Protecting Your Azure Key Vault: Why Azure RBAC Is Critical for Security
Introduction In today’s cloud-centric landscape, misconfigured access controls remain one of the most critical weaknesses in the cyber kill chain. When access policies are overly permissive, they create opportunities for adversaries to gain unauthorized access to sensitive secrets, keys, and certificates. These credentials can be leveraged for lateral movement, privilege escalation, and establishing persistent footholds across cloud environments. A compromised Azure Key Vault doesn’t just expose isolated assets it can act as a pivot point to breach broader Azure resources, potentially leading to widespread security incidents, data exfiltration, and regulatory compliance failures. Without granular permissioning and centralized access governance, organizations face elevated risks of supply chain compromise, ransomware propagation, and significant operational disruption. The Role of Azure Key Vault in Security Azure Key Vault plays a crucial role in securely storing and managing sensitive information, making it a prime target for attackers. Effective access control is essential to prevent unauthorized access, maintain compliance, and ensure operational efficiency. Historically, Azure Key Vault used Access Policies for managing permissions. However, Azure Role-Based Access Control (RBAC) has emerged as the recommended and more secure approach. RBAC provides granular permissions, centralized management, and improved security, significantly reducing risks associated with misconfigurations and privilege misuse. In this blog, we’ll highlight the security risks of a misconfigured key vault, explain why RBAC is superior to legacy Access Policies and provide RBAC best practices, and how to migrate from access policies to RBAC. Security Risks of Misconfigured Azure Key Vault Access Overexposed Key Vaults create significant security vulnerabilities, including: Unauthorized access to API tokens, database credentials, and encryption keys. Compromise of dependent Azure services such as Virtual Machines, App Services, Storage Accounts, and Azure SQL databases. Privilege escalation via managed identity tokens, enabling further attacks within your environment. Indirect permission inheritance through Azure AD (AAD) group memberships, making it harder to track and control access. Nested AAD group access, which increases the risk of unintended privilege propagation and complicates auditing and governance. Consider this real-world example of the risks posed by overly permissive access policies: A global fintech company suffered a severe breach due to an overly permissive Key Vault configuration, including public network access and excessive permissions via legacy access policies. Attackers accessed sensitive Azure SQL databases, achieved lateral movement across resources, and escalated privileges using embedded tokens. The critical lesson: protect Key Vaults using strict RBAC permissions, network restrictions, and continuous security monitoring. Why Azure RBAC is Superior to Legacy Access Policies Azure RBAC enables centralized, scalable, and auditable access management. It integrates with Microsoft Entra, supports hierarchical role assignments, and works seamlessly with advanced security controls like Conditional Access and Defender for Cloud. Access Policies, on the other hand, were designed for simpler, resource-specific use cases and lack the flexibility and control required for modern cloud environments. For a deeper comparison, see Azure RBAC vs. access policies. Best Practices for Implementing Azure RBAC with Azure Key Vault To effectively secure your Key Vault, follow these RBAC best practices: Use Managed Identities: Eliminate secrets by authenticating applications through Microsoft Entra. Enforce Least Privilege: Precisely control permissions, granting each user or application only minimal required access. Centralize and Scale Role Management: Assign roles at subscription or resource group levels to reduce complexity and improve manageability. Leverage Privileged Identity Management (PIM): Implement just-in-time, temporary access for high-privilege roles. Regularly Audit Permissions: Periodically review and prune RBAC role assignments. Detailed Microsoft Entra logging enhances auditability and simplifies compliance reporting. Integrate Security Controls: Strengthen RBAC by integrating with Microsoft Entra Conditional Access, Defender for Cloud, and Azure Policy. For more on the Azure RBAC features specific to AKV, see the Azure Key Vault RBAC Guide. For a comprehensive security checklist, see Secure your Azure Key Vault. Migrating from Access Policies to RBAC To transition your Key Vault from legacy access policies to RBAC, follow these steps: Prepare: Confirm you have the necessary administrative permissions and gather an inventory of applications and users accessing the vault. Conduct inventory: Document all current access policies, including the specific permissions granted to each identity. Assign RBAC Roles: Map each identity to an appropriate RBAC role (e.g., Reader, Contributor, Administrator) based on the principle of least privilege. Enable RBAC: Switch the Key Vault to the RBAC authorization model. Validate: Test all application and user access paths to ensure nothing is inadvertently broken. Monitor: Implement monitoring and alerting to detect and respond to access issues or misconfigurations. For detailed, step-by-step instructions—including examples in CLI and PowerShell—see Migrate from access policies to RBAC. Conclusion Now is the time to modernize access control strategies. Adopting Role-Based Access Control (RBAC) not only eliminates configuration drift and overly broad permissions but also enhances operational efficiency and strengthens your defense against evolving threat landscapes. Transitioning to RBAC is a proactive step toward building a resilient and future-ready security framework for your Azure environment. Overexposed Azure Key Vaults aren’t just isolated risks — they act as breach multipliers. Treat them as Tier-0 assets, on par with domain controllers and enterprise credential stores. Protecting them requires the same level of rigor and strategic prioritization. By enforcing network segmentation, applying least-privilege access through RBAC, and integrating continuous monitoring, organizations can dramatically reduce the blast radius of a potential compromise and ensure stronger containment in the face of advanced threats. Want to learn more? Explore Microsoft's RBAC Documentation for additional details.Become a Microsoft Defender for Cloud Ninja
[Last update: 08/29/2025] All content has been reviewed and updated for August 2025. 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 Microsoft Defender for Cloud expands U.S. Gov Cloud support for CSPM and server security (08/29/2025) 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 Resource Manager 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 for App Service Considerations for Multi-Tenant Scenario 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 Enhance your Secure Score in Microsoft Defender for Cloud Security recommendations Active User (Public Preview) Resource exemption Customizing Endpoint Protection Recommendation in Microsoft Defender for Cloud Deliver a Security Score weekly briefing Send Microsoft Defender for Cloud Recommendations to Azure Resource Stakeholders Secure Score Reduction Alert Average Time taken to remediate resources 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 Creating Custom Dashboard for Microsoft Defender for Cloud 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 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 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 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 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 Microsoft Defender for Containers Basics of Defender for Containers Secure your Containers from Build to Runtime 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 Microsoft Defender for Storage Protect your storage resources against blob-hunting Malware Scanning in Defender for Storage What's New in Defender for Storage 🎉Malware scanning add-on is now generally available in Azure Gov Secret and Top-Secret clouds (08/29/2025) Defender for Storage: Malware Scan Error Message Update Protecting Cloud Storage in the Age of AI 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 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 Linux 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 Closing an Incident in Azure Sentinel and Dismissing an Alert in Microsoft Defender for Cloud 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 Obtaining Secure Score via REST API 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 Workload Protection Secure your AI applications from code to runtime with Microsoft Defender for Cloud AI security posture management AI threat protection 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 Exposing hidden threats across the AI development lifecycle in the cloud (08/29/2025) Learn Live: Enable advanced threat protection for AI workloads with Microsoft Defender for Cloud Microsoft AI Security Story: Protection Across the Platform 🚀 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 PMExposing hidden threats across the AI development lifecycle in the cloud
