Microsoft Sentinel’s AI-driven UEBA ushers in the next era of behavioral analytics
Co-author - Ashwin Patil Security teams today face an overwhelming challenge: every data point is now a potential security signal and SOCs are drowning in complex logs, trying to find the needle in the haystack. Microsoft Sentinel User and Entity Behavior Analytics (UEBA) brings the power of AI to automatically surface anomalous behaviors, helping analysts cut through the noise, save time, and focus on what truly matters. Microsoft Sentinel UEBA has already helped SOCs uncover insider threats, detect compromised accounts, and reveal subtle attack signals that traditional rule-based methods often miss. These capabilities were previously powered by a core set of high-value data sources - such as sign-in activity, audit logs, and identity signals - that consistently delivered rich context and accurate detections. Today, we’re excited to announce a major expansion: Sentinel UEBA now supports six new data sources including Microsoft first- and third-party platforms like Azure, AWS, GCP, and Okta, bringing deeper visibility, broader context, and more powerful anomaly detection tailored to your environment. This isn’t just about ingesting more logs. It’s about transforming how SOCs understand behavior, detect threats, and prioritize response. With this evolution, analysts gain a unified, cross-platform view of user and entity behavior, enabling them to correlate signals, uncover hidden risks, and act faster with greater confidence. Newly supported data sources are built for real-world security use cases: Authentication activities MDE DeviceLogonEvents – Ideal for spotting lateral movement and unusual access. AADManagedIdentitySignInLogs – Critical for spotting stealthy abuse of non - human identities. AADServicePrincipalSignInLogs - Identifying anomalies in service principal usage such as token theft or over - privileged automation. Cloud platforms & identity management AWS CloudTrail Login Events - Surfaces risky AWS account activity based on AWS CloudTrail ConsoleLogin events and logon related attributes. GCP Audit Logs - Failed IAM Access, Captures denied access attempts indicating reconnaissance, brute force, or privilege misuse in GCP. Okta MFA & Auth Security Change Events – Flags MFA challenges, resets, and policy modifications that may reveal MFA fatigue, session hijacking, or policy tampering. Currently supports the Okta_CL table (unified Okta connector support coming soon). These sources feed directly into UEBA’s entity profiles and baselines - enriching users, devices, and service identities with behavioral context and anomalies that would otherwise be fragmented across platforms. This will complement our existing supported log sources - monitoring Entra ID sign-in logs, Azure Activity logs and Windows Security Events. Due to the unified schema available across data sources, UEBA enables feature-rich investigation and the capability to correlate across data sources, cross platform identities or devices insights, anomalies, and more. AI-powered UEBA that understands your environment Microsoft Sentinel UEBA goes beyond simple log collection - it continuously learns from your environment. By applying AI models trained on your organization’s behavioral data, UEBA builds dynamic baselines and peer groups, enabling it to spot truly anomalous activity. UBEA builds baselines from 10 days (for uncommon activities) to 6 months, both for the user and their dynamically calculated peers. Then, insights are surfaced on the activities and logs - such as an uncommon activity or first-time activity - not only for the user but among peers. Those insights are used by an advanced AI model to identify high confidence anomalies. So, if a user signs in for the first time from an uncommon location, a common pattern in the environment due to reliance on global vendors, for example, then this will not be identified as an anomaly, keeping the noise down. However, in a tightly controlled environment, this same behavior can be an indication of an attack and will surface in the Anomalies table. Including those signals in custom detections can help affect the severity of an alert. So, while logic is maintained, the SOC is focused on the right priorities. How to use UEBA for maximum impact Security teams can leverage UEBA in several key ways. All the examples below leverage UEBA’s dynamic behavioral baselines looking back up to 6 months. Teams can also leverage the hunting queries from the "UEBA essentials" solution in Microsoft Sentinel's Content Hub. Behavior Analytics: Detect unusual logon times, MFA fatigue, or service principal misuse across hybrid environments. Get visibility into geo-location of events and Threat Intelligence insights. Here’s an example of how you can easily discover Accounts authenticating without MFA and from uncommonly connected countries using UEBA behaviorAnalytics table: BehaviorAnalytics | where TimeGenerated > ago(7d) | where EventSource == "AwsConsoleSignIn" | where ActionType == "ConsoleLogin" and ActivityType == "signin.amazonaws.com" | where ActivityInsights.IsMfaUsed == "No" | where ActivityInsights.CountryUncommonlyConnectedFromInTenant == True | evaluate bag_unpack(UsersInsights, "AWS_") | where InvestigationPriority > 0 // Filter noise - uncomment if you want to see low fidelity noise | project TimeGenerated, _WorkspaceId, ActionType, ActivityType, InvestigationPriority, SourceIPAddress, SourceIPLocation, AWS_UserIdentityType, AWS_UserIdentityAccountId, AWS_UserIdentityArn Anomaly detection Identify lateral movement, dormant account reactivation, or brute-force attempts, even when they span cloud platforms. Below are examples of how to discover UEBA Anomalous AwsCloudTrail anomalies via various UEBA activity insights or device insights attributes: Anomalies | where AnomalyTemplateName in ( "UEBA Anomalous Logon in AwsCloudTrail", // AWS ClousTrail anomalies "UEBA Anomalous MFA Failures in Okta_CL", "UEBA