Pending Approval/Provisioning for Microsoft Defender XDR Lab/Trial Environment
Hello Microsoft Community Team, On June 26, 2026, our organization applied for a Microsoft 365 Developer Environment / Free Trial to support evaluation of the Microsoft Defender XDR Lab environment. To date, the environment has not been provisioned, and we have not received any status updates or confirmation. Impact: Current Status: We are currently utilizing our production environment to test project capabilities, which poses risks and limitations. Future Intent: Our organization plans to transition to a full, paid Business/Enterprise purchase immediately upon proving the platform’s benefits. Urgency: This delay is stalling our evaluation phase. We urgently need this environment onboarded and activated so we can proceed with deployment tests and subsequent procurement. Request: Please review the status of our registration and expedite the onboarding/provisioning of this developer environment. Thank you for your prompt assistance.Looking for a simple deployment guide
MS Learn is a great starting point, but it just doesn't seem to cover the steps needed to get up and running safely. I have concerns about adding or setting something that suddenly creates a vulnerability or exposure. Where is the installation guide that installs and configures the solution then tells you, "You are now protected". Do I really want to set my own policies? Why aren't the default set of rules good enough, safe enough. I can't have a solution that is so complicated I need to hire a team to manage it 24 hours a day. I am okay investigating an alert and helping a user solve a pop-up question. Why is every major corporation around the world required to re-invent the same or similar policies the company next door is creating to make this tool work? I want to onboard all of our Intune devices and monitor anything that CAN'T be stopped by default security measures. Just the fact that Sentinel appears to be changing as an embedded tool within Defender gives me hope that this will be getting closer to a more manageable tool. But that still seems a way off. I am ready to do the reading and research to get this set up but I am hoping for a guide that is specific enough to achieve a final result. Thank for understanding my challenges here.
SentinelHealth: Scheduled Rule Retry Logging Does Not Match Docs
## Objective I am working on a health checks architecture for Microsoft Sentinel analytic rules. The goal is to build a set of monitoring queries/approaches that cover rule execution failures, configuration issues (entity mapping, partial success), rule audit tracking, and auto-disabled rule detection. ## My Current Approach So far I have built monitoring for the following areas using the SentinelHealth and SentinelAudit tables: - Scheduled rule window failures (retry exhaustion) - NRT rule execution delays (cumulative delay over 25 minutes) - Partial success and configuration issues (entity mapping drops, alert size limits, semantic errors) with transient error codes filtered out - Auto-disabled rules detection - Rule disable/delete audit tracking via SentinelAudit + AzActivity ## The Issue: Scheduled Rule Retry Logging The documentation at https://learn.microsoft.com/en-us/azure/sentinel/monitor-analytics-rule-integrity#scheduled-rules states that when a scheduled rule fails, it is retried 5 more times on the same window (6 total attempts). It also provides this query to detect completely skipped windows: ```kql _SentinelHealth() | where SentinelResourceType == @"Analytics Rule" | where SentinelResourceKind == "Scheduled" | where Status != "Success" | extend startTime = tostring(ExtendedProperties["QueryStartTimeUTC"]) | summarize failuresByStartTime = count() by startTime, SentinelResourceId | where failuresByStartTime == 6 | summarize count() by SentinelResourceId ``` This query assumes that each retry attempt is logged as a separate event in SentinelHealth, all sharing the same QueryStartTimeUTC. You would then count 6 failure records per startTime to identify a fully skipped window. However, in practice I am seeing different behavior. I ran a diagnostic query with a 90-day lookback (480 non-success events total, 73 unique rules). Every single event had a count of 1 per unique (SentinelResourceName, startTime) combination. No grouping of retries was observed at all. I then found an actual failed-window event that confirms this. Here is the record: - Rule: Port scan detected (ASIM Network Session schema) - Status: Failure - Description: "Rule's scheduled run at 06/01/2026 10:43:55 failed after numerous attempts. It will be re-executed over the next scheduled time." - Issue Code: SemanticErrorInQuery - Only 1 SentinelHealth record exists for this failed window The Description field says "failed after numerous attempts" which indicates the retries happened internally, but only one consolidated Failure event was written to SentinelHealth after all retries were exhausted. The individual retry attempts do not appear as separate records. This means the failuresByStartTime == 6 query from the documentation would never match this pattern, because there is only 1 record per failed window, not 6. ## Why This Matters Yes, completely skipped windows are rare. In my 90-day dataset most failures were permanent types (SemanticErrorInQuery, QueryGeneralError) that would not benefit from retries anyway. But they still happen, and if a tenant experiences a transient issue that causes a higher rate of failed windows, the documented query would silently return nothing. For my health checks I have rewritten the detection to simply look for Status == "Failure" with Description containing "failed after numerous attempts" which matches the actual consolidated event Sentinel writes. ## Questions Is the documented failuresByStartTime == 6 query still accurate? Or has the retry logging behavior changed to write a single consolidated event per failed window? Are there specific failure types or conditions where individual retries are logged as separate events? Perhaps transient failures behave differently from permanent ones in this regard? For anyone else building health monitoring on SentinelHealth - am I missing any important use cases beyond what I described above? Any clarification would be appreciated.What’s new in Microsoft Sentinel: June 2026
Welcome back to What's new in Microsoft Sentinel. In June, Sentinel SIEM’s Advanced Security Information Model (ASIM) broadens its normalization, so one analytic rule can reach more sources with less per-source work and, additionally, two new ASIM schemas can now bring asset inventory and AI agent telemetry into common form. In Microsoft Sentinel data lake, the Agent Identities Asset Connector adds the identity context