Secure containers from code to runtime | Microsoft Defender
Correlate cross-cloud attacks into a single incident, catch runtime threats that image scanning misses, and block vulnerable images before they reach production. Investigate container hijacking, isolate compromised pods with Security Copilot-guided remediation, and close the loop from SOC to dev by pushing CVE fixes to GitHub and syncing resolution back to Defender. Matt McSpirit, Microsoft Azure expert, shares how to detect, investigate, and remediate container threats in one connected workflow. Expose container hijacking at runtime. Binary drift detection flags the moment a container runs anything outside its original image. Try it now. Detection to containment in one flow. Security Copilot in Defender generates your incident report, classifies the attack, and surfaces pod isolation as a guided remediation step. Check it out. Close the loop from SOC to dev. Create GitHub issues for vulnerable container dependencies, trigger Copilot’s fix as a Pull Request, and bring “resolved” status back to Defender the moment it merges. See how it works. QUICK LINKS: 00:00 — Secure containers in Microsoft Defender 01:02 — Cross-cloud incident 03:07- Runtime detection 04:10 — Investigate and build context 04:49 — Security Copilot incident report & containment 06:03 — Prevention 07:34 — Recommendations and take action 09:04 — Wrap up Link References Get started at https://aka.ms/DefenderCloudSecurity Unfamiliar with Microsoft Mechanics? As Microsoft’s official video series for IT, you can watch and share valuable content and demos of current and upcoming tech from the people who build it at Microsoft. Subscribe to our YouTube: https://www.youtube.com/c/MicrosoftMechanicsSeries Talk with other IT Pros, join us on the Microsoft Tech Community: https://techcommunity.microsoft.com/t5/microsoft-mechanics-blog/bg-p/MicrosoftMechanicsBlog Watch or listen from anywhere, subscribe to our podcast: https://microsoftmechanics.libsyn.com/podcast Keep getting this insider knowledge, join us on social: Follow us on Twitter: https://twitter.com/MSFTMechanics Share knowledge on LinkedIn: https://www.linkedin.com/company/microsoft-mechanics/ Enjoy us on Instagram: https://www.instagram.com/msftmechanics/ Loosen up with us on TikTok: https://www.tiktok.com/@msftmechanics Video Transcript: -Containers are the foundation for how modern business critical apps are built, running across Kubernetes environments and multiple clouds. These are high value targets for attackers, who are now using AI to accelerate ways to find and exploit vulnerabilities faster than humanly possible, from code to runtime. And it’s hard to respond effectively when security signals and responsibilities are fragmented across different teams and tools. The challenge isn’t just finding security issues, it’s understanding how they connect across the lifecycle from development to production. -So, rather than treating supply chain and runtime as separate problems, Microsoft Defender for Cloud brings them together into a single, unified model to catch issues early in code by identifying vulnerabilities and misconfigurations, prioritizing risk across clusters, workloads, and configurations, and detecting and responding to threats in your running containers in near real time, all while tracing them back to the source to put the right mitigations in place. So let me walk you through a multi-stage attack across a containerized environment to show this in action from the perspective of a SOC analyst. I’m in Microsoft Defender in the incidents view, filtered to container related incidents, and I can see four related to containers. -Let’s start with this one: a multi-stage incident involving multiple users. And notice this is already correlated into a single incident, not multiple alerts. You can see it’s flagged as high impact, with a critical asset label, and the crown icon in the incident graph showing asset criticality. On the left, is the attack story with alerts listed in chronological order, color-coded by severity. If we go back to the incident graph, container resources are in black, and related identities and services are in white. This is runtime threat detection correlated across layers. -In this case, Defender has detected 19 alerts for this incident in near-real time and stitched them into a single timeline. So, let’s walk through the attack. Here we can see the initial access signal was from a Kubernetes API request from a proxy IP address. Identity compromise and escalation then occurred with credentials accessed from a service principal. And finally, Defender for Cloud’s cross-cloud correlation surfaces lateral movement spanning environments, revealing a brute-force attack originating from an exposed Azure Kubernetes cluster targeting an AWS Relational Database service resource. -Back in the graph, you’ll notice a blue icon, this indicates there are related attack paths. And if we ungroup similar nodes, we see a user indicated by the blue icon, and we can pivot directly into the attack path view. Here’s where it gets interesting. This is where we connect runtime activity back to security posture and exposure. It’s concluded that the container is internet-exposed with high-severity