Accelerate 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.RSAC 2026: New Microsoft Sentinel Connectors Announcement
Microsoft Sentinel helps organizations detect, investigate, and respond to security threats across increasingly complex environments. With the rollout of the Microsoft Sentinel data lake in the fall, and the App Assure-backed Sentinel promise that supports it, customers now have access to long-term, cost-effective storage for security telemetry, creating a solid foundation for emerging Agentic AI experiences. Since our last announcement at Ignite 2025, the Microsoft Sentinel connector ecosystem has expanded rapidly, reflecting continued investment from software development partners building for our shared customers. These connectors bring diverse security signals together, enabling correlation at scale and delivering richer investigation context across the Sentinel platform. Below is a snapshot of Microsoft Sentinel connectors newly available or recently enhanced since our last announcement, highlighting the breadth of partner solutions contributing data into, and extending the value of, the Microsoft Sentinel ecosystem. New and notable integrations Acronis Cyber Protect Cloud Acronis Cyber Protect Cloud integrates with Microsoft Sentinel to bring data protection and security telemetry into a centralized SOC view. The connector streams alerts, events, and activity data - spanning backup, endpoint protection, and workload security - into Microsoft Sentinel for correlation with other signals. This integration helps security teams investigate ransomware and data-centric threats more effectively, leverage built-in hunting queries and detection rules, and improve visibility across managed environments without adding operational complexity. Anvilogic Anvilogic integrates with Microsoft Sentinel to help security teams operationalize detection engineering at scale. The connector streams Anvilogic alerts into Microsoft Sentinel, giving SOC analysts centralized visibility into high-fidelity detections and faster context for investigation and triage. By unifying detection workflows, reducing alert noise, and improving prioritization, this integration supports more efficient threat detection and response while helping teams extend coverage across evolving attack techniques. CyberArk Audit CyberArk Audit integrates with Microsoft Sentinel to centralize visibility into privileged identity and access activity. By streaming detailed audit logs - covering system events, user actions, and administrative activity - into Microsoft Sentinel, security teams can correlate identity-driven risks with broader security telemetry. This integration supports faster investigations, improved monitoring of privileged access, and more effective incident response through automated workflows and enriched context for SOC analysts. Cyera Cyera integrates with Microsoft Sentinel to extend AI-native data security posture management into security operations. The connector brings Cyera’s data context and actionable intelligence across multi-cloud, on-premises, and SaaS environments into Microsoft Sentinel, helping teams understand where sensitive data resides and how it is accessed, exposed, and used. Built on Sentinel’s modern framework, the integration feeds context-rich data risk signals into the Sentinel data lake, enabling more informed threat hunting, automation, and decision-making around data, user, and AI-related risk. TacitRed CrowdStrike IOC Automation Data443 TacitRed CS IOC Automation integrates with Microsoft Sentinel to streamline the operationalization of compromised credential intelligence. The solution uses Sentinel playbooks to automatically push TacitRed indicators of compromise into CrowdStrike via Sentinel playbooks, helping security teams turn identity-based threat intelligence into action. By automating IOC handling and reducing manual effort, this integration supports faster response to credential exposure and strengthens protection against account-driven attacks across the environment. TacitRed SentinelOne IOC Automation Data443 TacitRed SentinelOne IOC Automation integrates with Microsoft Sentinel to help operationalize identity-focused threat intelligence at the endpoint layer. The solution uses Sentinel playbooks to automatically consume TacitRed indicators and push curated indicators into SentinelOne via Sentinel playbooks and API-based enforcement, enabling faster enforcement of high-risk IOCs without manual handling. By automating the flow of compromised credential intelligence from Sentinel into EDR, this integration supports quicker response to identity-driven attacks and improves coordination between threat intelligence and endpoint protection workflows. TacitRed Threat Intelligence Data443 TacitRed Threat Intelligence integrates with Microsoft Sentinel to provide enhanced visibility into identity-based risks, including compromised credentials and high-risk user exposure. The solution ingests curated TacitRed intelligence directly into Sentinel, enriching incidents with context that helps SOC teams identify credential-driven threats earlier in the attack lifecycle. With built-in analytics, workbooks, and hunting queries, this integration supports proactive identity threat detection, faster triage, and more informed response across the SOC. Cyren Threat Intelligence Cyren Threat Intelligence integrates with Microsoft Sentinel to enhance detection of network-based threats using curated IP reputation and malware URL intelligence. The connector ingests Cyren threat feeds into Sentinel using the Codeless Connector Framework (CCF), transforming raw indicators into actionable insights, dashboards, and enriched investigations. By adding context to suspicious traffic and phishing infrastructure, this integration helps SOC teams improve alert accuracy, accelerate triage, and make more confident response decisions across their environments. TacitRed Defender Threat Intelligence Data443 TacitRed Defender Threat Intelligence integrates with Microsoft Sentinel to surface early indicators of credential exposure and identity-driven risk. The solution automatically ingests compromised credential intelligence from TacitRed into Sentinel and can support synchronization of validated indicators with Microsoft Defender Threat Intelligence through Sentinel workflows, helping SOC teams detect account compromise before abuse occurs. By enriching Sentinel incidents with actionable identity context, this integration supports faster triage, proactive remediation, and stronger protection against credential-based attacks. Datawiza Access Proxy (DAP) Datawiza Access Proxy integrates with Microsoft Sentinel to provide centralized visibility into application access and authentication activity. By streaming access and MFA logs from Datawiza into Sentinel, security teams can correlate identity and session-level events with broader security telemetry. This integration supports detection of anomalous access patterns, faster investigation through session traceability, and more effective