MCP safety & evaluation with the Agent 365 CLI & Agent Governance Toolkit
Co Author: JiteshThakur AI agents are useful because they can act. They call tools, query databases, send messages, and hand work to other agents. That same freedom creates a problem: access control can tell you which service an agent may reach, but it does not always tell you whether a particular action is sensible, safe, or permitted. MCP is how most agents now act. Two Control Points: This post examines two control points that address different parts of the MCP lifecycle. Agent 365 CLI evaluates the MCP server before an agent uses it. Agent Governance Toolkit (AGT) governs sensitive tool calls while the agent runs. One improves what the agent sees. The other governs what the agent does. The Agent 365 CLI is a cross-platform command-line tool for Agent 365 applications on Azure. Its evaluation command examines MCP tool definitions and scores their quality. AGT evaluates actions against policy and records each decision. Together, these tools support a practical model: evaluate the server first, then provide proper scaffolding for the developer to test this in a dry run. Agent 365 CLI: Score an MCP server from the command line: The Agent 365 CLI can evaluate an MCP server against research-based practices for production readiness. The result is more useful than a simple pass or fail. The evaluation gives you: A score for each tool name, description, and parameter schema; A prioritized list of improvements; An overall maturity score for the server; and Local output that you can use early in development. This report turns a vague question, "Is this MCP server ready?", into a concrete list of work. The evaluate command a365 develop-mcp evaluate --server-url <server-url> [--auth-token <auth-token>] [options] The command reads the tool schemas from the server. It then produces guidance for names, descriptions, parameters, and schema structure. A local coding-agent CLI scores the semantic checks. You can use GitHub Copilot CLI or Claude Code under your account and AI subscription. The command does not send tool-schema data to Microsoft. Prerequisites: Install the following software: Agent 365 CLI; Node.js 18 or later for GitHub Copilot CLI; and A supported coding-agent CLI for semantic scoring. For example, install GitHub Copilot CLI with this command: powershell npm install -g @github/copilot This bring-your-own-LLM model keeps the scoring step in your local development environment. It is useful when model calls must remain inside an approved deployment. How the evaluation works The command runs a five-step pipeline and logs progress as it goes. Fig 1: MCP Evaluation using Agent 365 Cli Connect to the MCP server and collect its tool schemas. Generate an evaluation checklist in the output directory. Score the semantic checks with the selected coding agent. Calculate the maturity level and action priorities. Write the JSON and HTML reports. The evaluation contains two types of checks: Deterministic checks use exact rules in the CLI. For example, a tool name cannot be empty. Semantic checks use a coding agent to score clarity and meaning. Each result includes a reason for the score. Examples Set the authentication token in an environment variable. Then evaluate an authenticated server and write the artifacts to a subfolder. powershell $env:A365_MCP_AUTH_TOKEN = "<bearer-token>" a365 develop-mcp evaluate --server-url "https://my-mcp-server.contoso.com/mcp" --output-dir "./eval" Use a specific scoring engine with the `--eval-engine` option: powershell a365 develop-mcp evaluate --server-url "http://localhost:5000/mcp" --eval-engine claude-code Scenario: Evaluate a malicious MCP server For this demonstration, we hosted a deliberately malicious MCP server at `http://127.0.0.1:8124/`. It exposes tools that demonstrate tool poisoning, credential leakage, prompt injection, schema mismatch, sandbox escape, and other attacks. The server is intentionally unsafe and is for demonstration only. Fig 2: Setting up a test MCP server for evaluation We ran the evaluation in two steps. First, we generated the checklist without automatic semantic scoring: a365 develop-mcp evaluate --server-url "http://127.0.0.1:8124/" --eval-engine none Fig 3: Agent365CLI MCP Evaluation The command wrote the checklist and a semantic-evaluation prompt to the output directory. It also displayed the next steps. Second, we gave the prompt and checklist to a coding agent. The agent completed each unscored semantic check with a Boolean score and a short reason. After we saved the completed checklist, we ran the command again to generate the report: a365 develop-mcp evaluate --server-url "http://127.0.0.1:8124/" --output-dir "C:\temp\MaliciousMCP" Fig 4: Creating the report with Agent365 CLI MCP Evaluate command Understanding the evaluation report Open `<server-name>_eval_report.html` from the output directory. The report contains: The overall score from 0 to 100; The maturity level from 0 to 4; Scores for each tool and quality category; and A prioritized action list for the next maturity level. Fig 5: MCP Evaluation Report In our demonstration, the server scored 86.0 and reached Level 3: Optimized for AI. That strong overall score did not mean that every tool was safe or clear. The report found 58 action items, including one critical item and 33 high-priority items. That contrast matters. A server can have valid schemas and consistent names while still exposing misleading or dangerous tools. Fig 6: MCP Evaluation Report - Tool-By-Tool Detail What to look for Read the per-tool results before the overall score. A single weak tool can create more risk than the server average suggests. Focus on these report sections: Tool names: Can an agent select the correct tool from its name? Tool descriptions: Does each description explain the purpose and correct use? Parameter names: Do the names identify the data that the tool requires? Parameter descriptions: Do they explain the format, type, and constraints? Schema structure: Are