behind your AI agents, helping you see who owns an agent and what permissions it holds. In Sentinel MCP, graph tools help security teams investigate threats and optimize security coverage by visualizing relationships across identities, devices, alerts, and signals in a unified graph experience. Read on for the details, and explore the resources at the end to go deeper. Sentinel innovations: Sentinel SIEM Sentinel data lake Sentinel MCP Microsoft Security Store Sentinel SIEM Advanced Security Information Model (ASIM) parsers and schemas [Generally available] The Advanced Security Information Model (ASIM) in Sentinel normalizes logs into common schemas, so one analytic rule can cover many sources without managing each native schema. ASIM coverage has expanded across more Azure services, broader AWS CloudTrail activity, and a range of third-party firewall, identity, and proxy products, so your detections reach more of your environment with less per-source work. Two schemas also join ASIM: Asset Entities normalizes asset inventory so you can correlate files and assets across investigations, and AI Agent Events normalizes telemetry from AI-driven workflows and autonomous agents. Browse the ASIM parsers on GitHub to explore, file issues, or contribute. Learn more in our blog. Sentinel transition to Defender blog series By March 31, 2027, all Microsoft Sentinel customers transition to Defender. This six-part series guides you through moving your Sentinel experience from the Azure portal to Defender, where SIEM, XDR, threat intelligence, AI, and automation come together in one experience. Your analytics rules, playbooks, workbooks, log analytics workspace, and access assignments all carry forward while the operational layer becomes more connected and intelligent. Starting early matters because you realize the benefits sooner, including a unified incident queue, cross-product correlation, Security Copilot, Sentinel data lake, and SOC optimization. Across the six-part blog series you get 1) the strategic shift, 2) the anatomy of incident and data changes, 3) detection and automation, 4) the governance shift across roles and access, 5) a readiness playbook with the adoption helper and cost guidance, and 6) a look at the AI-first SOC. Each part stands alone, so you can read in order or jump to what matters most to you. Sentinel data lake Agent Identities Asset Connector [Public preview] The Agent Identities Asset Connector brings identity context for AI agents into Sentinel. Activity connectors like Agent 365 and Microsoft 365 Copilot already show you what AI agents do, but activity alone cannot tell you who owns an agent, what permissions it holds, or how it is governed. This connector fills that gap with four asset tables covering agent owners, agent identities, agent blueprints, and the service principals tied to those blueprints. Together they form a connected agent identity graph you can trace from owner to identity to blueprint to permissions to the resources an agent touches. Joining this asset data with activity data in Sentinel data lake lets you detect anomalous behavior relative to permissions, spot over-permissioned or misconfigured agents, and follow full execution chains for end-to-end traceability. To get started, install the Agent 365 and Microsoft 365 Copilot solutions in Content Hub and enable the asset and activity connectors. Learn more. Sentinel MCP Sentinel MCP graph tools [Public preview] Microsoft Security Graph MCP tools, recently introduced in the Microsoft Sentinel MCP Server data exploration collection helps security teams investigate threats by exploring relationships between identities and device assets, and threat and activity signals ingested by data connectors and surfaced by analytic rules. Starting from an alert, analysts can follow the exposure path across connected entities — tracing lateral movement, understanding blast radius, and identifying configuration gaps — all from a single, interactive workspace. The tool provides a clear graph view that highlights dependencies and makes it easier to understand how content interacts across your environment. This helps security teams assess coverage, optimize content deployment, and identify areas that may need tuning or additional data sources. Executing graph queries via the MCP tools will trigger the graph meter. Learn more. Microsoft Security Store Partner testimonials from Adaquest and Glueckkanja For partners like Adaquest and Glueckkanja, the Microsoft Security Store helps not only put their years of knowledge, understanding, and best practices into a scalable, packaged solution, it gives them the ability to democratize that expertise and take it to market globally. Security Store operationalizes their expertise as always-on defenses — discoverable, deployable, and driving real outcomes inside the tools that security teams rely on every day. See how the Security Store is helping security teams act on threats faster with the right solutions and to be ready when it matters most: Watch: Adaquest unlocks faster response times for customers (testimonial) Watch: Glueckkanja builds agents with purpose (testimonial) Additional resources Blogs and documentation: The Advanced Security Information Model (ASIM) Process Event normalization schema reference How BlueVoyant's ASIM-First Strategy Simplifies Threat Detection in Microsoft Sentinel Migrate Sentinel to Defender – Why It Is a Security Architecture Decision, Not Just a Portal Change Connect Microsoft Sentinel to the Microsoft Defender portal Agent 365 connector: Monitor, hunt, and investigate AI agent activity in Microsoft Sentinel Get started with Microsoft Sentinel MCP server Upcoming webinars and events: July 15–16: Microsoft Virtual Training Day: Predict and Defend Against Cybersecurity Threats July 22: Microsoft Security Immersion Event: Shadow Hunter July 23-24: Microsoft Virtual Training Day: Introduction to Microsoft Security July 28: Tech Brief: Modernize security operations with a unified platform July 29: Security Immersion Event: Into the Breach Stay connected Check back each month for the latest innovations, updates, and events to ensure you’re getting the most out of Microsoft Sentinel. We’ll see you in the next edition!
Unusual user agent found in table AADNonInteractiveUserSignInLogs
Hello, Investigating the registers of the table "AADNonInteractiveUserSignInLogs", I have found a user-agent "Rich Client 4.40.0.0", which investigating via web I have not found information about it, neither I have knowledge of what this user-agent is about. Has anyone seen this in a case related to Azure log-ins? Regards.
Reminder: Next Tuesday 6/23 at 9AM PST we will be hosting an 'Ask Microsoft Anything' session on Tech Community for the Sentinel SIEM Migration Experience!
