Microsoft Sentinel’s AI-driven UEBA ushers in the next era of behavioral analytics
Co-author - Ashwin Patil Security teams today face an overwhelming challenge: every data point is now a potential security signal and SOCs are drowning in complex logs, trying to find the needle in the haystack. Microsoft Sentinel User and Entity Behavior Analytics (UEBA) brings the power of AI to automatically surface anomalous behaviors, helping analysts cut through the noise, save time, and focus on what truly matters. Microsoft Sentinel UEBA has already helped SOCs uncover insider threats, detect compromised accounts, and reveal subtle attack signals that traditional rule-based methods often miss. These capabilities were previously powered by a core set of high-value data sources - such as sign-in activity, audit logs, and identity signals - that consistently delivered rich context and accurate detections. Today, we’re excited to announce a major expansion: Sentinel UEBA now supports six new data sources including Microsoft first- and third-party platforms like Azure, AWS, GCP, and Okta, bringing deeper visibility, broader context, and more powerful anomaly detection tailored to your environment. This isn’t just about ingesting more logs. It’s about transforming how SOCs understand behavior, detect threats, and prioritize response. With this evolution, analysts gain a unified, cross-platform view of user and entity behavior, enabling them to correlate signals, uncover hidden risks, and act faster with greater confidence. Newly supported data sources are built for real-world security use cases: Authentication activities MDE DeviceLogonEvents – Ideal for spotting lateral movement and unusual access. AADManagedIdentitySignInLogs – Critical for spotting stealthy abuse of non - human identities. AADServicePrincipalSignInLogs - Identifying anomalies in service principal usage such as token theft or over - privileged automation. Cloud platforms & identity management AWS CloudTrail Login Events - Surfaces risky AWS account activity based on AWS CloudTrail ConsoleLogin events and logon related attributes. GCP Audit Logs - Failed IAM Access, Captures denied access attempts indicating reconnaissance, brute force, or privilege misuse in GCP. Okta MFA & Auth Security Change Events – Flags MFA challenges, resets, and policy modifications that may reveal MFA fatigue, session hijacking, or policy tampering. Currently supports the Okta_CL table (unified Okta connector support coming soon). These sources feed directly into UEBA’s entity profiles and baselines - enriching users, devices, and service identities with behavioral context and anomalies that would otherwise be fragmented across platforms. This will complement our existing supported log sources - monitoring Entra ID sign-in logs, Azure Activity logs and Windows Security Events. Due to the unified schema available across data sources, UEBA enables feature-rich investigation and the capability to correlate across data sources, cross platform identities or devices insights, anomalies, and more. AI-powered UEBA that understands your environment Microsoft Sentinel UEBA goes beyond simple log collection - it continuously learns from your environment. By applying AI models trained on your organization’s behavioral data, UEBA builds dynamic baselines and peer groups, enabling it to spot truly anomalous activity. UBEA builds baselines from 10 days (for uncommon activities) to 6 months, both for the user and their dynamically calculated peers. Then, insights are surfaced on the activities and logs - such as an uncommon activity or first-time activity - not only for the user but among peers. Those insights are used by an advanced AI model to identify high confidence anomalies. So, if a user signs in for the first time from an uncommon location, a common pattern in the environment due to reliance on global vendors, for example, then this will not be identified as an anomaly, keeping the noise down. However, in a tightly controlled environment, this same behavior can be an indication of an attack and will surface in the Anomalies table. Including those signals in custom detections can help affect the severity of an alert. So, while logic is maintained, the SOC is focused on the right priorities. How to use UEBA for maximum impact Security teams can leverage UEBA in several key ways. All the examples below leverage UEBA’s dynamic behavioral baselines looking back up to 6 months. Teams can also leverage the hunting queries from the "UEBA essentials" solution in Microsoft Sentinel's Content Hub. Behavior Analytics: Detect unusual logon times, MFA fatigue, or service principal misuse across hybrid environments. Get visibility into geo-location of events and Threat Intelligence insights. Here’s an example of how you can easily discover Accounts authenticating without MFA and from uncommonly connected countries using UEBA behaviorAnalytics table: BehaviorAnalytics | where TimeGenerated > ago(7d) | where EventSource == "AwsConsoleSignIn" | where ActionType == "ConsoleLogin" and ActivityType == "signin.amazonaws.com" | where ActivityInsights.IsMfaUsed == "No" | where ActivityInsights.CountryUncommonlyConnectedFromInTenant == True | evaluate bag_unpack(UsersInsights, "AWS_") | where InvestigationPriority > 0 // Filter noise - uncomment if you want to see low fidelity noise | project TimeGenerated, _WorkspaceId, ActionType, ActivityType, InvestigationPriority, SourceIPAddress, SourceIPLocation, AWS_UserIdentityType, AWS_UserIdentityAccountId, AWS_UserIdentityArn Anomaly detection Identify lateral movement, dormant account reactivation, or brute-force attempts, even when they span cloud platforms. Below are examples of how to discover UEBA Anomalous AwsCloudTrail anomalies via various UEBA activity insights or device insights attributes: Anomalies | where AnomalyTemplateName in ( "UEBA Anomalous Logon in AwsCloudTrail", // AWS ClousTrail anomalies "UEBA Anomalous MFA Failures in Okta_CL", "UEBA Anomalous Activity in Okta_CL", // Okta Anomalies "UEBA Anomalous Activity in GCP Audit Logs", // GCP Failed IAM access anomalies "UEBA Anomalous Authentication" // For Authentication related anomalies ) | project TimeGenerated, _WorkspaceId, AnomalyTemplateName, AnomalyScore, Description, AnomalyDetails, ActivityInsights, DeviceInsights, UserInsights, Tactics, Techniques Alert optimization Use UEBA signals to dynamically adjust alert severity in custom detections—turning noisy alerts into high-fidelity detections. The example below shows all the users with anomalous sign in patterns based on UEBA. Joining the results with any of the AWS alerts with same AWS identity will increase fidelity. BehaviorAnalytics | where TimeGenerated > ago(7d) | where EventSource == "AwsConsoleSignIn" | where ActionType == "ConsoleLogin" and ActivityType == "signin.amazonaws.com" | where ActivityInsights.FirstTimeConnectionViaISPInTenant == True or ActivityInsights.FirstTimeUserConnectedFromCountry == True | evaluate bag_unpack(UsersInsights, "AWS_") | where InvestigationPriority > 0 // Filter noise - uncomment if you want to see low fidelity noise | project TimeGenerated, _WorkspaceId, ActionType, ActivityType, InvestigationPriority, SourceIPAddress, SourceIPLocation, AWS_UserIdentityType, AWS_UserIdentityAccountId, AWS_UserIdentityArn, ActivityInsights | evaluate bag_unpack(ActivityInsights) Another example shows anomalous key vault access from service principal with uncommon source country location. Joining this activity with other alerts from the same service principle increases fidelity of the alerts. You can also join the anomaly UEBA Anomalous Authentication with other alerts from the same identity to bring the full power of UEBA into your detections. BehaviorAnalytics | where TimeGenerated > ago(1d) | where EventSource == "Authentication" and SourceSystem == "AAD" | evaluate bag_unpack(ActivityInsights) | where LogonMethod == "Service Principal" and Resource == "Azure Key Vault" | where ActionUncommonlyPerformedByUser == "True" and CountryUncommonlyConnectedFromByUser == "True" | where InvestigationPriority > 0 Final thoughts This release marks a new chapter for Sentinel UEBA—bringing together AI, behavioral analytics, and cross-cloud and identity management visibility to help defenders stay ahead of threats. If you haven’t explored UEBA yet, now’s the time. Enable it in your workspace settings and don’t forget to enable anomalies as well (in Anomalies settings). And if you’re already using it, these new sources will help you unlock even more value. Stay tuned for our upcoming Ninja show and webinar (register at aka.ms/secwebinars), where we’ll dive deeper into use cases. Until then, explore the new sources, use the UEBA workbook, update your watchlists, and let UEBA do the heavy lifting. UEBA onboarding and setting documentation Identify threats using UEBA UEBA enrichments and insights reference UEBA anomalies reference
XDR advanced hunting region specific endpoints
Hi, I am exploring XDR advanced hunting API to fetch data specific to Microsoft Defender for Endpoint tenants. The official documentation (https://learn.microsoft.com/en-us/defender-xdr/api-advanced-hunting) mentions to switch to Microsoft Graph advanced hunting API. I had below questions related to it: 1. To fetch the region specific(US , China, Global) token and Microsoft Graph service root endpoints(https://learn.microsoft.com/en-us/graph/deployments#app-registration-and-token-service-root-endpoints ) , is the recommended way to fetch the OpenID configuration document (https://learn.microsoft.com/en-us/entra/identity-platform/v2-protocols-oidc#fetch-the-openid-configuration-document) for a tenant ID and based on the response, the region specific SERVICE/TOKEN endpoints could be fetched? Since using it, there is no need to maintain different end points for tenants in different regions. And do we use the global service URL https://login.microsoftonline.com to fetch OpenID config document for a tenantID in any region? 2. As per the documentation, Microsoft Graph Advanced hunting API is not supported in China region (https://learn.microsoft.com/en-us/graph/api/security-security-runhuntingquery?view=graph-rest-1.0&tabs=http). In this case, is it recommended to use Microsoft XDR Advanced hunting APIs(https://learn.microsoft.com/en-us/defender-xdr/api-advanced-hunting) to support all region tenants(China, US, Global)?Microsoft Sentinel for SAP Agentless connector GA