Introduction: The AI Lifecycle in the Cloud and Its Risks As organizations increasingly adopt AI to drive innovation, the development and deployment of AI models, applications and agents is now taking place in the cloud more than ever before. Leading cloud platforms make it easier than ever to build, train, and deploy AI systems at scale - offering powerful compute, seamless integrations, and collaborative tools. However, this shift also introduces new security challenges at every stage of the development lifecycle. Whether you're training an AI model or deploying an AI application or agent, the AI development lifecycle in the cloud includes multiple stages, including data collection, model training, Fine-tuning pipelines, and the deployment of AI applications and agents. If attackers compromise even one part of this lifecycle, it can put the entire AI system and the business operations it supports at risk. What adds to the complexity of this landscape is the rapid evolution of cloud-based AI platforms. New features are released at a fast pace, often outpacing the maturity of existing security controls - leaving gaps that attackers can exploit. This blog will examine the risks associated with each phase of the AI development lifecycle in the cloud – whether it’s models, applications, or agents. We’ll explore how attackers can abuse them, and how Microsoft Defender for Cloud helps organizations reduce AI posture risks with AI Posture management across their multi cloud environment. Understanding the Threat Landscape Across the AI Lifecycle Whether it’s poisoning training data, stealing proprietary models, or hijacking deployed AI systems to manipulate outputs, securing the cloud-based AI development lifecycle requires a comprehensive understanding of the risks associated with every phase. Let’s explore how attackers can target various stages of the AI development lifecycle and the specific consequences of those compromises. Data and training It all begins with data, which is often the most valuable and the most vulnerable asset. Whether it's customer records, transaction logs, emails, or images, this data is used to train models that will eventually make decisions on behalf of the organization. In cloud AI environments, such data is typically stored in cloud storage. If attackers gain access to such storage account with training data, due to misconfigured storage or overly permissive cloud account permissions, the consequences can be severe. For instance, they might inject poisoned or manipulated data into the training set, subtly altering the behavior of the model. In one scenario, they could bias a credit scoring model to approve fraudulent applications. In another, they could insert a hidden backdoor - causing the model to behave normally most of the time but output incorrect or malicious predictions when triggered by a specific input. Once the data is prepared, it flows into the training pipeline: a critical but often overlooked attack surface. This pipeline automates the full training workflow: ingesting data, executing transformation scripts, spinning up GPU-powered training jobs, and saving the resulting model. If attackers infiltrate this pipeline, they can gain persistent control over the AI system. For example, they could modify preprocessing scripts to inject subtle distortions into the data, or they might replace a model artifact with a manipulated one that appears legitimate but behaves maliciously under specific conditions. Since pipelines often run with elevated permissions and can access cloud storage, compute resources, and secrets, they also become convenient pivot points for lateral movement across cloud infrastructure. Model Artifacts & Registries Once trained, models in the cloud are typically stored in model registries or artifact repositories. These are often considered secure because they’re not directly exposed to users. However, they represent a high-value target. Attackers who gain access to stored models can steal intellectual property, especially if the model architecture or parameters represent years of R&D. In addition to theft, an attacker might attempt to delete critical models to disrupt business and operations. Even more concerning, they could upload a malicious model in place of a legitimate one. Such a model could be designed to behave subtly but incorrectly, introduce biases, leak data during inference, or provide manipulated outputs that mislead downstream systems and users. This type of tampering not only undermines trust in AI systems but can also have serious operational and security consequences. Model Fine-tuning In addition to full model training, many organizations rely on fine-tuning: a process where a pre-trained foundation model is adapted using domain-specific data. Fine-tuning offers a faster and more cost-effective path to building specialized models, but it also introduces new attack vectors. The fine-tuning inherits all the risks of traditional training, plus a few more. For instance, attackers can target fine-tuning jobs or the associated fine-tuning files (e.g., in storage buckets) to manipulate the behavior of a pre-trained model without raising suspicion. By injecting poisoned fine-tuning data, they can create task-specific vulnerabilities, such as altering outputs related to a particular customer or product. The risk is especially high because fine-tuned models are often deployed directly into production environments. This means attackers don’t need to compromise the full model training workflow to achieve impact - they can introduce malicious behavior just by manipulating a smaller, faster process with fewer controls. Given this, securing fine-tuning pipelines and datasets is just as critical as protecting full-scale training jobs. Models Inference & Endpoints After deployment, models are exposed to the outside world through inference endpoints, typically REST APIs that receive input data and return predictions, decisions, text, or other outputs. The main risk at this stage is unbounded consumption. This occurs when attackers or even legitimate users are able to perform excessive, uncontrolled requests, especially with resource-intensive models like Large Language Models (LLMs). Such abuse can lead to denial of service (DoS), inflated operational costs, and overall service degradation. In cloud environments, where resource usage drives cost and performance, this kind of exploitation can have serious financial and operational impacts. In addition to consumption-based abuse, attackers with access to a poorly secured endpoint may attempt destructive actions such as deleting the endpoint to disrupt availability and business operations, or deploying a different model to the endpoint, potentially replacing trusted outputs with manipulated or malicious ones. Securing inference endpoints is critical to maintaining the integrity, availability, and cost-effectiveness of AI services in the cloud. The rise of AI Agents and apps AI agents, autonomous LLM-driven systems that can search, retrieve, write code, execute workflows, and make decisions, are rapidly becoming a central component in modern AI systems. Unlike traditional models that simply return predictions or text, agents are designed to perform complex, goal-oriented tasks by autonomously chaining multiple actions, tools, and reasoning steps. They can interact with external systems, call APIs, query databases, invoke tools like code execution environments or vector stores, and even communicate with other agents. This growing autonomy and connectivity unlock powerful capabilities - but it also introduces a new and expanding attack surface. One of the biggest concerns with AI agents is the amplification of existing risks. Vulnerabilities like prompt injection, which might have limited impact in a basic chatbot, can become far more dangerous when exploited in an agent that has access to tools and can take real actions. A single malicious input could cause an agent to leak sensitive information, perform unintended