What’s new in Microsoft Sentinel: RSAC 2026
Security is entering a new era, one defined by explosive data growth, increasingly sophisticated threats, and the rise of AI-enabled operations. To keep pace, security teams need an AI-powered approach to collect, reason over, and act on security data at scale. At RSA Conference 2026 (RSAC), we’re unveiling the next wave of Sentinel innovations designed to help organizations move faster, see deeper, and defend smarter with AI-ready tools. These updates include AI-driven playbooks that accelerate SOC automation, Granular Delegated Admin Privileges (GDAP) and granular role-based access controls (RBAC) that let you scale your SOC, accelerated data onboarding through new connectors, and data federation that enables analysis in place without duplication. Together, they give teams greater clarity, control, and speed. Come see us at RSAC to view these innovations in action. Hear from Sentinel leaders during our exclusive Microsoft Pre-Day, then visit Microsoft booth #5744 for demos, theater sessions, and conversations with Sentinel experts. Read on to explore what’s new. See you at RSAC! Sentinel feature innovations: Sentinel SIEM Sentinel data lake Sentinel graph Sentinel MCP Threat Intelligence Microsoft Security Store Sentinel promotions Sentinel SIEM Playbook generator [Now in public preview] The Sentinel playbook generator delivers a new era of automation capabilities. You can vibe code complex automations, integrate with different tools to ensure timely and compliant workflows throughout your SOC and feel confident in the results with built in testing and documentation. Customers and partners are already seeing benefit from this innovation. “The playbook generator gives security engineers the flexibility and speed of AI-assisted coding while delivering the deterministic outcomes that enterprise security operations require. It's the best of both worlds, and it lives natively in Defender where the engineers already work.” – Jaime Guimera Coll | Security and AI Architect | BlueVoyant Learn more about playbook generator. SIEM migration experience [General availability now] The Sentinel SIEM migration experience helps you plan and execute SIEM migrations through a guided, in-product workflow. You can upload Splunk or QRadar exports to generate recommendations for best‑fit Sentinel analytics rules and required data connectors, then assess migration scope, validate detection coverage, and migrate from Splunk or QRadar to Sentinel in phases while tracking progress. “The tool helps turn a Splunk to Sentinel migration into a practical decision process. It gives clear visibility into which detections are relevant, how they align to real security use cases, and where it makes sense to enable or prioritize coverage—especially with cost and data sources in mind.” – Deniz Mutlu | Director | Swiss Post Cybersecurity Ltd Learn more about SIEM migration experience. GDAP, unified RBAC, and row-level RBAC for Sentinel [Public preview, April 1] As Sentinel environments grow for enterprises, MSSPs, hyperscalers, and partners operating across shared or multiple environments, the challenge becomes managing access control efficiently and consistently at scale. Sentinel’s expanded permissions and access capabilities are designed to meet these needs. Granular Delegated Admin Privileges (GDAP) lets you streamline management across multiple governed tenants using your primary account, based on existing GDAP relationships. Unified RBAC allows you to opt in to managing permissions for Sentinel workspaces through a single pane of glass, configuring and enforcing access across Sentinel experiences in the analytics tier and data lake in the Defender portal. This simplifies administration and improves operational efficiency by reducing the number of permission models you need to manage. Row-level RBAC scoping within tables enables precise, scoped access to data in the Sentinel data lake. Multiple SOC teams can operate independently within a shared Sentinel environment, querying only the data they are authorized to see, without separating workspaces or introducing complex data flow changes. Consistent, reusable scope definitions ensure permissions are applied uniformly across tables and experiences, while maintaining strong security boundaries. To learn more, read our technical deep dives on RBAC and GDAP. Sentinel data lake Sentinel data federation [Public preview, April 1] Sentinel data federation lets you analyze security data in place without copying or duplicating your data. Powered by Microsoft Fabric, you can now federate data from Fabric, Azure Data Lake Storage (ADLS), and Azure Databricks into Sentinel data lake. Federated data appears alongside native Sentinel data, so you can use familiar tools like KQL hunting, notebooks, and custom graphs to correlate signals and investigate across your entire digital estate, all while preserving governance and compliance. You can start analyzing data in place and progressively ingest data into Sentinel for deeper security insights, advanced automation, and AI-powered defense at scale. You are billed only when you run analytics on federated data using existing Sentinel data lake query and advanced insights meters. les for unified investigation and hunting Sentinel cost estimation tool [Public Preview, April 9] The new Sentinel cost estimation tool offers all Microsoft customers and partners a guided, meter-level cost estimation experience that makes pricing transparent and predictable. A built-in three-year cost projection lets you model data growth and ramp-up over time, anticipate spend, and avoid surprises. Get transparent estimates into spend as you scale your security operations. All other customers can continue to use the Azure calculator for Sentinel pricing estimates. See the Sentinel pricing page for more information. Sentinel data connectors A365 connector [Public preview, May 5] Bring AI agent telemetry into the Sentinel data lake to investigate agent behavior, tool usage, prompts, reasoning and execution using hunting, graph, and MCP workflows. GitHub audit log connector using API polling [General availability, March 6] Ingest GitHub enterprise audit logs into Sentinel to monitor user and administrator activity, detect risky changes, and investigate security events across your development environment. Google Kubernetes Engine (GKE) connector [General availability, March 6] Collect Google Kubernetes Engine (GKE) audit and workload logs in Sentinel to monitor cluster activity, analyze workload behavior, and detect security threats across Kubernetes environments. Microsoft Entra and Azure Resource Graph (ARG) connector enhancements [Public preview, April 15] Enable new Entra assets (EntraDevices, EntraOrgContacts) and ARG assets (ARGRoleDefinitions) in existing asset connectors, expanding inventory coverage and powering richer, built‑in graph experiences for greater visibility. With over 350 Sentinel data connectors, customers achieve broad visibility into complex digital environments and can expand their security operations effectively. “Microsoft Sentinel data lake forms the core of our agentic SOC. By unifying large volumes of Microsoft and third-party data, enabling graph-based analysis, and supporting MCP-driven workflows, it allows us to investigate faster, at lower cost, and with greater confidence.” – Øyvind Bergerud | Head of Security Operations | Storebrand Learn more about Sentinel data connectors. Sentinel connector builder agent using Sentinel Visual Studio Code extension [Public preview, March 31] Build Sentinel data connectors in minutes instead of weeks using the AI‑assisted Connector Builder agent in Visual Studio Code. This low‑code experience guides developers and ISVs end-to-end, automatically generating schemas, deployment assets, connector UI, secure secret handling, and polling logic. Built‑in validation surfaces issues early, so you can validate event logs before deployment and ingestion. Example prompt in GitHub Copilot