Dear Community, Today is the day: Our new agentless connector for Microsoft Sentinel Solution for SAP applications is Generally Available now! Fully onboarded to SAP’s official Business Accelerator Hub and ready for prime time wherever your SAP systems are waiting – on-premises, hyperscalers, RISE, or GROW – to be protected. Let’s hear from an agentless customer: “With the Microsoft Sentinel Solution for SAP and its new agentless connector, we accelerated deployment across our SAP landscape without the complexity of containerized agents. This streamlined approach elevated our SOC’s visibility into SAP security events, strengthened our compliance posture, and enabled faster, more informed incident response” SOC Specialist, North American aviation company Use the video below to kick off your own agentless deployment today. #Kudos to the amazing mvigilante for showing us around the new connector! But we didn’t stop there! Security is being reengineered for the AI era - moving from static, rule-based controls to platform-driven, machine-speed defence that anticipates threats before they strike. Attackers think in graphs - Microsoft does too. We’re bringing relationship-aware context to Microsoft Security - so defenders and AI can see connections, understand the impact of a potential compromise (blast radius), and act faster across pre-breach and post-breach scenarios including SAP systems - your crown jewels. See it in action in below phishing-compromise which lead to an SAP login bypassing MFA with followed operating-system activities on the SAP host downloading trojan software. Enjoy this clickable experience for more details on the scenario. Shows how a phishing compromise escalated to an SAP MFA bypass, highlighting cross-domain correlation. The Sentinel Solution for SAP has AI-first in mind and directly integrates with our security platform on the Defender portal for enterprise-wide signal correlation, Security Copilot reasoning, and Sentinel Data Lake usage. Your real-time SAP detections operate on the Analytics tier for instant results and threat hunting, while the same SAP logs get mirrored to the lake for cost-efficient long-term storage (up to 12 years). Access that data for compliance reporting or historic analysis through KQL jobs on the lake. No more – yeah, I have the data stored somewhere to tick the audit report check box – but be able to query and use your SAP telemetry in long term storage at scale. Learn more here. Findings from the Agentless Connector preview During our preview we learned that majority of customers immediately profit from the far smoother onboarding experience compared to the Docker-based approach. Deployment efforts and time to first SAP log arrival in Sentinel went from days and weeks to hours. ⚠️ Deprecation notice for containerized data connector agent ⚠️ The containerised SAP data connector will be deprecated on 30 September 2026. This change aligns with the discontinuation of the SAP RFC SDK, SAP's strategic integration roadmap, and customer demand for simpler integration. Migrate to the new agentless connector for simplified onboarding and compliance with SAP’s roadmap. All new deployments starting October 31, 2025, will only have the new agentless connector option, and existing customers should plan their migration using the guidance on Microsoft Learn. It will be billed at the same price as the containerized agent, ensuring no cost impact for customers. Note📌: To support transition for those of you on the Docker-based data connector, we have enhanced our built-in KQL functions for SAP to work across data sources for hybrid and parallel execution. Spotlight on new Features Inspired by the feedback of early adopters we are shipping two of the most requested new capabilities with GA right away. Customizable polling frequency: Balance threat detection value (1min intervals best value) with utilization of SAP Integration Suite resources based on your needs. ⚠️Warning! Increasing the intervals may result in message processing truncation to avoid SAP CPI saturation. See this blog for more insights. Refer to the max-rows parameter and SAP documentation to make informed decisions. Customizable API endpoint path suffix: Flexible endpoints allow running all your SAP security integration flows from the agentless connector and adherence to your naming strategies. Furthermore, you can add the community extensions like SAP S/4HANA Cloud public edition (GROW), the SAP Table Reader, and more. Displays the simplified onboarding flow for the agentless SAP connector You want more? Here is your chance to share additional feature requests to influence our backlog. We would like to hear from you! Getting Started with Agentless The new agentless connector automatically appears in your environment – make sure to upgrade to the latest version 3.4.05 or higher. Sentinel Content Hub View: Highlights the agentless SAP connector tile in Microsoft Defender portal, ready for one-click deployment and integration with your security platform The deployment experience on Sentinel is fully automatic with a single button click: It creates the Azure Data Collection Endpoint (DCE), Data Collection Rule (DCR), and Microsoft Entra ID app registration assigned with RBAC role "Monitoring Metrics Publisher" on the DCR to allow SAP log ingest. Explore partner add-ons that build on top of agentless The ISV partner ecosystem for the Microsoft Sentinel Solution for SAP is growing to tailor the agentless offering even further. The current cohort has flagship providers like our co-engineering partner SAP SE themselves with their security products SAP LogServ & SAP Enterprise Threat Detection (ETD), and our mutual partners Onapsis and SecurityBridge. Ready to go agentless? ➤ Get started from here ➤ Explore partner add-ons here. ➤ Share feature requests here. Next Steps Once deployed, I recommend to check AryaG’s insightful blog series for details on how to move to production with the built-in SAP content of agentless. Looking to expand protection to SAP Business Technology Platform? Here you go. #Kudos to the amazing Sentinel for SAP team and our incredible community contributors! That's a wrap 🎬. Remember: bringing SAP under the protection of your central SIEM isn't just a checkbox - it's essential for comprehensive security and compliance across your entire IT estate. Cheers, MartinOperationalizing the Sentinel data lake: A Practitioner’s Guide
This article is part of The Sentinel data lake Practitioner Series. Part 1 of the series focuses on operationalizing the Sentinel data lake and our strategic vision for the customers. This series is evolving based on inputs and feedback from the community as well as various components of turning raw security data and workflows into operational security engine. Why This Series? Microsoft recently announced Sentinel data lake unlocking massive potential for security teams. Security data lakes are the foundation of modern detection and investigation. This blog series is designed to empower you to fully leverage your Sentinel data lake investment – providing practical tools, actionable workflows, and analyst-ready templates that simplify querying datalake-tier data and enable SOC teams to turn raw logs into meaningful security insights. With the right guidance, you can maximize the value you get from your Sentinel data lake. Microsoft Security research team has worked extensively on modular Jupyter notebooks, Python-based data analysis, enrichment, and visualization libraries, and security-driven analysis workflows at scale. We believe the key to adoption lies in researcher-driven operationalization—bringing these methods directly to practitioners in ways they can use immediately. Strategic Vision for Operationalization of Sentinel data lake Our approach centers on researcher-led enablement with ready-to-use workflows and customer community activation. The above infographic outlines four building blocks that brings a security data lake to life: Research curated and Community-Powered Content Hub Researcher-curated GitHub repository. Shared notebooks, detection templates, and models. Continuous contributions from the security researchers and community. Notebook & Model Templates Jupyter & VS Code notebooks tailored for analyst use. ML/GenAI models tailored for security data enrichment and anomaly detection. Modular queries for detections and investigations. Historical Data Enablement Analytics to data lake tier automation for cost-efficient historical queries. Dynamic baselining over months/years of logs to tune detections. Unlocking long-tail investigation scenarios otherwise left dormant. Practical real world Use Cases Historical threat hunting on network, identity, and cloud logs. Dynamic detection tuning at scale. GenAI-powered investigations. Post-incident deep dives to uncover the full blast radius. Getting Started Notebook: Building Familiarity with the Data Lake Framework Before diving into advanced workflows, we’ve published a Getting Started Notebook designed to help practitioners quickly onboard to the Sentinel Data Lake environment. This notebook introduces foundational concepts that will be used across subsequent examples and pipelines. What it covers: Connecting to the Data Lake: Learn how to establish authenticated Spark sessions and securely read data from the Sentinel Data Lake workspace. Exploring Data with Apache Spark: A short hands-on tour using PySpark to inspect schema, preview records, and perform lightweight data transformations at scale. Writing Back to the Lake: Understand the pattern of persisting processed or enriched datasets back to data lake tier for reuse in analytic notebooks and downstream detection pipelines via elevating them to analytics tier. Running Modular Pipelines: Step through a simple example of how pipeline jobs ingest raw security logs (e.g., SigninLogs), apply filters and enrichments, and output ready-to-use tables for later detection development. This foundational notebook ensures analysts and engineers are comfortable with the basic Spark + Sentinel data lake interaction model — the same model used in the advanced operational notebooks (for example, Password Spray Detection or Anomaly Detection workflows) later in this series. Our Commitment This new blog series will serve as a practitioner’s guide for operationalizing security data lakes. In the following weeks, we’ll gradually deliver: Modular Notebook templates to accelerate hunting, baselining, and investigations. End-to-end workflows connecting datalake-tier → analytics-tier → Sentinel detections. Enrichment and Gen AI-driven tools to reduce repetitive manual work and investigation friction. Reusable examples and walkthroughs based on real-world high-volume data sources Our goal is to make Sentinel data lake practical for customers by delivering actionable notebooks, workflows, and enablement. Expected Outcomes for Customers By operationalizing the Sentinel data lake in this way, enterprise customers can expect: Reduced Time-to-Value – Analysts can move from raw logs to actionable detections in days, not months. Improved Detection Quality – Long-term baselining and historical analysis reduce false positives and increase fidelity in your detections. Operational Efficiency – Automated enrichment and packaged workflows minimize manual investigation effort. Cost Optimization – analytics tier -to-data lake tier data workflows avoid expensive, ad-hoc queries and make historical data practical to use. Join the Journey This series is built by practitioners, for practitioners. Alongside blogs, we’ll also share: GitHub repository with reusable notebooks and model templates. Webinars and demos to walk through the workflows. Together, we’ll move beyond storage and make the security data lake truly operational, analyst-friendly, and impactful. Upcoming articles will demonstrate how notebooks and templates can turn research into workflows that are ready for analysts, featuring practical notebook examples available on GitHub. What's next? Join us at Microsoft Ignite in San Francisco on November 17–21, or online, November 18–20, for deep dives and practical labs to help you maximize your Microsoft Defender investments and to get more from the Microsoft capabilities you already use. Security is a core focus at Ignite this year, with the Security Forum on November 17th, deep dive technical sessions, theater talks, and hands-on labs designed for security leaders and practitioners Featured sessions BRK237: Identity Under Siege: Modern ITDR from Microsoft Join experts in Identity and Security to hear how Microsoft is streamlining collaboration across teams and helping customers better protect, detect, and respond to threats targeting your identity fabric. BRK240 – Endpoint security in the AI era: What's new in Defender Discover how Microsoft Defender’s AI-powered endpoint security empowers you to do more, better, faster. BRK236 – Your SOC’s ally against cyber threats, Microsoft Defender Experts See how Defender Experts detect, halt, and manage threats for you, with real-world outcomes and demos. LAB541 – Defend against threats with Microsoft Defender Get hands-on with Defender for Office 365 and Defender for Endpoint, from onboarding devices to advanced attack mitigation. Explore and filter the full security catalog by topic, format, and role: aka.ms/SessionCatalogSecurity. Why attend? Ignite is the place to learn about the latest Defender capabilities, including new agentic AI integrations and unified threat protection. We will also share future-facing innovations in Defender, as part of our ongoing commitment to autonomous defense. Security Forum—Make day 0 count (November 17) Kick off with an immersive, in person preday focused on strategic security discussions and real-world guidance from Microsoft leaders and industry experts. Select Security Forum during registration. Register for Microsoft Ignite >
