Better together with Azure WAF + Microsoft Defender for Storage + Defender for Azure SQL Databases
Authored by: Fernanda_Vela , saikishor, Yura_Lee Reviewed by: YuriDiogenes, Mohit_Kumar, Amir_Dahan, eitanbremler , Kitt_Weatherman Introduction Often, customers ask why additional workload protection is needed when a web application firewall is already in place. Azure Web Application Firewall (WAF) serves as a critical control at the application edge, inspecting inbound HTTP/S traffic and blocking common web-based exploits before they reach backend services. However, modern attack paths are no longer limited to the web entry point. Attackers increasingly target components that bypass HTTP/S inspection altogether such as direct access to storage and SQL through SDKs, native integration tools, private endpoints, or compromised identities and third-party integrations. This is where Microsoft Defender for Cloud complements WAF. While WAF focuses on securing the application boundary, Defender for Cloud extends protection into the resource layer by providing Cloud-Native Application Protection Platform (CNAPP) capabilities, including security posture management and workload protection. Using resource-native signals, it helps identify misconfigurations and detect suspicious control-plane and data-plane activity that would otherwise remain invisible to perimeter controls. The Azure Networking Security blog post “Zero Trust with Azure Firewall, Azure DDoS Protection, and Azure WAF: A practical approach” highlights WAF’s role in inspecting inbound HTTP/S traffic, detecting malicious request patterns (such as OWASP Top 10 vulnerabilities), and reducing direct exposure of backend endpoints by enforcing a controlled application entry point. Building on that foundation, this blog focuses on a “better together” approach that combines WAF with Microsoft Defender for Cloud protecting storage and database. Through practical scenarios and posture insights, we will underline how these controls together: Reduces attack surface at the application entry point Continuously improves security posture through configuration and exposure analysis Detects and responds to threats targeting storage accounts and SQL databases beyond the web perimeter By the end of this post, you will understand how Defender for Cloud’s Storage and SQL protections extend the visibility provided by WAF, enabling protection not only at the edge, but also across the underlying data services. Together, these controls form a cohesive model that addresses both external attack vectors and internal or indirect access paths. Note: This is not a deep configuration guide for rule tuning, nor a replacement for official product documentation. It is intended to help architects and security teams align responsibilities and understand how these services reinforce each other. Architecture: The architecture below shows the traffic flow and where each service fits in the lab used in this blog to simulate the attacks. Azure Application Gateway with WAF is the internet-facing entry point, inspecting inbound HTTP/S traffic before it reaches the backend. Behind it, Azure Firewall provides both network- and application-layer inspection for inbound and outbound flows. In the backend subnet, multiple VMs host the workload. For our demonstration, we focus on a single host running: OWASP Juice Shop (port 3000), An upload API that writes to Azure Storage (port 8080) An API that connects to Azure SQL Database (port 5000). This setup allows us to simulate realistic attack paths originating both from the internet and from within the network. Figure 1: Architecture that shows resources with Application Gateway with WAF, Azure Firewall Premium and inbound traffic Note: The patterns in this blog apply to both Azure WAF platforms: Application Gateway WAF and Azure Front Door WAF. The lab uses Application Gateway WAF for the demonstration. Now, let’s head to the next section where we dive deep into these services to understand their capabilities with some attacks, alerts and insights. Azure Web Application Firewall at the Edge As we may have understood by now, Azure WAF is the first layer of protection, inspecting external web traffic for malicious patterns. Each incoming request is evaluated against its rulesets to either allow, block or log this traffic by using its managed and custom rulesets. Now, what are these rulesets? Azure WAF uses managed rule sets like the Default Rule Set (DRS) (version 2.2 as of this writing), which incorporate OWASP Top 10 protections and Microsoft threat intelligence to block common attacks (SQL injection, XSS, remote file inclusion, etc.) in real time. Additional managed sets include a Bot Protection rule set (to guard against malicious bots scraping content) and HTTP DDoS rule set (to detect Layer 7 DDoS patterns). Beyond the built-ins, you can define custom WAF rules for application-specific needs—blocking or allowing traffic based on attributes like geolocation, IP ranges, or specific URL paths. Now let’s talk about an example scenario. In our lab, Azure WAF is protecting multiple backend services on different paths and ports. When an external attacker tries to exploit the Juice Shop app with a crafted XSSattack, Azure WAF immediately detects the malicious pattern and blocks the request at the gateway as seen below. Figure 2: An XSS attack on the juiceshop website, immediately results in a 403 Forbidden as WAF catches this attack in the application layer. However, WAF’s inspection is inherently limited to traffic it can see, primarily, the HTTP/S flows it fronts. Let’s say our attacker changes tactics: instead of trying to force malicious code through the web interface, they obtain a stolen storage key or credentials through phishing and attempt to access the Azure Storage account directly via APIs. This request never goes through WAF, so WAF cannot assess or block it. In such a case, Microsoft Defender for Storage’s threat detection monitors for such suspicious activity, for example by raising an alert about the unusual direct access or flagging a malware file uploaded to a blob container. Likewise, if our attacker exploited a weakness in application code to run malicious SQL commands on the database (whether through potentially harmful application or a suspicious service account), Defender for SQL monitors for and alerts anomalous query patterns or suspicious logins. This illustrates why WAF and Defender for Cloud are complementary: WAF stops web attacks at the door, while Defender for Cloud watches for threats that get inside or come through alternate doors. Figure 3: Single-host lab architecture with Azure Application Gateway (WAF) and resource‑level protection Figure 3 illustrates the key distinction: WAF inspects and protects the application entry point, while Defender for Cloud provides visibility into the resources themselves. Together, they cover both the path into the application and the behavior within the environment—forming a complete protection model across layers. Because not all access to storage and databases may flow through the application gateway, you also need resource-level posture and threat detection to see and stop activity that never appears in WAF logs. Cloud Security Posture Management with Defender for Cloud With the edge covered, the next challenge is reducing risk that originates from misconfiguration and resource exposure. Most successful attacks originate from exposed services and misconfigurations rather than direct application-layer exploits. Microsoft Defender for Cloud’s storage and database protection provide security posture insights that help identify and prioritize these security gaps at the resource level. Defender for Cloud has visibility insights that capture the resources’ misconfigurations on the control and data plane via the Recommendations view in the Azure portal, as shown in the example below: Figure 4: Juice Shop’s storage account and SQL server recommendations Figure 4 is a list of recommendations organized by risk level for this particular environment. The security team should harden the “defendertestsai” storage asset by preventing shared access keys, and the “juiceshop” SQL database by provisioning an Entra administrator. Each recommendation will also provide guidance to remediate these findings. The “Data & AI Dashboard” in Defender for Cloud, with Defender CSPM, will also provide security posture insight into storage, database and AI resources by surfacing their risks, alerts and sensitive data discovery all in one dashboard. Figure 5: Juice Shop’s Sensitive data discovery and Data threat detection dashboard in Defender for Cloud’s “Data&AI section”. Under Data closer look, in Figure 5, you can see in this example, starting from the left, sensitive information found in scanned resources, level alerts for databases and storage resources based on severity, templatized queries from the Cloud Security Explorer, and a graph displaying all internet exposed data resources below. These powerful insights on data resources all come from Defender for Cloud, designed to help customers harden their environment by priority through visibility across their entire data ecosystem based on risk level. Figure 6: Juice Shop’s attack path “Internet exposed Azure VM with high severity vulnerabilities allows lateral movement to Critical Storage used by Azure AI Foundry”. Attack paths are potential avenues in which an attacker can infiltrate and compromise data. In Figure 6 above, we see insight into not only the storage account itself, but the context around it: an internet exposed storage account is connected to other assets like a virtual machine and a managed identity that has permissions to manipulate data. These Defender for Cloud security posture insights complement WAF and complete the defense-in-depth security approach: harden the data services so that even if an attacker reaches them the blast radius is smaller, and the likelihood of compromise is reduced. Defender for Cloud’s advanced threat protection Even in well-secured environments, attackers often interact directly with storage accounts or databases through identities, APIs, or trusted internal paths. Reducing exposure is critical but not sufficient. Detection is required once an attacker begins interacting with data Defender for Cloud’s advanced threat protection for Storage and SQL surfaces resource-level security alerts such as suspicious access patterns, anomalous queries, and malware detections—often with richer context than perimeter telemetry alone. Let’s use a malware alert for a storage account in the Defender portal as an example: Figure 7: Juice Shop’s storage account security alert “Malicious blob uploaded to storage account”. Malware scanning is a common requirement for teams that process user uploads or must meet security benchmarks. In this lab, Juice Shop allows users to upload files (for example, feedback attachments), and the upload API writes those files to Azure Blob Storage. Azure WAF inspects the HTTP request that delivers the upload headers, parameters, payload patterns and blocks web-layer attacks like XSS or SQLi. Scanning blob contents after they land is a different job, performed at the resource layer by Defender for Storage. With Defender for Storage malware scanning enabled, each uploaded blob is scanned; if the verdict is malware, Defender for Cloud raises an alert such as “Malicious blob uploaded to storage account” as shown in figure 7. Then, with Defender for Storage’s automated malware remediation, the malicious blog is set to soft-delete for quarantine and further analysis. SQL databases are high-value targets for data access, privilege escalation, and exploitation of vulnerable applications. Database protection in Defender for Cloud has the visibility to provide customers with control plane and data plane level insight to alert on suspicious activity such as anomalous logons, unusual client applications, and injection-like query patterns. For example, here’s a potential SQL injection alert for a database in the Defender portal: Figure 8: Juice Shop’s database security alert on a potential SQL injection. These alerts typically include investigation context such as the client application, client principal name, and the statement or pattern in question, along with severity to help you prioritize, as shown in Figure 8. From there, analysts can use recommended response actions (for example, to contain risky access paths or harden the database) to reduce the chance of repeat activity. In practice, Defender for Cloud threat detection gives SOC teams prioritized, resource-specific alerts with the context needed to investigate quickly and take action at the storage and database layers. Conclusion Azure Application Gateway with WAF is a necessary control to reduce application-layer risk at the edge. But defense in depth requires the assumption that some threats will reach or target data services directly. By layering Microsoft Defender for Storage and Microsoft Defender for SQL on top of Azure WAF, you add continuous posture insights to reduce preventable exposure, plus threat protection that detects suspicious activity at the resource layer. Operated together, these services provide stronger prevention, better detection coverage, and clearer response paths than single control alone.Microsoft Defender for Cloud Customer Newsletter
What's new in Defender for Cloud? Kubernetes gated deployment is now generally available for AKS automatic clusters. Use help to deploy the Defender for Containers sensor to use this feature. More information can be found here. Grouped recommendations are converted into individual ones to list each finding separately. While grouped recommendations are still available, new individual recommendations are now marked as preview and are not yet part of the Secure Score. This new format will allow for better prioritization, actionable context and better governance and tracking. For more details, please refer to this documentation. Check out other updates from last month here! Check out monthly news for the rest of the MTP suite here! Blogs of the month In March, our team published the following blog posts we would like to share: Defending Container Runtime from Malware with Defender for Containers Modern Database Protection: From Visibility to Threat Detection with Defender for Cloud New innovations in Microsoft Defender to strengthen multi-cloud, containers, and AI model security Defending the AI Era: New Microsoft Capabilities to Protect AI Defender for Cloud in the field Revisit the malware automated remediation announcement since this feature is now in GA! Automated remediation for malware in storage Visit our YouTube page GitHub Community Check out the new Module 28 in the MDC Lab: Defending Container Runtime from Malware with Microsoft Defender for Containers Defending Container Runtime from Malware Visit our GitHub page Customer journey Discover how other organizations successfully use Microsoft Defender for Cloud to protect their cloud workloads. This month we are featuring ManpowerGroup, a global workforce solutions leader, deployed Microsoft 365 E5, and Microsoft Security to modernize and future-proof their cyber security platform. ManpowerGroup leverages Entra ID, Defender for Endpoint, Defender for Identity, Defender for O365, Defender for Cloud, Microsoft Security Copilot and Purview to transform itself as an AI Frontier Firm. Join our community! We offer several customer connection programs within our private communities. By signing up, you can help us shape our products through activities such as reviewing product roadmaps, participating in co-design, previewing features, and staying up-to-date with announcements. Sign up at aka.ms/JoinCCP. We greatly value your input on the types of content that enhance your understanding of our security products. Your insights are crucial in guiding the development of our future public content. We aim to deliver material that not only educates but also resonates with your daily security challenges. Whether it’s through in-depth live webinars, real-world case studies, comprehensive best practice guides through blogs, or the latest product updates, we want to ensure our content meets your needs. Please submit your feedback on which of these formats do you find most beneficial and are there any specific topics you’re interested in https://aka.ms/PublicContentFeedback. Note: If you want to stay current with Defender for Cloud and receive updates in your inbox, please consider subscribing to our monthly newsletter: https://aka.ms/MDCNewsSubscribeBecome a Microsoft Defender for Cloud Ninja
[Last update: 03/30/2026] This blog post has a curation of many Microsoft Defender for Cloud (formerly known as Azure Security Center and Azure Defender) resources, organized in a format that can help you to go from absolutely no knowledge in Microsoft Defender for Cloud, to design and implement different scenarios. You can use this blog post as a training roadmap to learn more about Microsoft Defender for Cloud. On November 2nd, at Microsoft Ignite 2021, Microsoft announced the rebrand of Azure Security Center and Azure Defender for Microsoft Defender for Cloud. To learn more about this change, read this article. Every month we are adding new updates to this article, and you can track it by checking the red date besides the topic. If you already study all the modules and you are ready for the knowledge check, follow the procedures below: To obtain the Defender for Cloud Ninja Certificate 1. Take this knowledge check here, where you will find questions about different areas and plans available in Defender for Cloud. 2. If you score 80% or more in the knowledge check, request your participation certificate here. If you achieved less than 80%, please review the questions that you got it wrong, study more and take the assessment again. Note: it can take up to 24 hours for you to receive your certificate via email. To obtain the Defender for Servers Ninja Certificate (Introduced in 08/2023) 1. Take this knowledge check here, where you will find only questions related to Defender for Servers. 2. If you score 80% or more in the knowledge check, request your participation certificate here. If you achieved less than 80%, please review the questions that you got it wrong, study more and take the assessment again. Note: it can take up to 24 hours for you to receive your certificate via email. Modules To become an Microsoft Defender for Cloud Ninja, you will need to complete each module. The content of each module will vary, refer to the legend to understand the type of content before clicking in the topic’s hyperlink. The table below summarizes the content of each module: Module Description 0 - CNAPP In this module you will familiarize yourself with the concepts of CNAPP and how to plan Defender for Cloud deployment as a CNAPP solution. 1 – Introducing Microsoft Defender for Cloud and Microsoft Defender Cloud plans In this module you will familiarize yourself with Microsoft Defender for Cloud and understand the use case scenarios. You will also learn about Microsoft Defender for Cloud and Microsoft Defender Cloud plans pricing and overall architecture data flow. 2 – Planning Microsoft Defender for Cloud In this module you will learn the main considerations to correctly plan Microsoft Defender for Cloud deployment. From supported platforms to best practices implementation. 3 – Enhance your Cloud Security Posture In this module you will learn how to leverage Cloud Security Posture management capabilities, such as Secure Score and Attack Path to continuous improvement of your cloud security posture. This module includes automation samples that can be used to facilitate secure score adoption and operations. 4 – Cloud Security Posture Management Capabilities in Microsoft Defender for Cloud In this module you will learn how to use the cloud security posture management capabilities available in Microsoft Defender for Cloud, which includes vulnerability assessment, inventory, workflow automation and custom dashboards with workbooks. 5 – Regulatory Compliance Capabilities in Microsoft Defender for Cloud In this module you will learn about the regulatory compliance dashboard in Microsoft Defender for Cloud and give you insights on how to include additional standards. In this module you will also familiarize yourself with Azure Blueprints for regulatory standards. 6 – Cloud Workload Protection Platform Capabilities in Azure Defender In this module you will learn how the advanced cloud capabilities in Microsoft Defender for Cloud work, which includes JIT, File Integrity Monitoring and Adaptive Application Control. This module also covers how threat protection works in Microsoft Defender for Cloud, the different categories of detections, and how to simulate alerts. 7 – Streaming Alerts and Recommendations to a SIEM Solution In this module you will learn how to use native Microsoft Defender for Cloud capabilities to stream recommendations and alerts to different platforms. You will also learn more about Azure Sentinel native connectivity with Microsoft Defender for Cloud. Lastly, you will learn how to leverage Graph Security API to stream alerts from Microsoft Defender for Cloud to Splunk. 8 – Integrations and APIs In this module you will learn about the different integration capabilities in Microsoft Defender for Cloud, how to connect Tenable to Microsoft Defender for Cloud, and how other supported solutions can be integrated with Microsoft Defender for Cloud. 9 - DevOps Security In this module you will learn more about DevOps Security capabilities in Defender for Cloud. You will be able to follow the interactive guide to understand the core capabilities and how to navigate through the product. 10 - Defender for APIs In this module you will learn more about the new plan announced at RSA 2023. You will be able to follow the steps to onboard the plan and validate the threat detection capability. 