP of Cloud & AI Ecosystem, Gigamon “Through active collaboration with Microsoft Security Engineering and the App Assure team, we quickly created and published our CCF-Push connector to deliver enriched network-derived telemetry from the Gigamon Deep Observability Pipeline into Sentinel data lake. In a parallel sprint, with the introduction of our initial Security Copilot Agent, security teams can apply AI to this network intelligence within Sentinel to uncover threats hidden in encrypted and lateral traffic that might otherwise go undetected.” Mario Espinoza, Chief Product Officer, Illumio "Illumio is proud to partner with Microsoft, proving together that cybersecurity can scale. Microsoft's product management teams collaborated closely with Illumio on several integrations, most recently Illumio Insights Agent for Security Copilot and Illumio for Microsoft Sentinel Data Lake Connector. Together, Illumio and Sentinel solutions empower customers to correlate joint security threat findings and ensure breaches don't become disasters." Duncan Barnes, Director Global Alliances, RSA "The partnership between RSA and Microsoft, exemplified by the RSA Advisor for Admin Threats agent, underscores the value of the Sentinel Advisory Service. It highlights how collaborative innovation drives differentiated, outcome-driven security solutions, ensuring customers can migrate with confidence and harness the full potential of agentic AI to find, prioritize, and resolve threats faster and more efficiently." Vlad Sushitsky, Research Engineer, Semperis “We developed a Security Copilot agent that correlates Tier-0 classifications, identity attack paths, and Indicators of Exposure for any given identity. The correlation is powered by Semperis Lightning telemetry, streamed into the Data Lake through our new data connector. What used to take analysts hours of manually pivoting across multiple tables to piece together an identity's risk profile now happens instantly in a single conversation. This gives our joint customers significantly better visibility into identity threats, faster investigations, and substantial cost savings. Developing the agent on Security Copilot was smooth and fast — thanks to great collaboration with the Microsoft team, we had it up and running in a matter of days.” Harman Kaur, SVP Technology Strategy and AI, Tanium "This partnership with Microsoft represents a new level of AI and security integration. Through the Microsoft Sentinel Advisory Service, Tanium integrated AI agents into Microsoft Security Copilot, including the recently launched Tanium Security Triage Agent with Identity Insights. By unifying Tanium’s real-time endpoint intelligence with identity information from the Microsoft Sentinel data lake and Entra ID, security analysts gain the speed, precision and confidence needed to stop threats before they escalate." Ariel Negrin, Worldwide Head of Partnerships and Alliances, Upwind "Through the Sentinel Advisory Service and the broader App Assure engineering teams, Microsoft has been side‑by‑side with us, from connector and data model design to advanced AI scenarios, helping us architect for high‑quality ingestion, graph‑aware context, and AI security use cases. That level of hands‑on guidance and roadmap alignment means our joint customers get faster time to value, fewer integration rebuilds, and a more intelligent security experience built on top of the Microsoft security stack they already trust." Matthew Payne, Field Engineer, XBOW "The team worked alongside us from the start, not just on ingestion, but on designing how XBOW's penetration testing data should flow into Sentinel to actually drive downstream outcomes. Their engineering guidance helped us build agents for Security Copilot and a Sentinel data connector that turns validated exploit paths into actionable security telemetry. The result is that joint customers can trigger a pentest, see real findings in Sentinel alongside their existing alerts, and investigate and remediate without leaving the Microsoft security console." Paul Lopez, Principal Solutions Architect, Zscaler "Organizations looking to improve visibility across internet and private access activities benefit from integrating these signals. Through collaboration with Microsoft’s App Assure team, Zscaler’s ZIA–ZPA Correlation Agent for Security Copilot leverages data from the Sentinel Data Lake to deliver a single, cohesive view, simplifying investigations and enabling faster response times." Getting Started The Sentinel Advisory Service is available today for developers building on Microsoft Sentinel and Sentinel data lake. If you’re enhancing an existing connector, designing an AI-driven security solution, or planning how to translate data into action on the Sentinel platform, App Assure is here to help. As always, our focus remains on customer confidence, ensuring that as Sentinel evolves, the ecosystem around it can evolve just as reliably. The Sentinel Advisory Service is the next step in delivering on that promise. Reach out to us here.Microsoft partners with DataBahn to accelerate enterprise deployments for Microsoft Sentinel