Join us for a live demo and AMA on the Microsoft Sentinel SIEM migration experience. We’ll show how the experience helps teams move from legacy SIEMs like Splunk and QRadar into Microsoft Sentinel with a more guided, lower-friction path. We’ll cover what it does today, how it works, and the questions customers ask most, then open it up for live Q&A. Link here: Ask Microsoft Anything: The Microsoft Sentinel SIEM Migration Experience Hope to see you there!Campaign-Centric Hunting with Microsoft Defender XDR and Microsoft Sentinel
Phishing investigations usually start with one suspicious email. A user reports a message. An alert is generated. An analyst opens the email details, checks the sender, reviews the URL, and tries to understand whether the message is malicious. That is a normal starting point. However, in a real SOC investigation, one email is rarely the full story. Attackers usually operate in campaigns. They reuse sender infrastructure, similar subjects, URLs, payloads, templates, and delivery techniques. A single email may be only one part of a wider phishing or malware campaign targeting multiple users. This is why campaign-centric hunting is important. I wrote this article from the perspective of a SOC analyst who often needs to move quickly from a single suspicious email to the full campaign impact. The goal is simple: use Microsoft Defender XDR and Microsoft Sentinel together to understand who was targeted, what was delivered, who clicked, and what should be prioritized first. Why Campaign-Centric Hunting When investigating a phishing or malware email, analysts usually need to answer practical questions: How many users received messages from the same campaign? Were the messages blocked, junked, delivered, or remediated? Did any user click the URL? Did anyone click through a Safe Links warning? Were any priority or high-risk users affected? Was the email removed after delivery? Are there related Defender XDR or Sentinel incidents? If we only investigate one message, we may miss the bigger picture. Campaign-centric hunting helps the SOC move from this question: Is this email malicious? To this question: What is the full impact of this campaign? That shift is important because the response priority should be based on campaign impact, not only on a single alert. What Campaign Views Provides Campaign Views in Microsoft Defender for Office 365 help analysts investigate coordinated email attacks such as phishing and malware campaigns. From Campaign Views, analysts can review campaign-level information such as: Campaign name Campaign type Campaign subtype Targeted users Inboxed messages Clicked users Visited links Sender domains Sender IPs Payload URLs Delivery actions Campaign timeline Campaign flow This is useful during triage because it quickly shows whether an email is part of a wider attack. For example, one reported phishing message may look small at first. But if Campaign Views shows that the same campaign targeted 50 users, delivered messages to 15 inboxes, and had 2 users click the URL, the investigation becomes much more urgent. Where CampaignInfo Fits The CampaignInfo table gives analysts a KQL-based way to query campaign-related data. Some useful fields are: Field Purpose CampaignId Unique identifier for the campaign CampaignName Name of the campaign CampaignType Campaign category, such as Phish or Malware CampaignSubtype Additional context, such as brand being phished or malware family NetworkMessageId Unique identifier for the email message RecipientEmailAddress Recipient affected by the campaign Timestamp Time when the event was recorded For correlation, the most important field is usually: NetworkMessageId This field can help connect campaign data with other Defender XDR email tables, including: EmailEvents UrlClickEvents EmailPostDeliveryEvents EmailAttachmentInfo EmailUrlInfo This makes CampaignInfo a useful pivot table for campaign-level hunting. Important note: CampaignInfo is currently documented as Preview. Before using these queries in production analytics rules, validate the table availability, schema, and results in your own tenant. Practical Scenario An analyst receives a phishing alert in Microsoft Defender XDR. The alert is related to a user who received a suspicious email with a credential-harvesting URL. The analyst opens Campaign Views and sees that the message belongs to a wider phishing campaign. At that point, the investigation should not stop with the original user. The analyst should now ask: Who else received this campaign? How many messages were delivered? Which users clicked? Did any users click through the Safe Links warning? Were the messages removed after delivery? Are there related incidents in Microsoft Sentinel? The investigation flow could look like this: Start from Campaign Views in Microsoft Defender XDR. Identify the campaign details. Use CampaignInfo to list affected users and messages. Join with EmailEvents to validate delivery status. Join with UrlClickEvents to identify user interaction. Join with EmailPostDeliveryEvents to confirm remediation. Review related Microsoft XDR incidents in Microsoft Sentinel. Prioritize response based on campaign impact. Query 1: List Recent Campaigns The first query gives a simple overview of recent campaigns. CampaignInfo | where Timestamp > ago(14d) | summarize FirstSeen = min(Timestamp), LastSeen = max(Timestamp), AffectedUsers = dcount(RecipientEmailAddress), Messages = dcount(NetworkMessageId) by CampaignId, CampaignName, CampaignType, CampaignSubtype | order by LastSeen desc This helps analysts quickly identify campaigns that affected the organization during the selected period. Useful questions to ask from this output: Which campaigns are most recent? Which campaigns affected the most users? Are the campaigns phishing, malware, or spam? Is there a specific brand or malware family in the subtype? Are similar campaigns appearing repeatedly? Query 2: Understand Delivery Impact After identifying campaigns, the next step is to understand delivery impact. A campaign that was fully blocked is different from a campaign that reached user inboxes. let Campaigns = CampaignInfo | where Timestamp > ago(14d) | project CampaignId, CampaignName, CampaignType, CampaignSubtype, NetworkMessageId, RecipientEmailAddress; Campaigns | join kind=leftouter ( EmailEvents | where Timestamp > ago(14d) | project NetworkMessageId, RecipientEmailAddress, Subject, SenderFromAddress, SenderFromDomain, SenderIPv4, DeliveryAction, DeliveryLocation, ThreatTypes, DetectionMethods, Timestamp ) on NetworkMessageId, RecipientEmailAddress | summarize Messages = dcount(NetworkMessageId), AffectedUsers = dcount(RecipientEmailAddress), Subjects = make_set(Subject, 5), SenderDomains = make_set(SenderFromDomain, 10), SenderIPs = make_set(SenderIPv4, 10) by CampaignId, CampaignName, CampaignType, CampaignSubtype, DeliveryAction, DeliveryLocation | order by AffectedUsers desc, Messages desc This query helps separate campaigns that were blocked from campaigns that actually reached users. From a SOC perspective, delivered messages deserve closer attention, especially if they reached the inbox. Query 3: Identify Users Who Clicked