Anomalous Activity in Okta_CL", // Okta Anomalies "UEBA Anomalous Activity in GCP Audit Logs", // GCP Failed IAM access anomalies "UEBA Anomalous Authentication" // For Authentication related anomalies ) | project TimeGenerated, _WorkspaceId, AnomalyTemplateName, AnomalyScore, Description, AnomalyDetails, ActivityInsights, DeviceInsights, UserInsights, Tactics, Techniques Alert optimization Use UEBA signals to dynamically adjust alert severity in custom detections—turning noisy alerts into high-fidelity detections. The example below shows all the users with anomalous sign in patterns based on UEBA. Joining the results with any of the AWS alerts with same AWS identity will increase fidelity. BehaviorAnalytics | where TimeGenerated > ago(7d) | where EventSource == "AwsConsoleSignIn" | where ActionType == "ConsoleLogin" and ActivityType == "signin.amazonaws.com" | where ActivityInsights.FirstTimeConnectionViaISPInTenant == True or ActivityInsights.FirstTimeUserConnectedFromCountry == True | evaluate bag_unpack(UsersInsights, "AWS_") | where InvestigationPriority > 0 // Filter noise - uncomment if you want to see low fidelity noise | project TimeGenerated, _WorkspaceId, ActionType, ActivityType, InvestigationPriority, SourceIPAddress, SourceIPLocation, AWS_UserIdentityType, AWS_UserIdentityAccountId, AWS_UserIdentityArn, ActivityInsights | evaluate bag_unpack(ActivityInsights) Another example shows anomalous key vault access from service principal with uncommon source country location. Joining this activity with other alerts from the same service principle increases fidelity of the alerts. You can also join the anomaly UEBA Anomalous Authentication with other alerts from the same identity to bring the full power of UEBA into your detections. BehaviorAnalytics | where TimeGenerated > ago(1d) | where EventSource == "Authentication" and SourceSystem == "AAD" | evaluate bag_unpack(ActivityInsights) | where LogonMethod == "Service Principal" and Resource == "Azure Key Vault" | where ActionUncommonlyPerformedByUser == "True" and CountryUncommonlyConnectedFromByUser == "True" | where InvestigationPriority > 0 Final thoughts This release marks a new chapter for Sentinel UEBA—bringing together AI, behavioral analytics, and cross-cloud and identity management visibility to help defenders stay ahead of threats. If you haven’t explored UEBA yet, now’s the time. Enable it in your workspace settings and don’t forget to enable anomalies as well (in Anomalies settings). And if you’re already using it, these new sources will help you unlock even more value. Stay tuned for our upcoming Ninja show and webinar (register at aka.ms/secwebinars), where we’ll dive deeper into use cases. Until then, explore the new sources, use the UEBA workbook, update your watchlists, and let UEBA do the heavy lifting. UEBA onboarding and setting documentation Identify threats using UEBA UEBA enrichments and insights reference UEBA anomalies reference
Ingesting Akamai Audit Logs into Microsoft Sentinel using Azure Function Apps
Introduction Akamai provides extensive audit logs that can be valuable for security monitoring and compliance. To integrate Akamai Audit logs with Microsoft Sentinel, we can use Azure Function Apps to retrieve logs via the Akamai EdgeGrid API and send them to Log Analytics Workspace. In this guide, we will walk through deploying an Azure Function App that fetches Akamai Audit Logs and ingests them into Microsoft Sentinel. Prerequisites Before starting, ensure you have: An active Azure subscription with Microsoft Sentinel enabled. Akamai API credentials (EdgeGrid authentication: client_token, client_secret, and access_token). A Log Analytics Workspace (LAW) where logs will be ingested. Azure Function App deployed via VS Code. Python installed locally (Use the VSCode for the local deployment). High-Level Architecture Azure Function App calls Akamai API to fetch audit logs. Logs are parsed and sent to Microsoft Sentinel via Log Analytics API request to Azure Function App. Scheduled Execution ensures logs are fetched periodically. Step 1: Create an Azure Function App To deploy an Azure Function App via VS Code: Install the Azure Functions extension for VS Code. Install Azure Core Tools: npm install -g azure-functions-core-tools@4 --unsafe-perm true Create a Python-based Function App: func init AkamaiLogsFunction --python cd AkamaiLogsFunction func new --name FetchAkamaiLogs --template "HTTP trigger" --authlevel "anonymous" Step 2: Install Required Python Packages In your Function App directory, install the required dependencies: pip install requests akamai.edgegrid pip freeze > requirements.txt Step 3: Configure Environment Variables Instead of hardcoding API credentials, store them in Azure Function App settings: Go to Azure Portal > Function App. Navigate to Configuration > Application settings. Add the following environment variables: AKAMAI_CLIENT_TOKEN AKAMAI_CLIENT_SECRET AKAMAI_ACCESS_TOKEN WORKSPACE_ID (Log Analytics Workspace ID) SHARED_KEY (Log Analytics Shared Key) Step 4: Implement the Azure Function Code Create AkamaiLogFetcher.py with the following code: import azure.functions as func import logging import requests from akamai.edgegrid import EdgeGridAuth from urllib.parse import urljoin import os app = func.FunctionApp() # Azure Function HTTP Trigger @app.function_name(name="AkamaiLogFetcher") @app.route(route="fetchlogs", auth_level=func.AuthLevel.ANONYMOUS) def fetch_logs(req: func.HttpRequest) -> func.HttpResponse: logging.info("Processing Akamai log fetch request...") # Akamai API credentials (move these to Azure App Settings for security) baseurl = 'https://YOURBASEHOSTURL.luna.akamaiapis.net/' client_token = os.getenv("AKAMAI_CLIENT_TOKEN", "xxxxxxxxxxxxxx") client_secret = os.getenv("AKAMAI_CLIENT_SECRET", "xxxxxxxxxxxxx") access_token = os.getenv("AKAMAI_ACCESS_TOKEN", "xxxxxxxxxxxxxx") # Initialize session with authentication session = requests.Session() session.auth = EdgeGridAuth( client_token=client_token, client_secret=client_secret, access_token=access_token ) try: # Call Akamai API response = session.get(urljoin(baseurl, '/events/v3/events')) response.raise_for_status() # Raise an error for HTTP errors # Return response as JSON return func.HttpResponse(response.text, mimetype="application/json", status_code=response.status_code) except requests.exceptions.RequestException as e: logging.error(f"Error fetching logs: {e}") return func.HttpResponse(f"Failed to fetch logs: {str(e)}", status_code=500) Step 5: Deploy the Function to Azure Run the following command to deploy the function: func azure functionapp publish <YourFunctionAppName> Step 6: Setting Up the Logic App Workflow Create a new Logic App in Azure: Navigate to the Azure Portal -> Logic Apps -> Create. Choose Consumption Plan and select your preferred region. Click Review + Create, then Create. Add an HTTP Trigger: Select Recurrence as the trigger. Configure it to run every 10 minutes. Configure the HTTP Action to Fetch Logs from Akamai Function App API: Use the HTTP action in Logic Apps. Set the method to GET. Enter the Function App URL. Add the required headers (content type). Parse the JSON Response: Use the "Parse JSON" action to structure the response. Define the schema using a sample response from Akamai Audit Logs. Send Logs to Microsoft Sentinel: Use the "Azure Log Analytics - Send Data" action. Map the Akamai Audit log fields to the Log Analytics schema. Select the appropriate Custom Table in Log Analytics or use CommonSecurityLog. JSON Request body for Send Logs trigger Completed Logic App will look like this: Step 7: Testing and Validation Run a test execution of the Logic App. Check the Logic Apps run history to ensure successful Function App calls and data ingestion. Verify logs in Sentinel: Navigate to Microsoft Sentinel -> Logs. Run a KQL query: RadwareEvents_CL | where TimeGenerated > ago(10m) Summary This guide demonstrated how to use Azure Function Apps and Logic Apps to fetch Akamai Audit Logs via API and send them to Microsoft Sentinel. The serverless approach ensures efficient log collection without requiring dedicated infrastructure.Table Talk: Sentinel’s New ThreatIntel Tables Explained
Key updates On April 3, 2025, we publicly previewed two new tables to support STIX (Structured Threat Information eXpression) indicator and object schemas: ThreatIntelIndicators and ThreatIntelObjects. To summarize the important dates: 31 August 2025: We previously announced that data ingestion into the legacy ThreatIntelligenceIndicator table would cease on the 31 July 2025. This timeline has now been extended and the transition to the new ThreatIntelIndicators and ThreatIntelObjects tables will proceed gradually until the 31 st of August 2025. The legacy ThreatIntelligenceIndicator table (and its data) will remain accessible, but no new data will be ingested there. Therefore, any custom content, such as workbooks, queries, or analytic rules, must be updated to reference the new tables to remain effective. If you require additional time to complete the transition, you may opt into dual ingestion, available until the official retirement on the 21 st of May 2026, by submitting a service request. 31 May 2026: ThreatIntelligenceIndicator table support will officially retire, along with ingestion for those who opt-in to dual ingestion beyond 31 st of August 2025. What’s changing: ThreatIntelligenceIndicator VS ThreatIntelIndicators and ThreatIntelObjects Let’s summarise some of the differences. ThreatIntelligenceIndicator ThreatIntelIndicators ThreatIntelObjects Status Extended data ingestion until the 31st of August 2025, opt-in for additional transition time available. Deprecating on the 31st of May 2026 — no new data will be ingested after this date. Active and recommended for use. Active and complementary to ThreatIntelIndicators. Purpose Originally used to store threat indicators like IPs, domains, file hashes, etc. Stores individual threat indicators (e.g. IPs, URLs, file hashes). Stores STIX objects that provide contextual information about indicators. Examples: threat actors, malware families, campaigns, attack patterns. Characteristics Limitations: o Less flexible schema. o Limited support for STIX (Structured Threat Information eXpression) objects. o Fewer contextual fields for advanced threat hunting. Enhancements: o Supports STIX indicator schema. o Includes a Data column with full STIX object data for advanced hunting. o More metadata fields (e.g. LastUpdateMethod, IsDeleted, ExpirationDateTime). o Optimized ingestion: excludes empty key-value pairs and truncates long fields over 1,000 characters. Enhancements: o Enables richer threat modelling and correlation. o Includes fields like StixType, Data.name, and Data.id. Use cases Legacy structure for storing threat indicators. Migration Note: All custom queries, workbooks, and analytics rules referencing this table must be updated to use the new tables . Ideal for identifying and correlating specific threat indicators. Threat Hunting: Enables hunting for specific Indicators of Compromise (IOCs) such as IP addresses, domains, URLs, and file hashes. Alerting and detection rules: Can be used in KQL queries to match against telemetry from other tables (e.g. Heartbeat, SecurityEvent, Syslog). Example query correlating threat indictors with threat actors: Identify threat actors associated with specific threat indicators Useful for understanding relationships between indicators and broader threat entities (e.g. linking an IP to a known threat actor). Threat Hunting: Adds context by linking indicators to threat actors, malware families, campaigns, and attack patterns. Alerting and Detection rules: Enrich alerts with context like threat actor names or malware types. Example query listing TI objects related to a threat actor, “Sangria Tempest.” : List threat intelligence data related to a specific threat actor Benefits of the new ThreatIntelIndicators and ThreatIntelObjects tables In