Agent 365 connector: Monitor, hunt, and investigate AI agent activity in Microsoft Sentinel
As enterprises scale the use of AI agents, SOC teams need visibility into AI agent behavior. The Agent 365 connector, now in public preview, streams rich agent telemetry from Agent 365 into Microsoft Sentinel data lake. Agent activity, such as agent data exposure or access drift, is surfaced alongside other security data, giving SOC teams a unified view across digital environments. AI Agent actions are correlated with agent identity, endpoint, and cloud signals, enabling analysts to run end‑to‑end investigations using KQL, graph, and MCP-powered workflows. Why this matters for organizations By centralizing security and AI agent telemetry in Sentinel data lake, organizations establish a unified control plane for securing AI agents. This enables security teams to analyze agent activity in context with broader signals and investigate using familiar Sentinel tools. This unlocks the ability for SOCs to detect risky or anomalous agent behavior early, understand impact quickly, and respond with speed and confidence. As AI agents take on real operational responsibility, this level of visibility is critical to prevent blind spots, reduce risk, and ensure agents operate safely at enterprise scale. End‑to‑end visibility into AI agent behavior: A centralized view of AI agent behavior allows AI agents to be treated as first-class entities alongside users, identities, endpoints, and workloads. Advanced hunting with KQL: Hunt using KQL to proactively uncover unusual AI agent execution patterns, sensitive actions, or activity without clear human context. These hunts help surface potential risk early using the same workflows already used for other security data. Analyzing blast radius and impact with Sentinel graph: Security teams can correlate AI agent activity with identities, endpoints, and cloud resources to understand blast radius and potential impact during an investigation. By pivoting across related entities in Sentinel, analysts can assess how agent actions connect to the broader environment and support deeper, end‑to‑end investigations. Querying agent data through MCP: Use MCP to surface agent observability data through AI assistants, letting analysts pull agent telemetry into investigation workflows alongside other Sentinel data. Agent 365 connector key capabilities Install the Agent 365 connector with a single click using Sentinel Content Hub in the Defender portal. Once enabled, two capabilities come online automatically: Unified agent telemetry across Agent 365 agent experiences: Rich Agent 365 agent telemetry streams into Sentinel data lake, ready to analyze alongside identity, endpoint, and cloud signals using familiar SOC workflows. ASIM unified schema for AI agent observability: Agent 365 agent observability data is normalized into an ASIM-aligned schema so it is consistent, queryable, and ready for analytics and detections. With the connector in place, Sentinel data lake becomes the system of record and the control plane for Agent 365 agent security—turning agent behavior into first-class security signals across SecOps workflows like hunting, investigation, detection engineering, and response. Use cases Prevent sensitive data exposure from misconfigured agents When an AI agent is granted broader access than intended, a crafted prompt could override safeguards and expose confidential data. With agent telemetry, security teams can trace the full execution path—from prompt to tools to data access—to quickly identify the root cause and contain the exposure. Detect and control agent access drift over time As agents take on new tasks, their permissions can expand beyond the original scope, often without clear visibility. Agent telemetry enables continuous behavioral baselining, making it easier to spot abnormal access patterns early and prevent privilege misuse before it escalates. Uncover hidden lateral movement across agent workflows Agents often collaborate and delegate tasks across systems, creating complex chains of execution that are difficult to track. Agent telemetry provides visibility into these interactions, mapping delegation paths and helping teams understand and limit the potential blast radius. Defend against prompt injection and manipulation attacks Attackers can craft prompts to override agent instructions and manipulate behavior. By capturing prompts and reasoning flows, agent telemetry enables detection of these attacks and provides the context needed to investigate and remediate quickly. Accelerate SOC investigations with end-to-end visibility When an agent is involved in a security alert, understanding its actions can be challenging. Agent telemetry correlates prompts, identities, tools, and data access into a unified timeline, giving SOC teams the clarity needed to investigate faster and respond with confidence. Strengthen governance and compliance for AI agents Organizations need visibility into what agents exist and what data they can access. Agent telemetry provides a comprehensive audit trail of agent activity and access patterns, supporting compliance reporting and policy enforcement. Enable proactive threat hunting on agent behavior Security teams need to stay ahead of emerging risks as agent usage grows. Agent telemetry enables advanced hunting across agent activity, helping detect anomalies, uncover patterns, and identify