vulnerabilities, and it’s using a managed identity to access a protected storage account. We’ll come back to this in a moment to see if we can fix the root cause. So, we can see this wasn’t just an isolated runtime event; there was a clear path from exposure to exploitation. -So let’s move on to the second incident in our queue. This is another multi-stage attack. This time focused on initial access and discovery across multiple endpoints. In the Incident Graph, we can see a number of containers were accessed. Then in the attack story, we can see they were hijacked to be used as Cryptocoin miners. Now, containers should be immutable, only running what was in the original image. But this alert shows the initial binary drift. This signals to Defender that the container is executing something it was never built to run. And if we review the activities for this alert, we can see a suspicious parent process, and a shim file being used to execute it. And if we move to the right in evidence, we can see all the details for the process, file, pod and cluster. This is where runtime detection becomes critical, because this activity isn’t visible through image scanning or posture alone. -From here, we can investigate and build full context around the attack. For that, let’s drill into our Kubernetes cluster and start hunting to take a look at the processes run, across both control plane and data plane signals. There are tables that log these events for querying, CloudProcessEvents for runtime activity and CloudAuditEvents for control plane actions. Let’s run a query in the audit events table to search for the creation of a cluster-admin role binding. This would grant full administrative access to the cluster. Here, we can see the role binding and associated subject. It confirms our assumption that the privilege escalation did happen. Next, we can leverage Security Copilot to transition from investigation to response using guided recommendations informed by the attack insights uncovered. Here, Copilot has already generated an incident report. -At the top, we have a summary of the attack from correlated signals. If we expand it, under Credential Access, we see compromised identity activity. Under Discovery, there are network scanning tools. Under Execution, we see confirmation that the container was modified to run crypto mining processes. And below that, the Triage section then lets you classify the incident as true positive, informational, or false positive with corresponding subcategories. I’ll choose a True Positive Multi-stage attack. And Copilot also suggests two remediation actions: terminate the pod to stop activity or isolate the pod to contain the threat. So let’s follow that guidance and isolate the pod. So I’ll close Copilot and return to our Incident graph, then ungroup similar nodes again. Now selecting the Pod, under Actions, I can isolate the Pod. I’ll add a reason. And then I just need to confirm. And just like that, we’ve moved from detection to investigation and containment in a single flow. -So far, we’ve focused on detecting, responding to active threats. But security doesn’t start at runtime. And this is where Defender for Cloud helps your DevOps teams harden your environment to prevent issues from reaching production in the first place. Using policies, you can put rules in place to prevent containers with issues from being deployed. So for that, in the Azure portal in Defender for Cloud, under Environment Settings, we’ll head over to our Security Rules. -Here, we’ve already created a handful of rules to deny or audit activities. For example, if I take a look at this one to block vulnerable deployments of AI applications, this rule is configured to automatically deny deployment of any image violating the policy. It’s scoped specifically to the namespace of our AI applications. In the Configurations tab, we can see that this rule is set to trigger when a container image contains a high or critical severity vulnerability, with any count higher than zero. With our policy in place, let’s test it out by attempting to deploy a container image that contains a few high-severity vulnerabilities. This is exactly what happens with supply chain attacks. For that, from Cloud Shell, I’ll run an AKS command first to get my credentials. -Now I can execute my command. I’ll paste in this Kube Control command to run an image, including its location and namespace details. And immediately, we can see that the run was denied, stating that the image contains 415 CVEs, and we allowed zero in our policy. So it’s blocked. What’s more, Defender for Cloud continuously scans what’s in your registries and running environments in near real time, surfacing the risks that matter and turning them into clear, actionable fixes. Next over in Recommendations, we can see findings identified by Defender for Cloud with several critical risks highlighted, related to the same incident that we saw earlier in the Defender portal. And these are its recommendations. -Let’s look at this one to update busybox. Looks like this version has 13 known vulnerabilities. And digging into the Remediation Insights tab, we can see more details across the development phases. The Take Action button tells us to assign an owner and to create