Chat: @sentinel-connector-builder Create a new connector for OpenAI audit logs using https://api.openai.com/v1/organization/audit_logs Get started with custom connectors and learn more in our blog. Data filtering and splitting [Public preview, March 30] As security teams ingest more data, the challenge shifts from scale to relevance. With filtering and splitting now built into the Defender portal, teams can shape data before it lands in Sentinel, without switching tools or managing custom JSON files. Define simple KQL‑based transformations directly in the UI to filter low‑value events and intelligently route data, making ingestion optimization faster, more intuitive, and easier to manage at scale. Filtering at ingest time allows you to remove low-value or benign events to reduce noise, cut unnecessary processing, and ensure that high-signal data drives detections and investigations. Splitting enables intelligent routing of data between the analytics tier and the data lake tier based on relevance and usage. Together, these two capabilities help you balance cost and performance while scaling data ingestion sustainably as your digital estate grows. Create workbook reports directly from the data lake [Public preview, April 1] Sentinel workbooks can now directly run on the data lake using KQL, enabling you to visualize and monitor security data straight from the data lake. By selecting the data lake as the workbook data source, you can now create trend analysis and executive reporting. Sentinel graph Custom graphs [Public preview, April 1] Custom graphs let you build tailored security graphs tuned to your unique security scenarios using data from Sentinel data lake as well as non-Microsoft sources. With custom graph, powered by Fabric, you can build, query, and visualize connected data, uncover hidden patterns and attack paths, and help surface risks that are hard to detect when data is analyzed in isolation. These graphs provide the knowledge context that enables AI-powered agent experiences to work more effectively, speeding investigations, revealing blast radius, and helping you move from noisy, disconnected alerts to confident decisions at scale. In the words of our preview customers: “We ingested our Databricks management-plane telemetry into the Sentinel data lake and built a custom security graph. Without writing a single detection rule, the graph surfaced unusual patterns of activity and overprivileged access that we escalated for investigation. We didn't know what we were looking for, the graph surfaced the risk for us by revealing anomalous activity patterns and unusual access combinations driven by relationships, not alerts.” – SVP, Security Solutions | Financial Services organization Custom graph API usage for creating graph and querying graph will be billed starting April 1, 2026, according to the Sentinel graph meter. Creating custom graph Using the Sentinel VS Code extension, you can generate graphs to validate hunting hypotheses, such as understanding attack paths and blast radius of a phishing campaign, reconstructing multi‑step attack chains, and identifying structurally unusual or high‑risk behavior, making it accessible to your team and AI agents. Once persisted via a schedule job, you can access these custom graphs from the ready-to-use section in the graph experience in the Defender portal. Graphs experience in the Microsoft Defender portal After creating your custom graphs, you can access them in the graphs section of the Defender portal under Sentinel. From there, you’ll be able to perform interactive graph-based investigations, such as using a graph built for phishing analysis to help you quickly evaluate the impact of a recent incident, profile the attacker, and trace its paths across Microsoft telemetry and third-party data. The new graph experience lets you run Graph Query Language (GQL) queries, view the graph schema, visualize the graph, view graph results in tabular format, and interactively travers the graph to the next hop with a simple click. Sentinel MCP Sentinel MCP entity analyzer [General availability, April 1] Entity analyzer provides reasoned, out-of-the-box risk assessments that help you quickly understand whether a URL or user identity represents potential malicious activity. The capability analyzes data across modalities including threat intelligence, prevalence, and organizational context to generate clear, explainable verdicts you can trust. Entity analyzer integrates easily with your agents through Sentinel MCP server connections to first-party and third-party AI runtime platforms, or with your SOAR workflows through Logic Apps. The entity analyzer is also a trusted foundation for the Defender Triage Agent and delivers more accurate alert classifications and deeper investigative reasoning. This removes the need to manually engineer evaluation logic and creates trust for analysts and AI agents to act with higher accuracy and confidence. Learn more about entity analyzer and in our blog here. Entity analyzer will be billed starting April 1, 2026, based on Security Compute Units (SCU) consumption. Learn more about MCP billing. Sentinel MCP graph tool collection [Public preview, May 20] Graph tool collection helps you visualize and explore relationships between identities and device assets, threats and activities signals ingested by data connectors and alerted by analytic rules. The tool provides a clear graph view that highlights dependencies and configuration gaps, which 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, all from a single, interactive workspace. Executing graph queries via the MCP tools will trigger the graph meter. Claude MCP connector [Public preview, April 1] Anthropic Claude can connect to Sentinel through a custom MCP connector, giving you AI-assisted analysis across your Sentinel environment. Microsoft provides step-by-step guidance for configuring a custom connector in Claude that securely connects to a Sentinel MCP server. With this connection you can summarize incidents, investigate alerts, and reason over security signals while keeping data inside Microsoft's security boundary. Access to large language models (LLMs) is managed through Microsoft authentication and role-based controls, supporting faster triage and investigation workflows while maintaining compliance and visibility. Threat Intelligence CVEs of interest in the Threat Intelligence Briefing Agent [Public preview in April] The Threat Intelligence Briefing Agent delivers curated intelligence based on your organization’s configuration, preferences, and unique industry and geographic needs. CVEs of interest which highlights vulnerabilities actively discussed across the security landscape and assesses their potential impact on your environment, delivering more timely threat intelligence insights. The agent automatically incorporates internet exposure data powered by the Sentinel platform to surface threats targeting technologies exposed in your organization. Together, these enhancements help you focus faster on the threats that matter most, without manual investigation. Microsoft Security Store Security Store embedded in Entra [General availability, March 23] As identity environments grow more complex, teams need to move faster and extend Entra with trusted third‑party capabilities that address operational, compliance, and risk challenges. The Security Store embedded directly into Entra lets you discover and adopt Entra‑ready agents and solutions in your workflow. You can extend Entra with identity‑focused agents that surface privileged access risk, identity posture gaps, network access insights, and overall identity health, turning identity data into clear recommendations and reports teams can use immediately. You can also enhance Entra with Verified ID and External ID integrations that strengthen identity verification, streamline account recovery, and reduce fraud across workforce, consumer, and external identities. Security Store embedded in Microsoft Purview [General