the schemas valid and processable? Action items: Which changes have the highest effect on tool selection and use? The command processes static tool schemas from `tools/list`. It does not process runtime payloads, end-user data, or personal data. The command keeps the `--auth-token` value in memory. It sends the value only in the HTTP `Authorization` header. It does not write the token to disk or give it to the coding agent. AGT: Put governance in the execution path: Microsoft's open-source Agent Governance Toolkit (AGT) evaluates an action before execution. It adds identity and policy context, records the decision, and can send risky work for approval. This can be used by developers during the build time for dynamic evaluation of the MCP server. AGT lets developers put part of that intent into the execution path. Remote tools still need secure implementations, sandboxes need hard boundaries, and audit records need appropriate storage and access controls. You do not need to replace your agent framework to use it. What sits in the decision path? AGT wraps the tools that an agent already uses. You can start to govern a tool with two lines of Python: python from agentmesh.governance import govern safe_tool = govern(my_tool, policy="policy.yaml") On each call, `safe_tool` evaluates the configured policy. An allowed action reaches the original tool. A denied action raises `GovernanceDenied` and creates a decision record. This wrapper model reduces the cost of adoption. Teams can add governance to an existing agent stack without rebuilding it. AGT supports Python, TypeScript, .NET, Rust, and Go. Its documented integrations include popular agent frameworks, MCP, and A2A. Teams can also adopt AGT in stages. A team can begin with policy checks and audit records. It can add identity, approvals, sandboxing, and operational controls as risk increases. Each control answers a different question: Policy: Is this action allowed? Identity and trust: Which agent made the request? Runtime controls: What limits apply to execution? Audit evidence: Why did AGT allow or deny the action? A low-risk assistant can need only a deny rule and basic logging. An agent that moves money or changes production systems needs stronger controls. Fig 7: AGT Architecture Scenario: Govern the same malicious MCP server For this scenario demonstration, we used AGT Python packages as an MCP gateway. The gateway sat between an agent and the same malicious server from the earlier evaluation. This setup let us examine both control points against one target. The Agent 365 CLI examined the server's static tool definitions. The AGT gateway examined real requests and responses for the developer during its testing. Fig 8: AGT findings at runtime In the policy interface, a developer can edit runtime limits and detection rules. The developer can also validate the policy against sample tool metadata, save a revision, and activate it with a recorded reason. Fig 9: AGT control coverage The control-coverage view shows which AGT capabilities are active in the gateway. It also links each capability to package checks and end-to-end evidence. In our demonstration, we included the following controls: Tool metadata poisoning detection; Tool change and rug-pull detection; Dangerous argument blocking; Tool-response content scanning; Per-client tool-call budgets; and A redacted decision audit trail. You can build your detection & input security by reading more about it here. The gateway detected malicious content. For one blocked `tools/list` request, it recorded the findings. The important result was not only that AGT blocked the request. It also preserved the matched evidence, affected tool locations, policy modes, and request context. Fig 10: Example detection via AGT The dashboard then summarized block-mode findings, leading risk drivers, and tools that required review. This evidence can help a team prioritize policy changes and investigate repeated attacks. Fig 11: Sample AGT metrics AGT does not require this UI, gateway, or architecture. Its structured decisions can feed an admin console, SIEM, incident workflow, or approval queue. AGT also includes an Agent Compliance package with mappings for OWASP and other controls. These mappings give developers and governance teams a common record of applied controls. Teams do not need to reconstruct the agent's behavior after an incident. Check Compliance - Agent Governance Toolkit for more information. Conclusion: MCP safety needs controls before and during execution. The Agent 365 CLI improves the MCP interface before deployment. It exposes unclear tool definitions, scores server maturity, and turns quality gaps into prioritized work. While AGT is implemented at the build phase, it provides developers the ability to test policy, identity, execution context & preserve evidence for allowed or denied decisions. Neither tool replaces secure server code, strong sandbox boundaries, or protected audit storage. Instead, they make those controls easier to evaluate and explain. Start with one MCP server and one consequential tool call. Evaluate the server with the Agent 365 CLI. Then put an AGT policy around the action that carries the most risk. While these controls help secure the build phase of an agent, once agents move into production, runtime controls become essential. Agent365 provides those controls at runtime. With thanks to Ashik Kuppil for his inputs and collaboration on this post.Microsoft Defender for Cloud Customer Newsletter