11 - AI Posture Management and Workload Protection In this module you will learn more about the risks of Gen AI and how Defender for Cloud can help improve your AI posture management and detect threats against your Gen AI apps. Module 0 - Cloud Native Application Protection Platform (CNAPP) Improving Your Multi-Cloud Security with a CNAPP - a vendor agnostic approach Microsoft CNAPP Solution Planning and Operationalizing Microsoft CNAPP Understanding Cloud Native Application Protection Platforms (CNAPP) Cloud Native Applications Protection Platform (CNAPP) Microsoft CNAPP eBook Understanding CNAPP Why Microsoft Leads the IDC CNAPP MarketScape: Key Insights for Security Decision-Makers Module 1 - Introducing Microsoft Defender for Cloud What is Microsoft Defender for Cloud? A New Approach to Get Your Cloud Risks Under Control Getting Started with Microsoft Defender for Cloud Implementing a CNAPP Strategy to Embed Security From Code to Cloud Boost multicloud security with a comprehensive code to cloud strategy A new name for multi-cloud security: Microsoft Defender for Cloud Common questions about Defender for Cloud MDC Cost Calculator Breaking down security silos: Microsoft Defender for Cloud Expands into the Defender Portal Microsoft Defender for Cloud Customer Newsletter (03/2026) New innovations in Microsoft Defender to strengthen multi-cloud, containers, and AI model security (03/2026) Module 2 – Planning Microsoft Defender for Cloud Features for IaaS workloads Features for PaaS workloads Built-in RBAC Roles in Microsoft Defender for Cloud Enterprise Onboarding Guide Design Considerations for Log Analytics Workspace Onboarding on-premises machines using Windows Admin Center Understanding Security Policies in Microsoft Defender for Cloud Creating Custom Policies Centralized Policy Management in Microsoft Defender for Cloud using Management Groups Planning Data Collection for IaaS VMs Microsoft Defender for Cloud PoC Series – Microsoft Defender for Storage How to Effectively Perform an Microsoft Defender for Cloud PoC Microsoft Defender for Cloud PoC Series – Microsoft Defender CSPM Microsoft Defender for DevOps GitHub Connector - Microsoft Defender for Cloud PoC Series Grant tenant-wide permissions to yourself Simplifying Onboarding to Microsoft Defender for Cloud with Terraform Module 3 – Enhance your Cloud Security Posture How Secure Score affects your governance Cloud secure score in Microsoft Defender for Cloud - Microsoft Defender for Cloud Enhance your Secure Score in Microsoft Defender for Cloud Security recommendations Active User (Public Preview) Resource exemption Create custom security standards and recommendations - Microsoft Defender for Cloud Deliver a Security Score weekly briefing Send Microsoft Defender for Cloud Recommendations to Azure Resource Stakeholders User roles and permissions - Microsoft Defender for Cloud Secure Score Reduction Alert Improved experience for managing the default Azure security policies Security Policy Enhancements in Defender for Cloud Create custom recommendations and security standards Secure Score Overtime Workbook Automation Artifacts for Secure Score Recommendations Connecting Defender for Cloud with Jira Remediation Scripts Module 4 – Cloud Security Posture Management Capabilities in Microsoft Defender for Cloud CSPM in Defender for Cloud Take a Proactive Risk-Based Approach to Securing your Cloud Native Applications Predict future security incidents! Cloud Security Posture Management with Microsoft Defender Software inventory filters added to asset inventory Drive your organization to security actions using Governance experience Managing Asset Inventory in Microsoft Defender for Cloud Vulnerability Assessment Workbook Template Vulnerability Assessment for Containers Implementing Workflow Automation Workflow Automation Artifacts Using Microsoft Defender for Cloud API for Workflow Automation What you need to know when deleting and re-creating the security connector(s) in Defender for Cloud Connect AWS Account with Microsoft Defender for Cloud Video Demo - Connecting AWS accounts Microsoft Defender for Cloud PoC Series - Multi-cloud with AWS Onboarding your AWS/GCP environment to Microsoft Defender for Cloud with Terraform How to better manage cost of API calls that Defender for Cloud makes to AWS Cloud posture management adds serverless protection for Azure and AWS Integrate AWS CloudTrail logs with Microsoft Defender for Cloud Connect GCP Account with Microsoft Defender for Cloud Protecting Containers in GCP with Defender for Containers Video Demo - Connecting GCP Accounts Microsoft Defender for Cloud PoC Series - Multicloud with GCP All You Need to Know About Microsoft Defender for Cloud Multicloud Protection Custom recommendations for AWS and GCP 31 new and enhanced multicloud regulatory standards coverage Azure Monitor Workbooks integrated into Microsoft Defender for Cloud and three templates provided How to Generate a Microsoft Defender for Cloud exemption and disable policy report Exempt resources at scale - Microsoft Defender for Cloud Cloud security posture and contextualization across cloud boundaries from a single dashboard Best Practices to Manage and Mitigate Security Recommendations Defender CSPM Defender CSPM Plan Options Go Beyond Checkboxes: Proactive Cloud Security with Microsoft Defender CSPM What’s New in Microsoft Defender CSPM Cloud Security Explorer Identify and remediate attack paths Agentless scanning for machines Cloud security explorer and Attack path analysis Governance Rules at Scale Governance Improvements Data Security Aware Posture Management Fast-Start Checklist for Microsoft Defender CSPM: From Enablement to Best Practices Unlocking API visibility: Defender for Cloud Expands API security to Function Apps and Logic Apps A Proactive Approach to Cloud Security Posture Management with Microsoft Defender for Cloud Prioritize Risk remediation with Microsoft Defender for Cloud Attack Path Analysis Understanding data aware security posture capability Agentless Container Posture Agentless Container Posture Management Microsoft Defender for Cloud - Automate Notifications when new Attack Paths are created Proactively secure your Google Cloud Resources with Microsoft Defender for Cloud Demystifying Defender CSPM Discover and Protect Sensitive Data with Defender for Cloud Defender for cloud's Agentless secret scanning for virtual machines is now generally available! Defender CSPM Support for GCP Data Security Dashboard Agentless Container Posture Management in Multicloud Agentless malware scanning for servers Recommendation Prioritization Unified insights from Microsoft Entra Permissions Management Defender CSPM Internet Exposure Analysis Future-Proofing Cloud Security with Defender CSPM ServiceNow's integration now includes Configuration Compliance module Agentless code scanning for GitHub and Azure DevOps (preview) 🚀 Suggested Labs: Improving your Secure Posture Connecting a GCP project Connecting an AWS project Defender CSPM Agentless container posture through Defender CSPM Contextual Security capabilities for AWS using Defender CSPM Module 5 – Regulatory Compliance Capabilities in Microsoft Defender for Cloud Understanding Regulatory Compliance Capabilities in Microsoft Defender for Cloud Adding new regulatory compliance standards Regulatory Compliance workbook Regulatory compliance dashboard now includes Azure Audit reports Microsoft cloud security benchmark: Azure compute benchmark is now aligned with CIS! Updated naming format of Center for Internet Security (CIS) standards in regulatory compliance CIS Azure Foundations Benchmark v2.0.0 in regulatory compliance dashboard Spanish National Security Framework (Esquema Nacional de Seguridad (ENS)) added to regulatory compliance dashboard for Azure Microsoft Defender for Cloud Adds Four New Regulatory Frameworks | Microsoft Community Hub 🚀 Suggested Lab: Regulatory Compliance Module 6 – Cloud Workload Protection Platform Capabilities in Microsoft Defender for Clouds Understanding Just-in-Time VM Access Implementing JIT VM Access File Integrity Monitoring in Microsoft Defender Understanding Threat Protection in Microsoft Defender Performing Advanced Risk Hunting in Defender for Cloud Microsoft Defender for Servers Demystifying Defender for Servers Onboarding directly (without Azure Arc) to Defender for Servers Agentless secret scanning for virtual machines in Defender for servers P2 & DCSPM Vulnerability Management in Defender for Cloud File Integrity Monitoring using Microsoft Defender for Endpoint File Integrity Monitoring requires MDE agent version 10.8799+ for legacy Windows machines (03/2026) Microsoft Defender for Containers Basics of Defender for Containers Secure your Containers from Build to Runtime Guarding Kubernetes Deployments: Runtime Gating for Vulnerable Images Now Generally Available AWS ECR Coverage in Defender for Containers Upgrade to Microsoft Defender Vulnerability Management End to end container security with unified SOC experience Binary drift detection episode Binary drift detection Cloud Detection Response experience Exploring the Latest Container Security Updates from Microsoft Ignite 2024 Unveiling Kubernetes lateral movement and attack paths with Microsoft Defender for Cloud Onboarding Docker Hub and JFrog Artifactory Improvements in Container’s Posture Management New AKS Security Dashboard in Defender for Cloud The Risk of Default Configuration: How Out-of-the-Box Helm Charts Can Breach Your Cluster Your cluster, your rules: Helm support for container security with Microsoft Defender for Cloud Defending Container Runtime from Malware with Microsoft Defender for Containers (03/2026) Microsoft Defender for Storage Protect your storage resources against blob-hunting Malware Scanning in Defender for Storage What's New in Defender for Storage Defender for Storage: Malware Scan Error Message Update Protecting Cloud Storage in the Age of AI Key findings from product telemetry: top storage security alerts across industries Malware scan results now in blob tags (ADLS Gen2 HNS | Public Preview) (03/2026) Microsoft Defender for SQL New Defender for SQL VA Defender for SQL on Machines Enhanced Agent Update Microsoft Defender for SQL Anywhere New autoprovisioning process for SQL Server on machines plan Enhancements for protecting hosted SQL servers across clouds and hybrid environments Defender for Open-Source Relational Databases Multicloud Modern Database Protection: From Visibility to Threat Detection with Microsoft Defender for Cloud (03/2026) Microsoft Defender for KeyVault Microsoft Defender for AppService Microsoft Defender for Resource Manager Understanding Security Incident Security Alert Correlation Alert Reference Guide 'Copy alert JSON' button added to security alert details pane Alert Suppression Simulating Alerts in Microsoft Defender for Cloud Alert validation Simulating alerts for Windows Simulating alerts for Containers Simulating alerts for Storage Simulating alerts for Microsoft Key Vault Simulating alerts for Microsoft Defender for Resource Manager Integration with Microsoft Defender for Endpoint Auto-provisioning of Microsoft Defender for Endpoint unified solution Resolve security threats with Microsoft Defender for Cloud Protect your servers and VMs from brute-force and malware attacks with Microsoft Defender for Cloud Filter security alerts by IP address Alerts by resource group Defender for Servers Security Alerts Improvements From visibility to action: The power of cloud detection and response 🚀 Suggested Labs: Workload Protections Agentless container vulnerability assessment scanning Microsoft Defender for Cloud database protection Protecting On-Prem Servers in Defender for Cloud Defender for Storage Module 7 – Streaming Alerts and Recommendations to a SIEM Solution Continuous Export capability in Microsoft Defender for Cloud Deploying Continuous Export using Azure Policy Connecting Microsoft Sentinel with Microsoft Defender for Cloud Stream alerts to monitoring solutions - Microsoft Defender for Cloud | Microsoft Learn Microsoft Sentinel bi-directional alert synchronization 🚀 Suggested Lab: Exporting Microsoft Defender for Cloud information to a SIEM Module 8 – Integrations and APIs