addition to what’s mentioned in the table above. The main benefits of the new table include: Enhanced Threat Visibility More granular and complete representation of threat intelligence. Support for advanced hunting scenarios and complex queries. Enables attribution to threat actors and relationships. Improved Hunting Capabilities Generic parsing of STIX patterns. Support for all valid STIX IoCs, Threat Actors, Identity, and Relationships. Important considerations with the new TI tables Higher volume of data being ingested: o In the legacy ThreatIntelligenceIndicator table, only the IoCs with Domain, File, URL, Email, Network sources were ingested. o The new tables support a richer schema and more detailed data, which naturally increases ingestion volume. The Data column in both tables stores full STIX objects, which are often large and complex. o Additional metadata fields (e.g. LastUpdateMethod, StixType, ObservableKey, etc.) increase the size of each record. o Some fields like description and pattern are truncated if they exceed 1,000 characters, indicating the potential for large payloads. More Frequent Republishing: o Previously, threat intelligence data was republished over a 12-day cycle. Now, all data is republished every 7-10 days (depending on the volume), increasing the ingestion frequency and volume. o This change ensures fresher data but also leads to more frequent ingestion events. o Republishing is identifiable by LastUpdateMethod = "LogARepublisher" in the tables. Optimising data ingestion There are two mechanisms to optimise threat intelligence data ingestion and control costs. Ingestion Rules See ingestion rules in action: Introducing Threat Intelligence Ingestion Rules | Microsoft Community Hub Sentinel supports Ingestion Rules that allow organizations to curate data before it enters the system. In addition, it enables: Bulk tagging, expiration extensions, and confidence-based filtering, which may increase ingestion if more indicators are retained or extended. Custom workflows that may result in additional ingestion events (e.g. tagging or relationship creation). Reduce noise by filtering out irrelevant TI Objects such as low confidence indicators (e.g. drop IoCs with a confidence score of 0), suppressing known false positives from specific feeds. These rules act on TI objects before they are ingested into Sentinel, giving you control over what gets stored and analysed. Data Collection Rules/ Data transformation As mentioned above, the ThreatIntelIndicator and ThreatIntelObjects tables include a “Data” column which contains the full original STIX object and may or may not be relevant for your use cases. In this case, you can use a workspace transformation DCR to filter it out using a KQL query. An example of this KQL query is shown below, for more examples about using workspace transformations and data collection rules: Data collection rules in Azure Monitor - Azure Monitor | Microsoft Learn source | project-away Data A few things to note: o Your threat intelligence feeds will be sending the additional STIX objects data and IoCs, if you prefer not to receive these additional TI data, you can modify the filter out data according to your use cases as mentioned above. More examples are mentioned here: Work with STIX objects and indicators to enhance threat intelligence and threat hunting in Microsoft Sentinel (Preview) - Microsoft Sentinel | Microsoft Learn o If you are using a data collection rule to make schema changes such as dropping the fields, please make sure to modify the relevant Sentinel content (e.g. detection rules, Workbooks, hunting queries, etc.) that are using the tables. o There can be additional cost when using Azure Monitor data transformations (such as when adding extra columns or adding enrichments to incoming data), however, if Sentinel is enabled on the Log Analytics workspace, there is no filtering ingestion charge regardless of how much data the transformation filters. New Threat Intelligence solution pack available A new Threat Intelligence solution is now available in the Content Hub, providing out of the box content referencing the new TI tables, including 51 detection rules, 5 hunting queries, 1 Workbook, 5 data connectors and also includes 1 parser for the ThreatIntelIndicators. Please note, the previous Threat Intelligence solution pack will be deprecated and removed after the transition phase. We recommend downloading the new solution from the Content Hub as shown below: Conclusion The transition to the new ThreatIntelIndicators and ThreatIntelObjects tables provide enhanced support for STIX schemas, improved hunting and alerting features, and greater control over data ingestion allowing organizations to get deeper visibility and more effective threat detection. To ensure continuity and maximize value, it's essential to update existing content and adopt the new Threat Intelligence solution pack available in the Content Hub. Related content and references: Work with STIX objects and indicators to enhance threat intelligence and threat hunting in Microsoft Sentinel Curate Threat Intelligence using Ingestion Rules Announcing Public Preview: New STIX Objects in Microsoft Sentinel
Exciting Announcements: New Data Connectors Released Using the Codeless Connector Framework