Campaign URLs Delivery is important, but clicks usually increase the priority of the incident. This query joins campaign data with UrlClickEvents. let Campaigns = CampaignInfo | where Timestamp > ago(14d) | project CampaignId, CampaignName, CampaignType, CampaignSubtype, NetworkMessageId, RecipientEmailAddress; Campaigns | join kind=inner ( UrlClickEvents | where Timestamp > ago(14d) | project NetworkMessageId, AccountUpn, Url, ActionType, IsClickedThrough, ThreatTypes, DetectionMethods, IPAddress, Workload, ClickTime = Timestamp ) on NetworkMessageId | summarize FirstClick = min(ClickTime), LastClick = max(ClickTime), ClickEvents = count(), ClickedUsers = dcount(AccountUpn), ClickThroughUsers = dcountif(AccountUpn, IsClickedThrough == true), ClickedUrls = make_set(Url, 10), SourceIPs = make_set(IPAddress, 10) by CampaignId, CampaignName, CampaignType, CampaignSubtype | order by ClickThroughUsers desc, ClickedUsers desc, LastClick desc This query helps identify campaigns where users interacted with the payload. If a user clicked a phishing URL, the next step should usually include identity-focused investigation, such as reviewing sign-in activity, MFA status, session activity, and possible risky sign-ins. Query 4: Focus on Click-Through Events Safe Links may block access to a malicious site. In some cases, however, a user may continue through a warning page. Those cases should be reviewed carefully. let Campaigns = CampaignInfo | where Timestamp > ago(30d) | project CampaignId, CampaignName, CampaignType, CampaignSubtype, NetworkMessageId, RecipientEmailAddress; Campaigns | join kind=inner ( UrlClickEvents | where Timestamp > ago(30d) | where IsClickedThrough == true | project NetworkMessageId, AccountUpn, Url, ActionType, ThreatTypes, IPAddress, ClickTime = Timestamp ) on NetworkMessageId | project ClickTime, CampaignId, CampaignName, CampaignType, CampaignSubtype, AccountUpn, RecipientEmailAddress, Url, ActionType, ThreatTypes, IPAddress | order by ClickTime desc This is one of the most useful views during incident response. A click-through event does not automatically mean compromise, but it is a strong reason to investigate the user account further. Query 5: Confirm Post-Delivery Remediation A malicious message may be delivered first and removed later by ZAP, AIR, or manual remediation. This query joins CampaignInfo with EmailPostDeliveryEvents. let Campaigns = CampaignInfo | where Timestamp > ago(30d) | project CampaignId, CampaignName, CampaignType, CampaignSubtype, NetworkMessageId, RecipientEmailAddress; Campaigns | join kind=leftouter ( EmailPostDeliveryEvents | where Timestamp > ago(30d) | project NetworkMessageId, RecipientEmailAddress, RemediationTime = Timestamp, Action, ActionType, ActionTrigger, ActionResult, DeliveryLocation, SourceLocation ) on NetworkMessageId, RecipientEmailAddress | summarize RemediatedMessages = dcountif(NetworkMessageId, isnotempty(ActionType)), RemediationTypes = make_set(ActionType, 10), RemediationResults = make_set(ActionResult, 10), LastRemediation = max(RemediationTime) by CampaignId, CampaignName, CampaignType, CampaignSubtype | order by LastRemediation desc This helps answer a very important question: Were the delivered malicious messages actually removed? This is useful for both SOC triage and reporting because it shows not only detection, but also response. Query 6: Campaign Blast Radius Summary The following query combines campaign, delivery, click, and remediation data into one campaign-level view. let TimeRange = 30d; let Campaigns = CampaignInfo | where Timestamp > ago(TimeRange) | project CampaignId, CampaignName, CampaignType, CampaignSubtype, NetworkMessageId, RecipientEmailAddress; let Delivery = EmailEvents | where Timestamp > ago(TimeRange) | summarize DeliveryActions = make_set(DeliveryAction, 10), DeliveryLocations = make_set(DeliveryLocation, 10), DeliveredMessages = dcountif(NetworkMessageId, DeliveryAction =~ "Delivered"), JunkedMessages = dcountif(NetworkMessageId, DeliveryAction =~ "Junked"), BlockedMessages = dcountif(NetworkMessageId, DeliveryAction =~ "Blocked"), Subjects = make_set(Subject, 5), SenderDomains = make_set(SenderFromDomain, 10) by NetworkMessageId, RecipientEmailAddress; let Clicks = UrlClickEvents | where Timestamp > ago(TimeRange) | summarize ClickEvents = count(), ClickThroughEvents = countif(IsClickedThrough == true), FirstClick = min(Timestamp), LastClick = max(Timestamp), ClickedUrls = make_set(Url, 10) by NetworkMessageId; let Remediation = EmailPostDeliveryEvents | where Timestamp > ago(TimeRange) | summarize RemediationActions = make_set(ActionType, 10), LastRemediation = max(Timestamp) by NetworkMessageId, RecipientEmailAddress; Campaigns | join kind=leftouter Delivery on NetworkMessageId, RecipientEmailAddress | join kind=leftouter Clicks on NetworkMessageId | join kind=leftouter Remediation on NetworkMessageId, RecipientEmailAddress | summarize AffectedUsers = dcount(RecipientEmailAddress), Messages = dcount(NetworkMessageId), DeliveredMessages = sum(DeliveredMessages), JunkedMessages = sum(JunkedMessages), BlockedMessages = sum(BlockedMessages), TotalClickEvents = sum(ClickEvents), ClickThroughEvents = sum(ClickThroughEvents), Subjects = make_set(Subjects, 10), SenderDomains = make_set(SenderDomains, 10), ClickedUrls = make_set(ClickedUrls, 10), RemediationActions = make_set(RemediationActions, 10), LastClick = max(LastClick), LastRemediation = max(LastRemediation) by CampaignId, CampaignName, CampaignType, CampaignSubtype | extend SuggestedPriority = case( ClickThroughEvents > 0, "High", TotalClickEvents > 0, "Medium", DeliveredMessages > 0, "Medium", "Low" ) | order by SuggestedPriority asc, AffectedUsers desc, Messages desc This type of query can be useful during hunting sessions, incident review, and campaign reporting. The goal is not only to collect more data. The goal is to help the analyst decide what needs attention first. Correlating Campaign Activity with Microsoft Sentinel When Microsoft Defender XDR is connected to Microsoft Sentinel, incidents and alerts can be synchronized into the Sentinel incident queue. This allows the SOC to correlate campaign-related email activity with other security signals, such as: Suspicious sign-ins Identity alerts Endpoint alerts Cloud app activity OAuth consent activity Data exfiltration attempts Related Microsoft XDR incidents For example, if a user clicked a phishing URL, the SOC can then review whether the same user had suspicious sign-in activity shortly after the click. The following query is a simple starting point for reviewing Microsoft XDR incidents in Microsoft Sentinel. SecurityIncident | where TimeGenerated > ago(30d) | where ProviderName == "Microsoft XDR" | where Title has_any ("phish", "phishing", "email", "malware", "campaign") | summarize Incidents = count(), HighSeverity = countif(Severity == "High"), MediumSeverity = countif(Severity == "Medium"), Closed = countif(Status == "Closed"), Active = countif(Status == "Active") by bin(TimeGenerated, 1d) | order by TimeGenerated desc This query does not replace campaign hunting. It simply helps analysts understand how email-related activity is represented in the Sentinel incident