operations, or invoke tools in harmful ways. In addition, attackers with access to the agent itself, whether though a compromised cloud account permissions or leaked API keys, can access the agent tools, change the agent’s behavior by manipulating its instructions, or deleting it to disrupt business. As the adoption of AI agents grows, it's critical for organizations to integrate security thinking into their design and deployment. This includes implementing strict controls on agent permissions, monitoring and logging agent behavior, hardening agent tools and APIs, and applying layered protections against manipulation and misuse. Models’ and Agents dependencies Cloud-based AI systems increasingly rely on external data sources and tools to perform complex tasks accurately. For example, retrieval-augmented generation (RAG) models depend on grounding data from document stores or vector databases to generate up-to-date, context-aware responses. Similarly, AI agents may be configured to interact with APIs, databases, cloud functions, or internal systems as part of their reasoning or execution loop. These dependencies act as the AI system's supply chain, where a breach in one part can undermine the integrity of the entire system. If attackers tamper the grounding data, a model’s output can be intentionally skewed or poisoned. Likewise, if the tools an agent depends on - such as cloud automation function - are compromised or misconfigured, the agent could execute malicious actions or leak sensitive information. Securing these dependencies is essential, as attackers may exploit trust in the AI supply chain to manipulate behavior, exfiltrate data, or pivot deeper into the cloud infrastructure. Across all these components, one theme is clear: the interconnected nature of AI in the cloud means that a single weak link can compromise the entire lifecycle. Data corruption can lead to model failure. Pipeline compromise can lead to infrastructure access. Endpoint manipulation can lead to silent data leaks. This is why AI security posture must be end-to-end - from data to deployment. Securing AI in the cloud – it all starts with visibility AI Security Posture Management (AI-SPM), part of Microsoft Defender for Cloud's CNAPP solution, provides security from code to deployed AI models, applications and agents. It offers comprehensive visibility into AI assets, including data assets, models, endpoints, and agents. By identifying vulnerabilities and misconfigurations, AI-SPM enables organizations to reduce risks and detect and respond to AI applications. Reduce AI application risks with Defender for Cloud By leveraging its agentless detection capabilities, Defender for Cloud uncovers misconfigurations and attack paths that could be exploited to compromise AI components at every stage of the lifecycle outlined above. These insights empower security teams to focus on critical risks and address them effectively, minimizing the overall risk. For example, as illustrated in Figure 1, an attack path can demonstrate how an attacker might utilize a virtual machine with a high-severity vulnerability to gain access to an organization's AI platform. This visualization helps security admin teams to take preventative actions, safeguarding the AI environment from potential breaches. The AI-SPM capabilities in Defender for Cloud also supports multi-cloud resources. In another example, as shown in figure 2, the attack path illustrates how an attacker can exploit a vulnerable GCP compute instance to gain access to a custom model deployment in Vertex AI. This scenario underscores the importance of securing every layer of the AI environment, including cloud infrastructure and compute resources, to prevent unauthorized access to sensitive AI components. In yet another scenario, as depicted in figure 3, an attacker might exploit a vulnerable GCP compute instance not only to access the model itself, but also to target the data used to train the AI model. This type of data poisoning attack could lead to altered model and application behavior, potentially skewing outputs, introducing bias, or corrupting downstream processes. Such attacks emphasize the critical need to secure data integrity across all stages of the AI lifecycle, from ingestion and training pipelines to active deployment. Safeguarding the data layer is as vital as securing the underlying infrastructure to ensure that AI applications remain trustworthy and resilient against threats. Summary: Build AI Security from the Ground Up To address these challenges across the whole cloud AI development lifecycle, Microsoft Defender for Cloud provides a suite of security tools tailored for AI workloads. By enabling AI Security Posture Management (AI-SPM) within the Defender for Cloud Defender CSPM plan, organizations gain comprehensive multicloud posture visibility and risk prioritization across platforms such as Azure AI Foundry, OpenAI services, AWS Bedrock, and GCP Vertex AI. This multicloud approach ensures critical vulnerabilities and potential attack paths are effectively identified and mitigated, creating a unified and secure AI ecosystem. Additionally, Defender for AI Services introduces a runtime protection plan specifically designed for custom-built AI applications. This plan extends the security coverage to AI models deployed on Azure AI Foundry and OpenAI services, safeguarding the entire lifecycle - from code to runtime. Together, these integrated solutions empower enterprises to build, deploy, and operate AI technologies securely, even within a diverse and evolving threat landscape. To learn more about Security for AI with Defender for Cloud, visit our website and documentation.
Microsoft Defender for Cloud expands U.S. Gov Cloud support for CSPM and server security
U.S. government organizations face unique security and compliance challenges as they migrate essential workloads to the cloud. To help meet these needs, Microsoft Defender for Cloud has expanded support in the Government Cloud with Defender cloud security posture management (CSPM) and Defender for Servers Plan 2. This expansion helps strengthen security posture with advanced threat protection, vulnerability management, and contextual risk insights across hybrid and multi-cloud environments. Defender CSPM and Defender for Servers are available in the following Microsoft Government Clouds: Microsoft Azure Government (MAG) – FedRamp High, DISA IL4, DISA IL5 Government Community Cloud High (GCCH) – FedRamp High, DISA IL4 Defender for Cloud offers support for CSPM in U.S. Government Cloud First, Defender CSPM is generally available for U.S. Government cloud customers. This expansion brings advanced cloud security posture management capabilities to U.S. federal and government agencies—including the Department of Defense (DoD) and civilian agencies—helping them strengthen their security posture and compliance in the cloud. Defender CSPM empowers agencies to continuously discover, assess, monitor, and improve their cloud security posture, including the ability to monitor and correct configuration drift, ensuring they meet regulatory requirements and proactively manage risk in highly regulated environments. Additional benefits for government agencies: Continuous Compliance Assurance Unlike static audits, Defender CSPM provides real-time visibility into the security posture of cloud environments. This enables agencies to demonstrate ongoing compliance with federal standards—anytime, not just during audit windows Risk-Based Prioritization Defender CSPM uses contextual insights and attack path analysis to help security teams focus on the most critical risks first—maximizing impact while optimizing limited resources Agentless Monitoring With agentless scanning, agencies can assess workloads without deploying additional software—ideal for sensitive or legacy systems Security recommendations in Defender CSPM To learn more about Defender CSPM, visit our technical documentation. Defender for Cloud now offers full feature parity for server security in U.S. Government Cloud In addition to Defender CSPM, we’re also expanding our support for server security in the U.S. GovCloud. Government agencies face mounting challenges in securing the servers that support their critical operations and sensitive data. As server environments expand across on-premises, hybrid, and multicloud platforms, maintaining consistent security controls and compliance with federal standards like FedRAMP and NIST SP 800-53 becomes increasingly difficult. Manual processes and periodic audits can’t keep up with configuration drift, unpatched vulnerabilities, and evolving threats—leaving agencies exposed to breaches and compliance risks. Defender for Servers