Enterprise security teams are collecting more telemetry than ever across cloud platforms, endpoints, SaaS applications, and on-premises infrastructure. Security teams want broader data coverage and longer retention without losing control of cost and data quality. This post explains the new DataBahn integration with Microsoft Sentinel, why it matters for SIEM operations, and how to think about using a security data pipeline alongside Sentinel for onboarding, normalization, routing, and governance. DataBahn joins Microsoft Sentinel partner ecosystem This integration reflects Microsoft Sentinel’s open partner ecosystem, giving customers choice in the partners they use alongside Microsoft Sentinel to manage their security data pipelines. DataBahn joins a broader set of complementary partners, enabling customers to tailor solutions for their unique security data needs. DataBahn is available through Microsoft Marketplace and is eligible for customers to apply existing Azure Consumption Commitments toward the purchase of DataBahn. Why this matters for security operations teams Security teams are under relentless pressure to ingest more data, move faster through SIEM migrations, and preserve data fidelity for detections and investigations, all while managing costs effectively. The challenge isn’t just ingesting data, but ensuring the right telemetry arrives in a consistent, governed format that analysts and detections can trust. This is where a security data pipeline, alongside Microsoft Sentinel’s native connectors and DCRs, can add value. It helps streamline onboarding of third-party and custom sources, improve normalization consistency, and provide operational visibility across diverse environments as deployments scale. What DataBahn integration is positioned to do with Microsoft Sentinel Security teams want broader coverage and need to ensure third-party data is consistently shaped, routed, and governed at scale. This is where a security data pipeline like DataBahn complements Microsoft Sentinel. Sitting upstream of ingestion, the pipeline layer standardizes onboarding and shaping across sources while providing operational visibility into data flow and pipeline health. Together, the collaboration focuses on reducing onboarding friction, improving normalization consistency, enabling intentional routing, and strengthening governance signals so teams can quickly detect source changes, parser breaks, or data gaps—while staying aligned with Sentinel analytics and detection workflows. This model gives Sentinel customers more choice to move faster, onboard data at scale, and retain control over data routing. Key capabilities Bidirectional data integration The integration enables seamless delivery of telemetry into Sentinel while aligning with Sentinel detection logic and schema expectations. This helps ensure telemetry pipelines remain consistent with: Sentinel detection formats Custom analytics rules Sentinel data models and schemas Automated table and DCR management As detections evolve, pipeline configurations can adapt to maintain detection fidelity and data consistency. Advanced management API DataBahn provides an advanced management API that allows organizations to programmatically configure and manage pipeline integrations with Sentinel. This enables teams to: Automate pipeline configuration Manage operational workflows Integrate pipeline management into broader security or DevOps automation processes Automatic identification of configuration conflicts In complex environments with multiple telemetry sources and routing rules, configuration conflicts can arise across filtering logic, enrichment pipelines, and detection dependencies. The integration helps automatically: Detect conflicts in filtering rules and pipeline logic Identify clashes with detection dependencies Highlight missing configurations or coverage gaps This visibility allows SOC teams to quickly identify issues that could impact detection reliability. Centralized pipeline management The integration enables centralized management of data collection and transformation workflows associated with Sentinel telemetry pipelines. This provides unified visibility and control across telemetry sources while maintaining compatibility with Sentinel analytics and detections. Centralized management simplifies operations across large environments where multiple telemetry pipelines must be maintained. Flexible data transformation and customization Security telemetry often arrives in inconsistent formats across vendors and platforms. The platform supports flexible transformation capabilities that allow organizations to: Normalize logs into standard or custom Sentinel table formats Add or derive fields required by Sentinel detections Apply filtering or enrichment rules before ingestion Configuration can be performed through a single-screen workflow, enabling teams to modify schemas and define filtering logic without disrupting downstream analytics. The platform also provides schema drift detection and source health monitoring, helping teams maintain reliable telemetry pipelines as environments evolve. Closing Effective security operations depend on how quickly a SOC can onboard new data, scale effectively, and maintain high‑quality investigations. Sentinel provides a cloud‑native, AI-ready foundation to ingest security data from first- and third‑party data sources—while enabling economical, large‑scale retention and deep analytics using open data formats and multiple analytics engines. DataBahn’s partnership with Sentinel is positioned as a pipeline layer that can help teams onboard third-party sources, shape and normalize data, and apply routing and governance patterns before data lands in Sentinel. Learn more DataBahn for Microsoft Sentinel DataBahn Press Release - Databahn Deepens Partnership with Microsoft Sentinel Microsoft Sentinel data lake overview - Microsoft Security | Microsoft Learn Microsoft Sentinel—AI-Ready Platform | Microsoft Security Connect Microsoft Sentinel to the Microsoft Defender portal - Unified security operations | Microsoft Learn Microsoft Sentinel data lake is now generally available | Microsoft Community Hub