Microsoft Sentinel’s Codeless Connector Framework or ‘CCF’ (formerly called Codeless Connector Platform [CCP]) represents a paradigm shift in data ingestion, making it easier than ever for organisations to do more with Microsoft Sentinel by integrating diverse data sources seamlessly. Designed to simplify and expedite the onboarding of data sources, CCF eliminates the need for extensive coding expertise and maintaining additional services to facilitate ingestion, allowing security teams to focus on what truly matters – safeguarding their environment. Advantages of the Codeless Connector Framework The Codeless Connector Framework offers several compelling benefits: Ease of Use: CCF configuration-based templates allows advanced users to create data connectors without writing exhausting code, making the onboarding process quicker and more accessible to a broader audience. Flexibility: Users can customise data streams to meet their specific needs; optimizing efficacy while ensuring more control on the data being ingested. Scalability: The connectors built using CCF follows a true SaaS auto-expansion model making them highly scalable and natively reliable for large data volumes. Efficiency: By reducing the time and effort required to develop and deploy data connectors, CCF accelerates the availability of critical insights for security monitoring and more rapidly expands the value Microsoft Sentinel provides. What are we up to? We recognize that Codeless Connectors offer substantial advantages over Azure Function App based ingestion in Microsoft Sentinel in most cases. That motivates us to continue investing in modernizing our ingestion patterns for out-of-box connectors; one connector at a time. Another goal of modernizing these connectors is to replace the deprecated HTTP Data Collector API with the Log Ingestion API to send data to Microsoft Sentinel. Announcing the General Availability of New Data Connectors We are continually improving the Data Collection experience for our customers and are thrilled to announce that the following data connectors are now Generally Available (GA) on the Codeless Connector Framework. Atlassian Confluence Ingesting Confluence audit logs allows organizations to monitor collaboration activity, detect security risks, and troubleshoot configuration issues using Confluence audit records. Auth0 With the Auth0 Connector, organizations can effortlessly integrate authentication and authorization data from Auth0 into Microsoft Sentinel. This connector provides valuable insights into user activities and access patterns, bolstering identity security and compliance efforts. Azure DevOps Audit logs from Azure DevOps, allows security teams to monitor user activities, detect anomalous behavior, and investigate potential threats across DevOps environments. Box The Box Connector facilitates the ingestion of file storage and sharing data from Box into Microsoft Sentinel. By leveraging this connector, security teams can monitor file access and sharing activities, ensuring data integrity, and preventing unauthorized access. Google Cloud Platform Load Balancer With GCP Load Balancer and Web Application Firewall (Cloud Armor) logs, security teams can monitor inbound network activity, enforce security policies, and detect threats across GCP environments. Proofpoint POD The ingestion of email security logs allows organizations to monitor message traceability, detect threats, and investigate data exfiltration attempts by attackers and malicious insiders. Proofpoint TAP Email threat intelligence logs, including message and click events, provides visibility into malware and phishing activity to support custom alerts, dashboards, and threat investigation. SentinelOne The SentinelOne Connector enables seamless ingestion of threat intelligence and endpoint security data from SentinelOne into Microsoft Sentinel. This integration empowers security teams to enhance their threat detection capabilities and respond swiftly to potential threats. New Connectors in Public Preview CrowdStrike Falcon Data Replicator (S3 based Polling) Google Cloud Platform VPC Flow Google Cloud Platform DNS Google IAM These new additions are not new out-of-box sources in Microsoft Sentinel, but they do improve how data is collected. The previously Azure Function App based polling has now been upgraded to the Codeless Connector Framework for these products to ensure data collection adheres to the more scalable; advantageous pattern with CCF. As noted previously, the newer version of these connectors replaces the deprecated HTTP Data Collector API with the Log Ingestion API to send data to Microsoft Sentinel. Call to Action! Microsoft Sentinel customers collecting data from any of the mentioned sources using Azure Function Apps are advised to migrate their ingestion streams to newer versions to utilize the Codeless Connector Framework. While we continue to improve the data collection experience across all connectors, we encourage our customers and partners to join the Microsoft Security Communities to benefit from early insights about the latest and greatest with Microsoft Security. Call to Action for ISV Partners We invite our ISV partners to migrate their Azure Function App-based data connectors to the Codeless Connector Framework. By leveraging CCF for data ingestion, we can ensure that our mutual customers benefit from streamlined data integration and enhanced security monitoring in Microsoft Sentinel. We are committed to ensuring partners have all the support needed in this transformation. For any support, please reach out to us at Microsoft Sentinel Partners. Join us in this transformative journey to empower our customers by unlocking the full potential of their security investments with Microsoft Sentinel’s Codeless Connector Framework. References Create a codeless connector for Microsoft Sentinel Migrate from the HTTP Data Collector API to the Log Ingestion API to send data to Azure Monitor LogsManage cases from across tenants in one place
Are you managing the security needs of a large organization or a managed security service provider (MSSP)? Would you like a unified view of all the cases you are managing across these tenants? We are pleased to announce the latest addition to our case management solution, multi-tenant support, is now generally available (GA). This is the latest step in our journey towards providing a native, security-focused case management system that spans all SecOps workloads in the Microsoft Defender portal, removing customer reliance on third-party SIEM/XDR and ticketing systems. This capability is available for all Microsoft Sentinel customers that have onboarded to the Defender portal.