threats before they impact the organization. Get started with Agent 365 connector Getting started is straightforward. In the Microsoft Defender portal, navigate to Microsoft Sentinel Open Content hub and search for Agent 365 Install the Agent 365 Connector (if not already installed) Open the connector page and select Connect to begin ingestion Once connected, AI agent telemetry starts flowing into Sentinel, ready for hunting, investigation, and response. Data ingestion and analytics are billed using existing Sentinel meters. Learn more Find the Agent 365 data connector | Microsoft Learn Discover and manage Sentinel out-of-the-box content | Microsoft Learn Connect data sources to Sentinel by using data connectors | Microsoft Learn Sample KQL queries for Sentinel data lake | Microsoft Learn Watch the Sentinel data lake video playlist | Microsoft Security Get started with Sentinel data lake | Microsoft LearnMicrosoft Ignite 2025: Transforming Phishing Response with Agentic Innovation
Phishing attacks remain one of the most persistent and damaging threats to organizations worldwide. Security teams are under constant pressure to investigate a growing number of user reported phishing emails daily, ensuring accurate verdicts and timely responses. As threats grow in volume and sophistication, SOC teams are forced to spend valuable time triaging and investigating, often at the expense of strategic defense and proactive threat hunting. At Microsoft Ignite 2025 we are delivering innovation that showcases our continued commitment to infuse AI agents, and agentic workflows into the core of our email security solution and SOC operations to automate repetitive tasks, accelerate investigations, and provide transparent, actionable insights for every reported phishing email. In addition, we continue to invest in our ecosystem partnerships to empower customers with seamless integrations, as they adopt layered security solutions to comply with regulatory requirements, enhance detection, and ensure robust protection. Today I’m excited to announce: General Availability of the Security Alert Triage Agent (previously named Phishing Triage Agent) Agentic Email Grading System in Microsoft Defender Cisco and VIPRE Security Group join the Microsoft Defender ICES ecosystem Note: The Phishing Triage Agent has since been expanded and is now called the Security Alert Triage Agent. Learn more at aka.ms/SATA The Security Alert Triage Agent is now generally available In March 2025, we introduced the Security Alert Triage Agent, designed to autonomously handle user-submitted phishing reports at scale. The agent classifies incoming alerts, resolves false positives, and escalates only the malicious cases that require human expertise. Today, we’re announcing its general availability. We will also be extending the agent to triage alerts for identity and cloud alerts. The Security Alert Triage Agent automates repetitive tasks, accelerates investigations, and every decision is transparent, allowing security teams to focus on what matters most—investigating real threats and strengthening the overall security posture. Early results prove how it is transforming analyst work: Identified 6.5X more malicious alerts Improved verdict accuracy by 77% Agent supported analysts spent 53% more time investigating real threats Agentic email grading: Advanced analysis of phishing email submissions When customers report suspicious messages to Microsoft, they expect clarity, speed, and actionable insights to protect their environment. They expect a response they can trust, understand easily, and take additional investigation and response action for the organization. Previously, when customers reported messages to Microsoft, our response depended largely on manual human grader reviews, creating delays and inconsistent verdicts. Customers often waited several hours for a response, and sometimes it lacked clarity on how a verdict was reached. Today, we are excited to announce that we integrated an agentic grading system into the Microsoft Defender submission analysis and response workflow when customers report phishing messages to Microsoft. Image 2: Agentic Email Grading: Advanced analysis of phishing email submissions The agentic grading system brings a new level of speed and transparency to phishing analysis. It uses large language models (LLMs) orchestrated within an agentic workflow to analyze phishing emails, assess the full content of a submitted email, and communicate context and related metadata. This system combines advanced AI with existing machine learning models and human review for additional levels of accuracy and transparency for decision making. Every verdict comes with higher quality, clear verdicts, and context-rich explanations tailored to each phishing email submission. Additionally, it establishes a feedback mechanism that enhances continuous learning and self-healing, thereby strengthening and optimizing protection over time. By reducing reliance on manual reviews, users will experience lower wait times, faster responses and higher-quality results. It will enable security teams to respond promptly and act confidently against phishing threats. Over time we plan to expand beyond phishing verdicts to include spam, scam, bulk, and clean classifications, making the process more comprehensive. The system will continue to evolve through feedback and adapt to emerging attack patterns. How