Unified detection rule management
Hi, I attended the webinar yesterday regarding the new unified custom detection rules in Defender XDR. I was wondering about the management of a library of rules. As with any SOC, our solution has a library of custom rules which we manage in a release cycle for a number of clients in different Tenants. To avoid having to manage rules individually we use the JSON approach, importing the library so it will update rules that we need to tune. Currently I'm not seeing an option to import unified detection rules in Defender XDR via JSON. Is that a feature that will be added? Thanks ZivAutomating IOC hunts in Microsoft Sentinel data lake
Security operations are undergoing significant transformation driven by the introduction of AI and a rapidly evolving threat landscape. With Microsoft Sentinel data lake now generally available, organizations can centralize all their security data in a purpose-built security data lake. This helps optimize costs, simplify data management, and accelerate the adoption of AI in security operations. This empowers defenders to transcend legacy security controls, adopting advanced analytics and automation for more dynamic and effective protection. A key advantage of the Sentinel data lake is its cost-efficiency, making it ideal for ingesting and retaining large volumes of security logs, such as network logs, without incurring high expenses or compromising coverage. By storing all security data in a unified, cost-effective data lake, organizations gain comprehensive, long-term visibility for historical threat hunting and TI matching, enabling investigations across extended timelines without the prohibitive costs of traditional analytics solutions. In this blog we will explore how security teams can leverage KQL jobs in Sentinel data lake to automate threat hunting and threat intelligence matching across network logs, enabling scalable, cost-effective, and continuous threat detection. By doing so, SOCs can efficiently process large volumes of data and transform raw logs into actionable insights efficiently with minimal manual intervention. What are KQL jobs? KQL jobs in Sentinel data lake are automated one-time or scheduled jobs that run Kusto Query Language (KQL) queries on data lake. These jobs help security teams investigate and hunt for threats more easily by automating processes like checking logs against known threat data. By automating tasks such as IOC matching with historical or high-volume data, analysts are able to concentrate on higher-value activities. This results in more effective threat detection and response. The next section demonstrates how to use the data lake for Threat Intelligence (TI) matching across network logs. IOC matching on network log on data lake Network logs, such as firewall and proxy data, are essential for uncovering advanced threats and supporting investigations. However, storing all this data in the analytics tier is often expensive, leading to reduced retention and potential blind spots. With Sentinel data lake, SOCs can store all their raw telemetry, at a fraction of the cost, making it possible to hunt for threats across a much broader timeline without financial constraints. However, simply storing data isn’t enough. To turn raw logs into actionable insights, SOC teams need to automate both summarization and threat intelligence (TI) matching. Scheduled KQL jobs make this possible by scanning new data in a schedule as it arrives in the data lake, surfacing suspicious activity for analyst review. Schedule KQL job for TI matching on network logs Here’s a practical example of how a SOC can use a scheduled KQL job to summarize network activity and correlate it with threat intelligence indicators. In this scenario, a KQL job is run to identify network log entries from Palo Alto firewalls that match known malicious IPs from ThreatIntelIndicators table. The output provides the complete network log row, enriched with relevant threat intelligence fields for further investigation and response. Create your query: let IPRegex = '[0-9]{1,3}\\.[0-9]{1,3}\\.[0-9]{1,3}\\.[0-9]{1,3}'; let dt_lookBack = 75m; // Look back 1 hour for CommonSecurityLog events let ioc_lookBack = 14d; // Look back 14 days for threat intelligence indicators // Fetch threat intelligence indicators related to IP addresses let IP_Indicators = ThreatIntelIndicators //extract key part of kv pair | extend IndicatorType = replace(@"\[|\]|\""", "", tostring(split(ObservableKey, ":", 0))) | where IndicatorType in ("ipv4-addr", "ipv6-addr", "network-traffic") | extend NetworkSourceIP = toupper(ObservableValue) | extend TrafficLightProtocolLevel = tostring(parse_json(AdditionalFields).TLPLevel) | where TimeGenerated >= ago(ioc_lookBack) | extend TI_ipEntity = iff(isnotempty(NetworkSourceIP), NetworkSourceIP, NetworkSourceIP) | extend TI_ipEntity = iff(isempty(TI_ipEntity) and isnotempty(NetworkSourceIP), NetworkSourceIP, TI_ipEntity) | where ipv4_is_private(TI_ipEntity) == false and TI_ipEntity !startswith "fe80" and TI_ipEntity !startswith "::" and TI_ipEntity !startswith "127." | summarize LatestIndicatorTime = arg_max(TimeGenerated, *) by Id, ObservableValue | where IsActive and (ValidUntil > now() or isempty(ValidUntil)); // Perform a join between IP indicators and CommonSecurityLog events IP_Indicators | project-reorder *, Tags, TrafficLightProtocolLevel, NetworkSourceIP, TI_ipEntity // Use innerunique to keep performance fast and result set low, as we only need one match to indicate potential malicious activity that needs investigation | join kind=innerunique ( CommonSecurityLog | where TimeGenerated >= ago(dt_lookBack) | extend MessageIP = extract(IPRegex, 0, Message) | extend CS_ipEntity = iff((not(ipv4_is_private(SourceIP)) and isnotempty(SourceIP)), SourceIP, DestinationIP) | extend CS_ipEntity = iff(isempty(CS_ipEntity) and isnotempty(MessageIP), MessageIP, CS_ipEntity) | extend CommonSecurityLog_TimeGenerated = TimeGenerated ) on $left.TI_ipEntity == $right.CS_ipEntity // Filter out logs that occurred after the expiration of the corresponding indicator | where CommonSecurityLog_TimeGenerated < ValidUntil // Group the results by IndicatorId and CS_ipEntity, and keep the log entry with the latest timestamp | summarize CommonSecurityLog_TimeGenerated = arg_max(CommonSecurityLog_TimeGenerated, *) by Id, CS_ipEntity // Select the desired output fields | project timestamp = CommonSecurityLog_TimeGenerated, SourceIP, DestinationIP, MessageIP, Message, DeviceVendor, DeviceProduct, Id, ValidUntil, Confidence, TI_ipEntity, CS_ipEntity, LogSeverity, DeviceAction Source: Microsoft Sentinel GitHub repo Before submitting a KQL job you may want to test your query interactively, using the KQL queries page: Create a KQL job: To match against new logs periodically, we would like to schedule this job to run every hour to summarize network log and match against latest IOCs in ThreatInelIndicators. To avoid missing any logs, I suggest adding an overlap between lookback and schedules, to make sure all logs are scanned. For example, you can set lookback of the last 75 minutes and execute job runs every 60 minutes. KQL jobs can run ad-hoc or be scheduled based on your preferred frequency (by minutes, hourly, daily, weekly or monthly), automatically summarizing new network activity and highlighting matches with known malicious indicators. Analysts can then focus on the most relevant events, accelerating investigations and reducing noise. Results are automatically available in the analytics tier and can be used to set up an automated detection using Analytics rules. The cost of running KQL jobs in Sentinel data lake depends on the volume of data scanned and how frequently the jobs run. Data lake KQL queries and jobs are priced at $0.005 per GB scanned. For example, if a KQL job scans 1 TB of data daily, the monthly cost would be around $150 USD. This pricing model allows organizations to perform large-scale threat hunting and intelligence matching without the high expenses typically associated with traditional SIEMs. $0.005 per GB scanned. For more details around Microsoft Sentinel data lake costs for KQL queries and jobs, see https://azure.microsoft.com/en-us/pricing/calculator. Summary and next steps Threat hunting at scale within Sentinel data lake is simplified with KQL jobs. SOC teams can use this method for various hunting or anomaly detection scenarios such as efficiently aggregating and correlating network logs with threat intelligence, enhancing visibility, agility, and assurance, and transforming raw telemetry into actionable security insights. KQL jobs provide several benefits: Continuous threat coverage: Scheduled jobs utilizing KQL automatically correlate high-volume logs located directly in the data lake with up-to-date threat intelligence. This process helps minimize detection gaps and blind spots. Efficient use of resources: Automating TI matching saves analysts from repetitive queries, allowing them to focus on investigating validated alerts rather than sifting through raw logs. Faster response times: Suspicious connections flagged by minutes or every hour enable quicker triage and containment before threats escalate. Historical context: Matches are retained against long-term or high volume logs, enabling analysts to trace back patterns of malicious activity and support deeper investigations. Get started with Microsoft Sentinel data lake today. Microsoft Sentinel data lake overview - Microsoft Security | Microsoft Learn KQL and the Microsoft Sentinel data lake - Microsoft Security | Microsoft Learn Microsoft Sentinel Pricing | Microsoft Security What's next? Join us at Microsoft Ignite in San Francisco on November 17–21, or online, November 18–20, for deep dives and practical labs to help you maximize your Microsoft Defender investments and to get more from the Microsoft capabilities you already use. Security is a core focus at Ignite this year, with the Security Forum on November 17th, deep dive technical sessions, theater talks, and hands-on labs designed for security leaders and practitioners Featured sessions BRK237: Identity Under Siege: Modern ITDR from Microsoft Join experts in Identity and Security to hear how Microsoft is streamlining collaboration across teams and helping customers better protect, detect, and respond to threats targeting your identity fabric. BRK240 – Endpoint security in the AI era: What's new in Defender Discover how Microsoft Defender’s AI-powered endpoint security empowers you to do more, better, faster. BRK236 – Your SOC’s ally against cyber threats, Microsoft Defender Experts See how Defender Experts detect, halt, and manage threats for you, with real-world outcomes and demos. LAB541 – Defend against threats with Microsoft Defender Get hands-on with Defender for Office 365 and Defender for Endpoint, from onboarding devices to advanced attack mitigation. Explore and filter the full security catalog by topic, format, and role: aka.ms/SessionCatalogSecurity. Why attend? Ignite is the place to learn about the latest Defender capabilities, including new agentic AI integrations and unified threat protection. We will also share future-facing innovations in Defender, as part of our ongoing commitment to autonomous defense. Security Forum—Make day 0 count (November 17) Kick off with an immersive, in person preday focused on strategic security discussions and real-world guidance from Microsoft leaders and industry experts. Select Security Forum during registration. Register for Microsoft Ignite >Unlocking Developer Innovation with Microsoft Sentinel data lake