AI-Powered MITRE ATT&CK Tagging for SOC Optimization
This post is part of an update series highlighting new https://learn.microsoft.com/en-us/azure/sentinel/soc-optimization/soc-optimization-access?tabs=defender-portal capabilities designed to help SOC teams maximize security value with less manual effort. In this post, we focus on AI-powered MITRE ATT&CK Tagging, which streamlines the process of aligning your detections with the MITRE framework. For an overview of our other recent updates, stay tuned for related posts in this series. Security teams rely on precise, consistent tagging to understand detection coverage, align with frameworks like MITRE ATT&CK, and respond effectively to threats. Yet in practice, tagging detections manually is error-prone, inconsistent, and resource-intensive — leaving gaps in coverage and missed opportunities for insight. To address this challenge, we’re excited to introduce a powerful new capability within SOC Optimization: AI MITRE ATT&CK Tagging. Problem Statement In today’s evolving threat landscape, aligning detection rules with the MITRE ATT&CK framework is critical for understanding and improving an organization’s security posture. MITRE tagging provides a common language to describe attacker behaviour, enabling security teams to assess their threat coverage, identify detection gaps, and drive a threat-informed defence. It powers key SOC experiences in Microsoft Sentinel, such as MITRE coverage views, use case recommendations, incident investigation context, and coverage optimization workflows. When tagging is missing or incomplete — for example, when only tactics are mapped without corresponding techniques — the ability to accurately assess protection against known adversary behaviours is weakened. This limits visibility into which threats are covered, complicates incident correlation, and prevents clear communication of coverage gaps to stakeholders. As a result, security teams struggle to prioritize detection improvements and risk leaving critical areas under protected. These gaps lead to: Incomplete visibility into coverage against known threats Limited ability to recommend or prioritize relevant use cases Fragmented alignment between detection rules and incident response workflows Without consistent MITRE tagging, teams spend valuable time manually reviewing and mapping rules — delaying threat response and reducing overall SOC efficiency. The Solution AI MITRE ATT&CK Tagging automates this process using artificial intelligence models that run directly in your workspace. The model scans your detection content and identifies which MITRE ATT&CK tactics and techniques apply, offering recommended tags for detections that are currently untagged. These recommendations can be easily reviewed and applied, allowing you to: Achieve complete detection coverage aligned with the MITRE ATT&CK framework Eliminate manual effort and reduce human error in tagging Enhance detection clarity and response workflows Gain insights into security posture with more structured and actionable data “AI-based tagging helps us to reduce manual workload that previously we tagged detections manually, as well as helps faster triage. Besides, AI-based tagging will be standardized, helping to reduce inconsistencies due to human error”. Farid Kalaidji, Security Lead at Pink Elephant How it looks like Let’s say you’re reviewing your detection posture and come across a new card in SOC Optimization: “Coverage improvement by AI MITRE Tagging”. The card highlights a list of detection rules in your environment that are missing MITRE ATT&CK mappings and offers AI-suggested tags to help close those gaps. You click into the experience and the relevant rules, each with recommended tactic and technique tags. Now you can quickly get a sense of where coverage is missing and what can be improved. If you’re looking for efficiency, you can simply click “Apply All” to tag every recommended rule at once. It’s a quick way to bring your rules up to date and ensure your MITRE coverage reflects your true detection posture – no manual tagging required. This improves not just the MIRTE blade, but also use case recommendation, incident investigation context, and overall visibility into your threat coverage. lease note that by selecting "choose rule", you also have the option to review and tag individual rules from the list. y heading to the MITRE ATT&CK blade, you can validate the improved coverage. The updated view includes newly applied tactics and techniques, reflecting your improved posture. Next Steps Get started with SOC Optimization today. We hope this detailed walkthrough helps you understand the benefits of this feature and improve your security coverage. Microsoft will continue to invest in this feature to assist our customers in defending against evolving security threats. Learn More SOC optimization documentation: https://learn.microsoft.com/azure/sentinel/soc-optimization/soc-optimization-access ; https://learn.microsoft.com/azure/sentinel/soc-optimization/soc-optimization-reference Short overview and demo: https://www.youtube.com/watch?v=b0rbPZwBuc0 In depth webinar: https://www.youtube.com/watch?v=Uk9x60grT-o SOC optimization API: Introducing SOC Optimization API | Microsoft Community Hub MITRE ATT&CK coverage: https://learn.microsoft.com/en-us/azure/sentinel/mitre-coverageAnnouncing File Attachments for Case Management