*This will be the last monthly MDC newsletter. To keep up with the latest, please visit: What's new in MDC What's new in Defender for Cloud? Multiple container security features are now Generally Available: Container-level misconfiguration recommendations for Kubernetes, Upgrade AKS version recommendations, VA for runtime-discovered container images on EKS and GKE, Kubernetes notes VA for EKS and GKE, scanning support for Docker hardened container images. For more information, see this page here. Database-level recommendations for SQL VA now GA The SQL vulnerability assessment recommendations created as part of the transition from grouped to individual recommendations are now generally available. Each SQL vulnerability assessment rule is surfaced as its own recommendation, reported directly on the affected SQL database resource. For more details, please refer to this documentation . Blogs of the month In July, our team published the following blog posts we would like to share: 1. Built to Protect: The Architecture Behind Codename MDASH Customer journey Discover how other organizations successfully use Microsoft Defender for Cloud to protect their cloud workloads. This month we are featuring NTT Data. NTT Data, a top global IT services provider, leverages Azure, OpenAI and Microsoft Defender to launch their AI agents, adopting secure by design principles, to help enhance, competitiveness organization change and data usage. Defender for AI, and Defender CSPM, as part of the Defender family, address the emerging risks and threats like prompt injection and data poisoning that come with generative AI. 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/MDCNewsSubscribeSecuring AI Agents at Runtime: Real-Time Protection and Threat Detection for Microsoft Agent 365
Organizations are rapidly adopting AI agents to automate workflows, access enterprise data, invoke tools, and take actions on behalf of users. This autonomy creates a fundamentally new security challenge. Unlike traditional AI applications, agents operate across dynamic execution flows, interacting with external content, calling tools, and accessing sensitive resources. These interactions create new attack paths that traditional security controls were not designed to address. Today, we're announcing two major milestones for Security for AI in Microsoft Defender for Microsoft Agent 365: Threat detection for Microsoft Agent 365 agents — now in public preview. Real-time protection for Microsoft Agent 365 tooling servers — now generally available. Together, these capabilities help security teams detect, investigate, and block attacks targeting AI agents, extending Microsoft Defender's threat protection capabilities into the agent runtime. Threat detection for Microsoft Agent 365 Agents (Public Preview) Threat detection provides SOC teams with detailed visibility into attacks and suspicious activity targeting AI agents. By analyzing runtime signals across agent interactions, tool usage, and execution patterns, Microsoft Defender identifies suspicious and malicious behavior throughout the agent execution lifecycle and surfaces actionable security alerts for SOC teams. Threat detection supports cloud agent types that emit observability logs to Microsoft Agent 365, including: Microsoft Copilot Studio Microsoft Foundry Microsoft 365 Copilot Agent Builder Agents integrated through the Microsoft Agent 365 SDK This provides consistent threat visibility across supported Microsoft Agent 365 agent experiences, regardless of how the agent was built. Fig. 1. Microsoft Security for AI alerts in Microsoft Defender XDR (Preview) Microsoft Defender identifies a broad range of AI-specific threats, including: Indirect prompt injection (XPIA) — malicious instructions embedded in external content designed to manipulate agent behavior. Evasion techniques — attempts to bypass agent instructions or security controls. Malicious content propagation — attempts to use agents to generate or distribute malicious content. Secret leakage — exposure of credentials, API keys, or other sensitive information through agent interactions. LLM reconnaissance — attempts to probe agent capabilities, instructions, or security boundaries. Suspicious IP access — agent access originating from anonymized or suspicious IP addresses. Alerts are surfaced directly in Microsoft Defender, enabling SOC analysts to investigate and respond using familiar workflows, Advanced Hunting queries, and the Defender XDR investigation experience. Real-time protection for WorkIQ and Custom MCP servers (General Availability) Real-time protection moves beyond detection by blocking threats inline when AI agents interact with WorkIQ and custom MCP servers (see Microsoft Agent 365 tooling servers). When an agent invokes a registered tool or receives a tool response, Defender evaluates the interaction against configured security policies and determines whether to allow or block it directly within the agent's execution flow. This helps prevent malicious actions and data leakage in real time, without requiring agent developers to implement custom security logic. Fig. 2. Microsoft Security for AI Real-Time Protection policy in Defender Real-time protection currently guards against high-impact threats, including: Evasion techniques — attempts to bypass agent guardrails or security controls. Malicious content propagation — preventing agents from spreading malicious content through tool actions. Secret leakage — blocking agents from inadvertently exposing credentials or sensitive data through tool calls. Communication with untrusted domains — preventing agents from sending email or data to high-risk or untrusted email domains. Better Together: Detection and Protection Threat detection and real-time protection address complementary parts of the agent security lifecycle. Real-time protection provides inline enforcement to block malicious interactions during execution, while threat detection gives SOC teams the visibility and investigation context needed to identify attack patterns, assess impact, and respond to suspicious activity. Together, they provide a defense-in-depth approach that combines runtime enforcement with SOC-driven detection and investigation, purpose-built for AI agents. Getting Started Both capabilities are available through Microsoft Defender, using a dedicated Security for AI workload experience that brings together AI threat detections, investigations, and runtime protection policies. To learn more: Enable security for AI agents using Microsoft Defender Detect and investigate threats to AI agents using Microsoft Defender (Preview) Protect AI agents in real time using Microsoft Defender As AI agents become more autonomous and gain access to enterprise data and tools, securing their runtime behavior becomes critical. With Threat Detection and Real-Time Protection, Microsoft Defender helps organizations adopt AI agents with security controls designed for how agents actually operate—detecting attacks, enabling SOC investigation, and blocking malicious interactions at runtime.Microsoft Defender for Cloud Customer Newsletter
