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.Microsoft Defender for Cloud Customer Newsletter
What's new in Defender for Cloud? Defender for Cloud is now integrated into the Defender portal to bring together cloud security posture management and threat protection in a single experience. Read more about it here. Cloud security reporting in the Defender portal is now in public preview Customers can now create, customize, and share security insights across the organization through Defender portal’s integrated cloud security reporting capabilities. With these reporting capabilities, customers can view built-in reports like CNAPP Executive Summary, create custom reports, export to PDF and more. 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! Blog(s) of the month In May, our team published the following blog posts we would like to share: Better together with Azure WAF + Defender for Storage + Defender for Azure SQL Databases Public preview: Expanded coverage and unified management for SQL VA Express Configuration | Microsoft Community Hub Defender for Cloud in the field Check out the two short videos on Defender Portal integration and Start Secure Stay Secure with Defender for Cloud Microsoft Defender for Cloud deeply integrates with Microsoft Defender Start secure and stay secure with Microsoft Defender for Cloud Visit our YouTube page GitHub Community Check out this PS script and CLI to help you enable Defender for API at scale: Onboard to Defender for API at scale Visit our GitHub page Customer journey Discover how other organizations successfully use Microsoft Defender for Cloud to protect their cloud workloads. This month we are featuring Loyens & Loeff, a law and tax firm, that operates in a high complex environment, sought to modernize the digital workplace with Microsoft 365 Copilot, Defender for Cloud and Purview. 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
The end of patching era for containers: Microsoft Defender for Cloud expands hardened image support
Why hardened images are becoming the new baseline for container image security Container security is evolving beyond vulnerability scanning alone. Across the ecosystem - spanning container platforms, registries, and software supply chain tooling - customers are increasingly adopting hardened container images - images that are minimal by design, transparent in composition, and continuously maintained to reduce inherited risk at the base layer. This shift is happening against a backdrop of increasingly fast-moving attacks. AI-assisted techniques - such as those demonstrated by Mythos-class tooling - continue to compress the time between vulnerability discovery and exploitation. In this environment, reducing exposure to exploitable vulnerabilities and attack surfaces in container images before deployment is becoming just as critical as detecting vulnerabilities after the fact. Traditional container images are optimized for flexibility and reuse, not for security - meaning they are not designed to minimize included components, reduce attack surface, or limit inherited vulnerabilities by default. As a result, many base images include large package sets and transitive dependencies that significantly increase attack surface and vulnerability noise. Hardened images take a different approach: Minimal by construction, including only what’s required to run the workload Reduced attack surface, limiting exploitable components Strong transparency, with SBOMs and provenance metadata Continuous maintenance, so vulnerabilities are addressed through rebuilding rather than downstream patching For customers, this represents a shift from reactive CVE triage to preventative risk reduction at the image layer. In practice, this changes how container image risk is managed - from prioritizing and patching vulnerabilities in place to replacing images with updated, rebuilt versions, making remediation more predictable and easier to scale across environments. As hardened images become more widely adopted, organizations still need to continuously assess these images for vulnerabilities and compliance, since minimal or frequently rebuilt images can still introduce new risks over time or differ from expected configurations - making continuous image scanning and monitoring essential. Microsoft Defender for Cloud’s approach: support choice, centralize visibility Today, Microsoft Defender for Cloud already supports vulnerability assessment for hardened image providers such as Chainguard, alongside traditional Linux distributions. We recently expanded this coverage further with additional hardened image types, giving customers more flexibility to adopt secure-by-default images while continuing to scan these images and manage findings in a centralized Microsoft Defender for Cloud experience. Microsoft Defender for Cloud does not prescribe a single hardened image solution. Instead, it focuses on enabling customer choice while providing consistent, centralized vulnerability assessment and posture management. This capability builds on the container vulnerability assessment foundation powered by Microsoft Defender for Endpoint and Microsoft Defender Vulnerability Management (MDVM), bringing together high-fidelity vulnerability insights across the container lifecycle with support for modern, hardened image models. From now on, Microsoft