Introduction Microsoft Sentinel is evolving rapidly, transforming to be both an industry-leading SIEM and an AI-ready platform that empowers agentic defense across the security ecosystem. In our recent webinar: Introduction to Sentinel data lake for Developers, we explored how developers can leverage Sentinel’s unified data lake, extensible architecture, and integrated tools to build innovative security solutions. This post summarizes the key takeaways and actionable insights for developers looking to harness the full power of Sentinel. The Sentinel Platform: A Foundation for Agentic Security Unified Data and Context Sentinel centralizes security data cost-effectively, supporting massive volumes and diverse data types. This unified approach enables advanced analytics, graph-enabled context, and AI-ready data access—all essential for modern security operations. Developers can visualize relationships across assets, activities, and threats, mapping incidents and hunting scenarios with unprecedented clarity. Extensible and Open Platform Sentinel’s open architecture simplifies onboarding and data integration. Out-of-the-box connectors and codeless connector creation make it easy to bring in third-party data. Developers can quickly package and publish agents that leverage the centralized data lake and MCP server, distributing solutions through Microsoft Security Store for maximum reach. The Microsoft Security Store is a storefront for security professionals to discover, buy, and deploy vetted security SaaS solutions and AI agents from our ecosystem partners. These offerings integrate natively with Microsoft Security products—including the Sentinel platform, Defender, and Entra, to deliver end‑to‑end protection. By combining curated, deploy‑ready solutions with intelligent, AI‑assisted workflows, the Store reduces integration friction and speeds time‑to‑value for critical tasks like triage, threat hunting, and access management. Advanced Analytics and AI Integration With support for KQL, Spark, and ML tools, Sentinel separates storage and compute, enabling scalable analytics and semantic search. Jupyter Notebooks hosted in on-demand Spark environments allow for rich data engineering and machine learning directly on the data lake. Security Copilot agents, seamlessly integrated with Sentinel, deliver autonomous and adaptive automation, enhancing both security and IT operations. Developer Scenarios: Unlocking New Possibilities The webinar showcased several developer scenarios enabled by Sentinel’s platform components: Threat Investigations Over Extended Timelines: Query historical data to uncover slow-moving attacks and persistent threats. Behavioral Baselining: Model normal behavior using months of sign-in logs to detect anomalies. Alert Enrichment: Correlate alerts with firewall and NetFlow data to improve accuracy and reduce false positives. Retrospective Threat Hunting: React to new indicators of compromise by running historical queries across the data lake. ML-Powered Insights: Build machine learning models for anomaly detection, alert enrichment, and predictive analytics. These scenarios demonstrate how developers can leverage Sentinel’s data lake, graph capabilities, and integrated analytics to deliver powerful security solutions. End-to-End Developer Journey The following steps outline a potential workflow for developers to ingest and analyze their data within the Sentinel platform. Data Sources: Identify high-value data sources from your environment to integrate with Microsoft Security data. The journey begins with your unique view of the customer’s digital estate. This is data you have in your platform today. Bringing this data into Sentinel helps customers make sense of their entire security landscape at once. Data Ingestion: Import third-party data into the Sentinel data lake for secure, scalable analytics. As customer data flows from various platforms into Sentinel, it is centralized and normalized, providing a unified foundation for advanced analysis and threat detection across the customer’s digital environment. Sentinel data lake and Graph: Run Jupyter Notebook jobs for deep insights, combining contributed and first-party data. Once data resides in the Sentinel data lake, developers can leverage its graph capabilities to model relationships and uncover patterns, empowering customers with comprehensive insights into security events and trends. Agent Creation: Build Security Copilot agents that interact with Sentinel data using natural language prompts. These agents make the customer’s ingested data actionable, allowing users to ask questions or automate tasks, and helping teams quickly respond to threats or investigate incidents using their own enterprise data. Solution Packaging: Package and distribute solutions via the Microsoft Security Store, reaching customers at scale. By packaging these solutions, developers enable customers to seamlessly deploy advanced analytics and automation tools that harness their data journey— from ingestion to actionable insights—across their entire security estate. Conclusion Microsoft Sentinel’s data lake and platform capabilities open new horizons for developers. By centralizing data, enabling advanced analytics, and providing extensible tools, Sentinel empowers you to build solutions that address today’s security challenges and anticipate tomorrow’s threats. Explore the resources below, join the community, and start innovating with Sentinel today! App Assure: For assistance with developing a Sentinel Codeless Connector Framework (CCF) connector, you can contact AzureSentinelPartner@microsoft.com. Microsoft Security Community: aka.ms/communitychoice Next Steps: Resources and Links Ready to dive deeper? Explore these resources to get started: Get Educated! Sentinel data lake general availability announcement Sentinel data lake official documentation Connect Sentinel to Defender Portal Onboarding to Sentinel data lake Integration scenarios (e.g. hunt | jupyter) KQL queries Jupyter notebooks (link) as jobs (link) VS Code Extension Sentinel graph Sentinel MCP server Security Copilot agents Microsoft Security Store Take Action! Bring your data into Sentinel Build a composite solution Explore Security Copilot agents Publish to Microsoft Security Store List existing SaaS apps in Security StoreWhat’s New in Microsoft Sentinel: November 2025
Welcome to our new Microsoft Sentinel blog series! We’re excited to launch a new blog series focused on Microsoft Sentinel. From the latest product innovations and feature updates to industry recognition, success stories, and major events, you’ll find it all here. This first post kicks off the series by celebrating Microsoft’s recognition as a Leader in the 2025 Gartner Magic Quadrant for SIEM 1 . It also introduces the latest innovations designed to deliver measurable impact and empower defenders with adaptable, collaborative tools in an evolving threat landscape. Microsoft is recognized as a Leader in 2025 Gartner Magic Quadrant for Security Information and Event Management (SIEM) Microsoft Sentinel continues to drive security innovation—and the industry is taking notice. Microsoft was named a leader in the 2025 Gartner Magic Quadrant for Security Information and Event Management (SIEM) 1 , published on October 8, 2025. We believe this acknowledgment reinforces our commitment to helping organizations stay secure in a rapidly changing threat landscape. Read blog for more information. Take advantage of M365 E5 benefit and Microsoft Sentinel promotional pricing Microsoft 365 E5 benefit Customers with Microsoft 365 E5, A5, F5, or G5 licenses automatically receive up to 5 MB of free data ingestion per user per day, covering key security data sources like Azure AD sign-in logs and Microsoft Cloud App Security discovery logs—no enrollment required. Read more about M365 benefits for Microsoft Sentinel. New 50GB promotional pricing To make Microsoft Sentinel more accessible to small and mid-sized organizations, we introduced a new 50 GB commitment tier in public preview, with promotional pricing starting October 1, 2025, through March 31, 2026. Customers who choose the 50 GB commitment tier during this period will maintain their promotional rate until March 31, 2027. Available globally with regional variations in regional pricing it is accessible through EA, CSP, and Direct channels. For more information see Microsoft Sentinel pricing page. Partner Integrations: Strengthening TI collaboration and workflow automation Microsoft Sentinel continues to expand its ecosystem with powerful partner integrations that enhance security operations. With Cyware, customers can now share threat intelligence bi-directionally across trusted destinations, ISACs, and multi-tenant environments—enabling real-time intelligence exchange that strengthens defenses and accelerates coordinated response. Learn more about the Cyware integration. Learn more about the Cyware integration here. Meanwhile, BlinkOps integration combined with Sentinel’s SOAR capabilities empowers SOC teams to automate repetitive tasks, orchestrate complex playbooks, and streamline workflows end-to-end. This automation reduces operational overhead, cuts Mean Time to Respond (MTTR) and frees analysts for strategic threat hunting. Learn more about the BlinkOps integration. Learn more about the BlinkOps integration. Harnessing Microsoft Sentinel Innovations Security is being reengineered for the AI era, moving beyond static, rule-based controls and reactive post-breach response toward platform-led, machine-speed defense. To overcome fragmented tools, sprawling signals, and legacy architectures that cannot keep pace with modern attacks, Microsoft Sentinel has evolved into both a SIEM and a unified security platform for agentic defense. These updates introduce architectural enhancements and advanced capabilities that enable AI-driven security operations at scale, helping organizations detect, investigate, and respond with