provides continuous, automated threat protection, vulnerability management, and compliance monitoring across all server environments, enabling agencies to safeguard their infrastructure and maintain a strong security posture. We are excited to share that all capabilities in Defender for Servers Plan 2 are now available in U.S. GovCloud, including these newly added capabilities: Agent-based and agentless vulnerability assessment recommendations Secrets detection recommendations EDR detection recommendations Agentless malware detection File integrity monitoring Baseline recommendations Customers can start using all capabilities of Defender for Servers Plan 2 in U.S. Government Cloud starting today. To learn more about Defender for Servers, visit our technical documentation. Get started today! To gain access to the robust capabilities provided by Defender CSPM and Defender for Servers, you need to enable the plans on your subscription. To enable the Defender CSPM and Defender for Servers plans on your subscription: Sign in to the Azure portal. Search for and select Microsoft Defender for Cloud. In the Defender for Cloud menu, select Environment settings. Select the relevant Azure subscription On the Defender plans page, toggle the Defender CSPM plan and/or Defender for Servers to On. Select Save.From Healthy to Unhealthy: Alerting on Defender for Cloud Recommendations with Logic Apps
In today's cloud-first environments, maintaining strong security posture requires not just visibility but real-time awareness of changes. This blog walks you through a practical solution to monitor and alert on Microsoft Defender for Cloud recommendations that transition from Healthy to Unhealthy status. By combining the power of Kusto Query Language (KQL) with the automation capabilities of Azure Logic Apps, you’ll learn how to: Query historical and current security recommendation states using KQL Detect resources that have degraded in compliance over the past 14 days Send automatic email alerts when issues are detected Customize the email content with HTML tables for easy readability Handle edge cases, like sending a “no issues found” email when nothing changes Whether you're a security engineer, cloud architect, or DevOps practitioner, this solution helps you close the gap between detection and response and ensure that no security regressions go unnoticed. Prerequisites Before implementing the monitoring and alerting solution described in this blog, ensure the following prerequisites are met: Microsoft Defender for Cloud is Enabled Defender for Cloud must be enabled on the target Azure subscriptions/management group. It should be actively monitoring your resources (VMs, SQL, App Services, etc.). Make sure the recommendations are getting generated. Continuous Export is Enabled for Security Recommendations Continuous export should be configured to send security recommendations to a Log Analytics workspace. This enables you to query historical recommendation state using KQL. You can configure continuous export by going to: Defender for Cloud → Environment settings → Select Subscription → Continuous Export Then enable export for Security Recommendations to your chosen Log Analytics workspace. Detailed guidance on setting up continuous export can be found here: Set up continuous export in the Azure portal - Microsoft Defender for Cloud | Microsoft Learn High-Level Summary of the Automation Flow This solution provides a fully automated way to track and alert on security posture regressions in Microsoft Defender for Cloud. By integrating KQL queries with Azure Logic Apps, you can stay informed whenever a resource's security recommendation changes from Healthy to Unhealthy. Here's how the flow works: Microsoft Defender for Cloud evaluates Azure resources and generates security recommendations based on best practices and potential vulnerabilities. These recommendations are continuously exported to a Log Analytics workspace, enabling historical analysis over time. A scheduled Logic App runs a KQL query that compares: Recommendations from ~14 days ago (baseline), With those from the last 7 days (current state). If any resources are found to have shifted from Healthy to Unhealthy, the Logic App: Formats the data into an HTML table, and Sends an email alert with the affected resource details and recommendation metadata. If no such changes are found, an optional email can be sent stating that all monitored resources remain compliant — providing peace of mind and audit trail coverage. This approach enables teams to proactively monitor security drift, reduce manual oversight, and ensure timely remediation of emerging security issues. Logic Apps Flow This Logic App is scheduled to trigger daily. It runs a KQL query against a Log Analytics workspace to identify resources that have changed from Healthy to Unhealthy status over the past two weeks. If such changes are detected, the results are formatted into an HTML table and emailed to the security team for review and action. KQL Query used here: // Get resources that are currently unhealthy within the last 7 days let now_unhealthy = SecurityRecommendation | where TimeGenerated > ago(7d) | where RecommendationState == "Unhealthy" // For each resource and recommendation, get the latest record | summarize arg_max(TimeGenerated, *) by AssessedResourceId, RecommendationDisplayName; // Get resources that were healthy approximately 14 days ago (between 12 and 14 days ago) let past_healthy = SecurityRecommendation | where TimeGenerated between (ago(14d) .. ago(12d)) | where RecommendationState == "Healthy" // For each resource and recommendation, get the latest record in that time window | summarize arg_max(TimeGenerated, *) by AssessedResourceId, RecommendationDisplayName; // Join current unhealthy resources with their healthy state 14 days ago now_unhealthy | join kind=inner past_healthy on AssessedResourceId, RecommendationDisplayName | project AssessedResourceId, // Unique ID of the assessed resource RecommendationDisplayName, // Name of the security recommendation RecommendationSeverity, // Severity level of the recommendation Description, // Description explaining the recommendation State_14DaysAgo = RecommendationState1,// Resource state about 14 days ago (should be "Healthy") State_Recent = RecommendationState, // Current resource state (should be "Unhealthy") Timestamp_14DaysAgo = TimeGenerated1, // Timestamp from ~14 days ago Timestamp_Recent = TimeGenerated // Most recent timestamp Once this logic app executes successfully, you’ll get an email as per your configuration. This email includes: A brief introduction explaining the situation. The number of affected recommendations. A formatted HTML table with detailed information: AssessedResourceId: The full Azure resource ID. RecommendationDisplayName: What Defender recommends (e.g., “Enable MFA”). Severity: Low, Medium, High. Description: What the recommendation means and why it matters. State_14DaysAgo: The previous (Healthy) state. State_Recent: The current (Unhealthy) state. Timestamps: When the states were recorded. Sample Email for reference: What the Security Team Can Do with It? Review the Impact Quickly identify which resources have degraded in security posture. Assess if the changes are critical (e.g., exposed VMs, missing patching). Prioritize Remediation Use the severity level to triage what needs immediate attention. Assign tasks to the right teams — infrastructure, app owners, etc. Correlate with Other Alerts Cross-check with Microsoft Sentinel, vulnerability scanners, or SIEM rules. Investigate whether these changes are expected, neglected, or malicious. Track and Document Use the email as a record of change in security posture. Log it in ticketing systems (like Jira or ServiceNow) manually or via integration. Optional Step: Initiate Remediation Playbooks Based on the resource type and issue, teams may: Enable security agents, Update configurations, Apply missing patches, Isolate the resource (if necessary). Automating alerts for resources that go from Healthy to Unhealthy in Defender for Cloud makes life a lot easier for security teams. It helps you catch issues early, act faster, and keep your cloud environment safe without constantly watching dashboards. Give this Logic App a try and see how much smoother your security monitoring and response can be! Access the JSON deployment file for this Logic App here: Microsoft-Unified-Security-Operations-Platform/Microsoft Defender for Cloud/ResourcesMovingFromHealthytoUnhealthyState/ARMTemplate-HealthytoUnhealthyResources(MDC).json at main · Abhishek-Sharan/Microsoft-Unified-Security-Operations-PlatformNew innovations to protect custom AI applications with Defender for Cloud