What's new in Defender for Cloud? Microsoft Defender for Open-Source Relational Databases is now generally available for Amazon Web Services Relational Database Service (AWS RDS) instances. Receive database threat protection and sensitive data discovery insights for supported open-source relational databases, including Aurora PostgreSQL, Aurora MySQL, PostgreSQL, MySQL, and MariaDB on AWS RDS. For more information, see our public documentation. Expanded multicloud security coverage now GA Microsoft Defender for Cloud's expanded multicloud security coverage is now generally available. This release significantly broadens posture assessment for AWS and GCP environments, adding support for about 90 new resource types and over 200 new security recommendations across data, identity and access, networking, compute, and container categories. 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 June, our team published the following blog posts we would like to share: Now Generally Available: Microsoft Defender for open source relational databases on AWS RDS Start Secure, Stay Secure: How Microsoft is Closing the Gap from Code to Runtime The end of patching era for containers: Microsoft Defender for Cloud expands hardened image support Closing the loop on container security: From code to runtime in the AI era Microsoft Defender for Cloud expands multicloud coverage across AWS and Google Cloud Defender for Cloud in the field Watch the latest Defender for Cloud in the Field YouTube episode here: Stay Secure: AI-powered Faster fixes Visit our YouTube page GitHub Community Check out Defender for Cloud GitHub lab module 25. It walks through the integration between Defender for Cloud and XDR to provide a comprehensive CDR solution. Module 25 - MDC and Defender portal integration 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 Hassan Allam Holding. Hassan Allam Holding is an Egyptian private-sector company with operations spanning engineering, construction, and infrastructure investment and was facing operational complexity. To overcome, they leveraged an end-to-end Microsoft Security ecosystem with Defender XDR, which integrates with Defender for Cloud and other products to centralize detection and signals across endpoint, identity, email and cloud workloads. As a result, alert noise reduce by up to 90%, and Hassan Allam Holding is able to respond faster with far less complexity. 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/MDCNewsSubscribe
Now generally available: Serverless posture coverage in Microsoft Defender CSPM
Serverless workloads are a foundation of modern application development, powering everything from low-code and no-code solutions to AI applications and agentic workflows. Development teams use functions, app services, containers, APIs, and event-driven infrastructure to move quickly, scale on demand, and reduce operational overhead. As AI applications and autonomous workflows increasingly rely on distributed services, background tasks, retrieval pipelines, and event-driven components, serverless architectures have become a natural fit. At the same time, they introduce new visibility and posture management challenges. While cloud providers manage the underlying infrastructure, organizations remain responsible for securing their applications, including code, container images, dependencies, configurations, identities, permissions, and access paths. Today, we're announcing the general availability of Serverless Container posture in Microsoft Defender Cloud Security Posture Management (Defender CSPM). Building on the recent general availability of Serverless Compute posture in Defender CSPM, these capabilities extend agentless posture coverage across supported serverless containers, applications, and functions in Azure and AWS. With this expanded serverless posture coverage, security teams can: Discover serverless workloads as first-class assets in a unified cloud inventory Assess workload-specific vulnerabilities, insecure dependencies, and risky configurations Surface exposure, identity, permission, and configuration context that helps prioritize contextual attack paths to broader applications Prioritize risk using security graph context and attack path analysis Act on severity-ranked recommendations in Defender for Cloud Serverless posture coverage across containers, apps, and functions Defender CSPM extends posture management across supported serverless workloads in Azure and AWS. Category Covered workloads Serverless applications and functions Azure Functions, Azure Web Apps, AWS Lambda Serverless containers Azure Container Apps, Azure Container Instances, AWS ECS on Fargate These resources are automatically discovered and surfaced in Defender cloud inventory, helping security teams maintain visibility across dynamic, event-driven application environments. Across supported serverless workloads, multiple layers of posture assessment are provided: Inventory: Serverless compute and container workloads are automatically discovered and mapped with key properties, helping teams understand what is running across their environment. Misconfiguration assessment: identify risky settings such as internet exposure, missing HTTPS, and other configuration issues that can increase exposure. Vulnerability management: Vulnerabilities are detected across supported serverless workloads and tracked over time, helping teams understand where exposed workloads may also contain known CVEs. Attack path analysis: Serverless resources are connected into the Security Graph, so teams can understand how a compromised function or container could reach sensitive assets. Actionable recommendations: Findings are surfaced as resource-level recommendations, making it easier to assign ownership, remediate, and track progress. Secure Score impact: Serverless findings contribute to the broader Secure Score, so risk reduction is reflected in the organization’s overall posture. These findings are incorporated into Security Graph and attack path analysis, helping security teams understand risk in context and prioritize remediation based on how serverless workloads connect to other resources across the cloud environment. In practice Exposed function with access to sensitive data: A serverless function may look like a simple HTTP endpoint, but if it is internet-facing, running with broad identity permissions, and connected to sensitive storage, it can become part of a real attack path. Defender CSPM helps connect these signals across exposure, vulnerabilities, identity, and data access so teams can prioritize the workload based on actual risk, not just isolated findings. Serverless container running a vulnerable image: A serverless container running on Azure Container Apps, Azure Container Instances, or Amazon ECS on AWS Fargate may be fully managed at the infrastructure layer, but the customer still owns the image, application code, identity, configuration, and exposure. If that workload is internet-facing, built from an image with a critical CVE, and connected to Key Vault, storage, or other sensitive services, Defender CSPM helps teams discover it, assess the risk, and prioritize remediation in context. One consistent Defender experience Defender CSPM now natively includes serverless posture coverage. Discovered functions, web apps, and serverless containers appear in the unified cloud inventory, are evaluated through security recommendations, integrate into attack path analysis, and are queryable through Cloud Security Explorer. This consistency matters because serverless workloads rarely operate in isolation. A function might call an API, read from a queue, authenticate with a managed identity, and write to storage. A serverless container might expose an endpoint, pull an image from a registry, process events, and connect to secrets or databases. Defender CSPM helps security teams evaluate these workloads with surrounding context, including exposure, vulnerabilities, identity permissions, configuration risk, and relationships to other resources. That context helps teams move from isolated findings to prioritized remediation. Built for modern and AI-ready applications As organizations build AI-powered applications, agentic workflows, and distributed cloud services, serverless infrastructure continues to play a growing role in delivering scalability and operational efficiency. Defender CSPM helps security teams gain visibility into supported serverless containers, applications, and functions, assess vulnerabilities and misconfigurations, and prioritize remediation using Security Graph context and attack path analysis. By bringing serverless workloads into inventory, recommendations, Cloud Security Explorer, and attack path analysis experiences, Defender CSPM helps organizations better understand and reduce risk across their cloud environments. Explore the documentation for serverless protection and posture for serverless container workloads, and discover the latest innovations in Microsoft Defender for Cloud in the release notes.Microsoft Defender for Cloud expands multicloud coverage across AWS and Google Cloud
Organizations are building and running applications across multiple cloud platforms and hybrid environments to move faster, improve resilience, and choose the services that best fit each workload. But that flexibility also changes how teams need to manage exposure. A risk may start with an internet-facing resource, an over-permissive identity, a misconfigured managed service, a vulnerable container image, or a serverless workload with access to sensitive data. When those signals are spread across multiple cloud providers and tools, it becomes harder to understand how exposure is created and which actions will reduce risk fastest. Today, Microsoft is expanding multicloud coverage in Microsoft Defender for Cloud with general availability of approximately 90 new AWS and Google Cloud resource types and more than 200 recommendations. Building on recent enhancements in CIEM, identity security, containers, and serverless workloads, this expansion helps customers evaluate more of their cloud estate through a unified security experience. With broader coverage across cloud-native applications, data platforms, identity services, networking components, and managed services, security teams can move beyond isolated findings and gain more context across resources, configurations, identities, exposure signals, and prioritization. What’s new: broader AWS and Google Cloud coverage Security teams cannot reduce exposure they cannot see. The expanded coverage brings more AWS and Google Cloud resources into the Defender for Cloud experience, helping customers assess a wider set of modern cloud services through a unified security lens, reducing blind spots where teams increasingly build and operate: serverless applications, containers and build systems, identity and entitlement controls, data and analytics services, AI and ML, networking, messaging, storage, and other managed cloud services. App, platform, and serverless services, including Cloud Run and EventBridge, to help teams identify exposure in cloud-native applications and event-driven workloads. Containers, registries, and build systems, including Artifact Registry and CodePipeline, to connect software supply chain posture with workload risk. Identity, data, and managed services, including Cognito and BigQuery, to help teams understand how access, data, and platform configurations can increase exposure. Multicloud