unprecedented speed and precision. Microsoft Sentinel graph – Public Preview Unified graph analytics for deeper context and threat reasoning. Microsoft Sentinel graph delivers an interactive, visual map of entity relationships, helping analysts uncover hidden attack paths, lateral movement, and root causes for pre- and post-breach investigations. Read tech community blog for more details. Microsoft Sentinel Model Context Protocol (MCP) server – Public Preview Context is key to effective security automation. Microsoft Sentinel MCP server introduces a standardized protocol for building context-aware solutions, enabling developers to create smarter integrations and workflows within Sentinel. This opens the door to richer automation scenarios and more adaptive security operations. Read tech community blog for more details. Enhanced UEBA with New Data Sources – Public Preview We are excited to announce support for six new sources in our user entity and behavior analytics algorithm, including AWS, GCP, Okta, and Azure. Now, customers can gain deeper, cross-platform visibility into anomalous behavior for earlier and more confident detection. Read our blog and check out our Ninja Training to learn more. Developer Solutions for Microsoft Sentinel platform – Public Preview Expanded APIs, solution templates, and integration capabilities empower developers to build and distribute custom workflows and apps via Microsoft Security Store. This unlocks faster innovation, streamlined operations, and new revenue opportunities, extending Sentinel beyond out-of-the-box functionality for greater agility and resilience. Read tech community blog for more details. Growing ecosystem of Microsoft Sentinel data connectors We are excited to announce the general availability of four new data connectors: AWS Server Access Logs, Google Kubernetes Engine, Palo Alto CSPM, and Palo Alto Cortex Xpanse. Visit find your Microsoft Sentinel data connector page for the list of data connectors currently supported. We are also inviting Private Previews for four additional connectors: AWS EKS, Qualys VM KB, Alibaba Cloud Network, and Holm Security towards our commitment to expand the breadth and depth to support new data sources. Our customer support team can help you sign up for previews. New agentless data connector for Microsoft Sentinel Solution for SAP applications We’re excited to announce the general availability of a new agentless connector for Microsoft Sentinel solution for SAP applications, designed to simplify integration and enhance security visibility. This connector enables seamless ingestion of SAP logs and telemetry directly into Microsoft Sentinel, helping SOC teams monitor critical business processes, detect anomalies, and respond to threats faster—all while reducing operational overhead. Events, Webinars and Training Stay connected with the latest security innovation and best practices. From global conferences to expert-led sessions, these events offer opportunities to learn, network, and explore how Microsoft is shaping AI-driven, end-to-end security for the modern enterprise. Microsoft Ignite 2025 Security takes center stage at Microsoft Ignite, with dedicated sessions and hands-on experiences for security professionals and leaders. Join us in San Francisco, November 17–21, 2025, or online, to explore our AI-first, end-to-end security platform designed to protect identities, devices, data, applications, clouds, infrastructure—and critically—AI systems and agents. Register today! Microsoft Security Webinars Stay ahead of emerging threats and best practices with expert-led webinars from the Microsoft Security Community. Discover upcoming sessions on Microsoft Sentinel SIEM & platform, Defender, Intune, and more. Sign up today and be part of the conversation that shapes security for everyone. Learn more about upcoming webinars. Onboard Microsoft Sentinel in Defender – Video Series Microsoft leads the industry in both SIEM and XDR, delivering a unified experience that brings these capabilities together seamlessly in the Microsoft Defender portal. This integration empowers security teams to correlate insights, streamline workflows, and strengthen defenses across the entire threat landscape. Ready to get started? Explore our video series to learn how to onboard your Microsoft Sentinel experience and unlock the full potential of integrated security. Watch Microsoft Sentinel is now in Defender video series. MDTI Convergence into Microsoft Sentinel & Defender XDR overview Discover how Microsoft Defender Threat Intelligence Premium is transforming cybersecurity by integrating into Defender XDR, Sentinel, and the Defender portal. Watch this session to learn about new features, expanded access to threat intelligence, and how these updates strengthen your security posture. Partner Sentinel Bootcamp Transform your security team from Sentinel beginners to advanced practitioners. This comprehensive 2-day bootcamp helps participants master architecture design, data ingestion strategies, multi-tenant management, and advanced analytics while learning to leverage Microsoft's AI-first security platform for real-world threat detection and response. Register here for the bootcamp. Looking to dive deeper into Microsoft Sentinel development? Check out the official https://aka.ms/AppAssure_SentinelDeveloper. It’s the central reference for developers and security teams who want to build custom integrations, automate workflows, and extend Sentinel’s capabilities. Bookmark this link as your starting point for hands-on guidance and tools. Stay Connected Check back each month for the latest innovations, updates, and events to ensure you’re getting the most out of Microsoft Sentinel. 1 Gartner® Magic Quadrant™ for Security Information and Event Management, Andrew Davies, Eric Ahlm, Angel Berrios, Darren Livingstone, 8 October 2025Unlocking Business Value: Microsoft's Dual Approach to AI for Security and Security for AI
Overview In an era where cyber threats evolve at an unprecedented pace and artificial intelligence (AI) transforms business operations, Microsoft stands at the forefront with a comprehensive strategy that addresses both leveraging AI to bolster security and safeguarding AI systems themselves. This white paper, presented in blog post format, explores Microsoft's business value model for "AI for Security" – using AI to enhance threat detection, response, and prevention – and "Security for AI" – protecting AI deployments from emerging risks. Drawing from independent studies, real-world case studies, and economic analyses, we demonstrate how these approaches deliver tangible returns on investment (ROI) and total economic impact (TEI). Whether you're a CISO evaluating security investments or a business leader integrating AI, this post provides insights, visuals, and calculations to guide your strategy. Executive Summary The enterprise adoption of AI has transcended from a technological novelty to a strategic imperative, fundamentally altering competitive landscapes and business models. Organizations that fail to integrate AI risk operational inefficiency, diminished competitiveness, and missed revenue opportunities. However, the path from initial awareness to full-scale transformation is fraught with a new and complex class of security risks that traditional cybersecurity postures are ill-equipped to address. This report provides a comprehensive analysis of the enterprise AI adoption journey, the evolving threat landscape, and a data-driven financial case for securing AI initiatives exclusively through Microsoft's unified security ecosystem. The AI journey is a multi-stage process, beginning with Awareness and Experimentation before progressing to Operational deployment, Systemic integration, and ultimately, Transformational impact. Advancement through these stages is contingent not on technology alone, but on a clear executive vision, a structured roadmap that aligns AI potential with business reality, and a foundational commitment to responsible AI governance. This journey is paralleled by the emergence of a sophisticated AI threat landscape. Malicious actors are no longer targeting just infrastructure but the very logic and integrity of AI models. Threats such as data poisoning, model theft, prompt injection, risks to intellectual property, data privacy, regulatory compliance, and brand reputation. Furthermore, the proliferation of generative AI tools creates a novel "accidental insider" risk, where well-intentioned employees can inadvertently leak sensitive corporate data to third-party models. To counter these multifaceted threats, a fragmented, multi-vendor security approach is proving insufficient. Microsoft offers a cohesive, AI-native security platform that provides end-to-end protection across the entire AI lifecycle. This unified framework integrates Microsoft Purview for proactive data security and governance, Microsoft Sentinel for AI-powered threat detection and response, and Microsoft Defender alongside Azure AI Services for comprehensive endpoint, application, infrastructure protection and Microsoft Entra for securing and protecting the identity and access management control. The platform's strength lies in its deep, native integration, which creates a virtuous cycle of shared intelligence and automated response that siloed solutions cannot replicate. A rigorous market analysis, based on independent studies from Forrester and IDC, demonstrates that investing in this unified security framework is not a cost center but a significant value driver. The financial returns are compelling: Microsoft Purview delivers a 355% Return on Investment (ROI) over three years, driven by a 30% reduction in data breach likelihood and a 75% improvement in security investigation time. For more details: mccs-ms-purview-final-9-3.pdf Microsoft Sentinel generates a 234% ROI, reducing the Total Cost of Ownership (TCO) from legacy Security Information and Event Management (SIEM) solutions by 44% and cutting false positives by up to 79%. For more details: The Total Economic Impact™ Of Microsoft Sentinel Microsoft Defender provides a 242% ROI with a payback period of less than six months, fueled by significant savings from vendor consolidation and a 30% faster threat remediation time. For more details: TEI-of-M365Defender-FINAL.pdf Microsoft Entra Suite: 131% ROI over three years, with $14.4 million in benefits, $8.2 million net present value, payback in less than six months, 30% reduction in identity-related risk exposure, 60% reduction in VPN license usage, 80% reduction in user management time, and 90% fewer password reset tickets. For more details: The Total Economic Impact™ Of Microsoft Entra Suite Collectively, these solutions do more than mitigate risk; they enable innovation. By establishing a secure and trusted data environment, organizations can confidently accelerate their adoption of transformative AI technologies, unlocking the broader business value and competitive advantage that AI promises. This report concludes with a clear strategic recommendation: to successfully navigate the AI frontier, executive leadership must prioritize investment in a unified, AI-native security and governance framework as a foundational enabler of their digital transformation strategy. AI Risks/Challenges AI is transforming cybersecurity, but it also might introduce new vulnerabilities and attack surfaces. Organizations adopting AI must address risks such as data leakage, prompt