Today’s blog post introduced new capabilities to enhance AI security and governance across multi-model and multi-cloud environments. This follow-on blog post dives deeper into how Microsoft Defender for Cloud can help organizations protect their custom-built AI applications. The AI revolution has been transformative for organizations, driving them to integrate sophisticated AI features and products into their existing systems to maintain a competitive edge. However, this rapid development often outpaces their ability to establish adequate security measures for these advanced applications. Moreover, traditional security teams frequently lack the visibility and actionable insights needed, leaving organizations vulnerable to increasingly sophisticated attacks and struggling to protect their AI resources. To address these challenges, we are excited to announce the general availability (GA) of threat protection for AI services, a capability that enhances threat protection in Microsoft Defender for Cloud. Starting May 1, 2025, the new Defender for AI Services plan will support models in Azure AI and Azure OpenAI Services. Note: Effective August 1, 2025, the price for Defender for AI Services was updated to $0.0008 per 1,000 tokens per month (USD – list price). “Security is paramount at Icertis. That’s why we've partnered with Microsoft to host our Contract Intelligence platform on Azure, fortified by Microsoft Defender for Cloud. As large language models (LLMs) became mainstream, our Icertis ExploreAI Service leveraged generative AI and proprietary models to transform contract management and create value for our customers. Microsoft Defender for Cloud emerged as our natural choice for the first line of defense against AI-related threats. It meticulously evaluates the security of our Azure OpenAI deployments, monitors usage patterns, and promptly alerts us to potential threats. These capabilities empower our Security Operations Center (SOC) teams to make more informed decisions based on AI detections, ensuring that our AI-driven contract management remains secure, reliable, and ahead of emerging threats.” Subodh Patil, Principal Cyber Security Architect at Icertis With these new threat protection capabilities, security teams can: Monitor suspicious activity in Azure AI resources, abiding by security frameworks like the OWASP Top 10 threats for LLM applications to defend against attacks on AI applications, such as direct and indirect prompt injections, wallet abuse, suspicious access to AI resources, and more. Triage and act on detections using contextual and insightful evidence, including prompt and response evidence, application and user context, grounding data origin breadcrumbs, and Microsoft Threat Intelligence details. Gain visibility from cloud to code (right to left) for better posture discovery and remediation by translating runtime findings into posture insights, like smart discovery of grounding data sources. Requires Defender CSPM posture plan to be fully utilized. Leverage frictionless onboarding with one-click, agentless enablement on Azure resources. This includes native integrations to Defender XDR, enabling advanced hunting and incident correlation capabilities. Detect and protect against AI threats Defender for Cloud helps organizations secure their AI applications from the latest threats. It identifies vulnerabilities and protects against sophisticated attacks, such as jailbreaks, invisible encodings, malicious URLs, and sensitive data exposure. It also protects against novel threats like ASCII smuggling, which could otherwise compromise the integrity of their AI applications. Defender for Cloud helps ensure the safety and reliability of critical AI resources by leveraging signals from prompt shields, AI analysis, and Microsoft Threat Intelligence. This provides comprehensive visibility and context, enabling security teams to quickly detect and respond to suspicious activities. Prompt analysis-based detections aren’t the full story. Detections are also designed to analyze the application and user behavior to detect anomalies and suspicious behavior patterns. Analysts can leverage insights into user context, application context, access patterns, and use Microsoft Threat Intelligence tools to uncover complex attacks or threats that escape prompt-based content filtering detectors. For example, wallet attacks are a common threat where attackers aim to cause financial damage by abusing resource capacity. These attacks often appear innocent because the prompts' content looks harmless. However, the attacker's intention is to exploit the resource capacity when left unconstrained. While these prompts might go unnoticed as they don't contain suspicious content, examining the application's historical behavior patterns can reveal anomalies and lead to detection. Respond and act on AI detections effectively The lack of visibility into AI applications is a real struggle for security teams. The detections contain evidence that is hard or impossible for most SOC analysts to access. For example, in the below credential exposure detection, the user was able to solicit secrets from the organizational data connected to the Contoso Outdoors chatbot app. How would the analyst go about understanding this detection? The detection evidence shows the user prompt and the model response (secrets are redacted). The evidence also explicitly calls out what kind of secret was exposed. The prompt evidence of this suspicious interaction is rarely stored, logged, or accessible anywhere outside the detection. The prompt analysis engine also tied the user request to the model response, making sense of the interaction. What is most helpful in this specific detection is the application and user context. The application name instantly assists the SOC in determining if this is a valid scenario for this application. Contoso Outdoors chatbot is not supposed to access organizational secrets, so this is worrisome. Next, the user context reveals who was exposed to the data, through what IP (internal or external) and their supposed intention. Most AI applications are built behind AI gateways, proxies, or Azure API Management (APIM) instances, making it challenging for SOC analysts to obtain these details through conventional logging methods or network solutions. Defender for Cloud addresses this issue by using a straightforward approach that fetches these details directly from the application’s API request to Azure AI. Now, the analyst can reach out to the user (internal) or block (external) the identity or the IP. Finally, to resolve this incident, the SOC analyst intends to remove and decommission the secret to mitigate the impact of the exposure. The final piece of evidence presented reveals the origin of the exposed data. This evidence substantiates the fact that the leak is genuine and originates from internal organizational data. It also provides the analyst with a critical breadcrumb trail to successfully remove the secret from the data store and communicate with the owner on next steps. Trace the invisible lines between your AI application and the grounding sources Defender for Cloud excels in continuous feedback throughout the application lifecycle. While posture capabilities help triage detections, runtime protection provides crucial insights from traffic analysis, such as discovering data stores used for grounding AI applications. The AI application's connection to these stores is often hidden from current control or data plane tools. The credential leak example provided a real-world connection that was then integrated into our resource graph, uncovering previously overlooked data stores. Tagging these stores improves attack path and risk factor identification during posture scanning, ensuring safe configuration. This approach reinforces the feedback loop between runtime protection and posture assessment, maximizing cloud-native application protection platform (CNAPP) effectiveness. Align with AI security frameworks Our guiding principle is widely recognized by OWASP Top 10 for LLMs. By combining our posture capabilities with runtime monitoring, we can comprehensively address a wide range of threats, enabling us to proactively prepare for and detect AI-specific breaches with Defender for Cloud. As the industry evolves and new regulations emerge, frameworks such as OWASP, the EU AI Act, and NIST 600-1 are shaping security expectations. Our detections are aligned with these frameworks as well as the MITRE ATLAS framework, ensuring that organizations stay compliant and are prepared for future regulations and standards. Get started with threat protection for AI services To get started with threat protection