Effective information sharing is crucial for resolving cases efficiently. Today, we are excited to announce the launch of File Attachments for Case Management, a capability designed to enhance your case investigations with the sharing of reports, emails, screenshots, log files, and more, all in one centralized location within a case. Key Benefits of Attachments for Case Management Centralized Information: File attachments ensure that all relevant documents, images, and data are stored in one place. Reduce your reliance on external file storage and stop hunting through emails—everything you need is right at your fingertips within the case. Comprehensive Documentation: From contracts and evidence to client communications and reports, file attachments provide a complete and organized record of all case-related materials. This comprehensive documentation is invaluable for audits, reviews, and future reference. More Accurate Response: Minimize errors and increase confidence in case outcomes by leveraging all relevant information related to a case. Comb over vulnerability assessments, patch documentation, configuration changes, etc. and ensure you have all the context you need about a case. Attachments Experience: The experience is super simple. Go to the Case details page, click the “Attachments” tab, click “Upload”, select your file, and wait for the upload. The file is malware scanned in the background. Once scanning is complete anyone with access to the case can download the file. If you need to upload malware samples, you can wrap them in password protected zip files. Q&A: How much file storage do I get? 500 GB. Do file attachments stay in region? Yes, they are stored in the same region as their tenant. What if I need to attach malware samples for case investigation? Zip and password protect your malware samples. Malware samples that have not been zipped and password protect will be removed by malware scan. Coming Soon! Add file attachments and screenshots directly to comments to quickly review attachments in the Activity Log. Conclusion Attachments for Case Management ensures you have all the necessary information to make quick and informed decisions about a case. This leads to better case outcomes and improved communication for your organization. Learn more Case management overview Case management blog postAnnouncing Rich Text for Case Management
We are excited to announce the public preview of Rich Text for Case Management. Clear and effective communication is critical for making fast and accurate decisions in case investigations. Learn more about how Rich Text can enhance your communication with your SOC team.Multi Workspace for Single tenant is now in Public Preview in Microsoft’s unified SecOps platform
We are excited to continue to expand the use cases addressed with our unified SecOps platform, which brings the capabilities of Microsoft Sentinel, Defender XDR, Security Copilot, Threat Intelligence and more into a single experience with new and more robust functionality. Now, customers can onboard and manage multiple workspaces across Microsoft Sentinel and Defender in one place. Key Benefits of Multi Workspace Experience The multi-workspace experience offers several key benefits that enhance security operations: Unified Entity View: Customers can view all relevant entity data from multiple workspaces in a single entity page, facilitating comprehensive investigations. Workspace Filtering: Users can filter data by workspace when needed, ensuring flexibility in investigations. Enhanced Context: Aggregates alerts, incidents, and timeline events from all workspaces, providing deeper insights into entity behavior. Introducing the Primary Workspace Concept A new concept in the unified SecOps platform is Primary Workspace, which acts as a central hub where Microsoft Sentinel alerts are correlated with XDR data, resulting in incidents that include both Microsoft Sentinel’s primary workspace and XDR alerts. All XDR alerts and incidents are synced back to this workspace, ensuring a cohesive and comprehensive view of security events. The XDR connector is automatically connected to the Primary Workspace upon onboarding and can be switched if necessary. One Primary Workspace must always be connected to use the unified platform effectively. Other onboarded workspaces are considered “Secondary” workspaces, with incidents created based on their individual data. We respect and protect your data boundaries- each workspace’s data will be synced with its own alerts only. Learn more: https://aka.ms/primaryWorkspace Multi Workspace Experience- Key Scenarios Onboarding multiple workspaces to the unified SecOps platform: Open the security portal: https://security.microsoft.com/ There are two options to connect workspaces, you can select either one: Option A: Connecting the workspace through the main home page: Click on” Connect a workspace” in the banner Select the workspaces you wish to onboard and click on “Next”. Select primary workspace Review the text and click on “Connect” After completing the connection, click on “Close”. Option B: Connecting the workspaces through the Settings page: Navigate to Settings and choose “Microsoft Sentinel” Click on "Connect workspace" Follow the same steps as Option A. Switching Primary Workspaces Navigate to Settings and choose "Microsoft Sentinel" On the workspace you wish to assign as Primary, click on the "3 dots" and choose "Set as primary" Confirm and proceed. Incidents and Alerts The incident queue is a single place for a SOC analyst to manage and investigate incidents. The alert queue centralized all your workspaces’ alert in the same place and provides the ability to see the alert page. In the unified queues, you are able now to view all incidents and alerts from all workloads and all workspaces and also filter by workspace. Each alert and incident are related to a single workspace to keep data boundaries. Bi-directional sync: Any change in the unified secOps portal is reflected to Sentinel portal and vice versa. Unified Entities The multi workspace aggregated view enhances entity pages in the unified portal by consolidating data from all relevant Sentinel workspaces into a single, unified experience. This feature enables security teams to gain a complete view of entity-related data without switching between workspaces, improving investigation efficiency and data accessibility. The unified entity page grants you with: Unified Entity View: Customers can see all relevant entity data from multiple workspaces in a single entity page. Workspace Filtering: Users can filter data by workspace when needed, ensuring flexibility in investigations. Enhanced Context: Aggregates alerts, incidents, and timeline events from all workspaces, providing deeper insights into entity behavior. Aggregated view: Provides a unified view of entity data across all workspaces. Supports a predefined logic to display key entity values across components. Introduces workspace filtering in Timeline, Incidents & Alerts, and Insights tabs. Entity Page Enhancements: Overview Section: Displays entity metadata aggregated from all workspaces. Timeline View: Supports events from all workspaces with workspace-based filtering. Incidents & Alerts: Aggregates incidents and alerts