injection attacks, model poisoning, identity and access management, and compliance gaps. These threats are not hypothetical—they are already impacting enterprises globally. Key Risks and Their Impact Data Security & Privacy 80%+ of security leaders cite leakage of sensitive data as their top concern when adopting AI. BYOAI (Bring Your Own AI) is rampant: 78% of employees use unapproved AI tools at work, increasing exposure to unmanaged risks. Source: Microsoft Work Trend Index & ISMG Study Emerging Threats Indirect Prompt Injection Attacks: 77% of organizations are concerned; 11% are extremely concerned. Hijacking & Automated Scams: 85% of respondents fear AI-driven scams and hijacking scenarios. Source: KPMG Global AI Study Compliance & Governance: 55% of leaders admit they lack clarity on AI regulations and compliance requirements. Agentic AI Risks: 88% of organizations are piloting AI agents, creating agent sprawl and new attack vectors. by 2029, 50%+ of successful attacks against AI agents will exploit access control weaknesses. The Numbers Tell the Story 97% of organizations reported security incidents related to Generative AI in the past year. Known AI security breaches jumped from 29% in 2023 to 74% in 2024, yet 45% of incidents go unreported. Source: Capgemini & HiddenLayer AI Threat Landscape Report Global AI cybersecurity market is projected to grow from $30B in 2024 to $134B by 2030, reflecting the urgency of securing AI systems. Source: Statista AI in Cybersecurity Where do we see customers in adoption Journey Understanding where an organization stands in its AI adoption journey is the critical first step in formulating a successful strategy. The transition from recognizing AI's potential to harnessing it for transformative business value is not a single leap but a structured progression through distinct stages of maturity. Many organizations falter by pursuing technologically interesting projects that fail to solve core business problems, leading to wasted resources and disillusionment. A coherent maturity model provides a diagnostic tool to assess current capabilities and a roadmap to guide future investments, ensuring that each step of the journey is aligned with measurable business goals. From Awareness to Transformation: A Unified AI Maturity Model By synthesizing frameworks from leading industry analysts and practitioners, a comprehensive five-stage maturity model emerges. This model provides a clear pathway for organizations, detailing the characteristics, challenges, and objectives at each level of AI integration. Stage 1: Aware / Exploration This initial stage is characterized by an early interest in AI, where organizations recognize its potential but have limited to no practical experience. Activities are focused on research and education, with internal teams exploring different tools to understand their capabilities and potential business use cases. A common and effective starting point is conducting brainstorming workshops with key stakeholders to identify pressing business pain points and map them to potential AI solutions. The primary goal is to build initial familiarity and garner buy-in from leadership to move beyond theoretical discussions. The most significant challenge at this stage is the "zero-to-one gap"—overcoming organizational inertia and a lack of executive sponsorship to secure the approval and resources needed for initial experimentation. Stage 2: Active / Experimentation In the experimentation phase, organizations have initiated small-scale pilot projects, often isolated within a data science team or a specific business unit. AI literacy remains limited, with only a few individuals or teams actively using AI tools in their daily work. A formal, enterprise-wide AI strategy is typically absent, leading to a fragmented approach where different teams may be experimenting with disparate tools. This is the stage where many organizations encounter the "Production Chasm." While they may successfully develop prototypes, they struggle to move these models into a live production environment. This difficulty arises from a critical skills gap; the expertise required for production-level AI—a multidisciplinary blend of data science, IT operations, and DevOps, often termed MLOps—is fundamentally different and far rarer than the skills needed for experimental modeling. This chasm is widened by a misleading perception of what constitutes professional-grade AI, often formed through exposure to public tools, which lack the security, scalability, and deep integration required for enterprise use. Stage 3: Operational / Optimizing Organizations reaching this stage have successfully deployed one or more AI solutions into production. The focus now shifts from experimentation to optimization and scalability. The primary challenge is to move from isolated successes to consistent, repeatable processes that can be applied across the enterprise. This requires a deliberate strategic shift from scattered efforts to a structured portfolio of AI initiatives, each with a clear business case and measurable goals. Key activities include defining a formal AI strategy, investing in enterprise-grade tools, and launching broader initiatives to improve the AI literacy of the entire workforce, not just specialized teams. The objective is to achieve tangible improvements in productivity, efficiency, and business performance through the integration of AI into key processes. Stage 4: Systemic / Standardizing At the systemic stage, AI is no longer a collection of discrete projects but is deeply integrated into core business operations and workflows. The organization makes significant investments in enterprise-wide technology, including modern data platforms and robust governance frameworks, to ensure standardized and responsible usage of AI. A culture of innovation is fostered, encouraging employees to leverage AI tools to drive the business forward. The focus is on maximizing efficiency at scale, automating complex processes, and creating a sustainable competitive advantage through widespread gains in productivity and creativity. Stage 5: Transformational / Monetization This is the apex of AI maturity, a level achieved by only a few organizations. Here, AI is a central pillar of the corporate strategy and a key priority in executive-level budget allocation.3 The organization is recognized as an industry leader, leveraging AI not just to optimize existing operations but to completely transform them, creating entirely new revenue streams, innovative business models, and disruptive market offerings.4 The focus is on maximizing the bottom-line impact of AI across every facet of the business, from employee productivity to customer satisfaction and financial performance. Why using AI in defense is imperative Cybersecurity has entered an era where the speed, scale, and sophistication of attacks outpace traditional defenses. AI is no longer optional—it’s a strategic necessity for organizations aiming to protect critical assets and maintain resilience: 1. The Threat Landscape Has Changed AI-powered attacks are real and growing fast: Breakout times for breaches have dropped to under an hour, making manual detection and response obsolete. Attackers use AI to craft polymorphic malware, deepfakes, and automated phishing campaigns that bypass legacy security controls. Source: [mckinsey.com] 93% of security leaders fear AI-driven attacks, yet 69% see AI as the answer, and 62% of enterprises already use AI in defense. 2. AI Delivers Asymmetric Advantage Predictive Threat Intelligence: AI analyzes billions of signals to anticipate attacks before they occur, reducing downtime and mitigating risk. Automated Response: AI-driven SOCs cut response times from hours to seconds, isolating compromised endpoints and revoking malicious access instantly. Source: [analyticsinsight.net] Behavioral Analytics: Detects insider threats and anomalous activities that traditional tools miss, safeguarding identities and sensitive data 3. Operational Efficiency & Talent Gap Cybersecurity teams face a global shortage of skilled professionals. AI acts as a force multiplier, automating repetitive tasks and enabling analysts to focus on strategic threats. Organizations report 76% improvement in early threat detection and $2M+ savings per breach when leveraging AI-powered security solutions. Source: AI-Powered Security: The Future of Threat Detection and Response Microsoft approach to AI security As AI adoption accelerates, Microsoft has developed a multi-layered security strategy to protect AI systems, data, and identities while enabling innovation. This approach combines platform-level security, responsible AI principles, and advanced threat protection to ensure AI is deployed securely and ethically across enterprises. 1. Foundational Principles Microsoft’s AI security strategy is grounded in: Responsible AI Principles: Fairness, privacy & security, inclusiveness, transparency, accountability, and reliability. These principles guide every stage of AI development and deployment. Secure Future Initiative (SFI): Embedding security by design, default, and deployment across AI workloads. 2. The Secure AI Framework Microsoft’s Secure AI Framework (SAIF) provides a structured approach to securing AI environments: Prepare: Implement Zero Trust principles, secure identities, and configure environments for AI readiness. Discover: Gain visibility into AI usage, sensitive data flows, and potential vulnerabilities. Protect: Apply end-to-end security controls for data, models, and infrastructure. Govern: Enforce compliance with regulations like GDPR and the EU AI Act, and monitor AI interactions for risk. 3. Key Security Controls Data Security & Governance: o Microsoft Purview for Data Security Posture Management (DSPM) in AI prompts and completions. o Auto-classification, encryption, and risk-adaptive controls to prevent data leakage. Identity & Access Management: o Microsoft Entra for securing AI agents and enforcing least privileges with adaptive access policies. Threat Protection: o Microsoft Defender for AI integrates with Defender for Cloud to detect prompt injection, model poisoning, and jailbreak attempts in real time. Compliance & Monitoring: o Continuous posture assessments aligned with ISO 42001 and NIST AI RMF. 4. Security by Design Microsoft embeds security throughout the AI lifecycle: Secure Development Lifecycle (SDL) for AI models. AI Red Teaming using tools like PyRIT to simulate adversarial attacks and validate resilience. Content Safety Systems in Azure AI Foundry to block harmful or inappropriate outputs. 