Integration with Tenable Integrate security solutions in Microsoft Defender for Cloud Defender for Cloud integration with Defender EASM Defender for Cloud integration with Defender TI REST APIs for Microsoft Defender for Cloud Using Graph Security API to Query Alerts in Microsoft Defender for Cloud Automate(d) Security with Microsoft Defender for Cloud and Logic Apps Automating Cloud Security Posture and Cloud Workload Protection Responses Module 9 – DevOps Security Overview of Microsoft Defender for Cloud DevOps Security DevOps Security Interactive Guide Configure the Microsoft Security DevOps Azure DevOps extension Configure the Microsoft Security DevOps GitHub action Automate SecOps to Developer Communication with Defender for DevOps Compliance for Exposed Secrets Discovered by DevOps Security Automate DevOps Security Recommendation Remediation DevOps Security Workbook Remediating Security Issues in Code with Pull Request Annotations Code to Cloud Security using Microsoft Defender for DevOps GitHub Advanced Security for Azure DevOps alerts in Defender for Cloud Securing your GitLab Environment with Microsoft Defender for Cloud Bridging the Gap Between Code and Cloud with Defender for Cloud Integrate Defender for Cloud CLI with CI/CD pipelines Code Reachability Analysis 🚀 Suggested Labs: Onboarding Azure DevOps to Defender for Cloud Onboarding GitHub to Defender for Cloud Module 10 – Defender for APIs What is Microsoft Defender for APIs? Onboard Defender for APIs Validating Microsoft Defender for APIs Alerts API Security with Defender for APIs Microsoft Defender for API Security Dashboard Exempt functionality now available for Defender for APIs recommendations Create sample alerts for Defender for APIs detections Defender for APIs reach GA Increasing API Security Testing Visibility Boost Security with API Security Posture Management 🚀 Suggested Lab: Defender for APIs Module 11 – AI Posture Management and Threat Protection Secure your AI applications from code to runtime with Microsoft Defender for Cloud AI security posture management AI threat protection Extending Defender’s AI Threat Protection to Microsoft Foundry Agents Secure your AI applications from code to runtime Data and AI security dashboard Protecting Azure AI Workloads using Threat Protection for AI in Defender for Cloud Plug, Play, and Prey: The security risks of the Model Context Protocol Learn Live: Enable advanced threat protection for AI workloads with Microsoft Defender for Cloud Microsoft AI Security Story: Protection Across the Platform Microsoft Defender for AI Alerts Demystifying AI Security Posture Management Part 3: Unified Security Intelligence - Orchestrating GenAI Threat Detection with Microsoft Sentinel A new era of agents, a new era of posture Defending the AI Era: New Microsoft Capabilities to Protect AI (03/2026) 🚀 Suggested Lab: Security for AI workloads Are you ready to take your knowledge check? If so, click here. If you score 80% or more in the knowledge check, request your participation certificate here. If you achieved less than 80%, please review the questions that you got it wrong, study more and take the assessment again. Note: it can take up to 24 hours for you to receive your certificate via email. Other Resources Microsoft Defender for Cloud Labs Become an Microsoft Sentinel Ninja Become an MDE Ninja Cross-product lab (Defend the Flag) Release notes (updated every month) Important upcoming changes Have a great time ramping up in Microsoft Defender for Cloud and becoming a Microsoft Defender for Cloud Ninja!! Reviewer: Tom Janetscheck, Senior PM
Defending Container Runtime from Malware with Microsoft Defender for Containers
In cloud-native environments, malware protection is no longer traditional antivirus — it is runtime workload security, ensuring containerized applications remain safe throughout their lifecycle. Many organizations focus on scanning container images before deployment. While image scanning is important, this does not stop runtime attacks. Image scanning protects before deployment, but malware detection protects during execution. Malware can enter cloud environments through container images, compromised CI/CD pipelines, exposed services, or misuse of legitimate administrative tools, making runtime malware detection an essential security control rather than an optional enhancement. Runtime Malware detection and Prevention acts as the last line of defence when preventive controls fail. If malware executes successfully inside a container, it may attempt Privilege escalation, Container escape and Host compromise. Antimalware in Defender for Containers Defender for Containers antimalware, powered by Microsoft Defender Antivirus cloud protection, near-real-time malware detection directly into container environments. The antimalware feature is available via Helm with sensor version 0.10.2 for AKS, GKE, and EKS. Defender for Containers Sensor Defender for Containers Antimalware provides: Runtime monitoring of container activity Malware detection on Container Workloads Malware detection for Kubernetes nodes Alerts integrated into Defender XDR Anti-malware detection and blocking - Microsoft Defender for Cloud | Microsoft Learn Container antimalware protection in Defender for Containers is powered by three main components: 1) Defender Sensor - version 0.10.2 installed via Helm or arc-extension The Defender sensor runs inside the Kubernetes cluster and monitors workload activity in real time. It provides: Runtime visibility into container processes Binary execution monitoring Behavioral inspection Alert and Block Malware execution Multicloud Support (Azure Kubernetes Service, AWS EKS, GCP GKE) Prerequisites: Ensure the following components of the Defender for containers plan are enabled: Defender sensor Security findings Registry access Kubernetes API access To Install Defender Sensor for Antimalware, ensure there are sufficient resources on your Kubernetes Cluster and outbound connectivity. In addition to the core sensor memory and CPU requirements, you need: Component Request Limit CPU 50m 300m Memory 128Mi 500Mi All sensor components use outbound-only connectivity (no inbound access required). To install Defender for Containers sensor follow the guidance here To Verify the sensor deployed successfully on all nodes, use the commands as screenshot below: You should see the collectors pods in Running state with 3/3 containers. 2) Antimalware Policy Engine Policies define what happens when malware is detected: Alert only Block execution Ignore (allowlisted cases) Policies can be scoped to Azure subscriptions, AWS Accounts and GCP Projects and also to Specific clusters, Namespaces, Pods, Images, Labels or workloads. This allows organizations to reduce false positives while enforcing strict security where needed. Host vs Workload Protection — How Sensor Covers Both Antimalware Rules can be applied to Resource scopes: Scope What Is Protected Workload (Container) Processes inside containers Host (Node) Kubernetes node OS and runtime Default rules include: Default antimalware workload rule Default antimalware host rule This matters because attackers often escape containers and target kubelet, container runtime, and node filesystem. Blocking malware at both workload and host layers prevents cluster takeover. To configure the Antimalware policy follow the guidance here To verify the antimalware policy is deployed to the cluster, login to your K8s cluster and use the commands as screenshot below: 3) Cloud Protection (Microsoft Defender Antivirus Cloud) Defender for Containers Sensor integrates with Microsoft Defender Antivirus cloud protection, which provides Global threat intelligence, Machine learning classification, Reputation scoring, Zero-day detection. When suspicious binaries appear, cloud analysis determines whether they should be allowed or blocked. To test Malware detection and blocking, upload an EICAR file to a running Container on your cluster. If policy action = Block Malware, the sensor performs enforcement. Blocking actions include, Killing malicious process and Generates Defender for Cloud alert as below: The malware is detected and execution is blocked. Defender for Cloud Alerts are also available in Defender XDR portal. Security Operations teams can further investigate the infected file by navigating to the Incidents and Alerts section in the Defender portal. When a container or pod is determined to be compromised, Defender XDR enables Security Operations Team to take response actions. For more details : Investigate and respond to container threats in the Microsoft Defender portal Binary Drift Detection and Prevention : Containers are expected to be immutable. Running containers should only execute binaries that came from the original container image. This is extremely important because most container attacks involve Curl/wget downloading malware, Crypto miners dropped post-compromise, Attack tools installed dynamically. For more details refer Binary drift detection and blocking Defender detects runtime drift, such as New binaries downloaded after deployment Files written into container filesystem Tools installed via reverse shell Payloads dropped by attackers To Configure drift detection and prevention policy follow the guidance here . When a drift is detected on a container workload, Defender for Container sensor detects drift and prevents it from being drifted. To test drift prevention, deploy a container and introduce a drift in the running container. The drift will be detected by the sensor and prevents drift, and alert is generated as shown in the screenshot below: References: Anti-malware detection and blocking Install Defender for Containers sensor using Helm Binary drift detection and blocking Investigate and respond to container threats in the Microsoft Defender portal Reviewed by: Eyal Gur, Principal Product Manager, Microsoft Defender for CloudMicrosoft Defender for Cloud Customer Newsletter
