Sentinel - Defender XDR KQL Queries Library
Hello all, I’ve been building something over the past few weeks that I think the security community might find useful. https://goxdr.fyi is a searchable KQL query library for Microsoft Sentinel and Defender XDR. The name comes from a nickname my colleagues gave me (GoX) combined with XDR. I also picked up https://goxdr.fyi as a short and easy to remember domain for it. You can check it out here: https://goxdr.fyi The idea came from my own day to day work as someone working in IAM and SOC operations. I constantly find myself writing and refining KQL queries for threat hunting, detection engineering and incident investigation. Over time I realized I had a growing collection of queries that I kept going back to and I thought why not make these available to others? It currently has 117 queries covering identity security, BEC/AiTM detection, NTLM and LDAP attack hunting, OAuth governance, AI/Copilot security, Sentinel alert trending, SOC performance metrics and more. Some of these queries are ones I wrote from scratch based on real scenarios I encountered in production environments. Others are community queries I tested and validated in my own setup. Only the ones I found genuinely useful and that actually worked against real data made it in. Each query comes with a description explaining what it detects and why it matters, along with severity levels, platform tags (Sentinel, XDR or both) and a copy button so you can paste it directly into Advanced Hunting or use it as the basis for an Analytics Rule. The site is open source, hosted on GitHub Pages and licensed under CC BY 4.0. No sign-up, no paywall, no tracking. The source is available. I’ll keep adding queries as new scenarios come up. If there’s enough interest I’m also considering adding Cortex XQL queries for Palo Alto environments. Suggestions, feedback or ideas for new detections are always welcome. Feel free to reach out. Thanks
Defender of XDR - Quarantine - Lack of filter/search options
Hi Microsoft, I love what you're doing with the Defender XDR portal, but could you please show some love to the Quarantine section soon? On a daily basis, I have to review emails caught in quarantine for false positives, and the lack of search and filtering options is appalling. As a company based in Denmark, 99% of legitimate emails come from .dk domains. Yet there is no way to search for or filter on something this simple. If I type .dk into the search box, I get 0 results, even though I can clearly see .dk sender addresses on the page. The filter options only allow me to enter full sender or recipient email addresses, which is of course almost useless in a quarantine-review context. Some examples of filters that would be extremely useful: Sender domain ends with .dk Sender domain contains .dk URL domain filtering Attachment name filtering Saved filter views More flexible search across message properties The Quarantine experience could be made dramatically better with relatively little effort. So please, pretty please, give the Quarantine portal some attention. It's often the part of Defender that security teams interact with every single day.
Microsoft Defender will not let me log in on Windows 11
I have a subscription to the Personal Microsoft 365 plan which includes Microsoft Defender. When I try logging into Microsoft Defender on my Windows PC, I receive an error message stating "Couldn't sign in to Microsoft Defender. Something went wrong-please try again later". I have been having this issue for several months now. I have recently contacted Microsoft Tech support who just directed me to this community. The tech support representative mentioned that others may have experienced similar issues as mine. I would appreciate if anyone could advise. My PC is running Windows 11 and is up to date on updates. All of my 365 applications are also up to date. I have also tried running the repair tool on Microsoft Defender in addition to uninstalling and reinstalling the application. The tech support representative mentioned something to me about the issue could be because I am using a personal email account for my login for Microsoft Defender. I did not fully understand why that would be the issue. I would like to note that I have no issue logging into Microsoft Defender on my Android phone. The problem appears to only occur on my PC and only for the Microsoft Defender app. All other apps that come with my 365 subscription use the same login and appear to be working fine.Feature Request: Manual Invocation Mode for Embedded Security Copilot Experiences to reduce cost !