5. Integrated Security Ecosystem Microsoft’s AI security capabilities are deeply integrated across its portfolio: Microsoft Defender XDR: Correlates AI workload alerts with broader threat intelligence. Microsoft Sentinel: Provides graph-based context for AI-driven threat investigations. Security Copilot: AI-powered assistant for SOC teams, accelerating detection and response. Market research on ROI and Cost Savings from securing AI Investing in a robust security framework for AI is not merely a defensive measure or a cost center; it is a strategic investment that yields a quantifiable and compelling return. Independent market analysis conducted by leading firms like Forrester and IDC, along with real-world customer case studies, provides extensive evidence that deploying Microsoft's unified security platform delivers significant financial benefits. These benefits manifest in two primary ways: a "defensive" ROI derived from mitigating risks and reducing costs, and an "offensive" ROI achieved by enabling the secure and rapid adoption of high-value AI initiatives that drive business growth. A recurring and powerful theme across these studies is that platform consolidation is a major, often underestimated, value driver. A significant portion of the quantified ROI comes from retiring a fragmented stack of legacy point solutions and eliminating the associated licensing, infrastructure, and specialized labor costs, allowing the investment in the Microsoft platform to be funded, in part or in whole, by reallocating existing budget. The Total Economic Impact™ of a Unified Security Posture Microsoft has commissioned Forrester Consulting to conduct a series of Total Economic Impact™ (TEI) studies on its core security products. These studies, based on interviews with real-world customers, construct a "composite organization" to model the financial costs and benefits over a three-year period. The results consistently show a strong positive ROI across the platform. Microsoft Purview: The TEI study on Microsoft Purview found that the composite organization experienced benefits of $3.0 million over three years versus costs of $633,000, resulting in a net present value (NPV) of $2.3 million and an impressive 355% ROI. The primary value drivers included reduced data breach impact, significant efficiency gains for security and compliance teams, and the avoidance of costs associated with legacy data governance tools. Microsoft Sentinel: For Microsoft Sentinel, the Forrester study calculated an NPV of $7.9 million and a 234% ROI over three years. Key financial benefits were derived from a 44% reduction in TCO by replacing expensive, on-premises legacy SIEM solutions, a dramatic 79% reduction in false-positive alerts that freed up analyst time, and a 35% reduction in the likelihood of a data breach. Microsoft Defender: The unified Microsoft Defender XDR platform delivered an NPV of $12.6 million and a 242% ROI over three years, with an exceptionally short payback period of less than six months. The benefits were substantial, including up to $12 million in savings from vendor consolidation, $2.4 million from SecOps optimization, and $2.8 million from the reduced cost of material breaches. Microsoft Security Copilot: As a newer technology, the TEI for Security Copilot is a projection. Forrester projects a three-year ROI ranging from a low of 99% to a high of 348%, with a medium impact scenario yielding a 224% ROI and an NPV of $1.13 million. This return is driven almost entirely by amplified SecOps team efficiency, with projected productivity gains on security tasks ranging from 23% to 46.7%, and cost efficiencies from a reduced reliance on third-party managed security services. The following table aggregates the headline financial metrics from these independent Forrester TEI studies, providing a clear, at-a-glance summary of the platform's investment value. Table: Aggregated Financial Impact of Microsoft AI Security Solutions (Forrester TEI Data) Microsoft Solution 3-Year ROI (%) 3-Year NPV ($M) Payback Period (Months) Key Value Drivers Microsoft Purview 355% $2.3 < 6 Reduced breach likelihood by 30%, 75% faster investigations, 60% less manual compliance effort, legacy tool consolidation. Microsoft Sentinel 234% $7.9 < 6 44% TCO reduction vs. legacy SIEM, 79% reduction in false positives, 85% less effort for advanced investigations. Microsoft Defender 242% $12.6 < 6 Up to $12M in vendor consolidation savings, 30% faster threat remediation, 80% less effort to respond to incidents. Security Copilot 99% - 348% (Projected) $0.5 - $1.76 (Projected) Not Specified 23%-47% productivity gains for SecOps tasks, reduced reliance on third-party services, upskilling of security personnel. Microsoft Entra Suite 131% $8.2 Not Specified 30% reduction in identity risk, 80% reduction in user management time, 90% fewer password reset tickets, 60% VPN license reduction. Quantifying Risk Reduction and Its Financial Impact A core component of the ROI calculation is the direct financial savings from preventing and mitigating security incidents. Reduced Likelihood of Data Breaches: The Forrester study on Microsoft Purview quantified a 30% reduction in the likelihood of a data breach for the composite organization. This translated into over $225,000 in annual savings from avoided costs of security incidents and regulatory fines. The study on Microsoft Sentinel found a similar 35% reduction in breach likelihood, which was valued at $2.8 million over the three-year analysis period. These figures provide a tangible financial value for improved security posture. The Cost of Inaction: The financial case is further strengthened when contrasted with the high cost of failure. The Forrester study on Microsoft Defender highlights that organizations with insufficient incident response capabilities spend an average of $204,000 more per breach and experience nearly one additional breach per year compared to their more prepared peers. This underscores that the investment in a modern, unified platform is an effective insurance policy against significantly higher future costs. Driving SOC Efficiency and Cost Optimization Beyond risk reduction, the Microsoft security platform drives substantial cost savings through automation, AI-powered efficiency, and platform consolidation. These savings free up both budget and highly skilled personnel to focus on more strategic, value-added activities. Faster Mean Time to Respond (MTTR): Time is money during a security incident. The platform's AI and automation capabilities dramatically accelerate the entire response lifecycle. The Sentinel TEI found that its AI-driven correlation engine reduced the manual labor effort for advanced, multi-touch investigations by 85%. The Defender TEI noted that security teams could remediate threats 30% faster, reducing the mean time to acknowledge (MTTA) from 30 minutes to just 15, and cutting the mean time to resolve (MTTR) from up to three hours to less than one hour in many cases. Similarly, Purview was found to reduce the time security teams spent on investigations by 75%. Legacy Tool and Cost Avoidance: Consolidating on the Microsoft platform allows organizations to retire a host of redundant security and compliance tools. The Purview study identified nearly $500,000 in savings over three years from sunsetting legacy records management and data security solutions. The Defender study attributed up to a massive $12 million in benefits over three years to vendor consolidation, eliminating licensing, maintenance, and management costs from other tools. The Microsoft Entra Suite was found to reduce VPN license usage by 60%, saving an estimated $680,000 over three years. Reduced IT Overhead and Labor Costs: Automation extends beyond the SOC to general IT operations. The Microsoft Entra study found that automated governance and lifecycle workflows reduced the time IT spent on ongoing user management by 80%, yielding $4.6 million in time savings over three years. The same study noted a 90% reduction in password reset help desk tickets, from 80,000 to just 8,000 per year, avoiding $2.6 million in support costs. For more details: https://www.microsoft.com/en-us/security/blog/2025/09/23/microsoft-purview-delivered-30-reduction-in-data-breach-likelihood/ https://www.microsoft.com/en-us/security/blog/2025/08/04/microsoft-entra-suite-delivers-131-roi-by-unifying-identity-and-network-access/ https://azure.microsoft.com/en-us/blog/explore-the-business-case-for-responsible-ai-in-new-idc-whitepaper/ https://www.microsoft.com/en-us/security/blog/2025/09/18/microsoft-defender-delivered-242-return-on-investment-over-three-years/ https://tei.forrester.com/go/microsoft/microsoft_sentinel/ https://www.gartner.com/reviews/market/email-security-platforms/compare/abnormal-ai-vs-microsoft Fast-track generative AI security with Microsoft Purview | Microsoft Security Blog Conclusion Summary Consolidating security and compliance operations on the Microsoft platform delivers substantial cost savings and operational efficiencies. Studies have shown that moving away from legacy tools and embracing automation through Microsoft solutions not only reduces licensing and maintenance expenses, but also significantly lowers IT labor and support costs. By leveraging integrated tools like Microsoft Purview, Defender, and Entra Suite, organizations can realize millions of dollars in savings and free up valuable IT resources for higher-value work. Key Highlights Significant Cost Savings: Up to $12 million in benefits over three years from vendor consolidation, and $500,000 saved by retiring legacy records management and data security solutions. License Optimization: The Microsoft Entra Suite reduced VPN license usage by 60%, saving an estimated $680,000 over three years. IT Efficiency Gains: Automated governance and lifecycle workflows decreased IT time spent on user management by 80%, resulting in $4.6 million in time savings. Support Cost Reduction: Password reset help desk tickets dropped by 90%, from 80,000 to 8,000 per year, avoiding $2.6 million in support costs.6 truths about migrating Microsoft Sentinel to the Defender portal
The move from the Azure portal to the Microsoft Defender portal is one of the most significant transformations yet for Microsoft Sentinel SIEM. By July 1, 2026, every Sentinel environment will make this leap. But this isn’t just a new coat of paint—it’s a re-architecture of how Security Operations Centers (SOCs) detect, investigate, and respond to threats. For many teams, the shift will feel different, even surprising. But within those surprises lie opportunities to run leaner, smarter, and more resilient operations. Here are six insights that will help you prepare your SOC to not only manage the change but thrive in it. The migration landscape: what's at stake This comprehensive guide distills the six most impactful changes every SOC needs to understand before making the switch. The migration represents a fundamental paradigm shift in how security operations are conducted, moving from the familiar Azure portal environment to the Defender portal—delivering new capabilities such as enhanced correlation, streamlined workflows, and continued innovation. The stakes are high. With this shift, SOC teams must prepare not just for new interfaces, but for entirely new ways of thinking about incident management, automation, and security operations. To thrive in this new experience, organizations must recognize this migration as an opportunity to modernize their security operations, while those that treat it as a simple interface change may find themselves struggling with unexpected disruptions. Critical Timeline: All Microsoft Sentinel environments must migrate to the Defender portal by July 1, 2026. This is not optional. Truth #1: You will gain a more comprehensive view of incidents The Defender correlation engine automatically groups related alerts into a single incident and merges existing incidents it deems to be sufficiently alike based on shared entities, attack patterns, and timing. This process is designed to reduce noise and provide a more context-rich view of an attack, greatly benefiting the SOC by transforming scattered, benign signals into a complete incident view. More comprehensive incidents We observed up to an 80% drop in Sentinel incident counts for early adopters