Check out monthly news for the rest of the MTP suite here! What's new in Defender for Cloud? Now in public preview, Defender for Cloud provides threat protection for AI agents built with Foundry, as part of the Defender for AI Services plan. Learn more about this in our documentation. Defender for Cloud’s Defender for SQL on machines plan provides a simulated alert feature to help validate deployment and test prepared security team for detection, response and automation workflows. For more details, please refer to this documentation. Check out other updates from last month here. Blogs of the month In February, our team published the following blog post we would like to share: Extending Defender's AI Threat Protection to Microsoft Foundry Agents Defender for Cloud in the field Revisit the announcement on the new Secure Score model and the enhancements available in the Defender Portal. New Secure Score model and Defender portal enhancements GitHub Community Module 12 in Defender for Cloud’s lab has been updated to include alert simulation! Database protection lab - module 12 Customer journey Discover how other organizations successfully use Microsoft Defender for Cloud to protect their cloud workloads. This month we are featuring ContraForce. ContraForce, a cybersecurity startup, built its platform on Microsoft’s robust security and AI ecosystem. Contraforce, while participating in Microsoft for Startup Pegasus program, addressed the issue of traditional, complex, and siloed security stacks by leveraging Microsoft Sentinel, Defender XDR, Entra ID and Microsoft Foundry. ContraForce was able to deliver enterprise-grade protection at scale, without the enterprise-level overhead. As a result, measured key outcomes like 90%+ incident automation, 93% reduced cost per incident, and 60x faster incident response. Join our community! We offer several customer connection programs within our private communities. By signing up, you can help us shape our products through activities such as reviewing product roadmaps, participating in co-design, previewing features, and staying up-to-date with announcements. Sign up at aka.ms/JoinCCP. We greatly value your input on the types of content that enhance your understanding of our security products. Your insights are crucial in guiding the development of our future public content. We aim to deliver material that not only educates but also resonates with your daily security challenges. Whether it’s through in-depth live webinars, real-world case studies, comprehensive best practice guides through blogs, or the latest product updates, we want to ensure our content meets your needs. Please submit your feedback on which of these formats do you find most beneficial and are there any specific topics you’re interested in https://aka.ms/PublicContentFeedback. Note: If you want to stay current with Defender for Cloud and receive updates in your inbox, please consider subscribing to our monthly newsletter: https://aka.ms/MDCNewsSubscribeMicrosoft Defender for Cloud Customer Newsletter
What's new in Defender for Cloud? Now in public preview, Microsoft Security Private Link allows for private connectivity between Defender for Cloud and your workloads. For more information, see our public documentation. Blogs of the month In January, our team published the following blog posts we would like to share: Guarding Kubernetes Deployments: Runtime gating for vulnerable images now GA Architecting Trust: A NIST-Based Security Governance Framework for AI Agents Defender for Cloud in the field Revisit the announcement on the CloudStorageAggregatedEvents table in XDR’s Advanced Hunting experience. Storage aggregated logs in XDR’s advanced hunting Visit our YouTube page GitHub Community Update your Defender for SQL on machines extension at scale Update Defender for SQL extension at scale Visit our GitHub page Customer journey Discover how other organizations successfully use Microsoft Defender for Cloud to protect their cloud workloads. This month we are featuring Toyota Leasing Thailand. Toyota Leasing Thailand, a financial services subsidiary of Toyota, provides financing, insurance and mobility services and is entrusted with sensitive personal data. Integrating with Defender, Entra and Purview, Security Copilot provided the SOC and the IT team a unified view, streamlined operations and reporting to reduce response times on phishing attacks from hours to minutes. Join our community! We offer several customer connection programs within our private communities. By signing up, you can help us shape our products through activities such as reviewing product roadmaps, participating in co-design, previewing features, and staying up-to-date with announcements. Sign up at aka.ms/JoinCCP. We greatly value your input on the types of content that enhance your understanding of our security products. Your insights are crucial in guiding the development of our future public content. We aim to deliver material that not only educates but also resonates with your daily security challenges. Whether it’s through in-depth live webinars, real-world case studies, comprehensive best practice guides through blogs, or the latest product updates, we want to ensure our content meets your needs. Please submit your feedback on which of these formats do you find most beneficial and are there any specific topics you’re interested in https://aka.ms/PublicContentFeedback. Note: If you want to stay current with Defender for Cloud and receive updates in your inbox, please consider subscribing to our monthly newsletter: https://aka.ms/MDCNewsSubscribeMicrosoft Defender for Cloud Customer Newsletter
What's new in Defender for Cloud? Now in public preview, DCSPM (Defender for Cloud Security Posture Management) extends its capabilities to cover serverless workloads in both Azure and AWS, like Azure Web Apps and AWS Lambda. For more information, see our public documentation. Defender for Cloud’s integration with Endor Labs is now GA Focus on exploitable open-source vulnerabilities across the application lifecycle with Defender for Cloud and Endor Lab integration. This feature is now generally available! For more details, please refer to this documentation. Blogs of the month In December, our team published the following blog posts: Defender for AI Alerts Demystifying AI Security Posture Management Breaking down security silos: Defender for Cloud expands into the Defender portal Part 3: Unified Security Intelligence – Orchestrating Gen AI Threat Detection with Microsoft Sentinel Defender for Cloud in the field Watch the latest Defender for Cloud in the Field YouTube episode here: Malware Automated Remediation New Secure score in Defender for Cloud GitHub Community Check out Module 27 in the Defender for Cloud lab on GitHub. This module covers gating mechanisms to enforce security policies and prevent deployment of insecure container images. Click here for MDC Github lab module 27 Customer journeys Discover how other organizations successfully use Microsoft Defender for Cloud to protect their cloud workloads. This month we are featuring Ford Motor Company. Ford Motor Company, an American multinational automobile manufacturer, and its innovative and evolving technology footprint and infrastructure needed equally sophisticated security. With Defender and other Microsoft products like Purview, Sentinel and Entra, Ford was able to modernize and deploy end-to-end protection, with Zero-trust architecture, and reduce vulnerabilities across the enterprise. Additionally, Ford’s SOC continues to respond with speed and precision with the help of Defender XDR. Join our community! JANUARY 20 (8:00 AM- 9:00 AM PT) What's new in Microsoft Defender CSPM We offer several customer connection programs within our private communities. By signing up, you can help us shape our products through activities such as reviewing product roadmaps, participating in co-design, previewing features, and staying up-to-date with announcements. Sign up at aka.ms/JoinCCP. We greatly value your input on the types of content that enhance your understanding of our security products. Your insights are crucial in guiding the development of our future public content. We aim to deliver material that not only educates but also resonates with your daily security challenges. Whether it’s through in-depth live webinars, real-world case studies, comprehensive best practice guides through blogs, or the latest product updates, we want to ensure our content meets your needs. Please submit your feedback on which of these formats do you find most beneficial and are there any specific topics you’re interested in https://aka.ms/PublicContentFeedback. Note: If you want to stay current with Defender for Cloud and receive updates in your inbox, please consider subscribing to our monthly newsletter: https://aka.ms/MDCNewsSubscribeUpdating SDK for Java used by Defender for Server/CSPM in AWS
Hi, I have a customer who is Defender for Cloud/CSPM in AWS. Last week, Cloud AWS Health Dashboard lit up with a recommendation around the use of AWS SDK for Java 1.x in their organization. This version will reach end of support on December 31, 2025. The recommendation is to migrate to AWS SDK for Java 2.x. The issue is present in all of AWS workload accounts. They found that a large amount of these alerts is caused by the Defender CSPM service, running remotely, and using AWS SDK for Java 1.x. Customer attaching a couple of sample events that were gathered from the CloudTrail logs. Please note that in both cases: assumed-role: DefenderForCloud-Ciem sourceIP: 20.237.136.191 (MS Azure range) userAgent: aws-sdk-java/1.12.742 Linux/6.6.112.1-2.azl3 OpenJDK_64-Bit_Server_VM/21.0.9+10-LTS java/21.0.9 kotlin/1.6.20 vendor/Microsoft cfg/retry-mode/legacy cfg/auth-source#unknown Can someone provide guidance about this? How to find out if DfC is going to leverage AWS SDK for Java 2.x after Dec 31, 2025? Thanks, Terru
Part 3: Unified Security Intelligence - Orchestrating GenAI Threat Detection with Microsoft Sentinel
Why Sentinel for GenAI Security Observability? Before diving into detection rules, let's address why Microsoft Sentinel is uniquely positioned for GenAI security operations—especially compared to traditional or non-native SIEMs. Native Azure Integration: Zero ETL Overhead The problem with external SIEMs: To monitor your GenAI workloads with a third-party SIEM, you need to: Configure log forwarding from Log Analytics to external systems Set up data connectors or agents for Azure OpenAI audit logs Create custom parsers for Azure-specific log schemas Maintain authentication and network connectivity between Azure and your SIEM Pay data egress costs for logs leaving Azure The Sentinel advantage: Your logs are already in Azure. Sentinel connects directly to: Log Analytics workspace - Where your Container Insights logs already flow Azure OpenAI audit logs - Native access without configuration Azure AD sign-in logs - Instant correlation with identity events Defender for Cloud alerts - Platform-level AI threat detection included Threat intelligence feeds - Microsoft's global threat data built-in Microsoft Defender XDR - AI-driven cybersecurity that unifies threat detection and response across endpoints, email, identities, cloud apps and Sentinel There's no data movement, no ETL pipelines, and no latency from log shipping. Your GenAI security data is queryable in real-time. KQL: Built for Complex Correlation at Scale Why this matters for GenAI: Detecting sophisticated AI attacks requires correlating: Application logs (your code from Part 2) Azure OpenAI service logs (API calls, token usage, throttling) Identity signals (who authenticated, from where) Threat intelligence (known malicious IPs) Defender for Cloud alerts (platform-level anomalies) KQL's advantage: Kusto Query Language is designed for this. You can: Join across multiple data sources in a single query Parse nested JSON (like your structured logs) natively Use time-series analysis functions for anomaly detection and behavior patterns Aggregate millions of events in seconds Extract entities (users, IPs, sessions) automatically for investigation graphs Example: Correlating your app logs with Azure AD sign-ins and Defender alerts takes 10 lines of KQL. In a traditional SIEM, this might require custom scripts, data normalization, and significantly slower performance. User Security Context Flows Natively Remember the user_security_context you pass in extra_body from Part 2? That context: Automatically appears in Azure OpenAI's audit logs Flows into Defender for Cloud AI alerts Is queryable in Sentinel without custom parsing Maps to the same identity schema as Azure AD logs With external SIEMs: You'd need to: Extract user context from your application logs Separately ingest Azure OpenAI logs Write correlation logic to match them Maintain entity resolution across