Hello, I see that Copilot for Security in XDR dashboards , if used in embedded mode (I mean whenever you are opening a case to investigate) you get AUTOMATICALLY a summary of the incident , and you are consuming SCU costs. I want a way either globally as a tenant option, or through a pwsh to be able to DISABLE this, or be able to PRESS the AI button AND THEN generate the AI reply (and consume SCU credits ..) Current behavior: Open Incident --> Copilot automatically generates Incident Summary --> SCUs ARE consumed Desired behavior: Open Incident --> No AI execution --> Click "Generate Summary" MANUALLY --> SCUs consumed I am not talking about RBAC controls to assign WHO of my admins can use Security Copilot, I have set that, BUT I want my admin to decide IF they want AI to help them (and consume - pay for that SCU credits-costs) OR NOT !! At the moment I havent found any solution, except to educate my admin to press CANCEL the moment he/she opens such an XDR dashboard ! :) Does anyone knows something ? Regards, PanosCampaign-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.Operational Notes on Microsoft Security Copilot Agents in Defender XDR and Microsoft Entra ID
Microsoft Security Copilot is now becoming more visible inside day-to-day security operations, especially through embedded experiences and agent-based workflows across Microsoft Defender XDR, Microsoft Entra ID, Microsoft Intune, and Microsoft Purview. Instead of looking at Security Copilot only as a standalone prompt interface, SOC and identity teams should also understand how Security Copilot agents are deployed, how they consume Security Compute Units, how they appear in operational workflows, and where activity can be monitored. This post summarizes practical observations from a security operations perspective, with a focus on Microsoft Defender XDR, Microsoft Entra ID, usage monitoring, and KQL-based activity review. Licensing & Capacity Units Requirements Requires eligible Microsoft security licensing, typically: Microsoft 365 E5 Microsoft 365 E7 Security Compute Units (SCUs) Security Copilot capacity is measured using Security Compute Units (SCUs). SCUs are billed based on provisioned capacity. Indicative pricing: $4 per Provisionied SCU/hour $6 per Overage SCU/hour Billing is calculated hourly, based on the amount of SCUs provisioned. Included Capacity Organizations with: 1,000 Microsoft 365 E5 licenses Receive: 400 included SCUs Included SCUs are shared across the tenant within a common capacity pool. Scaling SCU capacity can be scaled dynamically based on operational requirements and workload demand. Data Retention Security Copilot session and interaction data without active SCU-backed retention is typically retained for: 90 days Security Copilot Agents - Microsoft Defender This section outlines the Microsoft Security Copilot agents currently available in the Microsoft Defender portal. NameKey characteristics Security Alert Triage Agent (Preview) Manual setup from Defender portal Automatically creates Unified RBAC custom role Runs automatically when a user reports a suspicious email or when a new supported alert is generated, supported alert sources: MDI, MDC, MDO If an alert tuning rule is enabled, it will be automatically disabled when the agent is deployed. Creates and connects with agentic user account: Phishing Triage Agent (Security Copilot) Automatic alert assignment to SecurityCopilotAgentUser-db16fec3-f1fb-4632-843e-46d07408c584@<tenant-domain>Alert was assigned to Phishing Triage Agent (Security Copilot). Adds Tag Agent to the created Incidents Threat Hunting Agent Manual setup from Defender portal Automatically creates Unified RBAC custom role This agent runs manually. There isn't an automatic trigger. Creates and connects with agentic user account: Threat Hunting Agent (Security Copilot) Analyst Questions in natural language Generates and executed KQL queries in Advanced hunting Provides charts, dynamic follow-up questions and remediation actions recommendations No activity is identified from agent's identity during agent execution Threat Intelligence Briefing Agent Manual setup from Defender portal Provides automated TI briefing summary Configured from https://security.microsoft.com/securitysettings/defender/agent_configuration-threatintelligencebriefingagent Security Analyst Agent Manual setup from Defender portal Dynamic Threat Detection Agent (Preview) Automatically enabled always-on, runs continuously in the background Correlates: Alerts, Security events, Behavioral anomalies, TI signals Generates Alerts with Detection Source: Security Copilot The Alerts can be correlated with existing Multi-Stage Incidents No agentic user account identity is used by this agent Available free of charge during public preview, will begin consuming Security Compute Units (SCUs) once generally available (GA) Incidents handled by Security Alert Triage Agent: Alerts created by Dynamic Threat Detection Agent: Execution of Threat Hunting Agent: View agents in use: https://security.microsoft.com/security-copilot/agents View Unified RBAC custom roles: https://security.microsoft.com/mtp_roles View Security Copilot user identities in Microsoft Entra ID: Notes: CloudAppEvents activity logs only from the following agents: Phishing Triage Agent Conditional Access Optimization Agent Security Copilot Agents - Microsoft Entra ID Conditional Access Optimization Agent Usage Monitoring Sign-in to Security Copilot portal using Global Admin account and navigate to the following location: https://securitycopilot.microsoft.com/usage-monitoring Reference: https://learn.microsoft.com/en-us/copilot/security/manage-usage Logging Activity Copilot Agents Management: CloudAppEvents | where ActionType contains "CopilotAgent" | extend AgentName = RawEventData.AgentName | extend Workload = RawEventData.Workload | extend ResultStatus = RawEventData.ResultStatus | project TimeGenerated, ActionType, ResultStatus, AgentName, Application, Workload All Copilot Workload data: CloudAppEvents | extend Workload = RawEventData.Workload | where Workload == "Copilot" | summarize EventCount = count() by ActionType, AccountDisplayName
Defender XDR - how to grant "undo action" Permissions on File Quarantine?