capabilities in Defender for Cloud, it’s as simple as one-click to enable it on your relevant subscription in Azure. The integration is agentless and requires zero intervention in the application dev lifecycle. More importantly, the native integration directly inside Azure AI pipeline does not entail scale or performance degradation in the application runtime. Consuming the detections is easy, it appears in Defender for Cloud’s portal, but is also seamlessly connected to Defender XDR and Sentinel, leveraging the existing connectors. SOC analysts can leverage the correlation and analysis capabilities of Defender XDR from day one. Explore these capabilities today with a free 30-day trial*. You can leverage your existing AI application and simply enable the “AI workloads” plan on your chosen subscription to start detecting and responding to AI threats. *Trial free period is limited to up to 75B tokens scanned. Learn more about the innovations designed to help your organization protect data, defend against cyber threats, and stay compliant. Join Microsoft leaders online at Microsoft Secure on April 9. Explore additional resources Learn more about Runtime protection Learn more about Posture capabilities Watch the Defender for Cloud in the Field episode on securing AI applications Get started with Defender for CloudAgentless code scanning for GitHub and Azure DevOps (preview)
🚀 Start free preview ▶️ Watch a video on agentless code scanning Most security teams want to shift left. But for many developers, "shift left" sounds like "shift pain". Coordination. YAML edits with extra pipeline steps. Build slowdowns. More friction while they're trying to go fast. 🪛 Pipeline friction YAML edits with extra steps ⏱️ Build slowdowns More friction, less speed 🧩 Complex coordination Too many moving parts That's the tension we wanted to solve. With agentless code scanning in Defender for Cloud, you get broad visibility into code and infrastructure risks across GitHub and Azure DevOps - without touching your CI/CD pipelines or installing anything. ✨ Just connect your environment. We handle the rest. Already in preview, here's what's new Agentless code scanning was released in November 2024, and we're expanding the preview with capabilities to make it more actionable, customizable, and scalable: ✅ GitHub & Azure DevOps Connect your GitHub org and scan every repository automatically 🎯 Scoping controls Choose exactly which orgs, projects, and repos to scan 🔍 Scanner selection Enable code scanning, IaC scanning, or both 🧰 UI and REST API Manage at scale, programmatically or in-portal or Cloud portal 🎁 Available for free during the preview under Defender CSPM How agentless code scanning works Agentless code scanning runs entirely outside your pipelines. Once a connector has been created, Defender for Cloud automatically discovers your repositories, pulls the latest code, scans for security issues, and publishes findings as security recommendations - every day. Here's the flow: 1 Discover Repositories in GitHub or Azure DevOps are discovered using a built-in connector. 2 Retrieve The latest commit from the default branch is pulled immediately, then re-scanned daily. 3 Analyze Built-in scanners run in our environment: Code Scanning – looks for insecure patterns, bad crypto, and unsafe functions (e.g., `pickle.loads`, `eval()`) using Bandit and ESLint. Infrastructure as Code (IaC) – detects misconfigurations in Terraform, Bicep, ARM templates, CloudFormation, Kubernetes manifests, Dockerfiles, and more using Checkov and Template Analyzer. 4 Publish Findings appear as Security recommendations in Defender for Cloud, with full context: file path, line number, rule ID, and guidance to fix. Get started in under a minute 1 In Defender for Cloud, go to Environment settings → DevOps Security 2 Add a connector: Azure DevOps – requires Azure Security Admin and ADO Project Collection Admin GitHub – requires Azure Security Admin and GitHub Org Owner to install the Microsoft Security DevOps app 3 Choose your scanning scope and scanners 4 Click Save – and we'll run the first scan immediately s than a minute No pipeline configuration. No agent installed. No developer effort. Do I still need in-pipeline scanning? Short answer: yes - if you want depth and speed in the development workflow. Agentless scanning gives you fast, wide coverage. But Defender for Cloud also supports in-pipeline scanning using Microsoft Security DevOps (MSDO) command line application for Azure DevOps or GitHub Action. Each method has its own strengths. Here's how to think about when to use which - and why many teams choose both: When to use... ☁️ Agentless Scanning 🏗️ In-Pipeline Scanning Visibility Quickly assess all repos at org-level Scans and enforce every PR and commit Setup Requires only a connector Requires pipeline (YAML) edits Dev experience No impact on build time Inline feedback inside PRs and builds Granularity Repo-level control with code and IaC scanners Fine-tuned control per tool or branch Depth Default branch scans, no build context Full build artifact, container, and dependency scanning 💡 Best practice: start broad with agentless. Go deeper with in-pipeline scans where "break the build" makes sense. Already using GitHub Advanced Security (GHAS)? GitHub Advanced Security (GHAS) includes built-in scanning for secrets, CodeQL, and open-source dependencies - directly in GitHub and Azure DevOps. You don't need to choose. Defender for Cloud complements GHAS by: Surfaces GHAS findings inside Defender for Cloud's Security recommendations Adds broader context across code, infrastructure, and identity Requires no extra setup - findings flow in through the connector You get centralized visibility, even if your teams are split across tools. One console. Full picture. Core scenarios you can tackle today 🛡️ Catch IaC misconfigurations early Scan for critical misconfigurations in Terraform, ARM, Bicep, Dockerfiles, and Kubernetes manifests. Flag issues like public storage access or open network rules before they're deployed. 🎯 Bring code risk into context All findings appear in the same portal you use for VM and container security. No more jumping between tools - triage issues by risk, drill into the affected repository and file, and route them to the right owner. 🔍 Focus on what matters Customize which scanners run and where. Continuously scan production repositories. Skip forks. Run scoped PoCs. Keep pace as repositories grow - new ones are auto-discovered. What you'll see - and where All detected security issues show up as security recommendations in the recommendations and DevOps Security blades in Defender for Cloud. Every recommendation includes: ✅ Affected repository, branch, file path, and line number 🛠️ The scanner that found it 💡 Clear guidance to fix What's next We're not stopping here. These are already in development: 🔐 Secret scanning Identify leaked credentials alongside code and IaC findings 📦 Dependency scanning Open-source dependency scanning (SCA) 🌿 Multi-branch support Scan protected and non-default branches Follow updates in our Tech Community and release notes. Try it now - and help us shape what comes next Connect GitHub or Azure DevOps to Defender for Cloud (free during preview) and enable agentless code scanning View your discovered DevOps resources in the Inventory or DevOps Security blades Enable scanning and review recommendations Microsoft Defender for Cloud → Recommendations Shift left without slowing down. Start scanning smarter with agentless code scanning today. Helpful resources to learn more Learn more in the Defender for Cloud in the Field episode on agentless code scanning Overview of Microsoft Defender for Cloud DevOps security Agentless code scanning - configuration, capabilities, and limitations Set up in-pipeline scanning in: Azure DevOps GitHub action Other CI/CD pipeline tools (Jenkins, BitBucket Pipelines, Google Cloud Build, Bamboo, CircleCI, and more)
Microsoft AI Security Story: Protection Across the Platform
Explore how Microsoft’s end-to-end AI security platform empowers organizations to confidently adopt generative AI. Learn how to discover and control shadow AI, protect sensitive data, and defend against emerging threats—so you can innovate securely and at scaleMicrosoft Defender for Cloud Adds Four New Regulatory Frameworks