different data sources With Sentinel: It just works. The end_user_id, source_ip, and application_name are already normalized across Azure services. Built-In AI Threat Detection Sentinel includes pre-built detections for cloud and AI workloads: Azure OpenAI anomalous access patterns (out of the box) Unusual token consumption (built-in analytics templates) Geographic anomalies (using Azure's global IP intelligence) Impossible travel detection (cross-referencing sign-ins with AI API calls) Microsoft Defender XDR (correlation with endpoint, email, cloud app signals) These aren't generic "high volume" alerts—they're tuned for Azure AI services by Microsoft's security research team. You can use them as-is or customize them with your application-specific context. Entity Behavior Analytics (UEBA) Sentinel's UEBA automatically builds baselines for: Normal request volumes per user Typical request patterns per application Expected geographic access locations Standard model usage patterns Then it surfaces anomalies: "User_12345 normally makes 10 requests/day, suddenly made 500 in an hour" "Application_A typically uses GPT-3.5, suddenly switched to GPT-4 exclusively" "User authenticated from Seattle, made AI requests from Moscow 10 minutes later" This behavior modeling happens automatically—no custom ML model training required. Traditional SIEMs would require you to build this logic yourself. The Bottom Line For GenAI security on Azure: Sentinel reduces time-to-detection because data is already there Correlation is simpler because everything speaks the same language Investigation is faster because entities are automatically linked Cost is lower because you're not paying data egress fees Maintenance is minimal because connectors are native If your GenAI workloads are on Azure, using anything other than Sentinel means fighting against the platform instead of leveraging it. From Logs to Intelligence: The Complete Picture Your structured logs from Part 2 are flowing into Log Analytics. Here's what they look like: { "timestamp": "2025-10-21T14:32:17.234Z", "level": "INFO", "message": "LLM Request Received", "request_id": "a7c3e9f1-4b2d-4a8e-9c1f-3e5d7a9b2c4f", "session_id": "550e8400-e29b-41d4-a716-446655440000", "prompt_hash": "d3b07384d113edec49eaa6238ad5ff00", "security_check_passed": "PASS", "source_ip": "203.0.113.42", "end_user_id": "user_550e8400", "application_name": "AOAI-Customer-Support-Bot", "model_deployment": "gpt-4-turbo" } These logs are in the ContainerLogv2 table since our application “AOAI-Customer-Support-Bot” is running on Azure Kubernetes Services (AKS). Steps to Setup AKS to stream logs to Sentinel/Log Analytics From Azure portal, navigate to your AKS, then to Monitoring -> Insights Select Monitor Settings Under Container Logs Select the Sentinel-enabled Log Analytics workspace Select Logs and events Check the ‘Enable ContainerLogV2’ and ‘Enable Syslog collection’ options More details can be found at this link Kubernetes monitoring in Azure Monitor - Azure Monitor | Microsoft Learn Critical Analytics Rules: What to Detect and Why Rule 1: Prompt Injection Attack Detection Why it matters: Prompt injection is the GenAI equivalent of SQL injection. Attackers try to manipulate the model by overriding system instructions. Multiple attempts indicate intentional malicious behavior. What to detect: 3+ prompt injection attempts within 10 minutes from similar IP let timeframe = 1d; let threshold = 3; AlertEvidence | where TimeGenerated >= ago(timeframe) and EntityType == "Ip" | where DetectionSource == "Microsoft Defender for AI Services" | where Title contains "jailbreak" or Title contains "prompt injection" | summarize count() by bin (TimeGenerated, 1d), RemoteIP | where count_ >= threshold What the SOC sees: User identity attempting injection Source IP and geographic location Sample prompts for investigation Frequency indicating automation vs. manual attempts Severity: High (these are actual attempts to bypass security) Rule 2: Content Safety Filter Violations Why it matters: When Azure AI Content Safety blocks a request, it means harmful content (violence, hate speech, etc.) was detected. Multiple violations indicate intentional abuse or a compromised account. What to detect: Users with 3+ content safety violations in a 1 hour block during a 24 hour time period. let timeframe = 1d; let threshold = 3; ContainerLogV2 | where TimeGenerated >= ago(timeframe) | where isnotempty(LogMessage.end_user_id) | where LogMessage.security_check_passed == "FAIL" | extend source_ip=tostring(LogMessage.source_ip) | extend end_user_id=tostring(LogMessage.end_user_id) | extend session_id=tostring(LogMessage.session_id) | extend application_name = tostring(LogMessage.application_name) | extend security_check_passed = tostring (LogMessage.security_check_passed) | summarize count() by bin(TimeGenerated, 1h),source_ip,end_user_id,session_id,Computer,application_name,security_check_passed | where count_ >= threshold What the SOC sees: Severity based on violation count Time span showing if it's persistent vs. isolated Prompt samples (first 80 chars) for context Session ID for conversation history review Severity: High (these are actual harmful content attempts) Rule 3: Rate Limit Abuse Why it matters: Persistent rate limit violations indicate automated attacks, credential stuffing, or attempts to overwhelm the system. Legitimate users who hit rate limits don't retry 10+ times in minutes. What to detect: Users blocked by rate limiter 5+ times in 10 minutes let timeframe = 1h; let threshold = 5; AzureDiagnostics | where ResourceProvider == "MICROSOFT.COGNITIVESERVICES" | where OperationName == "Completions" or OperationName contains "ChatCompletions" | extend tokensUsed = todouble(parse_json(properties_s).usage.total_tokens) | summarize totalTokens = sum(tokensUsed), requests = count(), rateLimitErrors = countif(httpstatuscode_s == "429") by bin(TimeGenerated, 1h) | where count_ >= threshold What the SOC sees: Whether it's a bot (immediate retries) or human (gradual retries) Duration of attack Which application is targeted Correlation with other security events from same user/IP Severity: Medium (nuisance attack, possible reconnaissance) Rule 4: Anomalous Source IP for User Why it matters: A user suddenly accessing from a new country or VPN could indicate account compromise. This is especially critical for privileged accounts or after-hours access. What to detect: User accessing from an IP never seen in the last 7 days let lookback = 7d; let recent = 1h; let baseline = IdentityLogonEvents | where Timestamp between (ago(lookback + recent) .. ago(recent)) | where isnotempty(IPAddress) | summarize knownIPs = make_set(IPAddress) by AccountUpn; ContainerLogV2 | where TimeGenerated >= ago(recent) | where isnotempty(LogMessage.source_ip) | extend source_ip=tostring(LogMessage.source_ip) | extend end_user_id=tostring(LogMessage.end_user_id) | extend session_id=tostring(LogMessage.session_id) | extend application_name = tostring(LogMessage.application_name) | extend security_check_passed = tostring (LogMessage.security_check_passed) | extend full_prompt_sample = tostring (LogMessage.full_prompt_sample) | lookup baseline on $left.AccountUpn == $right.end_user_id | where isnull(knownIPs) or IPAddress !in (knownIPs) | project TimeGenerated, source_ip, end_user_id, session_id, Computer, application_name, security_check_passed, full_prompt_sample What the SOC sees: User identity and new IP address Geographic location change Whether suspicious prompts accompanied the new IP Timing (after-hours access is higher risk) Severity: Medium (environment compromise, reconnaissance) Rule 5: Coordinated Attack - Same Prompt from Multiple Users Why it matters: When 5+ users send identical prompts, it indicates a bot network, credential stuffing, or organized attack campaign. This is not normal user behavior. What to detect: Same prompt hash from 5+ different users within 1 hour let timeframe = 1h; let threshold = 5; ContainerLogV2 | where TimeGenerated >= ago(timeframe) | where isnotempty(LogMessage.prompt_hash) | where isnotempty(LogMessage.end_user_id) | extend source_ip=tostring(LogMessage.source_ip) | extend end_user_id=tostring(LogMessage.end_user_id) | extend prompt_hash=tostring(LogMessage.prompt_hash) | extend application_name = tostring(LogMessage.application_name) | extend security_check_passed = tostring (LogMessage.security_check_passed) | project TimeGenerated, prompt_hash, source_ip, end_user_id, application_name, security_check_passed | summarize DistinctUsers = dcount(end_user_id), Attempts = count(), Users = make_set(end_user_id, 100), IpAddress = make_set(source_ip, 100) by prompt_hash, bin(TimeGenerated, 1h) | where DistinctUsers >= threshold What the SOC sees: Attack pattern (single attacker with stolen accounts vs. botnet) List of compromised user accounts Source IPs for blocking Prompt sample to understand attack goal Severity: High (indicates organized attack) Rule 6: Malicious model detected Why it matters: Model serialization attacks can lead to serious compromise. When Defender for Cloud Model Scanning identifies issues with a custom or opensource model that is part of Azure ML Workspace, Registry, or hosted in Foundry, that may be or may not be a user oversight. What to detect: Model scan results from Defender for Cloud and if it is being actively used. What the SOC sees: Malicious model Applications leveraging the model Source IPs and users accessed the model Severity: Medium (can be user oversight) Advanced Correlation: Connecting the Dots The power of Sentinel is correlating your application logs with other security signals. Here are the most valuable correlations: Correlation 1: Failed GenAI Requests + Failed Sign-Ins = Compromised Account Why: Account showing both authentication failures and malicious AI prompts is likely compromised within a 1 hour timeframe l let timeframe = 1h; ContainerLogV2 | where TimeGenerated >= ago(timeframe) | where isnotempty(LogMessage.source_ip) | extend source_ip=tostring(LogMessage.source_ip) | extend end_user_id=tostring(LogMessage.end_user_id) | extend session_id=tostring(LogMessage.session_id) | extend application_name = tostring(LogMessage.application_name) | extend security_check_passed = tostring (LogMessage.security_check_passed) | extend full_prompt_sample = tostring (LogMessage.full_prompt_sample) | extend message = tostring (LogMessage.message) | where security_check_passed == "FAIL" or message contains "WARNING" | join kind=inner ( SigninLogs | where ResultType != 0 // 0 means success, non-zero indicates failure | project TimeGenerated, UserPrincipalName, ResultType, ResultDescription, IPAddress, Location, AppDisplayName ) on $left.end_user_id == $right.UserPrincipalName | project TimeGenerated, source_ip, end_user_id, application_name, full_prompt_sample, prompt_hash, message, security_check_passed Severity: High (High probability of compromise) Correlation 2: Application Logs + Defender for Cloud AI Alerts Why: Defender for Cloud AI Threat Protection detects platform-level threats (unusual API patterns, data exfiltration attempts). When both your code and the platform flag the same user, confidence is very high. let timeframe = 1h; ContainerLogV2 | where TimeGenerated >= ago(timeframe) | where isnotempty(LogMessage.source_ip) | extend source_ip=tostring(LogMessage.source_ip) | extend end_user_id=tostring(LogMessage.end_user_id) | extend