Dear Defender XDR Community I have a question regarding the permissions to "undo action" on a file quarantine action in the action center. We have six locations, each location manages their own devices. We have created six device groups so that Accounts from Location 1 can only manage/see devices from Location 1 as well. Then we created a custom "Microsoft Defender XDR" Role with the following permissions. This way the admins from location 1 can manage all Defender for Endpoint Devices / incidents / recommendations etc. without touching devices they aren't managing.. very cool actually! BUT - if a file gets quarantined, it might want to be released again because of false positive etc. I can do that as a global admin, but not as an admin with granularly assigned rights - the option just isnt there.. I don't want to give them admins a more privileged role because of - you know - least privileges. but i don't have the option to allow "undo action" on file quarantine events, besides that being a critical feature for them to manage their own devices and not me having to de-quarantine files i dont care about.. Any thoughts on how to give users this permission?
Understand New Sentinel Pricing Model with Sentinel Data Lake Tier
Introduction on Sentinel and its New Pricing Model Microsoft Sentinel is a cloud-native Security Information and Event Management (SIEM) and Security Orchestration, Automation, and Response (SOAR) platform that collects, analyzes, and correlates security data from across your environment to detect threats and automate response. Traditionally, Sentinel stored all ingested data in the Analytics tier (Log Analytics workspace), which is powerful but expensive for high-volume logs. To reduce cost and enable customers to retain all security data without compromise, Microsoft introduced a new dual-tier pricing model consisting of the Analytics tier and the Data Lake tier. The Analytics tier continues to support fast, real-time querying and analytics for core security scenarios, while the new Data Lake tier provides very low-cost storage for long-term retention and high-volume datasets. Customers can now choose where each data type lands—analytics for high-value detections and investigations, and data lake for large or archival types—allowing organizations to significantly lower cost while still retaining all their security data for analytics, compliance, and hunting. Please flow diagram depicts new sentinel pricing model: Now let's understand this new pricing model with below scenarios: Scenario 1A (PAY GO) Scenario 1B (Usage Commitment) Scenario 2 (Data Lake Tier Only) Scenario 1A (PAY GO) Requirement Suppose you need to ingest 10 GB of data per day, and you must retain that data for 2 years. However, you will only frequently use, query, and analyze the data for the first 6 months. Solution To optimize cost, you can ingest the data into the Analytics tier and retain it there for the first 6 months, where active querying and investigation happen. After that period, the remaining 18 months of retention can be shifted to the Data Lake tier, which provides low-cost storage for compliance and auditing needs. But you will be charged separately for data lake tier querying and analytics which depicted as Compute (D) in pricing flow diagram. Pricing Flow / Notes The first 10 GB/day ingested into the Analytics tier is free for 31 days under the Analytics logs plan. All data ingested into the Analytics tier is automatically mirrored to the Data Lake tier at no additional ingestion or retention cost. For the first 6 months, you pay only for Analytics tier ingestion and retention, excluding any free capacity. For the next 18 months, you pay only for Data Lake tier retention, which is significantly cheaper. Azure Pricing Calculator Equivalent Assuming no data is queried or analyzed during the 18-month Data Lake tier retention period: Although the Analytics tier retention is set