compliance and data protection controls, improving visibility into encryption, logging, backup, auditability, and resilience scenarios. This broader view helps customers understand their real exposure surface and act on recommendations tied to the scenarios that matter most. Find the full list of recommendations here. See exposure in context Exposure is rarely created by a single finding. Consider a security team managing applications across AWS and Google Cloud. A publicly accessible BigQuery dataset, a cloud-native application running in Cloud Run, and an over-permissioned identity may each generate separate findings. Viewed independently, these issues can appear as routine posture alerts; together, they reveal a higher-risk exposure scenario that could lead to unauthorized access to sensitive data. More AWS and Google Cloud resources can now be assessed in the same security experience, helping teams move beyond isolated findings and toward a clearer understanding of potential exposure and remediation priority. Why this matters For most security teams, the bigger challenge isn't generating more findings, it's prioritizing the ones that matter most. By bringing more AWS and Google Cloud resources into inventory, evaluating them with recommendations, and correlating them with identity context, exposure signals, regulatory compliance results, Secure Score insights, and business criticality, Defender for Cloud helps teams focus on the exposures most likely to impact their organization, without adding another fragmented tool to the stack. As coverage expands, teams can answer practical questions across a broader part of their environment: Which AWS and Google Cloud services are now visible in my cloud inventory? Which newly evaluated resources have recommendations that should be reviewed? Which findings are tied to exposed, high-value, or security-sensitive resources? Where should my team prioritize remediation based on exposure, not just finding volume? Building on recent multicloud investments This release builds on a series of multicloud investments in Defender for Cloud over the past several months that bring deeper, more consistent protection across multicloud environments. CIEM and identity: Identity is one of the most common entry points for cloud exposure. Defender for Cloud evaluates overprovisioned identities, risky permissions, weak authentication, and privilege-escalation paths. Modernized CIEM logic now assesses identity risk based on actual entitlement usage rather than sign-in activity, using a 90-day lookback. Customers benefit from improved accuracy using log ingestion from AWS CloudTrail and Google Cloud Logging, and drive actionable recommendations. Learn more about permissions management. Containers and serverless: In containers and serverless, Microsoft expanded multicloud posture coverage across serverless compute, serverless containers, and modern Kubernetes environments. This expansion brings more cloud-native workloads into a unified code-to-runtime security model with vulnerability assessment, misconfiguration analysis, container-level recommendations, and a richer exposure context. Last month we introduced general availability of serverless compute posture coverage for AWS Lambda, Azure Functions, and Azure Web Apps, and the public preview of serverless container posture coverage for Azure Container Apps, Azure Container Instances, and Amazon ECS on AWS Fargate. Learn more about the latest in container security, and find documentation about serverless protection and serverless containers posture protection. Together, these investments give security teams a more complete view of exposure across Azure, AWS, and Google Cloud. Built into the Microsoft Security experience The expanded AWS and Google Cloud coverage strengthens the foundation for multicloud exposure management in Defender for Cloud. Customers can use the same experience they already rely on to understand inventory, posture, serverless and container risk, CIEM and identity context, compliance, Secure Score, and risk prioritization across more of their cloud estate. Because exposure is shaped by relationships across resources, identities, entitlements, workloads, configurations, controls, and reachable services, a more complete multicloud view helps security teams understand risk and act with greater confidence. For customers standardizing on Microsoft Security, this means broader multicloud exposure management in one place – without adding another fragmented tool to the stack. Get started Customers can begin reviewing the expanded coverage by exploring Cloud Inventory, filtering by cloud provider and resource category, reviewing newly introduced recommendations, and monitoring Secure Score changes as broader assessment becomes available. We recommend that security teams: Use Cloud Inventory to understand which additional AWS and Google Cloud resource types are now represented in Defender for Cloud. Review new recommendations across key workload, identity, compliance, data protection, and networking. Reassess top exposure scenarios across clouds, including serverless, containers, identity, data, and managed services. Prioritize remediation based on exposure, criticality, and business context, not only recommendation volume. Learn more With expanded AWS and Google Cloud coverage, Microsoft Defender for Cloud helps security teams improve multicloud visibility, assess more resources, and prioritize exposure across their cloud estate. To learn more, visit the Microsoft Defender for Cloud documentation, review the latest release notes, and follow the Microsoft Defender for Cloud Tech Community blog for updates on cloud security and posture management.Authenticating AWS Workloads to Azure Functions using Workload Identity Federation
Step-by-step guide to configuring Workload Identity Federation between AWS and Azure, enabling service-to-service authentication where AWS workloads can securely call Azure Functions using token-based access instead of stored credentials.