queue. Suggested SOC Workflow A practical campaign-centric workflow could look like this: Step 1: Start from Campaign Views Review campaigns with delivered messages, clicked users, visited links, or high user impact. Step 2: Pivot to KQL Use CampaignInfo to list campaign-related messages and affected recipients. Step 3: Validate Delivery Join with EmailEvents to confirm whether messages were blocked, junked, delivered, or replaced. Step 4: Review User Interaction Join with UrlClickEvents to identify users who clicked URLs or clicked through Safe Links warnings. Step 5: Confirm Remediation Join with EmailPostDeliveryEvents to confirm whether delivered messages were removed after delivery. Step 6: Correlate in Sentinel Review related Microsoft XDR incidents and correlate with identity, endpoint, and cloud activity. Step 7: Decide Response Depending on the impact, the SOC may decide to: Escalate the incident Notify affected users Review user sign-ins Revoke user sessions Reset passwords Block sender domains or URLs Submit false negatives Create a watchlist for related indicators Tune analytics rules or response processes Suggested Priority Logic Not every campaign needs the same level of response. A simple triage model could be: Condition Suggested priority Campaign blocked before delivery Low Campaign delivered to junk Low to Medium Campaign delivered to inbox Medium Campaign delivered to multiple inboxes Medium to High User clicked URL High User clicked through warning High Priority account clicked High Click followed by suspicious sign-in Critical This model should be adapted to each organization’s risk profile and response process. Limitations and Things to Validate Before using this approach in production, validate the following: Defender for Office 365 Plan 2 availability Campaign Views permissions CampaignInfo table availability Defender XDR connector configuration Advanced hunting event streaming Field names in your environment Retention period Data latency Join behavior using NetworkMessageId Whether click events can be joined to email metadata in all cases One important limitation is that some URL click events may not join cleanly with email metadata. For example, clicks from Drafts or Sent Items may not have the same message metadata available for correlation. Also, because CampaignInfo is currently documented as Preview, I would avoid depending on it alone for critical production automation without testing and validation.Microsoft Sentinel data lake FAQ
Microsoft Sentinel data lake (generally available) is a purpose‑built, cloud‑native security data lake. It centralizes all security data in an open format, serving as the foundation for agentic defense, enhanced security insights, and graph-based enrichment. It offers cost‑effective ingestion, long‑term retention, and advanced analytics. In this blog we offer answers to many of the questions we’ve heard from our customers and partners. General questions What is the Microsoft Sentinel data lake? Microsoft has expanded its industry-leading SIEM solution, Microsoft Sentinel, to include a unified, security data lake, designed to help optimize costs, simplify data management, and accelerate the adoption of AI in security operations. This modern data lake serves as the foundation for the Microsoft Sentinel platform. It has a cloud-native architecture and is purpose-built for security—bringing together all security data for greater visibility, deeper security analysis, contextual awareness and agentic defense. It provides affordable, long-term retention, allowing organizations to maintain robust security while effectively managing budgetary requirements. What are the benefits of Sentinel data lake? Microsoft Sentinel data lake is purpose built for security offering flexible analytics, cost management, and deeper security insights. Sentinel data lake: Centralizes security data delta parquet and open format for easy access. This unified data foundation accelerates threat detection, investigation, and response across hybrid and multi-cloud environments. Enables data federation by allowing customers to access data in external sources like Microsoft Fabric, ADLS and Databricks from the data lake. Federated data appears alongside native Sentinel data, enabling correlated hunting, investigation, and custom graph analysis across a broader digital estate. Offers a disaggregated storage and compute pricing model, allowing customers to store massive volumes of security data at a fraction of the cost compared to traditional SIEM solutions. Allows multiple analytics engines like Kusto, Spark, and ML to run on a single data copy, simplifying management, reducing costs, and supporting deeper security analysis. Integrates with GitHub Copilot and VS Code empowering SOC teams to automate enrichment, anomaly detection, and forensic analysis. Supports AI agents via the MCP server, allowing tools like GitHub Copilot to query and automate security tasks. The MCP Server layer brings intelligence to the data, offering Semantic Search, Query Tools, and Custom Analysis capabilities that make it easier to extract insights and automate workflows. Provides streamlined onboarding, intuitive table management, and scalable multi-tenant support, making it ideal for MSSPs and large enterprises. The Sentinel data lake is designed for security workloads, ensuring that processes from ingestion to analytics meet evolving cybersecurity requirements. Is Microsoft Sentinel SIEM going away? No. Microsoft is expanding Sentinel into an AI powered end-to-end security platform that includes SIEM and new platform capabilities - Security data lake, graph-powered analytics and MCP Server. SIEM remains a core component and will be actively developed and supported. Getting started What are the prerequisites for Sentinel data lake? To get started: Connect your Sentinel workspace to Microsoft Defender prior to onboarding to Sentinel data lake. Once in the Defender experience see data lake onboarding documentation for next steps. Note: Sentinel is moving to the Microsoft Defender portal and the Sentinel Azure portal will be retired by March 31, 2027. I am a Sentinel-only customer, and not a Defender customer. Can I use the Sentinel data lake? Yes. You must connect Sentinel to the Defender experience before onboarding to the Sentinel data lake. Microsoft Sentinel is generally available in the Microsoft Defender portal, with or without Microsoft Defender XDR or an E5 license. If you have created a log analytics workspace, enabled it for Sentinel and have the right Microsoft Entra roles (e.g. Global Administrator + Subscription Owner, Security Administrator + Sentinel Contributor), you can enable Sentinel in the Defender portal. For more details on how to connect Sentinel to Defender review these sources: Microsoft Sentinel in the Microsoft Defender portal In what regions is Sentinel data lake available? For supported regions see: Geographical availability and data residency in Microsoft Sentinel | Azure Docs. Is there an expected release date for Microsoft Sentinel data lake in GCC, GCC-H, and DoD? While the exact date is not yet finalized, we plan to expand Sentinel data lake to the