to view agentic submission responses in Microsoft Defender When you report a suspicious email—whether as an admin or an end user—you can now see how Microsoft Defender’s new agentic grading system evaluates your submission. To view agentic grading system responses, follow the steps below: Report the suspicious email Submit the email through the admin submission or user-reported submission process. Sign in to Microsoft Defender Go to https://security.microsoft.com. Navigate to Submissions From the left menu, select: Investigation & response > Actions & submissions > Submissions. Choose the correct tab Emails for admin submissions User reported for user submissions Open the submission details Click the email submission you want to review. A flyout panel will display Result details. Look for the Agentic AI note If the verdict was generated by Agentic AI, you’ll see: “AI-generated content may be incorrect. Check it for accuracy.” Image 3: AI generated explainable verdicts Expanding the Integrated Cloud Email Security (ICES) ecosystem In June, we introduced the Microsoft Defender ICES vendor ecosystem, a unified framework that enables seamless integration of Microsoft’s Defender’s email security solution with trusted third-party vendors. Today we are excited to announce two new partners: Cisco and VIPRE Security Group. The addition of these partners to our ecosystem reinforces our ongoing commitment to support customers in their choice to strategically layer their email security solutions. Organizations benefit from a unified quarantine experience, and a deep integration across the various SOC experiences including threat explorer, advanced hunting, and the email entity page, while providing clear insight into detection efficacy of each solution. As we continue to innovate, our commitment remains steadfast: empowering defenders with intelligent, transparent, and integrated security solutions that adapt to the evolving threat landscape. By infusing agentic AI into every layer of Microsoft Defender, expanding our ecosystem of trusted partners, and delivering faster, more actionable insights, we’re helping organizations build resilience and stay ahead of attackers. Our strategy is rooted in delivering real value making security simpler, more effective, and adapted to the needs of every customer. Learn More: Want to know what else is new in Microsoft Defender at Ignite 2025 check out the blog here. For info on how to complete admin phish submissions, please see For end user reported phish submissions, you need to have it configured for reporting messages to Microsoft. Set it up today. Join us at Microsoft Ignite Join us at Microsoft Ignite to see these advancements in action and discover how intelligent, agentic defense is becoming accessible to every organization. Don’t miss our featured sessions: AI vs AI: Protect email and collaboration tools with Microsoft Defender on Thursday, November 20 th . Learn More. Microsoft Defender: Building the agentic SOC with guest Allie Mellen on Wednesday, November 19 th . Learn more. Empowering the SOC: Security Copilot and the rise of Agentic Defense on Friday, November 21 st . Learn more.
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.Announcing Public Preview: Security Copilot’s Email Summary in Microsoft Defender
Co-Authors: Cristina Da Gama Henriquez and Ajaj Shaikh AI is rapidly reshaping both sides of the security landscape, and email remains one of the most common and complex entry points for attacks. As adversaries use AI to scale more sophisticated phishing and email-based threats, defenders are under pressure not just to detect them, but to quickly understand what actually happened. Microsoft continues to apply generative and agentic AI across the email protection stack to help stop threats before they reach the inbox and catch what inevitably gets through in the SOC. Still, for security analysts, understanding an email threat requires piecing together context across the incident and its related artifacts. Much of that context exists within the Email entity experience, but it is spread across metadata, timelines, URLs, and attachments, making it time-consuming to connect the dots and act with confidence. Today, we are excited to announce the public preview of Security Copilot’s Email summary capability, designed to bring those insights together and make email threat investigations faster, clearer, and more actionable. With Security Copilot included in Microsoft 365 E5, organizations will be able to bring AI directly into their flow of work—extending these benefits across the SOC at no additional cost.