Exempt - Azure CSPM Recommendation" (Terraform exemption
The reason you're not finding a standalone policyAssignmentId/policyDefinitionId for this specific recommendation is that it isn't a standalone assignment — it's one control inside the built-in CSPM initiative (the "ASC Default" / Microsoft Cloud Security Benchmark assignment). That initiative does have an assignment ID; you just need to target the specific control within it, not look for a separate one. In azurerm_resource_policy_exemption (or the subscription/resource-group variants), the relevant fields are: policy_assignment_id → the ID of the initiative assignment (ASC Default / MCSB), not a per-recommendation assignment policy_definition_reference_ids → an array scoping the exemption to just this one control instead of the whole initiative resource "azurerm_resource_policy_exemption" "function_app_network_exemption" { name = "exempt-function-network-restriction" resource_id = azurerm_linux_function_app.example.id policy_assignment_id = data.azurerm_subscription_policy_assignment.asc_default.id policy_definition_reference_ids = [ "<reference-id-for-the-specific-control>" ] exemption_category = "Waiver" # or "Mitigated" if an equivalent control exists expires_on = "2026-12-31T00:00:00Z" } To find the policy_definition_reference_id for this specific control: in the Azure Portal, go to Policy → Definitions, search for "Restricted network access should be configured on Internet exposed Function app" to get its definition ID, then open the initiative definition (ASC Default) and find the matching entry in its policyDefinitions[].policyDefinitionReferenceId array — that string is what goes in the array above. Two things worth deciding upfront before automating this: Waiver vs Mitigated — if you've genuinely restricted access another way (e.g., Private Endpoint), use Mitigated so it's distinguishable from accepted risk in reporting. Consider whether the exemption belongs at the resource scope (just this Function App) vs resource group/subscription — narrower is safer, but if you have a pattern of similar apps, a tagged-based resourceSelectors block can scale this without per-resource blocks.Closing the loop on container security: From code to runtime in the AI era
Containers are the backbone of modern cloud-native apps — and increasingly, the infrastructure powering AI, from AI assistants to a new wave of intelligent agents. They also blur the line between build, deploy, and runtime: a single code change can become a running workload in minutes. A misconfiguration committed in the morning can be deployed in minutes and exploited before noon. At that speed, container security can no longer be a point-in-time check, it has to work as one continuous loop. The numbers back this up. For the first time, 31% of breaches now begin with an attacker exploiting a software vulnerability — overtaking stolen credentials as the most common way in — and 15% of attack techniques are now accelerated by generative AI, with adversaries using it to find gaps and write malware faster at every stage. Source: Verizon 2026 Data Breach Investigations Report (incidents Nov 2024–Oct 2025). Over the last few quarters, Microsoft Defender for Cloud has been evolving to offer you this continuous security, end to end. Explore container security’s new capabilities across posture, shift-left, runtime, multicloud coverage, and operations. Collectively they form a more comprehensive approach to container security — one that offers security right during developing a code to a running pod across Azure, AWS, and GCP. There is a second reason why container security matters more in 2026: containers are increasingly where AI runs. Many AI workloads — from model-serving APIs to retrieval systems and intelligent agents — now live as pods on AKS, EKS, and GKE (the managed Kubernetes services from Azure, AWS, and Google), often connected to some of an organization’s most sensitive models and data. As those crown jewels move into the cluster, the same posture, code‑to‑runtime, and runtime protections described in this post extend to AI workloads. The contest is increasingly AI against AI: attackers use it to find and reach the cluster faster, while defenders use it to push back — surfacing the risks that matter most and turning runtime findings into AI‑assisted code fixes. One platform, code to runtime A container finding is not treated as an isolated issue; it is connected to the identity it runs under, the registry and code repository it came from, and the cluster where it is running - all unified under one Microsoft Defender platform. Container posture and shift-left security are now redesigned for least vulnerabilities in production Conventional container security posture offered challenges to scale: a single grouped recommendation could stack thousands of findings under one bucket, making ownership, exemptions, and risk scoring too coarse to act on. That experience is now evolved. We have rebuilt the experience so that each finding is its own recommendation — per software, per image, per container. If two CVEs in the same image belong to two different teams, they can now be triaged, exempted, and reported separately. The grouped recommendations are deprecated and will be removed on July 30, 2026, We suggest updating any automation, export rules, and ServiceNow integrations to target the new per-finding recommendations before that date. That per-finding precision becomes even more powerful once you connect each finding to its source code and to the runtime resources it impacts. Defender for Cloud — part of Microsoft Defender suite — connects this code-to-runtime chain end-to-end. For example, an image built through Azure DevOps or GitHub, pushed to ACR, ECR, Google Artifact Registry, Docker Hub, or JFrog, and pulled by AKS, EKS, or GKE is one continuous evidence chain — traceable from a running container back to the pull request (PR) and line of code that introduced the risk. With GitHub Advanced Security integrated (GA), secrets, code, and dependency findings