response using Sentinel automation, helping organizations strengthen Zero Trust controls and meet auditing and compliance requirements. Endace Endace integrates with Microsoft Sentinel to provide deep network visibility by providing always-on, packet-level evidence. The connector enables one-click pivoting from Sentinel alerts directly to recorded packet data captured by EndaceProbes. This helps SOC and NetOps teams reconstruct events and validate threats with confidence. By combining Sentinel’s AI-driven analytics with Endace’s always-on, full-packet capture across on-premises, hybrid, and cloud environments, this integration supports faster investigations, improved forensic accuracy, and more decisive incident response. Feedly Feedly integrates with Microsoft Sentinel to ingest curated threat intelligence directly into security operations workflows. The connector automatically imports Indicators of Compromise (IoCs) from Feedly Team Boards and folders into Sentinel, enriching detections and investigations with context from the original intelligence articles. By bringing analyst‑curated threat intelligence into Sentinel in a structured, automated way, this integration helps security teams stay current on emerging threats and reduce the manual effort required to operationalize external intelligence. Gigamon Gigamon integrates with Microsoft Sentinel through a new connector that provides access to Gigamon Application Metadata Intelligence (AMI), delivering high-fidelity network-derived telemetry with rich application metadata from inspected traffic directly into Sentinel. This added context helps security teams detect suspicious activity, encrypted threats, and lateral movement faster and with greater precision. By enriching analytics without requiring full packet ingestion, organizations can reduce noise, manage SIEM costs, and extend visibility across hybrid cloud infrastructure. Halcyon Halcyon integrates with Microsoft Sentinel to provide purpose-built ransomware detection and automated containment across the Microsoft security ecosystem. The connector surfaces Halcyon ransomware alerts directly within Sentinel, enabling SOC teams to correlate ransomware behavior with Microsoft Defender and broader Microsoft telemetry. By supporting Sentinel analytics and automation workflows, this integration helps organizations detect ransomware earlier, investigate faster using native Sentinel tools, and isolate affected endpoints to prevent lateral spread and reinfection. Illumio The Illumio platform identifies and contains threats across hybrid multi-cloud environments. By integrating AI-driven insights with Microsoft Sentinel and Microsoft Graph, Illumio Insights enables SOC analysts to visualize attack paths, prioritize high-risk activity, and investigate threats with greater precision. Illumio Segmentation secures critical assets, workloads, and devices and then publishes segmentation policy back into Microsoft Sentinel to ensure compliance monitoring. Joe Sandbox Joe Sandbox integrates with Microsoft Sentinel to enrich incidents with dynamic malware and URL analysis. The connector ingests Joe Sandbox threat intelligence and automatically detonates suspicious files and URLs associated with Sentinel incidents, returning behavioral and contextual analysis results directly into investigation workflows. By adding sandbox-driven insights to indicators, alerts, and incident comments, this integration helps SOC teams validate threats faster, reduce false positives, and improve response decisions using deeper visibility into malicious behavior. Keeper Security The Keeper Security integration with Microsoft Sentinel brings advanced password and secrets management telemetry into your SIEM environment. By streaming audit logs and privileged access events from Keeper into Sentinel, security teams gain centralized visibility into credential usage and potential misuse. The connector supports custom queries and automated playbooks, helping organizations accelerate investigations, enforce Zero Trust principles, and strengthen identity security across hybrid environments. Lookout Mobile Threat Defense (MTD) Lookout Mobile Threat Defense integrates with Microsoft Sentinel to extend SOC visibility to mobile endpoints across Android, iOS, and Chrome OS. The connector streams device, threat, and audit telemetry from Lookout into Sentinel, enabling security teams to correlate mobile risk signals such as phishing, malicious apps, and device compromise, with broader enterprise security data. By incorporating mobile threat intelligence into Sentinel analytics, dashboards, and alerts, this integration helps organizations detect mobile driven attacks earlier and strengthen protection for an increasingly mobile workforce. Miro Miro integrates with Microsoft Sentinel to provide centralized visibility into collaboration activity across Miro workspaces. The connector ingests organization-wide audit logs and content activity logs into Sentinel, enabling security teams to monitor authentication events, administrative actions, and content changes alongside other enterprise signals. By bringing Miro collaboration telemetry into Sentinel analytics and dashboards, this integration helps organizations detect suspicious access patterns, support compliance and eDiscovery needs, and maintain stronger oversight of collaborative environments without disrupting productivity. Obsidian Activity Threat The Obsidian Threat and Activity Feed for Microsoft Sentinel delivers deep visibility into SaaS and AI applications, helping security teams detect account compromise and insider threats. By streaming user behavior and configuration data into Sentinel, organizations can correlate application risks with enterprise telemetry for faster investigations. Prebuilt analytics and dashboards enable proactive monitoring, while automated playbooks simplify response workflows, strengthening security posture across critical cloud apps. OneTrust for Purview DSPM OneTrust integrates with Microsoft Sentinel to bring privacy, compliance, and data governance signals into security operations workflows. The connector enriches Sentinel with privacy relevant events and risk indicators from OneTrust, helping organizations detect sensitive data exposure, oversharing, and compliance risks across cloud and non-Microsoft data sources. By unifying privacy intelligence with Sentinel analytics and automation, this integration enables security and privacy teams to respond more quickly to data risk events and support responsible data use and AI-ready governance. Pathlock Pathlock integrates with Microsoft Sentinel to bring SAP-specific threat detection and response signals into centralized security operations. The connector forwards security-relevant SAP events into Sentinel, enabling SOC teams to correlate SAP activity with broader enterprise telemetry and investigate threats using familiar SIEM workflows. By enriching Sentinel with SAP security context and focused detection logic, this integration helps organizations improve visibility into SAP