US Government environments. . How will URBAC and Entra RBAC work together to manage the data lake given there is no centralized model? Entra RBAC will provide broad access to the data lake (URBAC maps the right permissions to specific Entra role holders: GA/SA/SO/GR/SR). URBAC will become a centralized pane for configuring non-global delegated access to the data lake. For today, you will use this for the “default data lake” workspace. In the future, this will be enabled for non-default Sentinel workspaces as well – meaning all workspaces in the data lake can be managed here for data lake RBAC requirements. Azure RBAC on the Log Analytics (LA) workspace in the data lake is respected through URBAC as well today. If you already hold a built-in role like log analytics reader, you will be able to run interactive queries over the tables in that workspace. Or, if you hold log analytics contributor, you can read and manage table data. For more details see: Roles and permissions in the Microsoft Sentinel platform | Microsoft Learn Data ingestion and storage How do I ingest data into the Sentinel data lake? To ingest data into the Sentinel data lake, you can use existing Sentinel data connectors or custom connectors to bring data from Microsoft and third-party sources. Data can be ingested into the analytics tier or the data lake tier. Data ingested into the analytics tier is automatically mirrored to the lake (at no additional cost). Alternatively, data that is not needed in the analytics tier can be ingested directly into the data lake. Data retention is configured directly in table management, for both analytics retention and data lake storage. Note: Certain tables do not support data lake-only ingestion via either API or data connector UI. See here for more information: Custom log tables. What is Microsoft’s guidance on when to use analytics tier vs. the data lake tier? Sentinel data lake offers flexible, built-in data tiering (analytics and data lake tiers) to effectively meet diverse business use cases and achieve cost optimization goals. Analytics tier: Is ideal for high-performance, real-time, end-to-end detections, enrichments, investigation and interactive dashboards. Typically, high-fidelity data from EDRs, email gateways, identity, SaaS and cloud logs, threat intelligence (TI) should be ingested into the analytics tier. Data in the analytics tier is best monitored proactively with scheduled alerts and scheduled analytics to enable security detections Data in this tier is retained at no cost for up to 90 days by default, extendable to 2 years. A copy of the data in this tier is automatically available in the data lake tier at no extra cost, ensuring a unified copy of security data for both tiers. Data lake tier: Is designed for cost-effective, long-term storage. High-volume logs like NetFlow logs, TLS/SSL certificate logs, firewall logs and proxy logs are best suited for data lake tier. Customers can use these logs for historical analysis, compliance and auditing, incident response (IR), forensics over historical data, build tenant baselines, TI matching and then promote resulting insights into the analytics tier. Customers can run full Kusto queries, Spark Notebooks and scheduled jobs over a single copy of their data in the data lake. Customers can also search, enrich and promote data from the data lake tier to the analytics tier for full analytics. For more details see documentation. What does it mean that a copy of all new analytics tier data will be available in the data lake? When Sentinel data lake is enabled, a copy of all new data ingested into the analytics tier is automatically duplicated into the data lake tier. This means customers don’t need to manually configure or manage this process, every new log or telemetry added to the analytics tier becomes instantly available in the data lake. This allows security teams to run advanced analytics, historical investigations, and machine learning models on a single, unified copy of data in the lake, while still using the analytics tier for real-time SOC workflows. It’s a seamless way to support both operational and long-term use cases—without duplicating effort or cost. What is the guidance for customers using data federation capability in Sentinel data lake? Starting April 1, 2026, federate data from Microsoft Fabric, ADLS, and Azure Databricks into Sentinel data lake. Use data federation when data is exploratory, infrequently accessed, or must remain at source due to governance, compliance, sovereignty, or contractual requirements. Ingest data directly into Sentinel to unlock full SIEM capabilities, always-on detections, advanced automation, and AI‑driven defense at scale. This approach lets security teams start where their data already lives — preserving governance, then progressively ingest data into Sentinel for full security value. Is there any cost for retention in the analytics tier? Analytics ingestion includes 90 days of interactive retention, at no additional cost. Simply set analytics retention to 90 days or less. Analytics retention beyond 90 days will incur a retention cost. Data can be retained longer within the data lake by using the “total retention” setting. This allows you to extend retention within the data lake for up to 12 years. While data is retained within the analytics tier, there is no charge for the mirrored data within the lake. Retaining data in the lake beyond the analytics retention period incurs additional storage costs. See documentation for more details: Manage data tiers and retention in Microsoft Sentinel | Microsoft Learn What is the guidance for Microsoft Sentinel Basic and Auxiliary Logs customers? If you previously enabled Basic or Auxiliary Logs plan in Sentinel: You can view Basic Logs in the Defender portal but manage it from the Log Analytics workspace. To manage it in the Defender portal, you must change the plan from Basic to Analytics. Once the table is transitioned to the analytics tier, if desired, it can then be transitioned to the data lake. Existing Auxiliary Log tables will be available in the data lake tier for use once the Sentinel data lake is enabled. Billing for these tables will automatically switch to the Sentinel data lake meters. Microsoft Sentinel customers are recommended to start planning their data management strategy with the data lake. While Basic and Auxiliary Logs are still available, they are not being enhanced further. Sentinel data lake offers more capabilities at a lower price point. Please plan on onboarding your security data to the Sentinel data lake. Azure Monitor customers can continue to use Basic and Auxiliary Logs for observability scenarios. What happens to customers that already have Archive logs enabled? If a customer has already configured tables for Archive retention, existing retention settings will not change and will be automatically inherited by the Sentinel data lake. All data, including existing data in archive retention will be billed using the data lake storage meter, benefiting from 6x data compression. However, the data itself will not move. Existing data in archive will continue to be accessible through Sentinel search and