availability, March 31] Extending data security across the digital estate requires visibility and enforcement into new data sources and risk surfaces, often requiring a partnered approach. The Security Store embedded directly into Purview lets you discover and evaluate integrated solutions inside your data security workflows. Relevant partner capabilities surface alongside context, making it easier to strengthen data protection, address regulatory requirements, and respond to risk without disrupting existing processes. You can quickly assess which solutions align to data security scenarios, especially with respect to securing AI use, and how they can leverage established classifiers, policies, and investigation workflows in Purview. Keeping integration discovery in‑flow and purchases centralized through the Security Store means you move faster from evaluation to deployment, reducing friction and maintaining a secure, consistent transaction experience. Security Store Advisor [General availability, March 23] Security teams today face growing complexity and choice. Teams often know the security outcome they need, whether that's strengthening identity protection, improving ransomware resilience, or reducing insider risk, but lack a clear, efficient way to determine which solutions will help them get there. Security Store Advisor provides a guided, natural-language discovery experience that shifts security evaluation from product‑centric browsing to outcome‑driven decision‑making. You can describe your goal in plain language, and the Advisor surfaces the most relevant Microsoft and partner agents, solutions, and services available in the Security Store, without requiring deep product knowledge. This approach simplifies discovery, reduces time spent navigating catalogs and documentation, and helps you understand how individual capabilities fit together to deliver meaningful security outcomes. Sentinel promotions Extending signups for promotional 50 GB commitment tier [Through June 2026] The Sentinel promotional 50 GB commitment tier offers small and mid-sized organizations a cost-effective entry point into Sentinel. Sign up for the 50 GB commitment tier until June 30, 2026, and maintain the promotional rate until March 31, 2027. This promotion is available globally with regional variations in pricing and accessible through EA, CSP, and Direct channels. Visit the Sentinel pricing page for details and to get started. Sentinel RSAC 2026 sessions All week – Sentinel product demos, Microsoft Booth #5744 Mon Mar 23, 3:55 PM – RSAC 2026 main stage Keynote with CVP Vasu Jakkal [KEY-M10W] Ambient and autonomous security: Building trust in the agentic AI era Tue Mar 24, 10:30 AM – Live Q&A session, Microsoft booth #5744 and online Ask me anything with Microsoft Security SMEs and real practitioners Tue Mar 24, 11 AM – Sentinel data lake theater session, Microsoft booth #5744 From signals to insights: How Microsoft Sentinel data lake powers modern security operations Tue Mar 24, 2 PM – Sentinel SIEM theater session, Microsoft booth #5744 Vibe-coding SecOps automations with the Sentinel playbook generator Wed Mar 25, 12 PM – Executive event at Palace Hotel with Threat Protection GM Scott Woodgate The AI risk equation: Visibility, control, and threat acceleration Wed Mar 25, 1:30 PM – Sentinel graph theater session, Microsoft booth #5744 Bringing knowledge-driven context to security with Microsoft Sentinel graph Wed Mar 25, 5 PM – MISA theater session, Microsoft booth #5744 Cut SIEM costs without reducing protection: A Sentinel data lake case study Thu Mar 26, 1 PM – Security Store theater session, Microsoft booth #5744 What's next for Security Store: Expanding in portal and smarter discovery All week – 1:1 meetings with Microsoft security experts Meet with Microsoft Defender and Sentinel SIEM and Defender Security Operations Additional resources Sentinel data lake video playlist Explore the full capabilities of Sentinel data lake as a unified, AI-ready security platform that is deeply integrated into the Defender portal Sentinel data lake FAQ blog Get answers to many of the questions we’ve heard from our customers and partners on Sentinel data lake and billing AI‑powered SIEM migration experience ninja training Walk through the SIEM migration experience, see how it maps detections, surfaces connector requirements, and supports phased migration decisions SIEM migration experience documentation Learn how the SIEM migration experience analyzes your exports, maps detections and connectors, and recommends prioritized coverage Accenture collaborates with Microsoft to bring agentic security and business resilience to the front lines of cyber defense Stay connected Check back each month for the latest innovations, updates, and events to ensure you’re getting the most out of Sentinel. We’ll see you in the next edition!Identity Attack Graph in Microsoft Sentinel
Identity is now one of the most important attack surfaces in cloud security. In many real-world incidents, attackers do not rely only on malware or network movement. Instead, they abuse identities, permissions, role assignments, group memberships, service principals, and misconfigured access paths to move from an initial compromise to high-value resources. This is why the new Identity Attack Graph in Microsoft Sentinel is an important capability. It helps security teams visualize how identities are connected to Azure resources and how an attacker could potentially move from one identity to another resource through permissions and relationships. What is the Identity Attack Graph? The Identity Attack Graph in Microsoft Sentinel provides a visual way to understand how identities, permissions, groups, and Azure resources are connected. Instead of manually checking multiple portals, logs, and role assignments, the graph helps analysts understand relationships such as: Which identities have access to specific Azure resources Which users or service principals are over-privileged Which groups provide indirect access to sensitive resources Which identities may have a path to critical assets What the potential blast radius of a compromised identity could be How attackers could move laterally through identity and permission relationships This is especially useful because identity risk is often not obvious when looking at a single user, group, or role assignment in isolation. The real risk usually appears when these relationships are connected together. A user may not directly have access to a sensitive resource, but the user may be a member of a group that has access to another resource, which then has permissions that create a path toward a high-value asset. The Identity Attack Graph helps expose these hidden relationships. Why this matters In many Azure environments, permissions grow over time. Users change roles, groups are reused, emergency access is granted, service principals are created, and temporary permissions are not always removed. As a result, organizations often end up with: Too many privileged identities Unused or stale permissions Service principals with excessive access Guest users with unnecessary permissions Groups that provide indirect access to sensitive resources Subscription-level roles that are broader than required Lack of visibility into who can reach critical assets Traditional investigation usually requires analysts to move between several places, including Microsoft Entra ID, Azure RBAC, Azure Activity logs, Sentinel queries, Defender XDR, and Azure Resource Graph. The Identity Attack Graph reduces this complexity by presenting identity relationships as a connected graph. This makes it easier to answer practical security questions such as: “What can this identity access?” “What happens if this user is compromised?” “Which identities have a path to critical resources?” “Which access path should we remediate first?” “Which permissions create the highest risk?” “Why does this identity have access to this asset?” Key use cases The feature can support several important identity security and cloud security scenarios. 