to 6 months, the first 3 months of retention fall under the free retention limit, so retention charges apply only for the remaining 3 months of the analytics retention window. Azure pricing calculator will adjust accordingly. Scenario 1B (Usage Commitment) Now, suppose you are ingesting 100 GB per day. If you follow the same pay-as-you-go pricing model described above, your estimated cost would be approximately $15,204 per month. However, you can reduce this cost by choosing a Commitment Tier, where Analytics tier ingestion is billed at a discounted rate. Note that the discount applies only to Analytics tier ingestion—it does not apply to Analytics tier retention costs or to any Data Lake tier–related charges. Please refer to the pricing flow and the equivalent pricing calculator results shown below. Monthly cost savings: $15,204 – $11,184 = $4,020 per month Now the question is: What happens if your usage reaches 150 GB per day? Will the additional 50 GB be billed at the Pay-As-You-Go rate? No. The entire 150 GB/day will still be billed at the discounted rate associated with the 100 GB/day commitment tier bucket. Azure Pricing Calculator Equivalent (100 GB/ Day) Azure Pricing Calculator Equivalent (150 GB/ Day) Scenario 2 (Data Lake Tier Only) Requirement Suppose you need to store certain audit or compliance logs amounting to 10 GB per day. These logs are not used for querying, analytics, or investigations on a regular basis, but must be retained for 2 years as per your organization’s compliance or forensic policies. Solution Since these logs are not actively analyzed, you should avoid ingesting them into the Analytics tier, which is more expensive and optimized for active querying. Instead, send them directly to the Data Lake tier, where they can be retained cost-effectively for future audit, compliance, or forensic needs. Pricing Flow Because the data is ingested directly into the Data Lake tier, you pay both ingestion and retention costs there for the entire 2-year period. If, at any point in the future, you need to perform advanced analytics, querying, or search, you will incur additional compute charges, based on actual usage. Even with occasional compute charges, the cost remains significantly lower than storing the same data in the Analytics tier. Realized Savings Scenario Cost per Month Scenario 1: 10 GB/day in Analytics tier $1,520.40 Scenario 2: 10 GB/day directly into Data Lake tier $202.20 (without compute) $257.20 (with sample compute price) Savings with no compute activity: $1,520.40 – $202.20 = $1,318.20 per month Savings with some compute activity (sample value): $1,520.40 – $257.20 = $1,263.20 per month Azure calculator equivalent without compute Azure calculator equivalent with Sample Compute Conclusion The combination of the Analytics tier and the Data Lake tier in Microsoft Sentinel enables organizations to optimize cost based on how their security data is used. High-value logs that require frequent querying, real-time analytics, and investigation can be stored in the Analytics tier, which provides powerful search performance and built-in detection capabilities. At the same time, large-volume or infrequently accessed logs—such as audit, compliance, or long-term retention data—can be directed to the Data Lake tier, which offers dramatically lower storage and ingestion costs. Because all Analytics tier data is automatically mirrored to the Data Lake tier at no extra cost, customers can use the Analytics tier only for the period they actively query data, and rely on the Data Lake tier for the remaining retention. This tiered model allows different scenarios—active investigation, archival storage, compliance retention, or large-scale telemetry ingestion—to be handled at the most cost-effective layer, ultimately delivering substantial savings without sacrificing visibility, retention, or future analytical capabilities.Welcome to the Microsoft Security Community!