restore experiences: o Data will not be backfilled into the data lake. o Data will be billed using the data lake storage meter. New data ingested after enabling the data lake: o Will be automatically mirrored to the data lake and accessible through data lake explorer. o Data will be billed using the data lake storage meter. Example: If a customer has 12 months of total retention enabled on a table, 2 months after enabling ingestion into the Sentinel data lake, the customer will still have access to 10 months of archived data (through Sentinel search and restore experiences), but access to only 2 months of data in the data lake (since the data lake was enabled). Key considerations for customers that currently have Archive logs enabled: The existing archive will remain, with new data ingested into the data lake going forward; previously stored archive data will not be backfilled into the lake. Archive logs will continue to be accessible via the Search and Restore tab under Sentinel. If analytics and data lake mode are enabled on table, which is the default setting for analytics tables when Sentinel data lake is enabled, all new data will be ingested into the Sentinel data lake. There will only be one storage meter (which is data lake storage) going forward. Archive will continue to be accessible via Search and Restore. If Sentinel data lake-only mode is enabled on table, new data will be ingested only into the data lake; any data that’s not already in the Sentinel data lake won’t be migrated/backfilled. Only data that was previously ingested under the archive plan will be accessible via Search and Restore. What is the guidance for customers using Azure Data Explorer (ADX) alongside Microsoft Sentinel? Some customers might have set up ADX cluster for their DIY lake setup. Customers can choose to continue using that setup and gradually migrate to Sentinel data lake for new data that they want to manage. The lake explorer will support federation with ADX to enable the customers to migrate gradually and simplify their deployment. What happens to the Defender XDR data after enabling Sentinel data lake? By default, Defender XDR tables are available for querying in advanced hunting, with 30 days of analytics tier retention included with the XDR license. To retain data beyond this period, an explicit change to the retention setting is required, either by extending the analytics tier retention or the total retention period. You can extend the retention period of supported Defender XDR tables beyond 30 days and ingest the data into the analytics tier. For more information see Manage XDR data in Microsoft Sentinel. You can also ingest XDR data directly into the data lake tier. See here for more information. A list of XDR advanced hunting tables supported by Sentinel are documented here: Connect Microsoft Defender XDR data to Microsoft Sentinel | Microsoft Learn. KQL queries and jobs Is KQL and Notebook supported over the Sentinel data lake? Yes, via the data lake KQL query experience along with a fully managed Notebook experience which enables spark-based big data analytics over a single copy of all your security data. Customers can run queries across any time range of data in their Sentinel data lake. In the future, this will be extended to enable SQL query over lake as well. Note: Triggering a KQL job directly via an API or Logic App is not yet supported but is on the roadmap. Why are there two different places to run KQL queries in Sentinel experience? Advanced hunting queries both XDR and analytics tables, with compute cost included. Data lake explorer only queries data in the lake and incurs a separate compute cost. Consolidating advanced hunting and KQL explorer user interfaces is on the roadmap. This will provide security analysts a unified query experience across both analytics and data lake tiers. Where is the output from KQL jobs stored? KQL jobs are written into existing or new custom tables in the analytics tier. Is it possible to run KQL queries on multiple data lake tables? Yes, you can run KQL interactive queries and jobs using operators like join or union. Can KQL queries (either interactive or via KQL jobs) join data across multiple workspaces? Security teams can run multi-workspace KQL queries for broader threat correlation Pricing and billing How does a customer pay for Sentinel data lake? Billing is automatically enabled at the time of onboarding based on Azure Subscription and Resource Group selections. Customers are then charged based on the volume of data ingested, retained, and analyzed (e.g. KQL Queries and Jobs). See Sentinel pricing page for more details. 2. What are the pricing components for Sentinel data lake? Sentinel data lake offers a flexible pricing model designed to optimize security coverage and costs. At a high level, pricing is based on the volume of data ingested/processed, the volume of data retained, and the volume of data processed. For specific meter definitions, see documentation. 3. How does the business model for Sentinel SIEM change with the introduction of the data lake? There is no change to existing Sentinel analytics tier ingestion business model. Sentinel data lake has separate meters for ingestion, storage and analytics. 4. What happens to the existing Sentinel SIEM and related Azure Monitor billing meters when a customer onboards to Sentinel data lake? When a customer onboards to the Sentinel data lake, nothing changes with analytic ingestion or retention. Customers using data archive and Auxiliary Logs will automatically transition to the new data lake meters. How does data lake storage affect cost efficiency for high volume data retention? Sentinel data lake offers cost-effective, long-term storage with uniform data compression of 6:1 across all data sources, applicable only to data lake storage. Example: For 600GB of data stored, you are only billed for 100GB compressed data. This approach allows organizations to retain greater volumes of security data over extended periods cost-effectively, thereby reducing security risks without compromising their overall security posture. here How “Data Processing” billed? To support the ingestion and standardization of diverse data sources, the Data Processing feature applies a $0.10 per GB (US East) charge for all data ingested into the data lake. This feature enables a broad array of transformations like redaction, splitting, filtering and normalization. The data processing charge is applied per GB of uncompressed data Note: For regional pricing, please refer to the “Data processing” meter within the Microsoft Sentinel Pricing official documentation. Does “Data processing” meter apply to analytics tier data mirrored in the data lake? No. Data processing charge will not be applied to mirrored data. Data mirrored from the analytic tier is not subject to either data ingestion or processing charges. How is retention billed for tables that use data lake-only ingestion & retention? Sentinel data lake decouples ingestion, storage, and analytics meters. Customers have the flexibility to pay based on how data is retained and used. For tables that use data lake‑only ingestion, there is no included free