1. Attack path discovery Security teams can use the graph to identify how an attacker could move from a compromised identity to a sensitive Azure resource. This is useful during both proactive assessments and active incident response. For example, if a user account is suspected to be compromised, the graph can help identify which resources may be reachable through that identity’s direct or indirect permissions. 2. Blast-radius analysis When an identity is compromised, one of the first questions is: What could the attacker access with this identity? The Identity Attack Graph can help analysts understand the potential impact of a compromised user, group, service principal, or managed identity. This can help with containment, prioritization, and communication with stakeholders. 3. Over-privileged identity detection The graph can help identify identities that have more permissions than they need. Include: Users with Owner or Contributor access at subscription level Service principals with broad permissions Guest users with privileged access Groups that grant access to sensitive resources Identities that have access to multiple critical assets This is useful for enforcing least privilege and reducing identity attack surface. 4. Privileged access review IAM and cloud security teams can use the graph to support access reviews. Instead of only reviewing a list of role assignments, teams can understand the real impact of those permissions. This helps answer: Is this role assignment still required? Does this group create unnecessary risk? Does this identity have access to critical resources? Is this access direct or inherited? Is this path expected or suspicious? 5. Incident response and threat hunting For SOC teams, the graph can support investigations involving: Suspicious sign-ins Compromised users Privilege escalation Suspicious role assignments Lateral movement Service principal abuse Unusual access to sensitive resources The graph does not replace logs or hunting queries, but it gives analysts a faster way to understand relationships and prioritize what to investigate next. Important prerequisites and setup notes During my evaluation, there were a few important setup requirements that should be clearly highlighted. Microsoft Sentinel permissions The environment must already be onboarded to Microsoft Sentinel, and the user testing or configuring the feature must have the appropriate Microsoft Sentinel permissions. The documented role requirement includes Microsoft Sentinel Contributor. However, in my experience, this is not always enough for the full onboarding and validation experience. Subscription-level Owner permission One important prerequisite that should be clearly mentioned is that Owner permissions at the Azure subscription level may be required. This is especially important during onboarding and activation, because the graph depends on access to Azure resource and permission relationships. If the user does not have sufficient subscription-level permissions, some setup steps or visibility into resources and relationships may not work as expected. Recommended permission note: In addition to Microsoft Sentinel permissions, ensure that the user configuring the preview has Owner permissions at the subscription level for the subscriptions that should be represented in the graph. This should be made very clear in the onboarding documentation to avoid confusion during deployment. Required data connector: Azure Resource Graph Another very important setup step is the Azure Resource Graph data connector. The Azure Resource Graph connector must be: Installed manually Activated manually Connected to the relevant Sentinel workspace This is a key point. The connector is not automatically enabled just because the Identity Attack Graph feature is available. Without this connector, Sentinel may not have the required Azure resource relationship data needed to build a useful graph. Why Azure Resource Graph is important Azure Resource Graph provides visibility across Azure resources, subscriptions, and relationships. For an identity attack graph, this data is essential. The graph needs to understand not only identities, but also the resources those identities can reach. This may include: Subscriptions Resource groups Storage accounts Key Vaults Virtual machines Managed identities Role assignments Resource relationships Resource hierarchy Critical assets Without Azure Resource Graph data, the attack graph may not provide the full picture of how identities connect to Azure resources. For this reason, I believe the onboarding instructions should explicitly state: The Azure Resource Graph data connector must be manually installed and activated before using the Identity Attack Graph. Recommended onboarding checklist Before using the Identity Attack Graph, I would recommend validating the following: Requirement Recommendation Microsoft Sentinel workspace Ensure the workspace is active and accessible Sentinel role Microsoft Sentinel Contributor or equivalent access Subscription permissions Owner permissions at subscription level Azure Resource Graph connector Manually install and activate the connector Azure RBAC visibility Ensure access to relevant role assignments Microsoft Entra ID visibility Ensure identity and group data is available Resource visibility Validate that relevant subscriptions and resources are visible Data freshness Allow enough time for data collection and graph population This checklist can help avoid issues where the feature appears available but does not show the expected relationships. How the Identity Attack Graph improves investigation Before using a graph-based approach, an analyst often needs to manually collect and correlate data from multiple sources. A typical investigation may include: Checking the user in Microsoft Entra ID Reviewing group memberships Reviewing Azure RBAC assignments Checking subscription-level access Looking at resource-level permissions Reviewing PIM activations Searching Sentinel logs Running KQL queries Checking Azure Activity logs Validating access with cloud or IAM teams This process can be time-consuming. The Identity Attack Graph helps reduce this effort by showing relationships visually. This allows the analyst to understand the possible path faster and decide where to focus. For example, instead of manually asking: “Does this user have access to this resource through any group, role, or inherited permission?” The graph can help show the relationship directly. This is valuable because many risky permissions are indirect. The user may not have direct access, but may inherit access through a group, role assignment, nested relationship, or service principal path. Where validation is still needed Although the graph provides strong visibility, I would still validate findings before taking remediation action. This is especially important because removing access can affect business operations or production systems. I would still validate with: Microsoft Sentinel KQL queries Microsoft Entra sign-in logs Microsoft Entra audit logs Azure Activity logs Azure RBAC role assignments PIM activation history Defender XDR signals Defender for Cloud recommendations Azure Resource Graph queries IAM team input Cloud platform team input Application owner confirmation The graph is very useful for discovery and prioritization, but final remediation decisions should still be validated. GQL and graph-based investigation One of the interesting aspects of this feature is the use of graph-based thinking. Security teams are already familiar with query languages such as KQL for log analytics. However, graph investigation is different. KQL is excellent for searching and analyzing events over time, such as sign-ins, alerts, audit logs, and activity logs. Graph Query Language, or GQL, is designed for querying connected data. Instead of only asking what happened at a specific time, graph queries help answer how entities are connected. In identity security, this