Defender for Cloud’s vulnerability assessment supports hardened image ecosystems including: Chainguard images, rebuilt from source and designed to minimize inherited vulnerabilities Minimus images, which are minimal and continuously rebuilt to ship with zero known CVEs at publish time Docker Hardened Images (DHI), secure, minimal, production-ready base images maintained by Docker (recently added) Photon OS-based images and other minimal operating system distributions Across all of these, Microsoft Defender for Cloud’s experience remains consistent: Images are scanned through the existing container vulnerability assessment pipeline Findings surface in the same Azure and Defender portals Policy evaluation, alerting, and compliance reporting stay centralized Security teams do not need to onboard new scanners, manage separate dashboards, or maintain parallel remediation workflows. Hardened image adoption fits directly into existing Microsoft Defender for Cloud posture management. What this means for customers As hardened image adoption accelerates, Microsoft Defender for Cloud enables customers to adopt secure‑by‑default foundations without fragmenting their security posture. The benefits are tangible: Reduced vulnerability noise from inherited base‑image packages Earlier risk reduction at the image layer Consistent vulnerability assessment across hardened image providers Centralized security posture, compliance, and reporting Whether customers choose Chainguard, Minimus, Docker Hardened Images, Photon OS–based images, or a combination, Microsoft Defender for Cloud provides a single control plane for understanding and managing container image risk - without forcing a change in operational model. How this works across hardened image providers Microsoft Defender for Cloud supports multiple hardened image providers, enabling organizations to adopt secure‑by‑default container images while maintaining a consistent approach to vulnerability assessment and posture management. While each provider takes a different approach to minimizing risk at the image layer, Microsoft Defender for Cloud ensures that all images are scanned through the same vulnerability assessment pipeline, with findings surfaced centrally for security teams to monitor, prioritize, and remediate. Examples: Minimus Minimal, continuously rebuilt container images designed to ship with zero known CVEs at publish time. Microsoft Defender for Cloud enables native scanning of Minimus images stored in Azure Container Registry, allowing security teams to assess vulnerabilities and maintain centralized visibility without introducing new workflows. Docker Hardened Images (DHI) Production‑ready, minimal base images designed as drop‑in replacements for standard container images. By supporting DHI, Microsoft Defender for Cloud allows customers to adopt these hardened images while continuing to rely on the same vulnerability scanning, governance, and reporting capabilities. Looking ahead Hardened images are no longer niche - they are becoming a foundational element of modern container security. As attacker automation and AI‑assisted attack techniques continue to shorten response windows, reducing exposure at build and image layers becomes increasingly important. Microsoft Defender for Cloud will continue expanding support for hardened and minimal image ecosystems, ensuring customers can evolve their image strategies without sacrificing visibility, control, or operational simplicity. Security should start with what you build on - not with what you fix later. Learn more: Scanning support for Docker Hardened container imagesStart Secure, Stay Secure: How Microsoft is Closing the Gap from Code to Runtime
At Build 2026, Microsoft announces two advances in shift-left security: the expanded private preview of Codename MDASH, a multi-model agentic scanning system that finds and validates exploitable vulnerabilities end to end, and the general availability of the Microsoft Defender for Cloud and GitHub Code Security native integration, which connects runtime risk signals directly to code. Together, they help security and development teams prioritize what matters, fix it faster, and work from a single shared workflow.Now Generally Available: Microsoft Defender for open source relational databases on AWS RDS
Securing multicloud databases to help reduce risks Open‑source (OSS) relational databases are becoming increasingly critical and increasingly targeted in organization of all sizes. As organizations adopt multicloud architectures, these databases often run across Azure and Amazon Web Services (AWS), while security tools remain fragmented. The result is inconsistent visibility into sensitive data, disconnected alerts, and limited insight into how database exposure translates into real risk. Today, Microsoft announces the general availability (GA) of Microsoft Defender for open‑source relational databases with support for Amazon Relational Database Service (AWS RDS). Customers can gain visibility into potentially sensitive data, identify indicators of database threats, and support risk prioritization across Azure and AWS through a unified experience in Microsoft Defender for Cloud, with capabilities that continue to expand across environments. This GA release highlights Microsoft’s existing protection for open‑source relational databases in Azure