of the unified SOC. Fewer duplications When Defender XDR merges one incident into another, the original "source incident" is automatically closed, given a Redirected tag, and disappears from the main queue. A shorter incident queue Analysts will enjoy fewer singleton alerts with the enhanced correlation engine. Leveraging the new correlation engine in Defender allows customers to transform signals into stories. With more comprehensive incidents and fewer singleton alerts, customers can connect the dots across their environment. How the correlation engine decides The Defender correlation engine uses sophisticated algorithms to determine which incidents should be merged. It analyzes multiple factors simultaneously to make these decisions, creating a more comprehensive view of security events. Shared entities (users, devices, IP addresses) Attack patterns and techniques Temporal proximity of events Contextual relationships between alerts Threat intelligence correlations This automated correlation is designed to mirror the thought process of an experienced analyst who would naturally connect related security events. The engine operates at scale and speed that human analysts cannot match, processing thousands of potential correlations simultaneously. Truth #2: Automation will level up Think of this migration as a chance to strengthen the foundation of your Security Orchestration, Automation, and Response (SOAR) strategy. The new unified schema encourages automation that’s more resilient and future-proof. By keying off stable fields like AnalyticsRuleName and tags, SOC teams gain precision and durability in their workflows—ensuring automation continues to work as correlation logic evolves. Some playbooks and automations built in the Azure portal will need adjustment. Fields like IncidentDescription and IncidentProvider behave differently now, and titles aren’t always stable identifiers. Adapting your automation rules and playbooks: The Description field is gone Customers will need to update automations, playbooks and integrations that rely on the SecurityIncident table. This table no longer populates the Description field, which impacts rules that use dynamic content from KQL queries to populate descriptions. IncidentProvider property removed Since all incidents in the unified portal are considered to come from XDR, this property is now obsolete. Automation rules that use a condition like "Incident Provider equals Microsoft Sentinel" will need to be updated or removed entirely. Incident titles are unstable The Defender correlation engine may change an incident's title when it merges alerts or other incidents. Instead, use Analytics rule name or tags. Fixing Automation: The Action Plan Audit existing automation Conduct a comprehensive review of all automation rules, SOAR playbooks, and integration scripts that interact with Sentinel incidents. Document dependencies on deprecated fields and identify potential failure points. Update field references Modify your rules to use more stable identifiers. Replace references to IncidentDescription and IncidentProvider with fields like AnalyticsRuleName and tags for conditions, ensuring more reliable automation execution. Implement granular control Use Analytics rule name as the recommended method for ensuring an automation rule only targets incidents generated from a specific Sentinel rule, preserving granular control over your workflows. Test and validate Thoroughly test updated automation in a staging environment before deployment. Create test scenarios that simulate the new correlation behaviors to ensure automation responds appropriately to merged incidents. Teams must move away from dependencies on dynamic content and toward predictable data points that will remain consistent even as the correlation engine modifies incident properties. Truth #3: The correlation engine is in charge The Defender correlation engine acts like an intelligent filter—connecting the dots between alerts at machine speed. You can’t turn it off, but you can work with it. Some creative SOCs are already finding ways to guide its behavior, like routing test incidents separately to prevent unwanted merges. While it may feel like giving up some manual control, the payoff is big: an analyst’s effort shifts from sorting noise to validating signals. In short, analysts spend less time as traffic cops and more time acting as investigators. Rapid response with less configuration With Defender’s advanced correlation algorithms handling incident grouping automatically, your security teams can confidently dedicate their attention to strategic decision-making and rapid incident response, rather than manual rule configuration. Truth #4: Some familiar tools stay behind, but new ones await A few Sentinel features you’ve grown used to—like Incident Tasks and manually created incidents—don’t carry over. Alerts that aren’t tied to incidents won’t appear in the Defender portal either. It’s a shift, yes. But one that nudges SOCs toward leaner workflows and built-in consistency. Instead of juggling multiple tracking models, analysts can focus on cases that Defender elevates as truly significant. Incident tasks The built-in checklist and workflow management feature used to standardize analyst actions within an incident is unavailable in the Defender portal. Teams must adopt alternative methods such as Cases for ensuring consistent investigation procedures. Alerts without incidents Analytics rules configured only to generate alerts (with createIncident set to false) will still write to the SecurityAlerts table. However, while the Advanced hunting query editor doesn’t recognize the SecurityAlerts table schema, you can still use the table in queries and analytics rules. Manually created incidents While incidents created manually or programmatically in Sentinel do not sync to the Defender portal, they remain fully manageable through API, ensuring specific workflows are handled separately. You can ingest the relevant events as custom logs and then create analytics rules to generate incidents from that data. Similar incidents feature Analysts will have access to a more advanced and integrated correlation engine that automatically merges related incidents from all sources. This built-in engine proactively groups threats by identifying common entities and attack behaviors, presenting a single, comprehensive incident rather than requiring manual investigation of separate alerts. Truth #5: Permissions grow more sophisticated Operating in the Defender portal introduces a dual permissions model: your Azure Role-Based Access Control (RBAC) roles still apply, but you’ll also need Microsoft Entra ID or Defender Unified RBAC for high-level tasks. This layered model means more careful planning up front, but it also opens the door for clearer separation of duties across teams—making it easier to scale SOC operations securely. Additive permission model After connecting to the Defender portal, your existing Azure RBAC permissions are still honored and allow you to work with the Microsoft Sentinel features you already have access to. The new permissions are for accessing the Defender portal itself and performing high-level onboarding actions. Dual permission requirements Analysts and engineers need either global Microsoft Entra ID roles or roles from the new Microsoft Defender XDR Unified RBAC model to access and operate within the Defender portal. Elevated setup requirements Key actions like onboarding a workspace for the first time or changing the primary workspace require either Global Administrator or Security Administrator roles in Microsoft Entra ID. Permission planning matrix User type Required permissions Access capabilities SOC Analyst Defender XDR role + existing Sentinel RBAC Incident investigation, alert triage, basic response actions SOC Manager Security Reader/Operator + Defender XDR roles Full operational access, reporting, dashboard configuration Security Engineer Contributor + Defender XDR Administrator Rule creation, automation management, advanced configuration Initial Setup Admin Global Admin or Security Admin Workspace onboarding, primary workspace designation MSSP Technician B2B Guest + appropriate Defender roles Customer tenant access via guest invitation model Planning permission transitions requires mapping current access patterns to the new dual-model requirements. Organizations should audit existing permissions, identify gaps, and develop migration plans that ensure continued access while meeting the new portal requirements. Truth #6: Primary vs. secondary workspace divide Not all workspaces are equal in the new model. The primary workspace is the one fully integrated with Defender . It’s where cross-signal correlation happens, enabling unified incidents that combine alerts across Defender and Microsoft Sentinel for a truly comprehensive view of threats. This forces a big but valuable decision: which workspace will anchor your organization’s most critical signals? Choosing wisely ensures you unlock the full power of cross-signal correlation, giving your SOC a panoramic view of complex threats. Primary workspace This is the only workspace with alerts that can be correlated with Defender alerts and included together in unified incidents. It receives the full benefit of cross-signal correlation and advanced threat detection capabilities. Secondary workspaces Secondary workspaces often exist for compliance, regulatory, or business unit separation reasons. While their alerts are not correlated with XDR or or other workspaces, this design keeps them logically and operationally isolated. Importantly, analysts who need visibility can still be granted URBAC permissions to view incidents and alerts, even if those originate outside their local workspace. Selecting your primary workspace is a strategic step that unlocks the full potential of our unified SIEM and XDR correlation capabilities, empowering you with deeper insights and stronger threat detection across your critical data sources. While secondary workspaces remain fully operational, consolidating workspaces strategically allows your organization to maximize the advanced correlation and proactive defense that define the core value of the unified platform. Thoughtful workspace consolidation, balanced with compliance and cost considerations, positions your organization to get the absolute best out of your security investments. Turning migration into momentum The Defender portal migration isn’t just a deadline to meet—it’s an opportunity to re-engineer how your SOC operates. By anticipating these six shifts, you can move past disruption and toward a strategic advantage: fewer distractions, stronger automation, richer incident context, and a unified XDR-driven defense posture. The future SOC isn’t just managing alerts—it’s mastering context. And the Defender portal is your launchpad. More Information Transition Your Microsoft Sentinel Environment to the Defender Portal | Microsoft Learn Frequently asked questions about the unified security operations platform | Microsoft Community Hub Alert correlation and incident merging in the Microsoft Defender portal - Microsoft Defender XDR | Microsoft Learn Planning your move to Microsoft Defender portal for all Microsoft Sentinel customers | Microsoft Community Hub Connect Microsoft Sentinel to the Microsoft Defender portal - Unified security operations | Microsoft Learn Microsoft Sentinel in the Microsoft Defender portal | Microsoft Learn