landscapes, reduce noise, and accelerate detection and response for risks affecting critical business systems. Quokka Q-scout Quokka Q-scout integrates with Microsoft Sentinel to centralize mobile application risk intelligence across Microsoft Intune-managed devices. The connector automatically ingests app inventories from Intune, analyzes them using Quokka’s mobile app vetting engines, and streams security, privacy, and compliance risk findings into Sentinel. By surfacing app-level risks through Sentinel analytics and alerts, this integration helps security teams identify malicious or high-risk mobile apps, prioritize remediation, and strengthen mobile security posture without deploying agents or disrupting users. Synqly Synqly integrates with Microsoft Sentinel to simplify and scale security integrations through a unified API approach. The connector enables organizations and security vendors to establish a bi‑directional connection with Sentinel without relying on brittle, point‑to‑point integrations. By abstracting common integration challenges such as authentication handling, retries, and schema changes, Synqly helps teams orchestrate security data flows into and out of Sentinel more reliably, supporting faster onboarding of new data sources and more maintainable integrations at scale. Versasec vSEC:CMS Versasec vSEC:CMS integrates with Microsoft Sentinel to provide centralized visibility into credential lifecycle and system health events. The connector securely streams vSEC:CMS and vSEC:CLOUD alerts and status data into Sentinel using the Codeless Connector Framework (CCF), transforming credential management activity into correlation-ready security signals. By bringing smart card, token, and passkey management telemetry into Sentinel, this integration helps security teams monitor authentication infrastructure health, investigate credential-related incidents, and unify identity security operations within their SIEM workflows. VirtualMetric DataStream VirtualMetric DataStream integrates with Microsoft Sentinel to optimize how security telemetry is collected, normalized, and routed across the Microsoft security ecosystem. Acting as a high-performance telemetry pipeline, DataStream intelligently filters and enriches logs, sending high-value security data to Sentinel while routing less-critical data to Sentinel data lake or Azure Blob Storage for cost-effective retention. By reducing noise upstream and standardizing logs to Sentinel ready schemas, this integration helps organizations control ingestion costs, improve detection quality, and streamline threat hunting and compliance workflows. VMRay VMRay integrates with Microsoft Sentinel to enrich SIEM and SOAR workflows with automated sandbox analysis and high-fidelity, behavior-based threat intelligence. The connector enables suspicious files and phishing URLs to be submitted directly from Sentinel to VMRay for dynamic analysis, while validated, high-confidence indicators of compromise (IOCs) are streamed back into Sentinel’s Threat Intelligence repository for correlation and detection. By adding detailed attack-chain visibility and enriched incident context, this integration helps SOC teams reduce investigation time, improve detection accuracy, and strengthen automated response workflows across Sentinel environments. Zero Networks Segment Audit Zero Networks Segment integrates with Microsoft Sentinel to provide visibility into micro-segmentation and access-control activity across the network. The connector can collect audit logs or activities from Zero Networks Segment, enabling security teams to monitor policy changes, administrative actions, and access events related to MFA-based network segmentation. By bringing segmentation audit telemetry into Sentinel, this integration supports compliance monitoring, investigation of suspicious changes, and faster detection of attempts to bypass lateral-movement controls within enterprise environments. Zscaler Internet Access (ZIA) Zscaler Internet Access integrates with Microsoft Sentinel to centralize cloud security telemetry from web and firewall traffic. The connector enables ZIA logs to be ingested into Sentinel, allowing security teams to correlate Zscaler Internet Access signals with other enterprise data for improved threat detection, investigation, and response. By bringing ZIA web, firewall, and security events into Sentinel analytics and hunting workflows, this integration helps organizations gain broader visibility into internet-based threats and strengthen Zero Trust security operations. In addition to these solutions from our third-party partners, we are also excited to announce the following connector published by the Microsoft Sentinel team: GitHub Enterprise Audit Logs Microsoft’s Sentinel Promise For Customers Every connector in the Microsoft Sentinel ecosystem is built to work out of the box. In the unlikely event a customer encounters any issue with a connector, the App Assure team stands ready to assist. For Software Developers Software partners in need of assistance in creating or updating a Sentinel solution can also leverage Microsoft’s Sentinel Promise to support our shared customers. For developers seeking to build agentic experiences utilizing Sentinel data lake, we are excited to announce the launch of our Sentinel Advisory Service to guide developers across their Sentinel journey. Customers and developers alike can reach out to us via our intake form. Learn More Microsoft Sentinel data lake Microsoft Sentinel data lake: Unify signals, cut costs, and power agentic AI Introducing Microsoft Sentinel data lake What is Microsoft Sentinel data lake Unlocking Developer Innovation with Microsoft Sentinel data lake Microsoft Sentinel Codeless Connector Framework (CCF) Create a codeless connector for Microsoft Sentinel Public Preview Announcement: Microsoft Sentinel CCF Push What’s New in Microsoft Sentinel Monthly Blog Microsoft App Assure App Assure home page App Assure services App Assure blog App Assure Request Assistance Form App Assure Sentinel Advisory Services announcement App Assure’s promise: Migrate to Sentinel with confidence App Assure’s Sentinel promise now extends to Microsoft Sentinel data lake Ignite 2025 new Microsoft Sentinel connectors announcement Microsoft Security Microsoft’s Secure Future Initiative Microsoft Unified SecOpsAgentic Use Cases for Developers on the Microsoft Sentinel Platform