We have moved! Registering for webinars is now easier than ever—you can add any session directly to your calendar with a single click using the link below. Please visit: https://securitycommunity.microsoft.com/VirtualEvents/ to sign up for future webinars!What’s new in Microsoft Defender XDR at Secure 2025
Protecting your organization against cybersecurity threats is more challenging than ever before. As part of our 2025 Microsoft Secure cybersecurity conference announcements, we’re sharing new product features that spotlight our AI-first, end-to-end security innovations designed to help - including autonomous AI agents in the Security Operations Center (SOC), as well as automatic detection and response capabilities. We also share information on how you can expand your protection by bringing data security and collaboration tools closer to the SOC. Read on to learn more about how these capabilities can help your organization stay ahead of today’s advanced threat actors. Expanding AI-Driven Capabilities for Smarter SOC Operations Introducing Microsoft Security Copilot’s Security Alert Triage Agent (previously named Phishing Triage Agent) Today, we are excited to introduce Security Copilot agents, a major step in bringing AI-driven automation to Microsoft Security solutions. As part of this, we’re unveiling our newest innovation in Microsoft Defender: the Security Alert Triage Agent. Acting as a force multiplier for SOC analysts, it streamlines the triage of user-submitted phishing incidents by autonomously identifying and resolving false positives, typically cleaning out over 95% of submissions. This allows teams to focus on the remaining incidents – those that pose the most critical threats. Phishing submissions are among the highest-volume alerts that security teams handle daily, and our data shows that at least 9 in 10 reported emails turn out to be harmless bulk mail or spam. As a result, security teams must sift through hundreds of these incidents weekly, often spending up to 30 minutes per case determining whether it represents a real threat. This manual triage effort not only adds operational strain but also delays the response to actual phishing attacks, potentially impacting protection levels. The Security Alert Triage Agent transforms this process by leveraging advanced LLM-driven analysis to conduct sophisticated assessments –such as examining the semantic content of emails– to autonomously determine whether an incident is a genuine phishing attempt or a false alarm. By intelligently cutting through the noise, the agent alleviates the burden on SOC teams, allowing them to focus on high-priority threats. Figure 1. A phishing incident triaged by the Security Copilot Security Alert Triage Agent To help analysts gain trust in its decision-making, the agent provides natural language explanations for its classifications, along with a visual representation of its reasoning process. This transparency enables security teams to understand why an incident was classified in a certain way, making it easier to validate verdicts. Analysts can also provide feedback in plain language, allowing the agent to learn from these interactions, refine its accuracy, and adapt to the organization’s unique threat landscape. Over time, this continuous feedback loop fine-tunes the agent’s behavior, aligning it more closely with organizational nuances and reducing the need for manual verification. The Security Copilot Security Alert Triage Agent is designed to transform SOC operations with autonomous, AI-driven capabilities. As phishing threats grow increasingly sophisticated and SOC analysts face mounting demands, this agent alleviates the burden of repetitive tasks, allowing teams to shift their focus to proactive security measures that strengthen the organization’s overall defense. Note: The Phishing Triage Agent has since been expanded and is now called the Security Alert Triage Agent. Learn more at aka.ms/SATA Security Copilot Enriched Incident Summaries and Suggested Prompts Security Copilot Incident Summaries in Microsoft Defender now feature key enrichments, including related threat intelligence and asset risk –enhancements driven by customer feedback. Additionally, we are introducing suggested prompts following incident summaries, giving analysts quick access to common follow-up questions for deeper context on devices, users, threat intelligence, and more. This marks a step towards a more interactive experience, moving beyond predefined inputs to a more dynamic, conversational workflow. Read more about Microsoft Security Copilot agent announcements here. New protection across Microsoft Defender XDR workloads To strengthen core protection across Microsoft Defender XDR workloads, we're introducing new capabilities while building upon existing integrations for enhanced protection. This ensures a more comprehensive and seamless defense against evolving threats. Introducing collaboration security for Microsoft Teams Email remains a prevalent entry point for attackers. But the fast adoption of collaboration tools like Microsoft Teams has opened new attack surfaces for cybercriminals. Our advancements within Defender for Office 365 allow organizations to continue to protect users in Microsoft Teams against phishing and other emerging cyberthreats with inline protection against malicious URLs, safe attachments, brand impersonation protection, and more. And to ensure seamless investigation and response at the incident level, everything is centralized across our SOC workflows in the unified security operations platform. Read the announcement here. Introducing Microsoft Purview Data Security Investigations for the SOC Understanding the extent of the data that has been impacted to better prioritize incidents has been a challenge for security teams. As data remains the main target for attackers it’s critical to dismantle silos between