and extends the same database‑focused security signals, risk context, and investigation capabilities to AWS RDS: helping organizations strengthen database security the way modern applications are actually deployed. What’s new with GA support for AWS RDS Defender for open-source relational databases now provides GA support for security capabilities designed for enterprise cloud environments, including: Amazon Aurora for PostgreSQL Amazon Aurora for MySQL Amazon RDS for PostgreSQL Amazon RDS for MySQL Amazon RDS for MariaDB These capabilities are integrated directly into Microsoft Defender for Cloud, providing consistent visibility and protection across Azure and AWS environments. Core security capabilities for multicloud databases Defender for Cloud delivers database‑specific security signals that help teams move beyond isolated alerts to risk‑based prioritization. This strengthens Defender for Cloud’s visibility into databases security by extending sensitive data discovery insights and threat protection specifically to supported AWS resources. As part of this delivery, we’ve also added recommendations that help validate AWS RDS resources’ enablement, discovery, scanning and protection status. Advanced threat protection at the database layer Defender for Cloud detects suspicious access patterns and brute force attempts that indicate active database threats. Alerts are enriched with cloud and workload context to help security teams quickly determine which issues require immediate attention. Built‑in sensitive data discovery Automated, recurring and agentless scans help identify data that may be sensitive, such as payment details or credentials without requiring additional configuration in supported AWS resources. This visibility helps teams understand where high-risk data resides and focus protection efforts where exposure matters most. Attack path analysis with cloud context Rather than viewing alerts in isolation, Defender provides visibility into potential attack paths, showing how exposed databases, weak authentication, and sensitive data can combine into real attack scenarios. This capability, provided by also enabling Defender CSPM, enables teams to prioritize remediation that breaks the attack chain to not only their Azure resources but also AWS RDS databases. Unified investigation with Microsoft Defender portal Database alerts integrate with Microsoft Defender portal, allowing security operations teams to correlate database incidents with signals from identities, endpoints, and workloads to support investigation and response workflows. This plan allows for supported AWS RDS signals to be added and correlated as well. Why this matters now Together, these capabilities help organizations move beyond isolated database alerts toward risk‑based prioritization, which becomes especially critical as open‑source databases increasingly store high‑value and regulated data in multicloud architectures. Customer outcomes: prioritized database risk across clouds With GA support for AWS RDS, organizations can move from fragmented database security to prioritized risk management across Azure and AWS: Detect real database threats by identifying risky access patterns tied directly to exposed databases. Understand where sensitive data lives through built‑in discovery that highlights high‑risk data stores automatically. See how attacks actually unfold using attack path analysis that connects exposure, misconfiguration, and data sensitivity and connecting those to actual alerts generated on the resource. Customer can respond faster with database alerts integrated into Microsoft Defender XDR for unified investigation across environments and correlation into incidents and attack stories across various resources and plans. Together, these outcomes help security teams move from reactive database monitoring to proactive risk reduction in multicloud architecture. Database security as part of a unified CNAPP strategy This GA milestone is part of Microsoft’s broader Cloud‑Native Application Protection Platform (CNAPP) approach, which brings together posture management, workload protection, and threat protection across the cloud lifecycle. By integrating database security into CNAPP, Defender for Cloud ensures databases are not isolated controls, but a critical part of a unified view across applications, identities, workloads, and data to support risk reduction while maintaining operational efficiency. Get started today GA support for AWS RDS is available now. Billing for this plan starts on June 1, 2026, and charges will appear on the July 2026 bill. Enable Microsoft Defender for open‑source relational databases in the Azure portal to begin applying additional protections for open-source databases across Azure and AWS with unified visibility and risk‑based security. Learn more → Cloud Security Solutions | Microsoft Security Resources: Learn more about Microsoft Defender for Cloud Read the Defender for open‑source relational databases documentation Explore sensitive data discovery Review available trial options Share your experience with Microsoft Defender for Cloud on Gartner Peer Insights
Why “Data in Switzerland” Is Not Enough