is very powerful because the risk often exists in the relationship between objects. Graph entities include: Users Groups Service principals Managed identities Roles Subscriptions Resource groups Azure resources Permissions Sessions Attack paths Graph relationships include: User is member of group Group has role assignment Identity has access to resource Service principal owns application Managed identity can access Key Vault User can escalate privilege Identity can reach critical asset This allows analysts to ask more relationship-focused questions, such as: Which identities can reach this resource? What is the shortest path from this user to a critical asset? Which groups create privileged access? Which service principals have paths to sensitive resources? Which identities have indirect access through nested relationships? Which attack paths include subscription Owner or Contributor permissions? KQL vs GQL: why both are useful KQL and GQL serve different but complementary purposes. Area KQL GQL / Graph Querying Main purpose Analyze logs and events Analyze relationships and paths Best for Time-based investigation Connected identity/resource investigation question “Did this user sign in from a risky location?” “What resources can this user reach?” Data model Tables Nodes and edges Common use Detection, hunting, analytics Attack path discovery, relationship mapping Strength Event correlation Path discovery In practice, security teams need both. KQL can identify a suspicious sign-in. The Identity Attack Graph can show what the compromised identity could access. KQL can then be used again to validate whether the attacker interacted with those resources. This creates a strong workflow between event-based detection and relationship-based investigation. Graph investigation scenarios The following are conceptual are the types of graph questions that would be useful in identity attack path analysis. Find paths from a user to critical resources A useful graph query would help answer: Show me all paths from this user to critical Azure resources. This could help determine whether a compromised identity has a direct or indirect route to sensitive assets. Find identities with paths to Key Vaults Key Vaults often contain secrets, certificates, and keys. A graph query could help identify: Which users, groups, service principals, or managed identities have a path to Key Vault resources? This would be useful for prioritizing access review and remediation. Find subscription-level privileged identities Subscription-level roles are high-impact because they can provide broad access. A graph query could help find: Which identities have Owner or Contributor access at subscription level? This is especially important because subscription-level permissions can create wide attack paths. Find indirect access through groups Many access paths are created through group membership. A graph query could help answer: Which users have access to this resource through group membership? This can help IAM teams clean up excessive or unnecessary group-based access. Find service principals with broad access Service principals are often used for automation and applications, but they can become high-risk if over-privileged. A useful query would identify: Which service principals have broad access to subscriptions or critical resources? This is important because service principal compromise can lead to significant impact. How GQL can improve analyst workflows Adding strong GQL support to the graph explorer would make the feature more powerful for advanced users. You could use graph queries to: Search for specific paths Filter by identity type Filter by role Filter by resource type Find shortest paths Find high-risk paths Exclude known approved paths Focus on critical assets Query only privileged relationships Identify unexpected permission chains This would help both SOC analysts and cloud security engineers move from visual exploration to repeatable analysis. A SOC analyst may want a quick visual graph during an incident, while a cloud security engineerSentinel 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.Extending Sentinel Data Integration: Azure Blob Storage Support for CCF Connectors
As organizations scale their security operations, the ability to ingest, process, and analyze high volumes of data reliably becomes increasingly critical. Microsoft Sentinel continues to expand its ecosystem through the Codeless Connector Framework (CCF), enabling ISVs to build and deliver integrations with Sentinel faster while simplifying deployment for customers. Today, CCF extends even further with support for Azure Blob Storage, introducing a new pattern for how data can be delivered into Sentinel. Expanding Connector Patterns with Azure Blob Storage CCF has traditionally enabled connectors that integrate directly with partner APIs and data sources. With this latest enhancement, ISVs can now build connectors that read data from Azure Blob Storage—unlocking new flexibility in how security data is collected and delivered. In this model, an ISV writes data to an Azure Blob Storage account. The Sentinel connector then reads from that storage layer, using Azure-native components such as Event Grid and storage queues to process events and forward them through data collection rules (DCR) into Log Analytics workspace. This approach introduces a durable data layer between the data source and Sentinel, enabling more resilient and scalable ingestion scenarios. Why a durable data layer matters By leveraging Azure Blob Storage as part of the ingestion pipeline, CCF connectors gain important operational advantages. This architecture allows data to be buffered and processed asynchronously, helping manage fluctuations in data volume and ensuring consistent delivery. Key benefits include: Resilience: Buffers spikes and handles backpressure to maintain steady ingestion Improved Compatibility: Supports widely adopted Azure Blob-based log streaming, enabling seamless integration with partners that already use Azure for audit data delivery Data protection: Reduces risk of data loss during outages or throttling Scalability: Supports high-volume ingestion scenarios across tenants Flexibility: Enables architectures that can support multiple SIEMs or data consumers Together, these capabilities make CCF Azure Blob Storage based connectors a strong fit for partners managing large, variable, or distributed data pipelines. Partner adoption Early partners are already taking advantage of this capability to modernize their integrations and support evolving customer needs. Cloudflare Cloudflare integrates with Microsoft Sentinel using the Codeless Connector Framework (CCF) to bring Cloudflare log data into centralized security operations workflows. The connector ingests Cloudflare logs—delivered via Logpush to Azure Blob Storage—into Sentinel for analysis, enabling security teams to correlate web, network, and application activity with other security signals. By combining Cloudflare’s global threat visibility with Sentinel analytics and automation, this integration supports more effective threat detection, investigation, and incident response across Cloudflare‑protected environments. Netskope Web Transaction Events Netskope integrates with Microsoft Sentinel to provide detailed visibility into web and cloud activity across users, applications, and SaaS services. The connector ingests Netskope web transaction logs into Sentinel—leveraging Azure Blob Storage as a staging layer for log streaming and ingestion—to enable near real‑time analysis of user behavior, policy violations, and potential threats. By combining Netskope’s inline web inspection with Sentinel’s analytics and correlation capabilities, this integration helps security teams detect risky activity, investigate incidents, and strengthen monitoring across modern cloud environments. These integrations demonstrate how Azure Blob Storage support can simplify ingestion architectures while improving reliability and scalability for customers. Here is what our partners say about the functionality. Cloudflare: Netskope: Get started Developers can begin building CCF Azure Blob Storage -enabled connectors today using the guidance on Microsoft Learn. This documentation provides step-by-step instructions for configuring storage, processing events, and connecting data to Sentinel. In the unlikely event that you encounter any issues in building or updating your connector, App Assure is here to help. We are an engineering-backed team committed to supporting customers and software development companies throughout their journey with Sentinel to streamline integration and accelerate time to market. Reach out to us via our intake form for assistance.What’s new in Microsoft Sentinel: April 2026