join the same attack story. The developer-first Defender for Cloud CLI runs the same scanner locally or in any CI/CD pipeline, with consistent exit codes for gating. In this diagram, you can see how we have embedded container security at every stage of the software development lifecycle (SDLC), not just the endpoints. At Code, GitHub Advanced Security and the Defender for Cloud CLI catch secrets, vulnerable dependencies, and insecure code before commit. At Build, the same scanner runs as a CI/CD gate — in GitHub Actions, Azure DevOps, Jenkins, or Bitbucket — failing the pipeline on critical findings. At Ship, registry scanning and Gated Deployment block risky or misconfigured images at the cluster door. And at Runtime, the sensor enforces anti-malware and binary-drift policy on the live workload. No stage is left as a blind spot, and a finding can be traced forward to the running pod or backward to the developer who introduced it. Visibility without enforcement only creates backlog. Gated Deployment — a Kubernetes admission controller — uses the same vulnerability signal, you trust, to block risky images at the cluster level. It supports phased rollout (audit, then deny), targets rules by cluster, namespace, pod, image, or label, and runs across AKS (including AKS Automatic), EKS, and GKE. A newer extension gates on Kubernetes misconfigurations too. Posture practitioners also get KSPM at container granularity — Kubernetes security posture management, available through both Defender for Containers and Defender CSPM — and, on Azure, a new actionable recommendation, Upgrade Azure Kubernetes Service Version (preview), that helps you remediate vulnerabilities in AKS-managed system pods. Coverage that matches containers’ evolution Historically, many container security programs concentrated on managed Kubernetes clusters in AKS, EKS, and GKE. The 2026 reality is broader: a growing share of production runs on serverless container platforms that abstract the cluster away, many sensitive workloads sit behind private, network-isolated clusters, and platform teams increasingly standardize on hardened or distroless base images. The surfaces that were blind spots are now part of the same posture graph as everything else. Serverless compute posture is now generally available across AWS Lambda, Azure Functions, and Web Apps, while Serverless containers posture (preview) takes the same idea to Azure Container Apps, ACI, and AWS Fargate. Together, they bring more of today’s cloud-native production footprint into the same posture graph. Coverage also improves where platform teams are standardizing on locked-down environments. The long-standing gap around private EKS and GKE clusters is closed, bringing some of the hardest-to-reach environments into the same security model. Scanning now works on hardened images from Docker Hardened or Minimus, and runtime protection supports BottleRocket on EKS — with the full feature set also available in Azure Government, which matters for teams running regulated workloads. Runtime threat protection that prevents, not just detects Posture closes the door on attackers; runtime threat protection guards the room if they still succeed. The key shift is that the Defender for Containers sensor now adds prevention on top of detection. The goal is simple: stop malicious code before it runs. Anti-malware detection and prevention (GA) scans container workloads and Kubernetes nodes and, based on the policies you define, blocks malicious execution instead of only alerting. Those alerts then flow into Microsoft Defender XDR’s unified incident model. The second is binary drift detection and prevention (preview). Containers are meant to be immutable. When a process starts from a binary that was not part of the original image, that is drift — and one of the highest-signal indicators of compromise in cloud-native workloads. Defender detects drifts and, with policy enabled, can now also block the drifted process before it executes. Anti-malware and Drift policies can be scoped by cloud, cluster, namespace, image, or label, with allow-lists for legitimate cases. Anti-malware policies can alert, block, or ignore — scoped to clusters, namespaces, pods, labels, or images. Rounding out runtime protection, DNS-based threat detection (GA) catches command-and-control beaconing, DGA traffic, and exfiltration over DNS. A unified approach to container security Step back, and the bigger picture is simple. The same platform that secured your VMs and identities now extends across AKS, EKS, GKE, private clusters, serverless containers, and serverless compute. The same Code-to-Runtime chain that once tied Infrastructure as Code (IaC) findings to running infrastructure now connects Dockerfile commits — through CI/CD and any major registry — to the running pod. Admission control turns posture findings into prevention at deploy time, and runtime protection actively blocks. That is a continuous container security loop living inside Microsoft Defender — not a checklist bolted onto Kubernetes. And it rebalances the fight: as attackers use AI to find and exploit gaps faster, the durable answer is security teams using AI of their own — protecting and triaging at machine speed. If you’ve already enabled container security with Microsoft, the clearest next step is to strengthen the core lifecycle stages first: Code + build: connect GitHub Advanced Security and integrate the Defender for Cloud CLI into your pipelines so findings are caught early and CI/CD gates can fail builds before an image is pushed. Ship: stand up Gated Deployment in audit mode on a non-production cluster, tune it, then flip to deny; extend it to Kubernetes misconfigurations. Run: enable the Defender for Containers sensor, extend it to private EKS and GKE clusters, then tune anti-malware and binary-drift rules in Block mode — starting with your crown-jewel namespaces. Extend protection: turn on serverless compute posture for Lambda, Functions, and Web Apps, and enable serverless container posture for Container Apps, ACI, or Fargate.