Interested in building an agent with Sentinel platform solutions but not sure where to start? This blog will help you understand some common use cases for agent development that we’ve seen across our partner ecosystem. SOC teams don’t need more alerts - they need fast, repeatable investigation and response workflows. Security Copilot agents can help orchestrate the steps analysts perform by correlating across the Sentinel data lake, executing targeted KQL queries, fetching related entities, enriching with context, and producing an evidence-backed decision without forcing analysts to switch tools. Microsoft Sentinel platform is a strong foundation for agentic experiences because it exposes a normalized security data layer, an investigation surface based on incidents and entities, and extensive automation capabilities. An agent can use these primitives to correlate identity, endpoint, cloud, and network telemetry; traverse entity relationships; and recommend remediation actions. In this blog, I will break down common agentic use cases that developers can implement on Sentinel platform, framed in buildable and repeatable patterns: Identify the investigation scenario Understand the required Sentinel data connectors and KQL queries Build enrichment and correlation logic Summarize findings with supporting evidence and recommended remediation steps Use Case 1: Identity & Access Intelligence Investigation Scenario: Is this risky sign-in part of an attack path? Signals Correlated: Identity access telemetry: Source user, IPs, target resources, MFA logs Authentication outcomes and diversity: Success vs. failure, Geographic spread Identity risk posture: User risk level/state Post-auth endpoint execution: Suspicious LOLBins Correlation Logic: An analyst receives a risky sign-in signal for a user and needs to determine whether the activity reflects expected behavior - such as travel, remote access, or MFA friction - or if it signals the early stage of an identity compromise that could escalate into privileged access and downstream workload impact. Practical Example: Silverfort Identity Threat Triage Agent, which is built on a similar framework, takes the user’s UPN as input and builds a bounded, last-24-hour investigation across authentication activity, MFA logs, user risk posture, and post-authentication endpoint behavior. Outcome: By correlating identity risk signals, MFA logs, sign-in success and failure patterns, and suspicious execution activity following authentication, the agent connects the initial risky sign-in to endpoint behavior, enabling the analyst to quickly assess compromise likelihood, identify escalation indicators, and determine appropriate remediation actions. “Our collaboration with Microsoft Sentinel and Security Copilot underscores the central role identity plays across every stage of attack path triage. By integrating Silverfort’s identity risk signals with Microsoft Entra ID and Defender for Endpoint, and sharing rich telemetry across platforms, we enable Security Copilot Agent to distinguish isolated anomalies from true identity-driven intrusions - while dramatically reducing the manual effort traditionally required for incident response and threat hunting. AI-driven agents accelerate analysis, enrich investigative context, reduce dwell time, and speed detection. Instead of relying on complex queries or deep familiarity with underlying data structures, security teams can now perform seamless, identity-centric reasoning within a single interaction.” - Frank Gasparovic, Director of Solution Architecture, Technology Alliances, Silverfort Use Case 2: Cyber Resilience, Backup & Recovery Investigation Scenario: Are the threats detected on a backup indicative of production impact and recovery risk? Signals Correlated: Backup threat telemetry: Backup threat scan alerts, risk analysis events, affected host/workload, detection timestamps Cross-vendor security alerts: Endpoint, network, and cloud security alerts for the same host/workload in the same time window Correlation Logic: The agent correlates threat signals originating from the backup environment with security telemetry associated with same host/workload to validate whether there is corroborating evidence in the production environment and whether activity aligns in time. Practical Example: Commvault Security Investigation Agent, which is built on a similar framework, takes a hostname as input and builds an investigation across Commvault Threat Scan / Risk Analysis events and third-party security telemetry. By correlating backup-originating detections with production security activity for the same host, the agent determines whether the backup threat signal aligns with observable production impact. Outcome: By correlating backup threat detections with endpoint, network, and cloud security telemetry while validating timing alignment, event spikes, and data coverage, the agent connects a backup originating threat signal to production evidence, enabling the analyst to quickly assess impact likelihood and determine appropriate actions such as containment or recovery-point validation. Use Case 3: Network, Exposure & Connectivity Investigation Scenario: Is this activity indicative of legitimate remote access, or does it demonstrate suspicious connectivity and access attempts that increase risk to private applications and internal resources. Signals Correlated: User access telemetry: Source user, source IPs/geo, device/context, destinations Auth and enforcement outcomes: Success vs. failure, MFA allow/block Behavior drift: new/rare IPs/locations, unusual destination/app diversity. Suspicious activity indicators: Risky URLs/categories, known-bad indicators, automated/bot-like patterns, repeated denied private app access attempts Correlation Logic: An analyst receives an alert for a specific user and needs to determine whether the activity reflects expected behavior such as travel, remote work, or VPN usage, or whether it signals the early stages of a compromise that could later extend into private application access. Practical Example: Zscaler ZIA ZPA Correlation Agent starts with a username and builds a bounded, last-24-hour investigation across Zscaler Internet Access and Zscaler Private Access activity. By correlating user internet behavior, access context, and private application interactions, the agent connects the initial Zscaler alert to any downstream access attempts or authentication anomalies, enabling the analyst to quickly assess risk, identify suspicious patterns, and determine whether Zscaler policy adjustments are required. Outcome: Provides a last‑24‑hour verdict on whether the activity reflects expected access patterns or escalation toward private application access, and recommends next actions—such as closing as benign drift, escalating for containment, or tuning access policy—based on correlated evidence. Use Case 4: Endpoint & Runtime Intelligence Investigation Scenario: Is this process malicious or a legitimate admin action? Signals Correlated: Execution context: Process chain, full command line, signer, unusual path Account & logon: Initiating user, logon type (RDP/service), recent risky sign-ins Tooling & TTPs: LOLBins, credential access hints, lateral movement tooling Network behavior: Suspicious connections, repeated callbacks/beaconing Correlation Logic: A PowerShell alert triggers on a production server. The agent ties the process to its parent (e.g., spawned by a web worker vs. an admin shell), validates the command-line indicators, correlates outbound connections from the same PID to a first-seen destination, and checks for immediate follow-on persistence and any adjacent runtime alerts in the same time window. Outcome: Classifies the