Welcome to the April 2026 edition of What's new in Microsoft Sentinel. April brings a broad set of updates, with RSAC 2026 announcements rolling out alongside new features. Highlights include cost limit enforcement to prevent runaway query costs, curated open-source intelligence in Threat Analytics, and new data connectors for CrowdStrike, Imperva, AWS, and Logstash. Together, these innovations help security teams control costs, stay ahead of emerging threats, and broaden visibility without added complexity. Read on to learn what's new with Sentinel. What's new OSINT reports in Threat Analytics [Preview] Customers can now consume curated OSINT articles alongside Microsoft-authored Threat Analytics reports, all in one place. (OSINT, or open-source intelligence, is any information readily available to the public.) These OSINT articles come enriched, as detailed in the following list, to help security teams move quickly from awareness to action. What’s included: Curated OSINT articles derived from trusted open-source research Clear summaries with links back to original sources Extracted indicators of compromise (IOCs) Mapped MITRE ATT&CK tactics and techniques Microsoft enrichment, analysis, and recommended actions (when available) By bringing OSINT directly into Threat Analytics, we’re reducing context switching, improving analyst efficiency, and helping customers operationalize open-source intelligence faster within their Defender workflows. Learn more. Cost limit enforcement for KQL queries and notebooks [Preview] Sentinel data lake cost policies do more than just send an alert when usage gets too high. You can set hard limits for KQL queries, jobs, and notebook sessions that block new work once a threshold is exceeded, eliminating surprise bills from runaway queries or heavy workloads. For example, instead of finding out about cost spikes after you run large queries against the data lake tier, enforcement stops further queries before the damage is done. Anything already running still finishes normally, and you get clear messaging about what happened and what to do next. You can lift guardrails temporarily, adjust thresholds, or disable enforcement on the fly. Learn more. Sentinel data connectors With 380 Sentinel data connectors, customers achieve broad visibility into complex digital environments and can expand their security operations effectively. Below are the latest updates. CrowdStrike API Connector [Generally Available] The CrowdStrike API Connector ingests logs from CrowdStrike APIs into Sentinel, fetching details on hosts, detections, incidents, alerts, and vulnerabilities from your CrowdStrike environment. Imperva Cloud WAF [Preview] The Imperva Cloud WAF data connector ingests Imperva logs into Sentinel through AWS S3 buckets, giving you visibility into web application traffic and threats detected by your Imperva deployment for monitoring, investigation, and threat hunting in Sentinel. AWS Elastic Load Balancer (ELB) [Preview] This connector allows you to ingest AWS Elastic Load Balancer (ALB, NLB, and GLB) logs into Sentinel. These logs contain detailed records for requests handled by your load balancers, including client IPs, latencies, request paths, and status codes. These logs are useful for monitoring traffic patterns, investigating anomalies, and ensuring security compliance. Logstash Output Plugin [Preview] For organizations that rely on Logstash to collect from on-premises, legacy, or air-gapped environments, the Sentinel Logstash Output Plugin has been rebuilt in Java to align with Microsoft's Secure Future Initiative (SFI) and provide improved security and long-term maintainability. The plugin uses the Azure Monitor Logs Ingestion API with Data Collection Rules (DCRs), giving you full schema control and the ability to ingest directly into Sentinel data lake as well as standard Sentinel tables. Learn more. Sentinel data federation [Preview] Sentinel data federation enables unified visibility and security analytics across federated and ingested data, without compromising data governance. Security teams can quickly query data in Microsoft Fabric, Azure Data Lake Storage (ADLS) Gen2, and Azure Databricks directly from Sentinel, no data movement required. This approach allows teams to explore data broadly through federation, then selectively ingest what matters most into Sentinel to unlock advanced detections, automation, and AI‑powered analytics. Learn more. Sentinel cost estimation tool [Preview] Customers and partners can confidently estimate Sentinel costs using the cost estimation tool. With meter-level guidance, you can model ingestion across analytics and data lake tiers, compare retention options, and estimate compute costs. Built‑in projections of up to three years offer transparency into spend, making it easier to plan, optimize, and share estimates. Try the Sentinel Cost Estimator. Microsoft Entra and Azure Resource Graph (ARG) connector enhancements [Preview] Enable new Entra assets (EntraDevices, EntraOrgContacts) and ARG assets (ARGRoleDefinitions) in existing asset connectors, expanding inventory coverage and powering richer, built‑in graph experiences for greater visibility. Create workbook reports directly from the data lake [Preview] Sentinel workbooks can directly run on the data lake using KQL, enabling you to visualize and monitor security data straight from the data lake. By selecting the data lake as the workbook data source, you can create trend analysis and executive reporting. Custom graphs [Preview] Custom graphs let you model relationships unique to your organization using data from Sentinel data lake, non-Microsoft sources, and federated data sources, all powered by Fabric. Instead of stitching together dozens of tables manually, you can build graphs that surface blast radius, trace attack paths, map privilege chains, and spot structural outliers like unusually broad access or anomalous email exfiltration. You can generate custom graphs using AI-assisted coding in the Microsoft Sentinel VS Code extension, persist them via a schedule job, and access them in the graphs experience in the Defender portal. Run Graph Query Language (GQL) queries, visualize results, and interactively traverse the graph to the next hop with a single click. These graphs also provide the knowledge context that enables AI-powered agent experiences to work more effectively, speeding investigations and helping you move from disconnected alerts to confident decisions at scale. Custom graph API usage for creating and querying graphs is billed according to the Sentinel graph meter. Learn more. MCP entity analyzer [General availability] Entity analyzer provides reasoned, out-of-the-box risk assessments that help you quickly understand whether a URL or user identity represents potential malicious activity. It analyzes data across threat intelligence, prevalence, and organizational context to generate clear, explainable verdicts you can trust. Entity analyzer integrates with your agents through Sentinel MCP server connections to first-party and third-party AI runtime platforms, or with your SOAR workflows through Logic Apps. It also serves as a trusted foundation for the Defender Triage Agent, delivering more accurate alert classifications