Moving from Residency to Control in Microsoft 365 Every conversation about data sovereignty in regulated industries tends to start the same way: “We use Multi-Geo. The data stays in Switzerland.” It’s the right starting point. Microsoft 365 Multi-Geo allows organizations to place selected workloads - SharePoint sites, OneDrive accounts, Teams data, or Exchange mailboxes - into specific regions, including Switzerland, while maintaining a single global tenant. This makes it possible to align sensitive data with regulatory or customer requirements without fragmenting the overall environment. But it only answers one question: Where is the data stored? It does not answer who accessed the data, from where, under which conditions, or what happened after access. That is where the real problem begins. A scenario that happens every day A Swiss engineering firm stores sensitive project documentation in Switzerland using Multi-Geo. An external contractor - working from an unmanaged device outside Switzerland - is granted access to review a file. The document opens. The data is now on a screen in an unknown location, on a device with no compliance posture, in a session with no restrictions. From the platform’s perspective, residency was enforced. From a sovereignty perspective, control was lost the moment access was granted without conditions. The file never left Switzerland. But sovereignty did. Residency is static. Control is not. The moment a document is opened, storage location stops being the relevant boundary. The file is no longer just “in Switzerland.” It moves instantly across endpoints and browsers, collaboration tools like Teams, external users and partners, and increasingly AI-driven contexts. The infrastructure remains unchanged. The data does not. From the platform’s perspective, everything is working as designed - access was granted, residency was enforced - and control was lost. Most “data in Switzerland” strategies fail at exactly this moment: when the data is used. The shift: from location to conditions If data sovereignty is the goal, the question must change. Not “Where is the data stored?” but: Under which conditions can data be accessed and used? This shift fundamentally changes the architecture. Control must be applied across three distinct layers - and all three must be connected. Layer 1: Access is conditional, not static Conditional Access extends control beyond authentication and turns it into continuous evaluation. Access decisions can depend on: Device compliance Location (geo-restriction) Identity and risk signals Multi-Geo ensures data is placed correctly. Conditional Access ensures it is reachable only under defined conditions. The two must work together - residency without access governance is an incomplete control. Layer 2: The session is the real risk surface Even with strict access controls, risk remains. A session is an exposure surface by design. During an active session, data is viewed, copied, shared, processed by applications, and connected to AI prompts. The gap does not appear at storage or authentication. It appears during active usage - inside the session. This is the layer most architectures do not explicitly address. Controls must extend into the session itself: limiting data transfer and replication, restricting interaction patterns, and enforcing policies in real time. Access is no longer a one-time event. It becomes continuously governed. This becomes even more critical as AI assistants consume content across SharePoint, Teams, Exchange, and other Microsoft 365 services. The question is no longer only where the source document resides - but whether the AI interaction itself is governed by the same access and protection controls as direct access. Layer 3: The document becomes the control point The most durable control does not sit in the network or in the session. It sits in the data itself. In regulated industries, organizations often arrive at this architecture having first evaluated sovereign or national encryption solutions. The decision to rely on native Microsoft 365 Purview encryption rather than a separate layer comes down to integration: AES-256 protection operating natively at file, user, and SharePoint level - including geo-based access restrictions - without an additional system to maintain. When protection is applied directly to the document through Microsoft Purview: Sensitivity labels define classification - automatically assigned based on content Encryption enforces access - AES-256, bound to the file itself IRM controls usage - view, copy, print, share, and presentation rights DLP governs movement across services - preventing data from leaving defined boundaries Dynamic watermarking tracks exposure - applied on open, view, or print At that point, access is enforced by the file, usage restrictions travel with it, and control persists regardless of location. The document becomes the perimeter. Platform control: limiting provider access One dimension often overlooked in sovereignty discussions is platform access itself. Even a perfectly configured tenant is only as sovereign as the controls placed on the operator. Customer Lockbox ensures that even Microsoft support cannot access customer data without explicit, logged, time-bound approval. Every access request is visible, auditable, and subject to customer veto. Data control applies not only to users - but also to the platform operating the service. Enforcement requires an integrated architecture Most organizations already have the required capabilities: Multi-Geo, Conditional Access, session control, Purview (labels, encryption, DLP, IRM), and monitoring. The issue is not capability. It is fragmentation. In practice, fragmentation looks like this: residency is configured in one project, Conditional Access policies are managed by a different team, and Purview labels were applied during a compliance initiative that never connected to the access layer. The tools exist. The signals do not flow between them. When designed as a single architecture: Data is placed intentionally - residency aligned to regulatory requirements Access is governed by context - device, location, and identity evaluated continuously Usage is controlled dynamically - session-level restrictions enforced in real time Protection is embedded in the document - encryption and IRM travel with the file Signals are connected across the platform - monitoring feeds access policy, not just audit logs “Data in Switzerland” becomes not just a statement - but an enforceable system property. Closing thought Placing data in Switzerland is the right first step. Multi-Geo makes it possible, even in global environments. But residency alone is not control. Data residency answers where information is stored. Data sovereignty requires proving who can access it, under which conditions, and what controls remain in place after access is granted. In Microsoft 365, sovereignty is no longer defined by geography alone. It is defined by the ability to enforce control wherever the data travels.Public preview: Expanded coverage and unified management for SQL VA Express Configuration
SQL Vulnerability Assessment (SQL VA) is a core capability in Defender for SQL that helps customers identify possible misconfigurations, excessive permissions, and other deviations from security best practices through continuous scanning of their databases. Traditionally, enabling SQL VA on SQL PaaS resources required customers to provision and maintain a dedicated Azure Storage account to hold scan results and baselines. In addition, managing SQL VA across resource types required different API endpoints, which made it harder to script consistent enablement and baseline management across a mixed SQL estate. For customers managing large SQL estates, this added operational overhead to onboarding and ongoing management. This friction may lead to inconsistent enablement across environments and leave gaps in vulnerability visibility. To simplify this experience, Microsoft introduced Express Configuration, which uses Microsoft-managed storage and does not require a customer-provisioned storage account. Express Configuration is generally available for Azure SQL Database and is the recommended enablement mode for SQL VA, where supported. This public preview extends Express Configuration to Azure SQL Managed Instance and Azure Synapse Analytics workspaces, and introduces a new preview API version that brings SQL VA management under a unified model across Azure SQL Database, SQL Managed Instance, Synapse workspaces, and SQL on machines (Azure VMs and Arc-enabled SQL Servers). Customers can now enable SQL VA on SQL Managed Instance and Synapse workspaces without provisioning a dedicated storage account and can manage SQL VA across all supported resource types through a single API. Together, these changes broaden Express Configuration coverage across Azure SQL PaaS services and consolidate SQL VA operations under a single API, helping standardize how SQL VA is enabled and managed and reduce operational overhead across a customer's SQL estate. What’s new in this release Express Configuration support for additional Azure SQL PaaS services: Azure SQL Managed Instance (public preview) and Azure Synapse Analytics workspaces (dedicated SQL pools, public preview); Express Configuration for Azure SQL Database remains generally available. Express Configuration is the default when enabling Defender for SQL on a resource from the UI. New preview API version for unified SQL VA management across Azure SQL Database, SQL Managed Instance, Azure Synapse Analytics workspaces (Express Configuration only), and SQL on machines (Azure Virtual Machines and Arc-enabled SQL Servers). Why use Express Configuration Express Configuration simplifies how SQL Vulnerability Assessment is enabled and managed for Azure SQL Managed Instance and Azure Synapse Analytics workspaces, without changing the security coverage or rule set provided by SQL VA. No customer-managed storage required. Express Configuration uses Microsoft-managed storage, so customers don’t need to provision or maintain storage accounts for scan results and baselines. Automatic weekly scans and on-demand scans through the UI, unified API, or scripts. Baseline management at scale, including setting baselines per finding or in bulk. Baseline changes take effect without waiting for the next scan to complete. Unified management across SQL platforms The latest preview API version enables a unified model for configuration, scanning, and governance for SQL Vulnerability Assessment across all supported SQL deployments: Manage SQL VA across Azure SQL Database, SQL Managed Instance, and Azure Synapse Analytics workspaces. Manage SQL VA across SQL on machines, including Azure Virtual Machines and Arc-enabled SQL Servers. Use a consistent model for configuration, scans, results retrieval, and baseline