activity as malicious vs. admin and produces an evidence pack (process tree, key command indicators, destinations, persistence/tamper artifacts) as well as the recommended containment step (isolate host and revoke/reset initiating credentials). Use Case 5: Exposure & Exploitability Investigation Scenario: What is the likelihood of exploitation and blast radius? Signals Correlated: Asset exposure: Internet-facing status, exposed services/ports, and identity or network paths required to reach the workload Exploit activity: Defender alerts on the resource, IDS/WAF hits, IOC matches, and first seen exploit or probing attempts Risk amplification signals: Internet communication, high privilege access paths, and indicators that the workload processes PII or sensitive data Blast radius: Downstream reachability to crown jewel systems (e.g., databases, key vaults) and trust relationships that could enable escalation Correlation Logic: An analyst receives a Medium/High Microsoft Defender for Cloud alert on a workload and needs to determine whether it’s a standalone detection or an exploitable exposure that can quickly progress into privilege abuse and data impact. The agent correlates exposure evidence signals such as internet reachability, high-privilege paths, and indicators that workload handles sensitive data by analyzing suspicious network connections in the same bounded time window. Outcome: Produces a resource-specific risk analysis that explains why the Defender for Cloud alert is likely to be exploited, based on asset attack surface and effective privileges, plus any supporting activity in the same 24-hour window. Use Case 6: Threat Intelligence & Adversary Context Investigation Scenario: Is this activity aligned with known attacker behavior? Signals Correlated: Behavior sequence: ordered events identity → execution → network. Technique mapping: MITRE ATT&CK technique IDs, typical progression, and required prerequisites. Threat intel match: campaign/adversary, TTPs, IOCs Correlation Logic: A chain of identity compromise, PowerShell obfuscation, and periodic outbound HTTPS is observed. The agent maps the sequence to ATT&CK techniques and correlates it with threat intel that matches a known adversary campaign. Outcome: Surfaces adversary-aligned behavioral insights and TTP context to help analysts assess intrusion likelihood and guide the next investigation steps. Summary This blog is intended to help developers better understand the key use cases for building agents with Microsoft Sentinel platform along with practical patterns to apply when designing and implementing agent scenarios. Need help? If you have any issues as you work to develop your agent, the App Assure team is available to assist via our Sentinel Advisory Service. Reach out via our intake form. Resources Learn more: For a practical overview of how ISVs can move from Sentinel data lake onboarding to building agents, see the Accelerate Agent Development blog - https://aka.ms/AppAssure_AccelerateAgentDev. Get hands-on: Explore the end-to-end journey from Sentinel data lake onboarding to a working Security Copilot agent through the accompanying lab modules available on GitHub Repo: https://github.com/suchandanreddy/Microsoft-Sentinel-Labs.Microsoft 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_PricingIgnite 2025: New Microsoft Sentinel Connectors Announcement
Microsoft Sentinel continues to set the pace for innovation in cloud-native SIEMs, empowering security teams to meet today’s challenges with scalable analytics, built-in AI, and a cost-effective data lake. Recognized as a leader by Gartner and Forrester, Microsoft Sentinel is a platform for all of security, evolving to unify signals, cut costs, and power agentic AI for the modern SOC. As Microsoft Sentinel’s capabilities expand, so does its connector ecosystem. With over 350+ integrations available, organizations can seamlessly bring data from a wide range of sources into Microsoft Sentinel’s analytics and data lake tiers. This momentum is driven by our partners, who continue to deliver new and enhanced connectors that address real customer needs. The past year has seen rapid growth in both the number and diversity of connectors, ensuring that Microsoft Sentinel remains robust, flexible, and ready to meet the demands of any security environment. Today we showcase some of the most recent additions to our growing Microsoft Sentinel ecosystem spanning categories such as cloud security, endpoint protection, identity, IT operations, threat intelligence, compliance, and more: New and notable integrations BlinkOps and Microsoft Sentinel BlinkOps is an enterprise-ready agentic security automation platform that integrates seamlessly with Microsoft Sentinel to accelerate incident response and streamline operations. With Blink, analysts can rapidly build sophisticated workflows and custom security agents—without writing a single line of code—enabling agile, scalable automation with both Microsoft Sentinel and any other security platform. This integration helps eliminate alert fatigue, reduce mean time to resolution (MTTR), and free teams to focus on what matters most: driving faster operations, staying ahead of cyber threats, and unlocking new levels of efficiency through reliable, trusted orchestration. Check Point for Microsoft Sentinel solutions Check Point’s External Risk Management (ERM) IOC and Alerts integration with Microsoft Sentinel streamlines how organizations detect and respond to external threats by automatically sending both alerts and indicators of compromise (IOCs) into Microsoft Sentinel. Through this integration, customers can configure SOAR playbooks to trigger automated actions such as updating security policies, blocking malicious traffic, and executing other security operations tasks. This orchestration reduces manual effort, accelerates response times, and allows IT teams, network administrators, and security personnel to focus on strategic threat analysis—strengthening the organization’s overall security posture. Cloudflare for Microsoft Sentinel Cloudflare’s integration with Microsoft Sentinel, powered by Logpush, brings detailed security telemetry from its Zero Trust and network services into your SIEM environment. By forwarding logs such as DNS queries, HTTP requests, and access events through Logpush, the connector enables SOC teams to correlate Cloudflare data with other sources for comprehensive threat detection. This integration supports automated workflows for alerting and investigation, helping organizations strengthen visibility across web traffic and identity-based access while reducing manual overhead. Contrast ADR for Microsoft Sentinel Contrast Security gives Microsoft Sentinel users their first-ever integration with Application Detection and Response (ADR), delivering real-time visibility into application and API attacks, eliminating the application-layer blind spot. By embedding security directly into applications, Contrast enables continuous monitoring and precise blocking of attacks, and with AI assistance, the