and deeper investigative reasoning. Entity analyzer is billed based on Security Compute Units (SCU) consumption. Learn more about entity analyzer and MCP billing. Claude MCP connector [Preview] Anthropic Claude can connect to Sentinel through a custom MCP connector, giving you AI-assisted analysis across your Sentinel environment. Microsoft provides step-by-step guidance for configuring a custom connector in Claude that securely connects to a Sentinel MCP server. With this connection you can summarize incidents, investigate alerts, and reason over security signals while keeping data inside Microsoft's security boundary. Access to large language models (LLMs) is managed through Microsoft authentication and role-based controls, supporting faster triage and investigation workflows while maintaining compliance and visibility. CVEs of interest in the Threat Intelligence Briefing Agent [Preview] The Threat Intelligence Briefing Agent delivers curated intelligence based on your organization’s configuration, preferences, and unique industry and geographic needs. The agent surfaces Common Vulnerabilities and Exposures (CVEs) of interest, highlighting vulnerabilities actively discussed across the security landscape and assessing their potential impact on your environment for more timely threat intelligence insights. The agent automatically incorporates internet exposure data powered by the Sentinel platform to surface threats targeting technologies exposed in your organization. Together, these enhancements help you focus faster on the threats that matter most, without manual investigation. Additional resources Blogs and documentation: Featured blog: App Assure launches its Sentinel Advisory Service Agentic use cases for developers on Microsoft Sentinel The Unified SecOps Transition: Why It Is a Security Architecture Decision, Not Just a Portal Change What's new in Microsoft Defender – April 2026 Webinars and training: Featured webinar: Powering the Agentic SOC with Scott Woodgate, General Manager, Microsoft Threat Protection Featured training: Introducing the Microsoft Sentinel Training Lab. Hands-On Security Operations in Minutes Beyond KQL – Unlocking SOC Insights with Sentinel data lake Jupyter Notebooks Hyper scale your SOC: Manage delegated access and role-based scoping in Microsoft Defender 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!Microsoft 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.Declutter and Defend: Reducing promotional mail noise with Microsoft Defender
Enterprise inboxes are overwhelmed with graymail — legitimate, bulk email like newsletters, vendor promotions, and product updates that isn't malicious but buries the messages that matter. When high volumes of these mails land in the inbox, it crowds out priority communications and can dull security vigilance. Employees conditioned to ignore repetitive emails may miss signs of a real threat. It also creates recurring work for admins and security teams who must continuously tune filters, manage exception requests, and chase noise from user reports for email that isn’t malicious. Because graymail passes every spam filter check, traditional defenses don't separate it — leaving this signal-to-noise gap unaddressed. Today we’re excited to announce that Microsoft Defender now includes built-in graymail filtering. It is delivered natively through a new Promotions experience in Outlook that automatically classifies and separates bulk email, so it no longer competes with business-critical communication in the inbox. Now in Public Preview, this capability learns from how users interact with graymail to become more accurate over time. Coupled with the existing Bulk Senders Insight report, Defender brings data-driven bulk classification and control into the security workflows you already use. What Is Graymail? Graymail is legitimate bulk email that isn't malicious—product newsletters, event announcements, marketing promotions, and software update notifications from reputable, authenticated senders. It is distinct from spam and from phishing - graymail comes from real organizations with proper authentication and traditional spam filters aren't designed to handle it. Graymail handling in Microsoft Defender Microsoft Defender's approach is built on three principles: classify intelligently, deliver natively, and learn continuously. Promotions Folder — Intelligent Inbox Organization A dedicated Promotions folder, natively provisioned in Outlook, now keeps legitimate bulk mail out of the primary inbox. Promotional content is separated from priority emails without being sent to Junk, which means users can still access and browse newsletters and updates at their own pace. The folder appears at the top level of the mailbox for easy discovery and is visible across all Outlook experiences. Non-spam bulk mail below the organization's configured Bulk Complaint Level threshold is automatically routed to the Promotions folder. Messages from senders the user has explicitly allowed continue to land in the Inbox. Messages identified as spam continue to go to Junk. To enable the Promotions folder administrators need to enable the "Bulk Moves Enabled" setting in their anti-spam policy. The Promotions folder is then created for all users and used for routing only when this setting is ON. Existing mail flow is unaffected. Promotional mail tagging and Mailbox Rule Support Messages classified as graymail will automatically be labeled with a "Promotions" system tag in Outlook. The tag provides instant visual context without requiring users to open each message and is visible in Outlook on the Web and the native Outlook desktop apps for Windows and Mac. During Public Preview, the tagging component is opt-in, requiring administrators to enable it by configuring an Exchange Transport Rule. Once generally available, it will be enabled by default. Because this classification is integrated at the client level, the Promotions tag can also be used as a condition in Outlook mailbox rules. This enables custom routing logic for advanced scenarios like moving all promotions-tagged messages from a specific sender to a custom folder, flagging certain promotional emails for follow-up, or auto-forwarding or deleting promotions that meet specific criteria. This transforms the Promotions classification from a one-way filter into a flexible building block for personal and organizational workflows—particularly valuable for power users and teams with compliance or archival requirements. Adaptive Learning Microsoft Defender's graymail filtering gets smarter with every interaction. The system learns directly from how users handle their mail. When a user moves a message out of the Promotions folder and back to the Inbox, future emails from that sender will no longer be placed in the Promotions folder. When a user moves a message from the Inbox into the Promotions folder, future emails from that sender will be routed to the Promotions folder automatically. This creates a personalized, self-improving experience that becomes more accurate over time - no manual rule configuration required, no safe-sender lists to maintain, and no filtering rules for IT teams to manage on behalf of individual employees. Built into existing Security Workflows Administrators also gain visibility through the Bulk Senders Insight report, which provides data-driven guidance on what your organization actually receives and can help tune your bulk mail filtering. Graymail has long been the unsolved middle ground of email security—too legitimate to block, too noisy to ignore. Microsoft Defender now handles it where it should be handled: inside the platform, inside the mailbox, and inside the security workflows your organization already relies on. No new portals, no new vendors, no compromise between security and user experience. Get Started Configure promotions tagging and the promotions folder today - Bulk email detection documentation on Microsoft Learn. Monitor the experience using the Bulk Senders Insight report.