As organizations accelerate their digital transformation and embrace artificial intelligence (AI) across industries, the regulatory landscape is evolving just as rapidly. From financial resilience to responsible AI governance, enterprises are under increasing pressure to demonstrate compliance with a growing number of global standards across multiple cloud platforms. At Microsoft, we are committed to helping customers meet these challenges with integrated, scalable, and intelligent security solutions. Today, we’re excited to announce the public preview of four new regulatory frameworks in Microsoft Defender for Cloud. These frameworks are now available across Microsoft Azure, Amazon Web Services (AWS), and Google Cloud Platform (GCP), further expanding our multicloud compliance capabilities. What’s New in Public Preview The following regulatory frameworks are now supported in Microsoft Defender for Cloud: Digital Operational Resilience Act (DORA) European Union Artificial Intelligence Act (EU AI Act) Korean Information Security Management System for Public Cloud (k-ISMS-P) Center for Internet Security (CIS) Microsoft Azure Foundations Benchmark v3.0 Each of these frameworks addresses a critical area of modern cloud security and compliance. Let’s explore what they are, why they matter, and how Defender for Cloud helps you stay ahead. Digital Operational Resilience Act (DORA) The Digital Operational Resilience Act is a groundbreaking regulation from the European Union aimed at strengthening the digital resilience of financial institutions. DORA applies to a wide range of financial entities, including banks, insurance companies, investment firms, and third-party ICT providers, and mandates that these organizations can withstand, respond to, and recover from all types of ICT-related disruptions and threats. Why DORA Matters In today’s interconnected financial ecosystem, operational disruptions can have cascading effects across markets and geographies. DORA introduces a unified regulatory framework that emphasizes: Rigorous ICT risk management Incident reporting and response Digital operational resilience testing Oversight of third-party ICT service providers With Defender for Cloud, organizations can now assess their compliance posture against DORA requirements, identify gaps, and implement recommended controls across Azure, AWS, and GCP. This helps financial institutions not only meet regulatory obligations but also build a more resilient digital infrastructure. European Union Artificial Intelligence Act (EU AI Act) The EU AI Act is the world’s first comprehensive legal framework for artificial intelligence. It introduces a risk-based classification system for AI systems, ranging from minimal to unacceptable risk, and imposes strict obligations on providers and users of high-risk AI applications. Why the EU AI Act Matters As AI becomes embedded in critical decision-making processes—from healthcare diagnostics to financial services, governments and regulators are stepping in to ensure these systems are safe, transparent, and accountable. The EU AI Act focuses on: Risk classification and governance Data quality and transparency Human oversight and accountability Robust documentation and monitoring Defender for Cloud now enables organizations to monitor AI workloads and evaluate their compliance posture under the EU AI Act. This includes mapping security controls to regulatory requirements and surfacing actionable recommendations to reduce risk. By integrating AI governance into your cloud security strategy, you can innovate responsibly and build trust with customers and regulators alike. Korean Information Security Management System for Public Cloud (k-ISMS-P) The k-ISMS-P is a South Korean regulatory standard that integrates personal information protection and information security management for public cloud services. It is a mandatory certification for cloud service providers and enterprises handling sensitive data in South Korea. Why k-ISMS-P Matters As cloud adoption grows in South Korea, so does the need for robust compliance frameworks that protect personal and organizational data. The k-ISMS-P standard covers: Organizational and technical security controls Personal data lifecycle management Incident response and audit readiness Defender for Cloud now supports k-ISMS-P, enabling organizations to assess their compliance posture and prepare for audits with confidence. This is especially valuable for multinational companies operating in or partnering with South Korean entities. CIS Microsoft Azure Foundations Benchmark v3.0 The Center for Internet Security (CIS) Azure Foundations Benchmark is a widely adopted set of best practices for securing Microsoft Azure environments. Version 3.0 introduces updated recommendations that reflect the latest cloud security trends and technologies. Why CIS v3.0 Matters Security benchmarks like CIS provide a foundational layer of protection that helps organizations reduce risk and improve their security posture. Key updates in version 3.0 include: Enhanced identity and access management controls Improved logging and monitoring configurations Updated recommendations for storage, networking, and compute Defender for Cloud now supports CIS Azure Foundations Benchmark v3.0, offering automated assessments and remediation guidance. This helps security teams stay aligned with industry standards and continuously improve their cloud security hygiene. Unified Compliance Across Multicloud Environments With the addition of these four frameworks, Microsoft Defender for Cloud now supports an extensive library of regulatory standards and benchmarks across Azure, AWS, and GCP. This multicloud support is critical for organizations operating in hybrid environments or managing complex supply chains. The Regulatory Compliance dashboard in Defender for Cloud provides a centralized view of your compliance posture, complete with: Framework-specific control mapping Assessments and scoring Actionable recommendations and remediation steps Integration with Microsoft Purview and Microsoft Entra for unified governance Get Started Today These new frameworks are available in public preview and can be enabled directly from the Microsoft Defender for Cloud portal. To get started: Navigate to the Regulatory Compliance blade. Select Manage compliance standards. Select an account or management account (Azure subscription or management group, AWS account or management account, GCP project or organization) to assign the security standard. Select Security policies. Locate the standard you want to enable and toggle the status to On. Review your compliance posture and implement recommended actions. For more information, visit our documentation. By expanding our regulatory coverage, we’re helping customers stay ahead of compliance requirements, reduce risk, and build trust in a rapidly evolving digital world. Whether you’re navigating AI governance, financial resilience, or regional data protection laws, Microsoft Defender for Cloud is here to support your journey.Optimizing Resource Allocation with Microsoft Defender CSPM
This article is part of our series on “Strategy to Execution: Operationalizing Microsoft Defender CSPM.” If you’re new to the series, or want broader strategic context, begin with our main overview article, then explore Article 1, Article 2, and Article 3 for details on risk identification, compliance, and DevSecOps workflows. Introduction Organizations today face an array of challenges in their cloud security efforts, ever-growing multicloud infrastructures, finite budgets, and evolving threat landscapes. Effectively allocating limited resources is critical: security teams must prioritize the vulnerabilities posing the highest risk while avoiding spending precious time and money on lower-priority issues. Defender CSPM (Cloud Security Posture Management) provides a data-driven approach to this problem. By continuously analyzing the security posture across Azure, AWS, and GCP, Defender CSPM calculates risk scores based on factors such as business impact, exposure, and potential exploitability. Armed with these insights, security teams can make informed decisions about where to focus resources, maximizing impact and reducing their overall risk. In this fourth, and last article of our series, we’ll examine how to operationalize resource allocation with Defender CSPM. We’ll discuss the common allocation challenges, explain how CSPM’s risk-based prioritization helps address them, and provide practical steps to implement an effective allocation strategy. Why Resource Allocation Matters in Multicloud Security Resource allocation is critical in multicloud security because securing environments that span multiple cloud providers introduces unique challenges that require careful planning. Before you can decide where to invest your time, budget, and headcount, you need to understand the hurdles that make multicloud allocation especially tough: Overwhelming Volume of Vulnerabilities Modern cloud environments are common with potential vulnerabilities. Multicloud setups compound