ability to fix underlying software vulnerabilities in minutes. This integration helps security teams prioritize actionable insights, reduce noise, and better understand the severity of threats targeting APIs and web apps. GreyNoise Enterprise Solution for Microsoft Sentinel GreyNoise helps Microsoft Sentinel users cut through the noise by identifying and filtering out internet background traffic that clutters security alerts. Drawing from a global sensor network, GreyNoise classifies IP addresses that are scanning the internet, allowing SOC teams to deprioritize benign activity and focus on real threats. The integration supports automated triage, threat hunting, and enrichment workflows, giving analysts the context they need to investigate faster and more effectively. iboss Connector for Microsoft Sentinel The iboss Connector for Microsoft Sentinel delivers real-time ingestion of URL event logs, enriching your SIEM with high-fidelity web traffic insights. Logs are forwarded in Common Event Format (CEF) over Syslog, enabling streamlined integration without the need for a proxy. With built-in parser functions and custom workbooks, the solution supports rapid threat detection and investigation. This integration is especially valuable for organizations adopting Zero Trust principles, offering granular visibility into user access patterns and helping analysts accelerate response workflows. Mimecast Mimecast’s integration with Microsoft Sentinel consolidates email security telemetry into a unified threat detection environment. By streaming data from Mimecast into Microsoft Sentinel’s Log Analytics workspace, security teams can craft custom queries, automate response workflows, and prioritize high-risk events. This connector supports a wide range of use cases, from phishing detection to compliance monitoring, while helping reduce mean time to respond (MTTR). MongoDB Atlas Solution for Microsoft Sentinel MongoDB Atlas integrates with Microsoft Sentinel to provide visibility into database activity and security events across cloud environments. By forwarding database logs into Sentinel, this connector enables SOC teams to monitor access patterns, detect anomalies, and correlate database alerts with broader security signals. The integration allows for custom queries and dashboards to be built on real-time log data, helping organizations strengthen data security, streamline investigations, and maintain compliance for critical workloads. Onapsis Defend Onapsis Defend integrates with Microsoft Sentinel Solution for SAP to deliver real-time security monitoring and threat detection from both cloud and on-premises SAP systems. By forwarding Onapsis's unique SAP exploit detection, proprietary SAP zero-day rules, and expert SAP-focused insights into Microsoft Sentinel, this integration enables SOC teams to correlate SAP-specific risks with enterprise-wide telemetry and accelerate incident response. The integration supports prebuilt analytics rules and dashboards, helping organizations detect suspicious behavior and malicious activity, prioritize remediation, and strengthen compliance across complex SAP application landscapes. Proofpoint on Demand (POD) Email Security for Microsoft Sentinel Proofpoint’s Core Email Protection integrates with Microsoft Sentinel to deliver granular email security telemetry for advanced threat analysis. By forwarding events such as phishing attempts, malware detections, and policy violations into Microsoft Sentinel, SOC teams can correlate Proofpoint data with other sources for a unified view of risk. The connector supports custom queries, dashboards, and automated playbooks, enabling faster investigations and streamlined remediation workflows. This integration helps organizations strengthen email defenses and improve response efficiency across complex attack surfaces. Proofpoint TAP Solution Proofpoint’s Targeted Attack Protection (TAP), part of its Core Email Protection, integrates with Microsoft Sentinel to centralize email security telemetry for advanced threat detection and response. By streaming logs and events from Proofpoint into Microsoft Sentinel, SOC teams gain visibility into phishing attempts, malicious attachments, and compromised accounts. The connector supports custom queries, dashboards, and automated playbooks, enabling faster investigations and streamlined remediation workflows. This integration helps organizations strengthen email defenses while reducing manual effort across incident response processes. RSA ID Plus Admin Log Connector The RSA ID Plus Admin Log Connector integrates with Microsoft Sentinel to provide centralized visibility into administrative activity within RSA ID Plus Connector. By streaming admin-level logs into Sentinel, SOC teams can monitor changes, track authentication-related operations, and correlate identity events with broader security signals. The connector supports custom queries and dashboards, enabling organizations to strengthen oversight and streamline investigations across their hybrid environments. Rubrik Integrations with Microsoft Sentinel for Ransomware Protection Rubrik’s integration with Microsoft Sentinel strengthens ransomware resilience by combining data security with real-time threat detection. The connector streams anomaly alerts, such as suspicious deletions, modifications, encryptions, or downloads, directly into Microsoft Sentinel, enabling fast investigations and more informed responses. With built-in automation, security teams can trigger recovery workflows from within Microsoft Sentinel, restoring clean backups or isolating affected systems. The integration bridges IT and SecOps, helping organizations minimize downtime and maintain business continuity when facing data-centric threats. Samsung Knox Asset Intelligence for Microsoft Sentinel Samsung’s Knox Asset Intelligence integration with Microsoft Sentinel equips security teams with near real-time visibility into mobile device threats across Samsung Galaxy enterprise fleets. By streaming security events and logs from managed Samsung devices into Microsoft Sentinel via the Azure Monitor Log Ingestion API, organizations can monitor risk posture, detect anomalies, and investigate incidents from a centralized dashboard. This solution is especially valuable for SOC teams monitoring endpoints for large mobile workforces, offering data-driven insights to reduce blind spots and strengthen endpoint security without disrupting device performance. SAP S/4HANA Public Cloud – Microsoft Sentinel SAP S/4HANA Cloud, public edition integrates with Microsoft Sentinel Solution for SAP to deliver unified, real-time security monitoring for cloud ERP environments. This connector leverages Microsoft’s native SAP integration capabilities to stream SAP logs into Microsoft Sentinel, enabling SOC teams to correlate SAP-specific events with enterprise-wide telemetry for faster, more accurate threat detection and response. SAP Enterprise Threat Detection – Microsoft Sentinel SAP Enterprise Threat Detection integrates with Microsoft Sentinel Solution for SAP to deliver