from multiple workspaces. Sentinel Tab: Defaults to the primary workspace but allows workspace filtering. Side Pane: Provides a summary view, dynamically updating based on workspace data. Advanced Hunting In Advanced Hunting, you'll be able to explore all your security data in a single place. For hunting and investigation purposes, you'll be able to: Query all Microsoft Sentinel workspaces data. Run queries across multiple workspaces using workspace operator. Access all Logs content of the workspace, including queries and functions, for read/ query Create custom detections on primary workspace Create Analytic rule with workspace operator on a secondary workspace. Microsoft Sentinel features + Using Workspace selector After you connect your workspace to the unified portal, Microsoft Sentinel is on the left-hand side navigation pane. Many of the existing Microsoft Sentinel features are integrated into the unified portal and are similar. Workspace selector: for users with permissions to multiple workspaces, in each Sentinel page, a workspace selector is added to the toolbox. User can easily switch between workspaces using the selector by clicking on “Select a workspace”. SOC Optimization The SOC Optimization feature is also available in the unified portal and contains data and recommendations for multiple workspaces. FAQ Who can onboard multiple workspaces? To onboard a primary workspace, user must be: Global admin/ Security admin AND Owner of subscription OR Global admin/ Security admin AND User access admin AND Microsoft Sentinel contributor To onboard secondary workspaces, user must be Owner of subscription OR User access admin and Microsoft Sentinel contributor. Who can change the primary workspace? Global admin or security admin can change workspace type (Primary/ Secondary) Do I need to onboard all my workspaces? You don’t need to onboard all your workspaces to use this feature, although we highly recommend you to, to ensure full coverage across all your environment. Will all users in my organization have access to all workspaces in the unified security operations portal? No - we respect the permissions granted for each user. Users can see only the data from the workspace they have permissions to. Will data from one workspace be synced to a second workspace? No, we keep the data boundaries between workspaces and ensure that each workspace will only be synced with its own data. When will multi-tenancy be available? Multi-tenancy in the unified SecOps platform for single workspace is already in GA. Multi-tenancy for multiple workspaces is released to public preview with this capability as well. Can I still access my environment in Azure? Yes, all experiences remain the same. We provide bi-directional sync to make sure all changes are up to date. Conclusion Microsoft’s unified SecOps platform support for multi workspace customers represents a significant leap forward in cybersecurity management. By centralizing operations and providing robust tools for detection, investigation, and automation, it empowers organizations to maintain a vigilant and responsive security posture. The platform’s flexibility and comprehensive view of security data make it an invaluable asset for modern security operations. With the public preview now available, organizations can experience firsthand the transformative impact of the Unified Security Operations Platform. Join us in pioneering a new era of cybersecurity excellence. Learn More Please visit our documentation to learn more on the scenarios supported and how to onboard multiple workspaces to the unified platform: https://aka.ms/OnboardMultiWSGo agentless with Microsoft Sentinel Solution for SAP
What a title during Agentic AI times 😂 Dear community, Bringing SAP workloads under the protection of your SIEM solution is a primary concern for many customers out there. The window for defenders is small “Critical SAP vulnerabilities being weaponized in less than 72 hours of a patch release and new unprotected SAP applications provisioned in cloud (IaaS) environments being discovered and compromised in less than three hours.” (SAP SE + Onapsis, Apr 6 2024) Having a turn-key solution as much as possible leads to better adoption of SAP security. Agent-based solutions running in Docker containers, Kubernetes, or other self-hosted environemnts are not for everyone. Microsoft Sentinel for SAP’s latest new capability re-uses the SAP Cloud Connector to profit from already existing setups, established integration processes, and well-understood SAP components. Meet agentless ❌🤖 The new integration path leverages SAP Integration Suite to connect Microsoft Sentinel with your SAP systems. The Cloud integration capability of SAP Integration Suite speaks all relevant protocols, has connectivity into all the places where your SAP systems might live, is strategic for most SAP customers, and is fully SAP RISE compatible by design. Are you deployed on SAP Business Technology Platform yet? Simply upload our Sentinel for SAP integration package (see bottom box in below image) to your SAP Cloud Integration instance, configure it for your environment, and off you go. Best of all: The already existing SAP security content (detections, workbooks, and playbooks) in Microsoft Sentinel continues to function the same way as it does for the Docker-based collector agent variant. The integration marks your steppingstone to bring your SAP threat signals into the Unified Security Operations Platform – a combination of Defender XDR and Sentinel – that looks beyond SAP at your whole IT estate. Microsoft Sentinel solution for SAP applications is certified for SAP S/4HANA Cloud, Private Edition RISE with SAP, and SAP S/4HANA on-premises. So, you are all good to go😎 You are already dockerized or agentless? Then proceed to this post to learn more about what to do once the SAP logs arrived in Sentinel. Final Words During the preview we saw drastically reduced deployment times for SAP customers being less familiar with Docker, Kubernetes and Linux administration. Cherry on the cake: the network challenges don’t have to be tackled again. The colleagues running your SAP Cloud Connector went through that process a long time ago. SAP Basis rocks 🤘 Get started from here on Microsoft Learn. Find more details on our blog on the SAP Community. Cheers Martin