* Bringing clarity into the investigation workflow Email summary brings AI-generated context directly into the Email entity page, transforming fragmented detection data into a clear, natural-language explanation of what happened and why. Analysts can access it from the Security Copilot right-side pane, the same place where Copilot activity across Microsoft Defender is surfaced. Instead of navigating across multiple views to reconstruct the story, analysts can generate a summary that connects the signals and highlights what matters most. And it all happens in seconds. Built on Security Copilot’s summarization capabilities, Email summary uses the same data analysts already rely on, like email metadata, timeline events, URLs, and attachments, and turns it into a cohesive narrative. It explains how a message was evaluated, what actions were taken, and where risk exists, without requiring manual correlation. A summary that follows how analysts think The experience is intentionally embedded in the Email entity page, where investigations already happen, so analysts don’t have to change how they work to benefit from it. The output is structured to match how analysts approach an investigation. It starts with a concise overview of the email, including what was detected, what actions were taken, and any key indicators. From there, it walks through the timeline of events, helping reconstruct how the email was delivered, interacted with, and remediated. It also breaks down URLs and attachments, calling out malicious signals and explaining associated risks in plain language. Importantly, this is a user-triggered experience. Analysts generate a summary when they need it, ensuring the capability is both intentional and efficient. From fragmented data to confident decisions Email summary is a foundational step toward making email threat investigations more explainable and efficient. Today, it brings together existing signals into a clear, actionable narrative. Over time, it will evolve to incorporate additional signal depth: detonation (sandboxing) results, submission responses, and more granular insights from the filtering stack, further strengthening the completeness and fidelity of each investigation. As threats continue to grow in speed and sophistication, the ability to quickly understand and act is just as critical as detection itself. Email summary helps close that gap, giving analysts the clarity they need to respond with confidence. *Eligible Microsoft 365 E5 customers will have 400 Security Compute Units (SCUs) per month for every 1,000 user licenses, up to 10,000 SCUs per month. This included capacity is expected to support typical scenarios. Customers will have an option to pay for scaling beyond the allocated amount at a future date with $6 per SCU on a pay-as-you-go basis, and will get a 30-day advanced notification when this option is available. Learn more.
MDO query of EmailEvents is not accepted in the flow which is why causing the badgateway error
When used the following MDO query of EmailEvents it is working in the Defender control panel but when applied through 'Advanced Hunting' action in Power automate application given bad gateway error. Is this query supported in this application?Full Automation Capabilities in Linux OS
Hello eveyone, We have configured Defender to detect viruses, and our goal is that if one of our assets downloads or encounters a virus, it is automatically hidden or removed. Based on the documentation regarding the automation levels in Automated Investigation and Remediation capabilities, we have set it to "Full - remediate threats automatically." While this works correctly on Windows devices, we have noticed that on Linux devices, the defender still detect the virus but it was not prevented. I was wondering if anyone has encountered this issue and, if so, how it was resolved? Additionally, as I am new to the Defender platform, I wanted to ask if could this issue potentially be resolved through specific Linux policies or functionalities? Best regards MathiewMicrosoft Sentinel MCP Entity Analyzer: Explainable risk analysis for URLs and identities
What makes this release important is not just that it adds another AI feature to Sentinel. It changes the implementation model for enrichment and triage. Instead of building and maintaining a chain of custom playbooks, KQL lookups, threat intel checks, and entity correlation logic, SOC teams can call a single analyzer that returns a reasoned verdict and supporting evidence. Microsoft positions the analyzer as available through Sentinel MCP server connections for agent platforms and through Logic Apps for SOAR workflows, which makes it useful both for interactive investigations and for automated response pipelines. Why this matters First, it formalizes Entity Analyzer as a production feature rather than a preview experiment. Second, it introduces a real cost model, which means organizations now need to govern usage instead of treating it as a free enrichment helper. Third, Microsoft’s documentation is now detailed enough to support repeatable implementation patterns, including prerequisites, limits, required tables, Logic Apps deployment, and cost behavior. From a SOC engineering perspective, Entity Analyzer is interesting because it focuses on explainability. Microsoft describes the feature as generating clear, explainable verdicts for URLs and user identities by analyzing multiple modalities, including threat intelligence, prevalence, and organizational context. That is a much stronger operational model than simple point-enrichment because it aims to return an assessment that analysts can act on, not just more raw evidence What Entity Analyzer actually does The Entity Analyzer tools are described as AI-powered tools that analyze data in the Microsoft Sentinel data lake and provide a verdict plus detailed insights on URLs, domains, and user entities. Microsoft explicitly says these tools help eliminate the need for manual data collection and complex integrations usually required for investigation and enrichment hat positioning is important. In practice, many SOC teams have built enrichment playbooks that fetch sign-in history, query TI feeds, inspect click data, read