session_id=tostring(LogMessage.session_id) | extend application_name = tostring(LogMessage.application_name) | extend security_check_passed = tostring (LogMessage.security_check_passed) | extend full_prompt_sample = tostring (LogMessage.full_prompt_sample) | extend message = tostring (LogMessage.message) | where security_check_passed == "FAIL" or message contains "WARNING" | join kind=inner ( AlertEvidence | where TimeGenerated >= ago(timeframe) and AdditionalFields.Asset == "true" | where DetectionSource == "Microsoft Defender for AI Services" | project TimeGenerated, Title, CloudResource ) on $left.application_name == $right.CloudResource | project TimeGenerated, application_name, end_user_id, source_ip, Title Severity: Critical (Multi-layer detection) Correlation 3: Source IP + Threat Intelligence Feeds Why: If requests come from known malicious IPs (C2 servers, VPN exit nodes used in attacks), treat them as high priority even if behavior seems normal. //This rule correlates GenAI app activity with Microsoft Threat Intelligence feed available in Sentinel and Microsoft XDR for malicious IP IOCs let timeframe = 10m; ContainerLogV2 | where TimeGenerated >= ago(timeframe) | where isnotempty(LogMessage.source_ip) | extend source_ip=tostring(LogMessage.source_ip) | extend end_user_id=tostring(LogMessage.end_user_id) | extend session_id=tostring(LogMessage.session_id) | extend application_name = tostring(LogMessage.application_name) | extend security_check_passed = tostring (LogMessage.security_check_passed) | extend full_prompt_sample = tostring (LogMessage.full_prompt_sample) | join kind=inner ( ThreatIntelIndicators | where IsActive == "true" | where ObservableKey startswith "ipv4-addr" or ObservableKey startswith "network-traffic" | project IndicatorIP = ObservableValue ) on $left.source_ip == $right.IndicatorIP | project TimeGenerated, source_ip, end_user_id, application_name, full_prompt_sample, security_check_passed Severity: High (Known bad actor) Workbooks: What Your SOC Needs to See Executive Dashboard: GenAI Security Health Purpose: Leadership wants to know: "Are we secure?" Answer with metrics. Key visualizations: Security Status Tiles (24 hours) Total Requests Success Rate Blocked Threats (Self detected + Content Safety + Threat Protection for AI) Rate Limit Violations Model Security Score (Red Team evaluation status of currently deployed model) ContainerLogV2 | where TimeGenerated > ago (1d) | extend security_check_passed = tostring (LogMessage.security_check_passed) | summarize SuccessCount=countif(security_check_passed == "PASS"), FailedCount=countif(security_check_passed == "FAIL") by bin(TimeGenerated, 1h) | extend TotalRequests = SuccessCount + FailedCount | extend SuccessRate = todouble(SuccessCount)/todouble(TotalRequests) * 100 | order by SuccessRate 1. Trend Chart: Pass vs. Fail Over Time Shows if attack volume is increasing Identifies attack time windows Validates that defenses are working ContainerLogV2 | where TimeGenerated > ago (14d) | extend security_check_passed = tostring (LogMessage.security_check_passed) | summarize SuccessCount=countif(security_check_passed == "PASS"), FailedCount=countif(security_check_passed == "FAIL") by bin(TimeGenerated, 1d) | render timechart 2. Top 10 Users by Security Events Bar chart of users with most failures ContainerLogV2 | where TimeGenerated > ago (1d) | where isnotempty(LogMessage.end_user_id) | extend end_user_id=tostring(LogMessage.end_user_id) | extend security_check_passed = tostring (LogMessage.security_check_passed) | where security_check_passed == "FAIL" | summarize FailureCount = count() by end_user_id | top 20 by FailureCount | render barchart Applications with most failures ContainerLogV2 | where TimeGenerated > ago (1d) | where isnotempty(LogMessage.application_name) | extend application_name=tostring(LogMessage.application_name) | extend security_check_passed = tostring (LogMessage.security_check_passed) | where security_check_passed == "FAIL" | summarize FailureCount = count() by application_name | top 20 by FailureCount | render barchart 3. Geographic Threat Map Where are attacks originating? Useful for geo-blocking decisions ContainerLogV2 | where TimeGenerated > ago (1d) | where isnotempty(LogMessage.application_name) | extend application_name=tostring(LogMessage.application_name) | extend source_ip=tostring(LogMessage.source_ip) | extend security_check_passed = tostring (LogMessage.security_check_passed) | where security_check_passed == "FAIL" | extend GeoInfo = geo_info_from_ip_address(source_ip) | project sourceip, GeoInfo.counrty, GeoInfo.city Analyst Deep-Dive: User Behavior Analysis Purpose: SOC analyst investigating a specific user or session Key components: 1. User Activity Timeline Every request from the user in time order ContainerLogV2 | where isnotempty(LogMessage.end_user_id) | project TimeGenerated, LogMessage.source_ip, LogMessage.end_user_id, LogMessage. session_id, Computer, LogMessage.application_name, LogMessage.request_id, LogMessage.message, LogMessage.full_prompt_sample | order by tostring(LogMessage_end_user_id), TimeGenerated Color-coded by security status AlertInfo | where DetectionSource == "Microsoft Defender for AI Services" | project TimeGenerated, AlertId, Title, Category, Severity, SeverityColor = case( Severity == "High", "🔴 High", Severity == "Medium", "🟠 Medium", Severity == "Low", "🟢 Low", "⚪ Unknown" ) 2. Session Analysis Table All sessions for the user ContainerLogV2 | where TimeGenerated > ago (1d) | where isnotempty(LogMessage.end_user_id) | extend end_user_id=tostring(LogMessage.end_user_id) | where end_user_id == "<username>" // Replace with actual username | extend application_name=tostring(LogMessage.application_name) | extend source_ip=tostring(LogMessage.source_ip) | extend session_id=tostri1ng(LogMessage.session_id) | extend security_check_passed = tostring (LogMessage.security_check_passed) | project TimeGenerated, session_id, end_user_id, application_name, security_check_passed Failed requests per session ContainerLogV2 | where TimeGenerated > ago (1d) | extend security_check_passed = tostring (LogMessage.security_check_passed) | where security_check_passed == "FAIL" | extend end_user_id=tostring(LogMessage.end_user_id) | extend session_id=tostring(LogMessage.session_id) | extend security_check_passed = tostring (LogMessage.security_check_passed) | summarize Failed_Sessions = count() by end_user_id, session_id | order by Failed_Sessions Session duration ContainerLogV2 | where TimeGenerated > ago (1d) | where isnotempty(LogMessage.session_id) | extend security_check_passed = tostring (LogMessage.security_check_passed) | where security_check_passed == "PASS" | extend end_user_id=tostring(LogMessage.end_user_id) | extend session_id=tostring(LogMessage.session_id) | extend application_name=tostring(LogMessage.application_name) | extend source_ip=tostring(LogMessage.source_ip) | summarize Start=min(TimeGenerated), End=max(TimeGenerated), count() by end_user_id, session_id, source_ip, application_name | extend DurationSeconds = datetime_diff("second", End, Start) 3. Prompt Pattern Detection Unique prompts by hash Frequency of each pattern Detect if user is fuzzing/testing boundaries Sample query for user investigation: ContainerLogV2 | where TimeGenerated > ago (14d) | where isnotempty(LogMessage.prompt_hash) | where isnotempty(LogMessage.full_prompt_sample) | extend prompt_hash=tostring(LogMessage.prompt_hash) | extend full_prompt_sample=tostring(LogMessage.full_prompt_sample) | extend application_name=tostring(LogMessage.application_name) | summarize count() by prompt_hash, full_prompt_sample | order by count_ Threat Hunting Dashboard: Proactive Detection Purpose: Find threats before they trigger alerts Key queries: 1. Suspicious Keywords in Prompts (e.g. Ignore, Disregard, system prompt, instructions, DAN, jailbreak, pretend, roleplay) let suspicious_prompts = externaldata (content_policy:int, content_policy_name:string, q_id:int, question:string) [ @"https://raw.githubusercontent.com/verazuo/jailbreak_llms/refs/heads/main/data/forbidden_question/forbidden_question_set.csv"] with (format="csv", has_header_row=true, ignoreFirstRecord=true); ContainerLogV2 | where TimeGenerated > ago (14d) | where isnotempty(LogMessage.full_prompt_sample) | extend full_prompt_sample=tostring(LogMessage.full_prompt_sample) | where full_prompt_sample in (suspicious_prompts) | extend end_user_id=tostring(LogMessage.end_user_id) | extend session_id=tostring(LogMessage.session_id) | extend application_name=tostring(LogMessage.application_name) | extend source_ip=tostring(LogMessage.source_ip) | project TimeGenerated, session_id, end_user_id, source_ip, application_name, full_prompt_sample 2. High-Volume Anomalies User sending too many requests by a IP or User. Assuming that Foundry Projects are configured to use Azure AD and not API Keys. //50+ requests in 1 hour let timeframe = 1h; let threshold = 50; AzureDiagnostics | where ResourceProvider == "MICROSOFT.COGNITIVESERVICES" | where OperationName == "Completions" or OperationName contains "ChatCompletions" | extend tokensUsed = todouble(parse_json(properties_s).usage.total_tokens) | summarize totalTokens = sum(tokensUsed), requests = count() by bin(TimeGenerated, 1h),CallerIPAddress | where count_ >= threshold 3. Rare Failures (Novel Attack Detection) Rare failures might indicate zero-day prompts or new attack techniques //10 or more failures in 24 hours ContainerLogV2 | where TimeGenerated >= ago (24h) | where isnotempty(LogMessage.security_check_passed) | extend security_check_passed=tostring(LogMessage.security_check_passed) | where security_check_passed == "FAIL" | extend application_name=tostring(LogMessage.application_name) | extend end_user_id=tostring(LogMessage.end_user_id) | extend source_ip=tostring(LogMessage.source_ip) | summarize FailedAttempts = count(), FirstAttempt=min(TimeGenerated), LastAttempt=max(TimeGenerated) by application_name | extend DurationHours = datetime_diff('hour', LastAttempt, FirstAttempt) | where DurationHours >= 24 and FailedAttempts >=10 | project application_name, FirstAttempt, LastAttempt, DurationHours, FailedAttempts Measuring Success: Security Operations Metrics Key Performance Indicators Mean Time to Detect (MTTD): let AppLog = ContainerLogV2 | extend application_name=tostring(LogMessage.application_name) | extend security_check_passed=tostring (LogMessage.security_check_passed) | extend session_id=tostring(LogMessage.session_id) | extend end_user_id=tostring(LogMessage.end_user_id) | extend source_ip=tostring(LogMessage.source_ip) | where security_check_passed=="FAIL" | summarize FirstLogTime=min(TimeGenerated) by application_name, session_id, end_user_id, source_ip; let Alert = AlertEvidence | where DetectionSource == "Microsoft Defender for AI Services" | extend end_user_id = tostring(AdditionalFields.AadUserId) | extend source_ip=RemoteIP | extend application_name=CloudResource | summarize FirstAlertTime=min(TimeGenerated) by AlertId, Title, application_name, end_user_id, source_ip; AppLog | join kind=inner (Alert) on application_name, end_user_id, source_ip | extend DetectionDelayMinutes=datetime_diff('minute', FirstAlertTime, FirstLogTime) | summarize MTTD_Minutes=round(avg (DetectionDelayMinutes),2) by AlertId, Title Target: <= 15 minutes from first malicious activity to alert Mean Time to Respond (MTTR): SecurityIncident | where Status in ("New", "Active") | where CreatedTime >= ago(14d) | extend ResponseDelay = datetime_diff('minute', LastActivityTime, FirstActivityTime) | summarize MTTR_Minutes = round (avg (ResponseDelay),2) by CreatedTime, IncidentNumber | order by CreatedTime, IncidentNumber asc Target: < 4 hours from alert to remediation Threat Detection Rate: ContainerLogV2 | where TimeGenerated > ago (1d) | extend security_check_passed = tostring (LogMessage.security_check_passed) | summarize SuccessCount=countif(security_check_passed == "PASS"), FailedCount=countif(security_check_passed == "FAIL") by bin(TimeGenerated, 1h) | extend TotalRequests = SuccessCount + FailedCount | extend SuccessRate = todouble(SuccessCount)/todouble(TotalRequests) * 100 | order by SuccessRate Context: 1-3% is typical for production systems (most traffic is legitimate) What You've Built By implementing the logging from Part 2 and the analytics rules in this post, your SOC now has: ✅ Real-time threat detection - Alerts fire within minutes of malicious activity ✅ User attribution - Every incident has identity, IP, and application context ✅ Pattern recognition - Detect both volume-based and behavior-based attacks ✅ Correlation across layers - Application logs + platform alerts + identity signals ✅ Proactive hunting - Dashboards for finding threats before they trigger rules ✅ Executive visibility - Metrics showing program effectiveness Key Takeaways GenAI threats need GenAI-specific analytics - Generic rules miss context like prompt injection, content safety violations, and session-based attacks Correlation is critical - The most sophisticated attacks span multiple signals. Correlating app logs with identity and platform alerts catches what individual rules miss. User context from Part 2 pays off - end_user_id, source_ip, and session_id enable investigation and response at scale Prompt hashing enables pattern detection - Detect repeated attacks without storing sensitive prompt content Workbooks serve different audiences - Executives want metrics; analysts want investigation tools; hunters want anomaly detection Start with high-fidelity rules - Content Safety violations and rate limit abuse have very low false positive rates. Add behavioral rules after establishing baselines. What's Next: Closing the Loop You've now built detection and visibility. In Part 4, we'll close the security operations loop with: Part 4: Platform Integration and Automated Response Building SOAR playbooks for automated incident response Implementing automated key rotation with Azure Key Vault Blocking identities in Entra Creating feedback loops from incidents to code improvements The journey from blind spot to full security operations capability is almost complete. Previous: Part 1: Securing GenAI Workloads in Azure: A Complete Guide to Monitoring and Threat Protection - AIO11Y | Microsoft Community Hub Part 2: Part 2: Building Security Observability Into Your Code - Defensive Programming for Azure OpenAI | Microsoft Community Hub Next: Part 4: Platform Integration and Automated Response (Coming soon)Breaking down security silos: Microsoft Defender for Cloud Expands into the Defender Portal
Picture this: You’re managing security across Azure, AWS, and GCP. Alerts are coming from every direction, dashboards are scattered and your team spends more time switching portals than mitigating threats. Sound familiar? That’s the reality for many organizations today. Now imagine a different world—where visibility, control and response converge into one unified experience, where posture management, vulnerability insights and incident response live side by side. That world is no longer a dream: Microsoft Defender for Cloud (MDC) is now integrated into Defender XDR in public preview. The expansion of MDC into the Defender portal isn’t just a facelift. It’s a strategic leap forward toward a Cloud-Native Application Protection Platform (CNAPP) that scales with your business. With Microsoft Defender for Cloud’s deep integration into the unified portal, we eliminate security silos and bring a modern, streamlined experience that is more intuitive and purpose-built for today’s security teams, while delivering a single pane of glass for hybrid and multi-cloud security. Here’s what makes this release a game-changer: Unified dashboard See everything with a single pane of glass—security posture, coverage, trends—across Azure, AWS and GCP. No more blind spots. Risk-based recommendations Prioritize by exploitability and business impact. Focus on what matters most, not just noise. Attack path analysis across all Defenders Visualize potential breach paths and cut them off before attackers can exploit them. Unified cloud assets inventory A consolidated view of assets, health data and onboarding state—so you know exactly where you stand. Cloud scopes & unified RBAC Create boundaries between teams, ensure each persona has access to the right level of data in the Defender portal. The enhanced in-portal experience includes all familiar Defender for Cloud capabilities and adds powerful new cloud-native workflows — now accessible directly within the Defender portal. Over time, additional features will be rolled out so that security teams can rely on a single pane of glass for all their pre- and post-breach operations. Unified cloud security dashboard A brand-new “Cloud Security→ Overview” page in Defender portal gives you a central place to assess your cloud posture across all connected clouds and environments (Azure, AWS, GCP, on-prem and onboarded environments such as Azure DevOps, Github, Gitlab, DockerHub, Jfrog). The unified dashboard displays the new Cloud Security Score, Threat Detection alerts and Defender coverage statistics. Amongst the high-level metrics, you can find the number of assessed resources, count of active recommendations, security alerts and more, giving you at-a-glance insight into your environment’s health. From here, you can drill into individual areas: Security posture, Exposure Management bringing visibility over Recommendations and Vulnerability Management, a unified asset inventory, workload specific insights and historical security posture data going back up to 6 months. Cloud Assets Inventory The cloud asset inventory view provides a unified, contextual inventory of all resources you have connected to Defender for Cloud — across cloud environments or on-premises. Assets are categorized by workload type, criticality, Defender coverage status, with integrated health data, risk signals, associated exposure management data, recommendations and related attack paths. Resources with unresolved security recommendations or alerts are clearly flagged — helping you quickly prioritize on risky or non-compliant assets. While you will get a complete list of cloud assets under "All assets", the rest of the tabs show you the complete view into each workload, with detailed and specific insights on each workload (VMs, Data, Containers, AI, API, DevOps, Identity and Serverless). Posture & Risk Management: From Secure Score to risk-based recommendations The traditional posture-management and CSPM capabilities of Defender for Cloud expand into the Defender portal under “Exposure Management.” A key upgrade is the new Cloud Secure Score — a risk-based model that factors in asset criticality and risk factors (e.g. internet exposure, data sensitivity) to give a more accurate, prioritized view of cloud security posture. The score ranges from 0 to 100, where 100 means perfect posture. It aggregates across all assets, weighting each asset by its criticality and the risk of its open recommendations. You can view the Cloud Secure Score overall, by subscription, cloud environment or workload type. This allows security teams to quickly understand which parts of their estate require urgent attention, and track posture improvements over time. Defender for Cloud continues to generate security recommendations based on assessments against built-in (or custom) security standards. When you have the Defender CSPM plan enabled in the Defender portal, these recommendations are surfaced with risk-based prioritization, where recommendations are tied to high-risk or critical assets show up first — helping you remediate what matters most. Each recommendation shows risk level, number of attack paths, MITRE ATT&CK tactics and techniques. For each recommendation you will see the remediation steps, attack map and the initiatives it contributes to - such as the Cloud Secure score. Continued remediation — across all subscriptions and environments — is the path toward a hardened cloud estate. Proactive Attack Surface Management: Attack path analysis A powerful addition is the "Attack paths" overview, which helps you visualize potential paths attackers could use — from external exposure zones to your most critical business assets to infiltrate your environment and access sensitive data. Defender’s algorithm models your network, resource interactions, vulnerabilities and external exposures to surface realistic, exploitable attack paths, rather than generic threat scenarios, while putting focus on the top targets, entry points and choke points involved in attack paths. The Attack Paths page organizes findings by risk level and correlates data across all Defender solutions, allowing users to rapidly detect high-impact attack paths and focus remediation on the most vulnerable assets. For some workloads, for example container-based or runtime workloads, additional prerequisites may apply (e.g. enabling agentless scanning or relevant Defender plans) to get full visualization. Governance, Visibility and Access: Cloud Scopes and Unified RBAC The expansion into the Defender portal doesn’t just bring new dashboards — it also brings unified access and governance using a single identity and RBAC model for the Defender solutions. Now you can manage cloud security permissions alongside identity, device and app permissions. Cloud Scopes ensure that teams with appropriate roles within the defined permission groups (e.g. Security operations, Security posture) can access the assets and features they need, scoped to specific subscriptions and environments. This unified scope system simplifies operations, reduces privilege sprawl and enforces consistent governance across cloud environments and across security domains. The expansion of Defender for Cloud into the Defender portal is more than a consolidation—it’s a strategic shift toward a truly integrated security ecosystem. Cloud security is no longer an isolated discipline. It is intertwined with exposure management, threat detection, identity protection and organizational governance. To conclude, this new experience empowers security teams to: Understand cloud risk in full context Prioritize remediation that reduces real-world threats Investigate attacks holistically across cloud and non-cloud systems Govern access and configurations with greater consistency Predict and prevent attack paths before they happen In this new era, cloud security becomes a continuous, intelligent and unified journey. The Defender portal is now the command center for that journey—one where insights, context and action converge to help organizations secure the present while anticipating the future. Ready to Explore? Defender for Cloud in the Defender portal Integration FAQ Enable Preview Features Azure portal vs Defender portal feature comparison What’s New in Defender for Cloud