this challenge by introducing platform-specific risks. Without a clear prioritization method, teams risk tackling too many issues at once, often leaving truly critical threats under-addressed. Competing Priorities Across Teams Security, DevOps, and IT teams frequently have diverging goals. Security may emphasize high-risk vulnerabilities, while DevOps focuses on uptime and rapid releases. Aligning everyone on which vulnerabilities matter most ensures strategic clarity and reduces internal friction. Limited Budgets and Skilled Personnel Constrained cybersecurity budgets and headcount force tough decisions about which fixes or upgrades to fund. By focusing on vulnerabilities that present the highest risk to the business, organizations can make the most of available resources. Lack of Centralized Visibility Monitoring and correlating vulnerabilities across multiple cloud providers can be time-intensive and fragmented. Without a unified view, it’s easy to miss critical issues or duplicate remediation efforts, both of which squander limited resources. How Defender CSPM Enables Risk-Based Resource Allocation To address the complex task of resource allocation in sprawling, multicloud estates, security teams need more than raw vulnerability data, they need a system that continually filters, enriches, and ranks findings by real-world impact. Microsoft Defender CSPM equips security teams with automated, prioritized insights and unified visibility. It brings together telemetry from Azure, AWS, and GCP, applies advanced analytics to assess which weaknesses pose the greatest danger, and then packages those insights into clear, actionable priorities. The following capabilities form the backbone of a risk-based allocation strategy: Risk Scoring and Prioritization Defender CSPM continuously evaluates vulnerabilities and security weaknesses, assigning each one a risk score informed by: Business Impact – How vital a resource or application is to daily operations. Exposure – Whether a resource is publicly accessible or holds sensitive data. Exploitability – Contextual factors (configuration, known exploits, network paths) that heighten or lower a vulnerability’s real-world risk. This approach ensures that resources, time, budget, and staff are channeled toward the issues that most endanger the organization. Centralized Visibility Across Clouds Multicloud support means you can view vulnerabilities across Azure, AWS, and GCP in a single pane of glass. This unified perspective helps teams avoid duplicative efforts and ensures each high-risk finding is appropriately addressed, no matter the platform. Automated, Context-Aware Insights Manual vulnerability evaluations are time-consuming and prone to oversight. Defender CSPM automates the risk-scoring process, updating risk levels as new vulnerabilities arise or resources change, so teams can act promptly on the most critical gaps. Tailored Remediation Guidance In addition to highlighting high-risk issues, Defender CSPM provides recommended steps to fix them, such as applying patches, adjusting access controls, or reconfiguring cloud resources. Having guided instructions accelerates remediation efforts and reduces the potential for human error. Step-by-Step: Operationalizing Resource Allocation with Defender CSPM Below is a practical workflow integrating both the strategic and operational aspects of allocating resources effectively. Step 1: Build a Risk Assessment Framework Identify Business-Critical Assets Collaborate with business leaders, application owners, and architects to label high-priority workloads (e.g., production apps, data stores with customer information). Use resource tagging (Azure tags, AWS tags, GCP labels) to systematically mark essential resources. Align Defender CSPM’s Risk Scoring with Business Impact Customize Defender CSPM’s scoring model to reflect your organization’s unique risk tolerance. Set up periodic risk-scoring workshops with security, compliance, and business stakeholders to keep definitions current. Categorize Vulnerabilities Group vulnerabilities into critical, high, medium, or low, based on the assigned risk score. Establish remediation SLAs for each severity level (e.g., 24-48 hours for critical; 7-14 days for medium). Step 2: Allocate Budgets and Personnel Based on Risk Prioritize Funding for High-Risk Issues Work with finance or procurement to ensure the biggest threats receive adequate budget. This may cover additional tooling, specialized consulting, or staff training. If a public-facing resource with sensitive data is flagged, you might immediately allocate budget for patching or additional third-party security review. Track Resource Utilization Monitor how much time and money go into specific vulnerabilities. Overinvesting in less severe issues can starve critical areas of necessary attention. Use dashboards in Power BI or similar tools to visualize resource allocation versus risk impact. Define Clear SLAs Set more aggressive SLAs for higher-risk items. For instance, fix critical vulnerabilities within 24-48 hours to minimize dwell time. Align your ticketing system (e.g., ServiceNow, Jira) with Defender CSPM so each newly discovered high-risk vulnerability automatically flags an urgent ticket. Step 3: Continuously Track Metrics and Improve Mean Time to Remediate (MTTR) Monitor how long it takes to fix vulnerabilities after they’re identified. Strive for a shorter MTTR on top-priority issues. Reduction in Risk Exposure Track how many high-priority vulnerabilities are resolved over time. A downward trend indicates effective remediation. Re-assess risk after major remediation efforts; scores should reflect newly reduced exposure. Resource Utilization Efficiency Compare security spending or labor hours to actual risk reduction outcomes. If you’re using valuable resources on low-impact tasks, reallocate them. Evaluate whether your investments, tools, staff, or specialized training, are paying off in measurable risk reduction. Compliance Improvement For organizations under regulations like HIPAA or PCI-DSS, measure compliance posture. Defender CSPM can highlight policy violations and track improvement over time. Benchmark Against Industry Standards Compare your results (MTTR, risk exposure, compliance posture) against sector-specific benchmarks. Adjust resource allocation strategies if you’re lagging behind peers. Strategic Benefits of a Risk-Based Approach Maximized ROI By focusing on truly critical issues, you’ll see faster, more tangible reductions in risk for each security dollar spent. Faster Remediation of High-Risk Vulnerabilities With Defender CSPM’s clear rankings, teams know which issues to fix first, minimizing exposure windows for the worst threats. Improved Collaboration Providing a transparent, data-driven explanation for why certain vulnerabilities get priority eases friction between security, DevOps, and operations teams. Scalable for Growth As you add cloud workloads, CSPM’s automated scoring scales with you. You’ll always have an updated queue of the most urgent vulnerabilities to tackle. Stronger Risk Management Posture Continuously focusing on top risks aligns security investments with business goals and helps maintain compliance with evolving standards and regulations. Conclusion Resource allocation is a central concern for any organization striving to maintain robust cloud security. Microsoft Defender for Cloud’s CSPM makes these decisions more straightforward by automatically scoring vulnerabilities according to impact, exposure, and other contextual factors. Security teams can thus prioritize their limited budgets, personnel, and time for maximum effect, reducing the window of exposure and minimizing the likelihood of critical breaches. By following the steps outlined here, building a risk assessment framework, allocating resources proportionally to risk severity, and monitoring metrics to drive continuous improvement, you can ensure your security program remains agile and cost-effective. In doing so, you’ll align cybersecurity investments with broader business objectives, ultimately delivering measurable risk reduction in today’s dynamic, multicloud environment. Microsoft Defender for Cloud - Additional Resources Strategy to Execution: Operationalizing Microsoft Defender CSPM Considerations for risk identification and prioritization in Defender for Cloud Strengthening Cloud Compliance and Governance with Microsoft Defender CSPM Integrating Security into DevOps Workflows with Microsoft Defender CSPM Download the new Microsoft CNAPP eBook at aka.ms/MSCNAPP Become a Defender for Cloud Ninja by taking the assessment at aka.ms/MDCNinja Reviewers Yuri Diogenes, Principal PM Manager, CxE Defender for Cloud