watchlists, and collect relevant alerts. Those workflows work, but they create maintenance overhead and produce inconsistent analyst experiences. Entity Analyzer centralizes that reasoning layer. For user entities, Microsoft’s preview architecture explains that the analyzer retrieves sign-in logs, security alerts, behavior analytics, cloud app events, identity information, and Microsoft Threat Intelligence, then correlates those signals and applies AI-based reasoning to produce a verdict. Microsoft lists verdict examples such as Compromised, Suspicious activity found, and No evidence of compromise, and also warns that AI-generated content may be incorrect and should be checked for accuracy. That warning matters. The right way to think about Entity Analyzer is not “automatic truth,” but “high-value, explainable triage acceleration.” It should reduce analyst effort and improve consistency, while still fitting into human review and response policy. Under the hood: the implementation model Technically, Entity Analyzer is delivered through the Microsoft Sentinel MCP data exploration tool collection. Microsoft documents that entity analysis is asynchronous: you start analysis, receive an identifier, and then poll for results. The docs note that analysis may take a few minutes and that the retrieval step may need to be run more than once if the internal timeout is not enough for long operations. That design has two immediate implications for implementers. First, this is not a lightweight synchronous enrichment call you should drop carelessly into every automation branch. Second, any production workflow should include retry logic, timeouts, and concurrency controls. If you ignore that, you will create fragile playbooks and unnecessary SCU burn. The supported access path for the data exploration collection requires Microsoft Sentinel data lake and one of the supported MCP-capable platforms. Microsoft also states that access to the tools is supported for identities with at least Security Administrator, Security Operator, or Security Reader. The data exploration collection is hosted at the Sentinel MCP endpoint, and the same documentation notes additional Entity Analyzer roles related to Security Copilot usage. The prerequisite many teams will miss The most important prerequisite is easy to overlook: Microsoft Sentinel data lake is required. This is more than a licensing footnote. It directly affects data quality, analyzer usefulness, and rollout success. If your organization has not onboarded the right tables into the data lake, Entity Analyzer will either fail or return reduced-confidence output. For user analysis, the following tables are required to ensure accuracy: AlertEvidence, SigninLogs, CloudAppEvents, and IdentityInfo. also notes that IdentityInfo depends on Defender for Identity, Defender for Cloud Apps, or Defender for Endpoint P2 licensing. The analyzer works best with AADNonInteractiveUserSignInLogs and BehaviorAnalytics as well. For URL analysis, the analyzer works best with EmailUrlInfo, UrlClickEvents, ThreatIntelIndicators, Watchlist, and DeviceNetworkEvents. If those tables are missing, the analyzer returns a disclaimer identifying the missing sources A practical architecture view An incident, hunting workflow, or analyst identifies a high-interest URL or user. A Sentinel MCP client or Logic App calls Entity Analyzer. Entity Analyzer queries relevant Sentinel data lake sources and correlates the findings. AI reasoning produces a verdict, evidence narrative, and recommendations. The result is returned to the analyst, incident record, or automation workflow for next-step action. This model is especially valuable because it collapses a multi-query, multi-tool investigation pattern into a single explainable decisioning step. Where it fits in real Sentinel operations Entity Analyzer is not a replacement for analytics rules, UEBA, or threat intelligence. It is a force multiplier for them. For identity triage, it fits naturally after incidents triggered by sign-in anomaly detections, UEBA signals, or Defender alerts because it already consumes sign-in logs, cloud app events, and behavior analytics as core evidence sources. For URL triage, it complements phishing and click-investigation workflows because it uses TI, URL activity, watchlists, and device/network context. Implementation path 1: MCP clients and security agents Microsoft states that Entity Analyzer integrates with agents through Sentinel MCP server connections to first-party and third-party AI runtime platforms. In practice, this makes it attractive for analyst copilots, engineering-side investigation agents, and guided triage experiences The benefit of this model is speed. A security engineer or analyst can invoke the analyzer directly from an MCP-capable client without building a custom orchestration layer. The tradeoff is governance: once you make the tool widely accessible, you need a clear policy for who can run it, when it should be used, and how results are validated before action is taken. Implementation path 2: Logic Apps and SOAR playbooks For SOC teams, Logic Apps is likely the most immediately useful deployment model. Microsoft documents an entity analyzer action inside the Microsoft Sentinel MCP tools connector and provides the required parameters for adding it to an existing logic app. These include: Workspace ID Look Back Days Properties payload for either