retention—unlike the analytics tier, which includes 90 days of analytics retention. Retention charges begin immediately once data is stored in the data lake. Data lake storage billing is based on compressed data size rather than raw ingested volume, which significantly reduces storage costs and delivers lower overall retention spend for customers. Does data federation incur charges? Data federation does not generate any ingestion or storage fees in Sentinel data lake. Customers are billed only when they run analytics or queries on federated data, with charges based on Sentinel data lake compute and analytics meters. This means customers pay solely for actual data usage, not mere connectivity. How do I understand Sentinel data lake costs? Sentinel data lake costs driven by three primary factors: how much data is ingested, how long that data is retained, and how the data is used. Customers can flexibly choose to ingest data into the analytics tier or data lake tier, and these architectural choices directly impact cost. For example, data can be ingested into the analytics tier—where commitment tiers help optimize costs for high data volumes—or ingested data directly into the Sentinel data lake for lower‑cost ingestion, storage, and on‑demand analysis. Customers are encouraged to work with their Microsoft account team to obtain an accurate cost estimate tailored to their environment. See Sentinel pricing page to understand Sentinel pricing. How do I manage Sentinel data lake costs? Built-in cost management experiences help customers with cost predictability, billing transparency, and operational efficiency. Reports provide customers with insights into usage trends over time, enabling them to identify cost drivers and optimize data retention and processing strategies. Set usage-based alerts on specific meters to monitor and control costs. For example, receive alerts when query or notebook usage passes set limits, helping avoid unexpected expenses and manage budgets. See our Sentinel cost management documentation to learn more. If I’m an Auxiliary Logs customer, how will onboarding to the Sentinel data lake affect my billing? Once a workspace is onboarded to Sentinel data lake, all Auxiliary Logs meters will be replaced by new data lake meters. Do we charge for data lake ingestion and storage for graph experiences? Microsoft Sentinel graph-based experiences are included as part of the existing Defender and Purview licenses. However, Sentinel graph requires Sentinel data lake and specific data sources to build the underlying graph. Enabling these data sources will incur ingestion and data lake storage costs. Note: For Sentinel SIEM customers, most required data sources are free for analytics ingestion. Non-entitled sources such as Microsoft Entra ID logs will incur ingestion and data lake storage costs. How is Entra asset data and ARG data billed? Data lake ingestion charges of $0.05 per GB (US EAST) will apply to Entra asset data and ARG data. Note: This was previously not billed during public preview and is billed since data lake GA. To learn more, see: https://learn.microsoft.com/azure/sentinel/datalake/enable-data-connectors When a customer activates Sentinel data lake, what happens to tables with archive logs enabled? To simplify billing, once the data lake is enabled, all archive data will be billed using the data lake storage meter. This provides consistent long-term retention billing and includes automatic 6x data compression. For most customers, this change results in lower long‑term retention costs. However, customers who previously had discounted archive retention pricing will not automatically receive the same discounts on the new data lake storage meters. In these cases, customers should engage their Microsoft account team to review pricing implications before enabling the Sentinel data lake. Thank you Thank you to our customers and partners for your continued trust and collaboration. Your feedback drives our innovation, and we’re excited to keep evolving Microsoft Sentinel to meet your security needs. If you have any questions, please don’t hesitate to reach out—we’re here to support you every step of the way. Learn more: Get started with Sentinel data lake today: https://aka.ms/Get_started/Sentinel_datalake Microsoft Sentinel AI-ready platform: https://aka.ms/Microsoft_Sentinel Sentinel data lake videos: https://aka.ms/Sentineldatalake_videos Latest innovations and updates on Sentinel: https://aka.ms/msftsentinelblog Sentinel pricing page: https://aka.ms/MicrosoftSentinel_PricingSentinel Foundry - MCP Server (Preview) (Github Community Release)
I’ve been cooking something that a lot of people in SOC have been struggling with — especially on the engineering side of Microsoft Sentinel. Thanks to the Microsoft Security team for shaping the capabilities of Sentinel even better with Sentinel Data Lake & Modern SecOps. Today’s the day I can finally share it. Note: This is not an official Microsoft product, but it is designed to make the Sentinel Build even better (complement) with much more intelligence. 🚀 Sentinel Foundry is now in public preview with 43 tools. (Sentinel Foundry - MCP Server) It’s an MCP server built to act like the brain of a strong Sentinel engineer — helping make building, improving, and operating Sentinel far more practical, faster, and honestly more enjoyable. For a lot of teams, the challenge is not understanding what Sentinel can do. The hard part is the engineering work around it: -> Deciding what data should actually be ingested -> Building a clean, scalable Sentinel foundation -> Writing useful detections instead of noisy ones -> Balancing security value with cost -> Turning ideas into deployable engineering outputs That is exactly why I built Sentinel Foundry to help communities grow stronger. It helps with the real engineering tasks behind Sentinel — from architecture thinking to detection design, deployment planning, ingestion strategy, automation ideas, and many of the workflows outlined in the GitHub project. How does it work? Here’s one of the flagship prompts I ran with it: “Give me a complete security posture report for our workspace. Score each pillar and tell me what to prioritise.” And within seconds, it produced a structured engineering blueprint that would normally take a lot longer to pull together manually. You can see the example prompts here in what it can do: https://github.com/prabhukiranveesam/Sentinel-Foundry#what-can-it-do I want building Sentinel to feel less like repetitive engineering overhead — and more like real security engineering that is fast, creative, and enjoyable. If you work with Sentinel as a SOC L2 analyst, engineer, detection engineer, consultant, or architect, I’d genuinely love for you to try it and tell me what you think. 🔗 Public Preview: https://github.com/prabhukiranveesam/Sentinel-Foundry This is just the start of an AI era — and I’m excited to keep shaping it with more powerful features over the coming days. This is very easy to set up and will be available to all of you at no cost during this month as part of the public preview, and your feedback is extremely valuable to shape this as a powerful solution.