unified, real-time security monitoring across SAP landscapes and the broader enterprise. Normalized SAP logs, alerts, and investigation reports flow into Microsoft Sentinel, enabling SOC teams to correlate SAP-specific alerts with enterprise telemetry for faster, more accurate threat detection and response. SecurityBridge: SAP Data to Microsoft Sentinel SecurityBridge extends Microsoft Sentinel for SAP’s reach into SAP environments, offering real-time monitoring and threat detection across both cloud and on-premises SAP systems. By funneling normalized SAP security events into Microsoft Sentinel, this integration enables SOC teams to correlate SAP-specific risks with broader enterprise telemetry. With support for S/4HANA, SAP BTP, and NetWeaver-based applications, SecurityBridge simplifies SAP security auditing and provides prebuilt dashboards and templates to accelerate investigations. Tanium Microsoft Sentinel Connector Tanium’s integration with Microsoft Sentinel bridges real-time endpoint intelligence and SIEM analytics, offering a unified approach to threat detection and response. By streaming real-time telemetry and alerts into Microsoft Sentinel,Tanium enables security teams to monitor endpoint health, investigate incidents, and trigger automated remediation, all from a single console. The connector supports prebuilt workbooks and playbooks, helping organizations reduce dwell time and align IT and security operations around a shared source of truth. Team Cymru Pure Signal Scout for Microsoft Sentinel Team Cymru’s Pure Signal™ Scout integration with Microsoft Sentinel delivers high-fidelity threat intelligence drawn from global internet telemetry. By enriching Microsoft Sentinel alerts with real-time context on IPs, domains, and adversary infrastructure, Scout enables security teams to proactively monitor third-party compromise, track threat actor infrastructure, and reduce false positives. The integration supports external threat hunting and attribution, enabling analysts to discover command-and-control activity, signals of data exfiltration and compromise with greater precision. For organizations seeking to build preemptive defenses by elevating threat visibility beyond their borders, Scout offers a lens into the broader threat landscape at internet scale. Veeam App for Microsoft Sentinel The Veeam App for Microsoft Sentinel enhances data protection by streaming backup and recovery telemetry into your SIEM environment. The solution provides visibility into backup job status, anomalies, and potential ransomware indicators, enabling SOC teams to correlate these events with broader security signals. With support for custom queries and automated playbooks, this integration helps organizations accelerate investigations, trigger recovery workflows, and maintain resilience against data-centric threats. WithSecure Elements via Function for Microsoft Sentinel WithSecure’s Elements platform integrates with Microsoft Sentinel to provide centralized visibility into endpoint protection and detection events. By streaming incident and malware telemetry into Microsoft Sentinel, organizations can correlate endpoint data with broader security signals for faster, more informed responses. The solution supports a proactive approach to cybersecurity, combining predictive, preventive, and responsive capabilities, making it well-suited for teams seeking speed and flexibility without sacrificing depth. This integration helps reduce complexity while enhancing situational awareness across hybrid environments, and for companies to prevent or minimize any disruption. In addition to these solutions from our third-party partners, we are also excited to announce the following connectors published by the Microsoft Sentinel team, available now in Azure Marketplace and Microsoft Sentinel content hub. Alibaba Cloud Action Trail Logs AWS: Network Firewall AWS: Route 53 DNS AWS: Security Hub Findings AWS: Server Access Cisco Secure Endpoint GCP: Apigee GCP: CDN GCP: Cloud Monitor GCP: Cloud Run GCP: DNS GCP: Google Kubernetes Engine (GKE) GCP: NAT GCP: Resource Manager GCP: SQL GCP: VPC Flow GCP: IAM OneLogin IAM Oracle Cloud Infrastructure Palo Alto: Cortex Xpanse CCF Palo Alto: Prisma Cloud CWPP Ping One Qualys Vulnerability Management Salesforce Service Cloud Slack Audit Snowflake App Assure: The Microsoft Sentinel promise Every connector in the Microsoft Sentinel ecosystem is built to work out of the box, backed by the App Assure team and the Microsoft Sentinel promise. In the unlikely event that customers encounter any issues, App Assure stands ready to assist to ensure rapid resolution. With the new Microsoft Sentinel data lake features, we extend our promise for customers looking to bring their data to the lake. To request a new connector or features for an existing one, contact us via our intake form. Learn More Microsoft Sentinel data lake Microsoft Sentinel data lake: Unify signals, cut costs, and power agentic AI Introducing Microsoft Sentinel data lake What is Microsoft Sentinel data lake Unlocking Developer Innovation with Microsoft Sentinel data lake Microsoft Sentinel Codeless Connector Framework (CCF) Create a codeless connector for Microsoft Sentinel What’s New in Microsoft Sentinel Microsoft App Assure App Assure home page App Assure services App Assure blog App Assure’s promise: Migrate to Sentinel with confidence App Assure’s Sentinel promise now extends to Microsoft Sentinel data lake RSAC 2025 new Microsoft Sentinel connectors announcement Microsoft Security Microsoft’s Secure Future Initiative Microsoft Unified SecOps
AI-Powered MITRE ATT&CK Tagging for SOC Optimization
This post is part of an update series highlighting new SOC optimization capabilities designed to help SOC teams maximize security value with less manual effort. In this post, we focus on AI-powered MITRE ATT&CK Tagging, which streamlines the process of aligning your detections with the MITRE framework. For an overview of our other recent updates, stay tuned for related posts in this series. Security teams rely on precise, consistent tagging to understand detection coverage, align with frameworks like MITRE ATT&CK, and respond effectively to threats. Yet in practice, tagging detections manually is error-prone, inconsistent, and resource-intensive — leaving gaps in coverage and missed opportunities for insight. To address this challenge, we’re excited to introduce a powerful new capability within SOC Optimization: AI MITRE ATT&CK Tagging. Problem Statement In today’s evolving threat landscape, aligning detection rules with the MITRE ATT&CK framework is critical for understanding and improving an organization’s security posture. MITRE tagging provides a common language to describe attacker behaviour, enabling security teams to assess their threat coverage, identify detection gaps, and drive a threat-informed defence. It powers key SOC experiences in Microsoft Sentinel, such as MITRE coverage views, use case recommendations, incident investigation context, and coverage optimization workflows. When tagging is