management across supported resource types. Limitations and prerequisites Permissions Task Required roles View SQL vulnerability assessment results in Microsoft Defender for Cloud recommendations Security Admin or Security Reader Change SQL vulnerability assessment settings Security Admin or SQL Security Manager Access resource-level scan results or automated email links Security Admin or SQL Security Manager Classic Configuration conflict: If Classic Configuration is already enabled on a resource, enabling Express Configuration through the API will fail with an error. To migrate an existing Classic Configuration to Express Configuration, use the updated migration script. UI enablement supports clearing Classic Configuration settings and re-enabling with Express Configuration. SQL Managed Instance prerequisite: A system-assigned managed identity is required for Express Configuration to work on SQL Managed Instance. Preview enablement scope: During public preview subscription-level enablement does not automatically apply Express Configuration to SQL Managed Instance or Synapse workspaces during public preview. Reverting to Classic Configuration: After migrating to Express Configuration, reverting to Classic Configuration is possible programmatically but not through the UI. Get started Try it through the portal: Enable Express Configuration on a SQL Managed Instance or Synapse workspace through the Defender for Cloud portal, run an on-demand scan, and review findings in Defender for Cloud recommendations. Automate your first steps: Use the SQL VA Express Configuration quickstart script to enable Express Configuration, discover databases, run scans, and manage baselines through the unified API. Migrate from Classic Configuration: If you have Classic Configuration enabled on existing resources, use the migration script to move to Express Configuration.State Explosion Security Problem in AI-Era Software Supply Chains
Introduction To see why this problem scales so quickly, start with the smallest possible change: a single line of code. In modern software, even a tiny edit is rarely just a local modification. It can change execution flow, introduce a new dependency, expose sensitive data, or quietly shift the purpose of the package itself. What looks trivial in a diff can create a materially different security outcome. That is why supply chain defenders cannot afford to treat small code changes as small security events. How a Single Line Changes Package Intent Every software package exists in a particular state at a particular moment in time. Imagine a benign version — State X — that behaves exactly as intended. Now add one line of code. That small edit can shift the package into a new state with different behavior and, potentially, a very different risk profile. The security issue is not the added line by itself. It is the fact that the package now has to be interpreted differently. A tiny diff can change the role of the entire component, which means defenders have to reason about the resulting behavior, not just the textual change. That is why file-level scanning breaks down so quickly. A change in one file can alter the behavior of the entire package because software semantics emerge from how components interact. Security systems therefore need to analyze packages as composed systems, not as a series of isolated file edits. Why the whole package matters This matters even more in modern supply chain attacks, where malicious intent is rarely concentrated in one obvious file. More often, the behavior is distributed across several files that look harmless when viewed independently. File A defines an encoded string constant. Looks like a config value. File B provides a decode function. Looks like a utility. File C (setup.py / postinstall) imports both, decodes, and executes. Viewed independently, each file may appear benign. No single file has to trigger a clear signature, rule, or heuristic. The malicious behavior only becomes visible when you reconstruct how the files interact as a system. Any scanner that evaluates files one by one without rebuilding that interaction is likely to miss the real behavior. Why every change demands re-analysis Every meaningful state change — a commit, pull request, version bump, or package publish — can alter the semantics of the software. That means defenders cannot stop at diff inspection or lightweight pattern matching. The real question is not only what changed, but what the software now does. Quantifying the problem The scale of the problem becomes clearer when you look at how many software state changes occur across the ecosystem every day: GitHub alone recorded nearly 1 billion commits in 2025, merged an average of 43.2 million pull requests per month, and now hosts roughly 630 million repositories. In 2026, GitHub was projected to reach roughly 38 million commits per day. npm has grown to well over 2 million packages, making JavaScript one of the largest public package ecosystems. PyPI published more than 130,000 new projects in 2025 and more than 3.9 million new files in the same year. NuGet serves package downloads at massive operational scale, with recent weekly totals in the 5 to 6 billion range. Maven Central indexed more than 20 million packages and published more than 3.2 million packages in 2025. Taken together, these ecosystems are