security and data security teams to enhance response times. At Microsoft, we’ve made significant investments in bringing SOC and data security teams closer together by integrating Microsoft Defender XDR and Microsoft Purview. We are continuing to build upon the rich set of capabilities and today, we are excited to announce that Microsoft Purview Data Security Investigations (DSI) can be initiated from the incident graph in Defender XDR. Ensuring robust data security within the SOC has always been important, as it helps protect sensitive information from breaches and unauthorized access. Data Security Investigations significantly accelerates the process of analyzing incident related data such as emails, files, and messages. With AI-powered deep content analysis, DSI reveals the key security and sensitive data risks. This integration allows analysts to further analyze the data involved in the incident, learn which data is at risk of compromise, and take action to respond and mitigate the incident faster, to keep the organization’s data protected. Read the announcement here. Figure 2. An incident that shows the ability to launch a data security investigation. OAuth app insights are now available in Exposure Management In recent years, we’ve witnessed a substantial surge in attackers exploiting OAuth applications to gain access to critical data in business applications like Microsoft Teams, SharePoint, and Outlook. To address this threat, Microsoft Defender for Cloud Apps is now integrating OAuth apps and their connections into Microsoft Security Exposure Management, enhancing both attack path and attack surface map experiences. Additionally, we are introducing a unified application inventory to consolidate all app interactions into a single location. This will address the following use cases: Visualize and remediate attack paths that attackers could potentially exploit using high-privilege OAuth apps to access M365 SaaS applications or sensitive Azure resources. Investigate OAuth applications and their connections to the broader ecosystem in Attack Surface Map and Advanced Hunting. Explore OAuth application characteristics and actionable insights to reduce risk from our new unified application inventory. Figure 3. An attack path infused with OAuth app insights Read the latest announcement here AI & TI are critical for effective detection & response To effectively combat emerging threats, AI has become critical in enabling faster detection and response. By combining this with the latest threat analytics, security teams can quickly pinpoint emerging risks and respond in real-time, providing organizations with proactive protection against sophisticated attacks. Disrupt more attacks with automatic attack disruption In this era of multi-stage, multi-domain attacks, the SOC need solutions that enable both speed and scale when responding to threats. That’s where automatic attack disruption comes in—a self-defense capability that dynamically pivots to anticipate and block an attacker’s next move using multi-domain signals, the latest TI, and AI models. We’ve made significant advancements in attack disruption, such as threat intelligence-based disruption announced at Ignite, expansion to OAuth apps, and more. Today, we are thrilled to share our next innovation in attack disruption—the ability to disrupt more attacks through a self-learning architecture that enables much earlier and much broader disruption. At its core, this technology monitors a vast array of signals, ranging from raw telemetry data to alerts and incidents across Extended Detection and Response (XDR) and Security Information and Event Management (SIEM) systems. This extensive range of data sources provides an unparalleled view of your security environment, helping to ensure potential threats do not go unnoticed. What sets this innovation apart is its ability learn from historical events and previously seen attack types to identify and disrupt new attacks. By recognizing similar patterns across data and stitching them together into a contextual sequence, it processes information through machine learning models and enables disruption to stop the attack much earlier in the attack sequence, stopping significantly more attacks in volume and variety. Comprehensive Threat Analytics are now available across all Threat Intelligence reports Organizations can now leverage the full suite of Threat Analytics features (related incidents, impacted assets, endpoints exposure, recommended actions) on all Microsoft Threat Intelligence reports. Previously only available for a limited set of threats, these features are now available for all threats Microsoft has published in Microsoft Defender Threat Intelligence (MDTI), offering comprehensive insights and actionable intelligence to help you ensure your security measures are robust and responsive. Some of these key features include: IOCs with historical hunting: Access IOCs after expiration to investigate past threats and aid in remediation and proactive hunting. MITRE TTPs: Build detections based on threat techniques, going beyond IOCs to block and alert on specific tactics. Targeted Industries: Filter threats by industry, aligning security efforts with sector-specific challenges. We’re proud of our new AI-first innovations that strengthen security protections for our customers and help us further our pledge to customers and our community to prioritize cyber safety above all else. Learn more about the innovations designed to help your organization protect data, defend against cyber threats, and stay compliant. Join Microsoft leaders online at Microsoft Secure on April 9. We hope you’ll also join us in San Francisco from April 27th-May 1 st 2025 at the RSA Conference 2025 to learn more. At the conference, we’ll share live, hands-on demos and theatre sessions all week at the Microsoft booth at Moscone Center. Secure your spot today.