missing or incomplete — for example, when only tactics are mapped without corresponding techniques — the ability to accurately assess protection against known adversary behaviours is weakened. This limits visibility into which threats are covered, complicates incident correlation, and prevents clear communication of coverage gaps to stakeholders. As a result, security teams struggle to prioritize detection improvements and risk leaving critical areas under protected. These gaps lead to: Incomplete visibility into coverage against known threats Limited ability to recommend or prioritize relevant use cases Fragmented alignment between detection rules and incident response workflows Without consistent MITRE tagging, teams spend valuable time manually reviewing and mapping rules — delaying threat response and reducing overall SOC efficiency. The Solution AI MITRE ATT&CK Tagging automates this process using artificial intelligence models that run directly in your workspace. The model scans your detection content and identifies which MITRE ATT&CK tactics and techniques apply, offering recommended tags for detections that are currently untagged. These recommendations can be easily reviewed and applied, allowing you to: Achieve complete detection coverage aligned with the MITRE ATT&CK framework Eliminate manual effort and reduce human error in tagging Enhance detection clarity and response workflows Gain insights into security posture with more structured and actionable data “AI-based tagging helps us to reduce manual workload that previously we tagged detections manually, as well as helps faster triage. Besides, AI-based tagging will be standardized, helping to reduce inconsistencies due to human error”. Farid Kalaidji, Security Lead at Pink Elephant How it looks like Let’s say you’re reviewing your detection posture and come across a new card in SOC Optimization: “Coverage improvement by AI MITRE Tagging”. The card highlights a list of detection rules in your environment that are missing MITRE ATT&CK mappings and offers AI-suggested tags to help close those gaps. You click into the experience and the relevant rules, each with recommended tactic and technique tags. Now you can quickly get a sense of where coverage is missing and what can be improved. If you’re looking for efficiency, you can simply click “Apply All” to tag every recommended rule at once. It’s a quick way to bring your rules up to date and ensure your MITRE coverage reflects your true detection posture – no manual tagging required. This improves not just the MIRTE blade, but also use case recommendation, incident investigation context, and overall visibility into your threat coverage. lease note that by selecting "choose rule", you also have the option to review and tag individual rules from the list. y heading to the MITRE ATT&CK blade, you can validate the improved coverage. The updated view includes newly applied tactics and techniques, reflecting your improved posture. Next Steps Get started with SOC Optimization today. We hope this detailed walkthrough helps you understand the benefits of this feature and improve your security coverage. Microsoft will continue to invest in this feature to assist our customers in defending against evolving security threats. Learn More SOC optimization documentation: SOC optimization overview ; Recommendations logic Short overview and demo: SOC optimization Ninja show In depth webinar: Manage your data, costs and protections with SOC optimization SOC optimization API: Introducing SOC Optimization API | Microsoft Community Hub MITRE ATT&CK coverage: View MITRE coverage for your organization from Microsoft Sentinel
Automate Extraction of Microsoft Sentinel Analytical Rules from GitHub Solutions
🔧 Enhancing Pre-Deployment Rule Insights Extracting metadata like Rule Name, Severity, MITRE Tactics, and Techniques for out-of-the-box analytical rules across multiple solutions can be time-consuming when done manually—especially before the rules are deployed. 🚀 Script Overview The PowerShell script, hosted on GitHub, lets you: Provide the exact Microsoft Sentinel solution name as input, from Microsoft Sentinel GitHub: Azure-Sentinel/Solutions at master · Azure/Azure-Sentinel · GitHub Automatically query the [Microsoft Sentinel GitHub repo] Parse all associated analytical rule YAMLs under that solution Export relevant metadata into a structured CSV 📥 GitHub Link This is My GitHub repository where the custom PowerShell script is hosted. It allows you to extract built-in analytical rules from Microsoft Sentinel solutions based on the solution name: 🔗 GitHub - SentinelArtifactExtract (Optimized Script) 📝 Pre-Requisites: Generate GitHub Personal Access token: GitHub official page to generate PAT: Managing your personal access tokens - GitHub Docs Why GitHub PAT token: It will help us to Authenticate and overcome the GitHub API rate limit Error (403). Download the Script from GitHub to Azure CloudShell: Use Invoke-WebRequest or curl to download the raw script: Command to Download the Raw Script from GitHub: Invoke-WebRequest -Uri "https://raw.githubusercontent.com/vdabhi123/SentinelArtifactExtract/main/Extract%20Sentinel%20Analytical%20Rule%20with%20Solution%20Name%20prompt/OptimizedVersionPromptforSolutionNameOnly" -OutFile "ExtractRules.ps1 Invoke-WebRequest in Azure CloudShell Update the Script with you GitHub PAT (generated in pre-requisite 1) in main script: To update the PAT token you can use vim and ensure to run the updated script. 🧪 How to Use the Script Open Azure Cloud Shell (PowerShell). Upload and run the script. (This is Optional if Pre-requisite 3 is followed) Run the Script and Enter the **exact** solution name (e.g., `McAfee ePolicy Orchestrator`). The script fetches rule metadata and exports to CSV in the same directory. Download the CSV from Cloud Shell. & 2 as highlighted. 📤 Sample Output The script generates a CSV with the following columns: - `Solution` - `AnalyticalRuleName` - `Description` - `Severity` - `MITRE_Tactics` - `MITRE_Techniques` Example file name: Formatted Output with all Analytical Rule and other metadata for the Solution: ✅ Benefits Streamlines discovery of built-in analytical rules for initial Microsoft Sentinel deployments. Accelerates requirements gathering by exporting rules into a shareable CSV format. Enables collaborative planning—output can be shared with clients or Microsoft to determine which rules to implement or recommend. Eliminates manual effort of browsing GitHub or Microsoft Sentinel UI or exporting and reviewing full JSON rule files individually. 💡 Pro Tips Always verify the solution name from the official Microsoft Sentinel GitHub Solutions folder. Azure-Sentinel/Solutions at master · Azure/Azure-Sentinel · GitHub 📌 Final Thoughts This script was created in response to a real-world project need and is focused on improving the discovery and extraction of Microsoft Sentinel analytical rules. A follow-up blog covering the export of additional Sentinel artifacts—such as Playbooks, Workbooks, and Hunting Queries—will be published soon.