generating an enormous stream of new software states. Some numbers describe repositories, some describe publishes, and some describe downloads, but they all point to the same reality: the scale of software movement is already massive before you even account for the acceleration from AI-assisted development. The number of state changes is already enormous, and AI-assisted development is increasing it even further. The result is not just more code, but more package states that may require meaningful security interpretation. Why the math breaks traditional scanning Assume a single semantic package analysis takes 30 seconds, which is a reasonable range for LLM-based inference. Scanning 50,000 packages would require roughly 1.5 million seconds of compute time per day — about 417 hours. But the ecosystem only gives defenders 24 hours before the next wave of packages arrives. Without aggressive parallelism and purpose-built infrastructure, backlog becomes inevitable. The scanning bottleneck This leaves modern scanning systems with a fundamental bottleneck: Heuristic and signature-based scanners are fast. They can match known patterns in milliseconds and work well for familiar malware families or repeated behaviors. Some systems also use emulation or detonation, but these approaches still struggle to deliver deep reasoning at ecosystem scale. That makes them easier to bypass with novel, well-structured, or AI-generated code that behaves maliciously without resembling previously known samples. LLM-based semantic analysis can reason about intent. It can follow behavior across files, recognize obfuscated exfiltration paths, and explain why a package is suspicious even when the code appears ordinary at first glance. The tradeoff is cost, latency, and trust: inference takes seconds rather than milliseconds, and a single package may require multiple reasoning passes. At ecosystem scale, that becomes a serious infrastructure challenge. Neither approach is sufficient on its own. Heuristics provide speed without deep understanding, while semantic models provide understanding without inherent scale. Closing the gap requires systems that combine both: package-level reasoning with the latency and throughput needed for production supply chains. Heuristics often miss novel attacks, while LLM-based approaches remain too slow to apply inline at large scale. That gap between understanding and throughput is where supply chain malware can persist. What needs to change Closing that gap will require a different class of supply chain security systems. Detonation can help in some cases, but it is too slow and expensive to apply inline to every package state change. What is needed is a system that can: Analyze entire packages as a unit — not individual files. The intent lives in the interaction between files, not within any single one. Run semantic analysis at data-plane speed — every package, every version, on the hot path, with latency low enough for inline enforcement. Not async advisories. Not CI-time checks. Inline, before delivery. Handle the state explosion — millions of state changes per day, each requiring full re-analysis. This is an infrastructure problem as much as a security problem: rate limiting, backpressure, connection pooling, regional failover, model versioning — the same hard distributed systems problems, with security stakes. Maintain high accuracy under evasion — attackers deliberately use encoding, string splitting, dynamic imports, polyglot files, and similar techniques to reduce detection quality. The scanner must continue to classify packages accurately even when the code is designed to obscure intent. The Latency-Accuracy Tradeoff: Malware Detection as an ML Problem At cloud scale, malware detection is governed by a hard tradeoff between latency, accuracy, throughput, and cost. The fastest detectors are typically shallow: signatures, heuristics, and lightweight models can make decisions in milliseconds, but they often miss novel, compositional, or intent-level attacks. Deeper semantic analysis can improve recall and resilience against evasion, but it also increases inference time, compute cost, and operational complexity. As a result, defenders cannot optimize for accuracy in isolation; they must deliver strong detection quality within strict performance constraints. This makes malware detection not just a cybersecurity problem, but a machine learning and distributed systems problem. In modern software supply chains, AI-assisted development increases the number of package states and enables attackers to generate variants at high speed, expanding the space defenders must reason over. The challenge is therefore to build detection architectures that preserve semantic depth while remaining fast enough for inline use at global scale. The gap between the rate of software change and the capacity to analyze it is widening. That gap is the attack surface. If defenders cannot inspect software at the speed it is being produced and published, attackers will continue to exploit the delay. What the industry needs now is a cloud-scale malware analysis capability that can deliver low latency, low cost, high accuracy, and the flexibility to meet different operational requirements , such as SLAs, false-positive tolerance, and enforcement policies , without compromising on package-level semantic analysis.