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.Why UK Enterprise Cybersecurity Is Failing in 2026 (And What Leaders Must Change)
Enterprise cybersecurity in large organisations has always been an asymmetric game. But with the rise of AI‑enabled cyber attacks, that imbalance has widened dramatically - particularly for UK and EMEA enterprises operating complex cloud, SaaS, and identity‑driven environments. Microsoft Threat Intelligence and Microsoft Defender Security Research have publicly reported a clear shift in how attackers operate: AI is now embedded across the entire attack lifecycle. Threat actors use AI to accelerate reconnaissance, generate highly targeted phishing at scale, automate infrastructure, and adapt tactics in real time - dramatically reducing the time required to move from initial access to business impact. In recent months, Microsoft has documented AI‑enabled phishing campaigns abusing legitimate authentication mechanisms, including OAuth and device‑code flows, to compromise enterprise accounts at scale. These attacks rely on automation, dynamic code generation, and highly personalised lures - not on exploiting traditional vulnerabilities or stealing passwords. The Reality Gap: Adaptive Attackers vs. Static Enterprise Defences Meanwhile, many UK enterprises still rely on legacy cybersecurity controls designed for a very different threat model - one rooted in a far more predictable world. This creates a dangerous "Resilience Gap." Here is why your current stack is failing- and the C-Suite strategy required to fix it. 1. The Failure of Traditional Antivirus in the AI Era Traditional antivirus (AV) relies on static signatures and hashes. It assumes malicious code remains identical across different targets. AI has rendered this assumption obsolete. Modern malware now uses automated mutation to generate unique code variants at execution time, and adapts behaviour based on its environment. Microsoft Threat Intelligence has observed threat actors using AI‑assisted tooling to rapidly rewrite payload components, ensuring that every deployment looks subtly different. In this model, there is no reliable signature to detect. By the time a pattern exists, the attacker has already moved on. Signature‑based detection is not just slow - it is structurally misaligned with AI‑driven attacks. The Risk: If your security relies on "recognising" a threat, you are already breached. By the time a signature exists, the attacker has evolved. The C-Suite Pivot: Shift investment from artifact detection to EDR/XDR (Extended Detection and Response). We must prioritise behavioural analytics and machine learning models that identify intent rather than file names. 2. Why Perimeter Firewalls Fail in a Cloud-First World Many UK enterprise still rely on firewalls enforcing static allow/deny rules based on IP addresses and ports. This model worked when applications were predictable and networks clearly segmented. Today, enterprise traffic is encrypted, cloud‑hosted, API‑driven, and deeply integrated with SaaS and identity services. AI‑assisted phishing campaigns abusing OAuth and device‑code flows demonstrate this clearly. From a network perspective, everything looks legitimate: HTTPS traffic to trusted identity providers. No suspicious port. No malicious domain. Yet the attacker successfully compromises identity. The Risk: Traditional firewalls are "blind" to identity-based breaches in cloud environments. The C-Suite Pivot: Move to Identity-First Security. Treat Identity as the new Control Plane, integrating signals like user risk, device health, and geolocation into every access decision. 3. The Critical Weakness of Single-Factor Authentication Despite clear NCSC guidance, single-factor passwords remain a common vulnerability in legacy applications and VPNs. AI-driven credential abuse has changed the economics of these attacks. Threat actors now deploy adaptive phishing campaigns that evolve in real-time. Microsoft has observed attackers using AI to hyper-target high-value UK identities- specifically CEOs, Finance Directors, and Procurement leads. The Risk: Static passwords are now the primary weak link in UK supply chain security. The C-Suite Pivot: Mandate Phishing‑resistant MFA (Passkeys or hardware security keys). Implement Conditional Access policies that evaluate risk dynamically at the moment of access, not just at login. Legacy Security vs. AI‑Era Reality 4. The Inherent Risk of VPN-Centric Security VPNs were built on a flawed assumption: that anyone "inside" the network is trustworthy. In 2026, this logic is a liability. AI-assisted attackers now use automation to map internal networks and identify escalation paths the moment they gain VPN access. Furthermore, Microsoft has tracked nation-state actors using AI to create synthetic employee identities- complete with fake resumes and deepfake communication. In these scenarios, VPN access isn't "hacked"; it is legally granted to a fraudster. The Risk: A compromised VPN gives an attacker the "keys to the kingdom." The C-Suite Pivot: Transition to Zero Trust Architecture (ZTA). Access must be explicit, scoped to the specific application, and continuously re‑evaluated using behavioural signals. 5. Data: The High-Velocity Target Sensitive data sitting unencrypted in legacy databases or backups is a ticking time bomb. In the AI era, data discovery is no longer a slow, manual process for a hacker. Attackers now use AI to instantly analyse your directory structures, classify your files, and prioritise high-value data for theft. Unencrypted data significantly increases your "blast radius," turning a containable incident into a catastrophic board-level crisis. The Risk: Beyond the technical breach, unencrypted data leads to massive UK GDPR fines and irreparable brand damage. The C-Suite Pivot: Adopt Data-Centric Security. Implement encryption by default, classify data while adding sensitivity labels and start board-level discussions regarding post‑quantum cryptography (PQC) to future-proof your most sensitive assets. 6. The Failure of Static IDS Traditional Intrusion Detection Systems (IDS) rely on known indicators of compromise - assuming attackers reuse the same tools and techniques. AI‑driven attacks deliberately avoid that assumption. Threat actors are now using Large Language Models (LLMs) to weaponize newly disclosed vulnerabilities within hours. While your team waits for a "known pattern" to be updated in your system, the attacker is already using a custom, AI-generated exploit. The Risk: Your team is defending against yesterday's news while the attacker is moving at machine speed. The C-Suite Pivot: Invest in Adaptive Threat Detection. Move toward Graph‑based XDR platforms that correlate signals across email, endpoint, and cloud to automate investigation and response before the damage spreads. From Static Security to Continuous Security Closing Thought: Security Is a Journey, Not a Destination For UK enterprises, the shift toward adaptive cybersecurity is no longer optional - it is increasingly driven by regulatory expectation, board oversight, and accountability for operational resilience. Recent UK cyber resilience reforms and evolving regulatory frameworks signal a clear direction of travel: cybersecurity is now a board‑level responsibility, not a back‑office technical concern. Directors and executive leaders are expected to demonstrate effective governance, risk ownership, and preparedness for cyber disruption - particularly as AI reshapes the threat landscape. AI is not a future cybersecurity problem. It is a current force multiplier for attackers, exposing the limits of legacy enterprise security architectures faster than many organisations are willing to admit. The uncomfortable truth for boards in 2026 is that no enterprise is 100% secure. Intrusions are inevitable. Credentials will be compromised. Controls will be tested. The difference between a resilient enterprise and a vulnerable one is not the absence of incidents, but how risk is managed when they occur. In mature organisations, this means assuming breach and designing for containment: Access controls that limit blast radius Least privilege and conditional access restricting attackers to the smallest possible scope if an identity is compromised Data‑centric security using automated classification and encryption, ensuring that even when access is misused, sensitive data cannot be freely exfiltrated As a Senior Enterprise Cybersecurity Architect, I see this moment as a unique opportunity. AI adoption does not have to repeat the mistakes of earlier technology waves, where innovation moved fast and security followed years later. We now have a rare chance to embed security from day one - designing identity controls, data boundaries, automated monitoring, and governance before AI systems become business‑critical. When security is built in upfront, enterprises don’t just reduce risk - they gain the confidence to move faster and unlock AI’s value safely. Security is no longer a “department”. In the age of AI, it is a continuous business function - essential to preserving trust and maintaining operational continuity as attackers move at machine speed. References: Inside an AI‑enabled device code phishing campaign | Microsoft Security Blog AI as tradecraft: How threat actors operationalize AI | Microsoft Security Blog Detecting and analyzing prompt abuse in AI tools | Microsoft Security Blog Post-Quantum Cryptography | CSRC Microsoft Digital Defense Report 2025 | Microsoft https://www.ncsc.gov.uk/news/government-adopt-passkey-technology-digital-servicesMicrosoft 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.