URL or User The documented payloads are straightforward: { "entityType": "Url", "url": "[URL]" } And { "entityType": "User", "userId": "[Microsoft Entra object ID or User Principal Name]" } Also states that the connector supports Microsoft Entra ID, service principals, and managed identities, and that the Logic App identity requires Security Reader to operate. This makes playbook integration a strong pattern for incident enrichment. A high-severity incident can trigger a playbook, extract entities, invoke Entity Analyzer, and post the verdict back to the incident as a comment or decision artifact. The concurrency lesson most people will learn the hard way Unusually direct guidance on concurrency: to avoid timeouts and threshold issues, turn on Concurrency control in Logic Apps loops and start with a degree of parallelism of . The data exploration doc repeats the same guidance, stating that running multiple instances at once can increase latency and recommending starting with a maximum of five concurrent analyses. This is a strong indicator that the correct implementation pattern is selective analysis, not blanket analysis. Do not analyze every entity in every incident. Analyze the entities that matter most: external URLs in phishing or delivery chains accounts tied to high-confidence alerts entities associated with high-severity or high-impact incidents suspicious users with multiple correlated signals That keeps latency, quota pressure, and SCU consumption under control. KQL still matters Entity Analyzer does not eliminate KQL. It changes where KQL adds value. Before running the analyzer, KQL is still useful for scoping and selecting the right entities. After the analyzer returns, KQL is useful for validation, deeper hunting, and building custom evidence views around the analyzer’s verdict. For example, a simple sign-in baseline for a target user: let TargetUpn = "email address removed for privacy reasons"; SigninLogs | where TimeGenerated between (ago(7d) .. now()) | where UserPrincipalName == TargetUpn | summarize Total=count(), Failures=countif(ResultType != "0"), Successes=countif(ResultType == "0"), DistinctIPs=dcount(IPAddress), Apps=make_set(AppDisplayName, 20) by bin(TimeGenerated, 1d) | order by TimeGenerated desc And a lightweight URL prevalence check: let TargetUrl = "omicron-obl.com"; UrlClickEvents | where TimeGenerated between (ago(7d) .. now()) | search TargetUrl | take 50 Cost, billing, and governance GA is where technical excitement meets budget reality. Microsoft’s Sentinel billing documentation says there is no extra cost for the MCP server interface itself. However, for Entity Analyzer, customers are charged for the SCUs used for AI reasoning and also for the KQL queries executed against the Microsoft Sentinel data lake. Microsoft further states that existing Security Copilot entitlements apply The April 2026 “What’s new” entry also explicitly says that starting April 1, 2026, customers are charged for the SCUs required when using Entity Analyzer. That means every rollout should include a governance plan: define who can invoke the analyzer decide when playbooks are allowed to call it monitor SCU consumption limit unnecessary repeat runs preserve results in incident records so you do not rerun the same analysis within a short period Microsoft’s MCP billing documentation also defines service limits: 200 total runs per hour, 500 total runs per day, and around 15 concurrent runs every five minutes, with analysis results available for one hour. Those are not just product limits. They are design requirements. Limitations you should state clearly The analyze_user_entity supports a maximum time window of seven days and only works for users with a Microsoft Entra object ID. On-premises Active Directory-only users are not supported for user analysis. Microsoft also says Entity Analyzer results expire after one hour and that the tool collection currently supports English prompts only. Recommended rollout pattern If I were implementing this in a production SOC, I would phase it like this: Start with a narrow set of high-value use cases, such as suspicious user identities and phishing-related URLs. Confirm that the required tables are present in the data lake. Deploy a Logic App enrichment pattern for incident-triggered analysis. Add concurrency control and retry logic. Persist returned verdicts into incident comments or case notes. Then review SCU usage and analyst value before expanding coverage.
Observed Automation Discrepancies
Hi Team ... I want to know the logic behind the Defender XDR Automation Engine . How it works ? I have observed Defender XDR Automation Engine Behavior contrary to expectations of identical incident and automation handling in both environments, discrepancies were observed. Specifically, incidents with high-severity alerts were automatically closed by Defender XDR's automation engine before reaching their SOC for review, raising concerns among clients and colleagues. Automation rules are clearly logged in the activity log, whereas actions performed by Microsoft Defender XDR are less transparent . A high-severity alert related to a phishing incident was closed by Defender XDR's automation, resulting in the associated incident being closed and removed from SOC review. Wherein the automation was not triggered by our own rules, but by Microsoft's Defender XDR, and sought clarification on the underlying logic.