a GitHub issue. I can create the GitHub issue from here. And if I head over to what our dev team would see in GitHub, you can see the details for the ticket, with deployment info, matching CVEs for this version, and below there, there are the remediation guidelines to mitigate the issue. From there, we can assign the issue to GitHub Copilot, and the GitHub Copilot Coding Agent will generate a draft Pull request fixing the CVEs for review. Once the pull request is actioned and complete and the version is updated, we can see that the CVEs were resolved. And finally as a developer, we can merge the pull request to get our app back into a healthy state. I’m going to return back to Microsoft Defender and our app recommendation, clicking on the GitHub issues shows that everything was resolved, and it sent the updated status back to Defender where your SOC lives. -So, that’s a hands-on look at how Microsoft Defender helps secure your container environments end to end. It doesn’t just detect security issues, it helps you to respond faster by connecting security signals across the entire lifecycle, from development through production. To learn more or get started, visit aka.ms/DefenderCloudSecurity. Be sure to subscribe to Microsoft Mechanics for the latest tech updates. And thanks for watching.Why UK Enterprise Cybersecurity Is Failing in 2026 (And What Leaders Must Change)
Enterprise cybersecurity in large organisations has always been an asymmetric game. But with the rise of AI‑enabled cyber attacks, that imbalance has widened dramatically - particularly for UK and EMEA enterprises operating complex cloud, SaaS, and identity‑driven environments. Microsoft Threat Intelligence and Microsoft Defender Security Research have publicly reported a clear shift in how attackers operate: AI is now embedded across the entire attack lifecycle. Threat actors use AI to accelerate reconnaissance, generate highly targeted phishing at scale, automate infrastructure, and adapt tactics in real time - dramatically reducing the time required to move from initial access to business impact. In recent months, Microsoft has documented AI‑enabled phishing campaigns abusing legitimate authentication mechanisms, including OAuth and device‑code flows, to compromise enterprise accounts at scale. These attacks rely on automation, dynamic code generation, and highly personalised lures - not on exploiting traditional vulnerabilities or stealing passwords. The Reality Gap: Adaptive Attackers vs. Static Enterprise Defences Meanwhile, many UK enterprises still rely on legacy cybersecurity controls designed for a very different threat model - one rooted in a far more predictable world. This creates a dangerous "Resilience Gap." Here is why your current stack is failing- and the C-Suite strategy required to fix it. 1. The Failure of Traditional Antivirus in the AI Era Traditional antivirus (AV) relies on static signatures and hashes. It assumes malicious code remains identical across different targets. AI has rendered this assumption obsolete. Modern malware now uses automated mutation to generate unique code variants at execution time, and adapts behaviour based on its environment. Microsoft Threat Intelligence has observed threat actors using AI‑assisted tooling to rapidly rewrite payload components, ensuring that every deployment looks subtly different. In this model, there is no reliable signature to detect. By the time a pattern exists, the attacker has already moved on. Signature‑based detection is not just slow - it is structurally misaligned with AI‑driven attacks. The Risk: If your security relies on "recognising" a threat, you are already breached. By the time a signature exists, the attacker has evolved. The C-Suite Pivot: Shift investment from artifact detection to EDR/XDR (Extended Detection and Response). We must prioritise behavioural analytics and machine learning models that identify intent rather than file names. 2. Why Perimeter Firewalls Fail in a Cloud-First World Many UK enterprise still rely on firewalls enforcing static allow/deny rules based on IP addresses and ports. This model worked when applications were predictable and networks clearly segmented. Today, enterprise traffic is encrypted, cloud‑hosted, API‑driven, and deeply integrated with SaaS and identity services. AI‑assisted phishing campaigns abusing OAuth and device‑code flows demonstrate this clearly. From a network perspective, everything looks legitimate: HTTPS traffic to trusted identity providers. No suspicious port. No malicious domain. Yet the attacker successfully compromises identity. The Risk: Traditional firewalls are "blind" to identity-based breaches in cloud environments. The C-Suite Pivot: Move to Identity-First Security. Treat Identity as the new Control Plane, integrating signals like user risk, device health, and geolocation into every access decision. 3. The Critical Weakness of Single-Factor Authentication Despite clear NCSC guidance, single-factor passwords remain a common vulnerability in legacy applications and VPNs. AI-driven credential abuse has changed the economics of these attacks. Threat actors now deploy adaptive phishing campaigns that evolve in real-time. Microsoft has observed attackers using AI to hyper-target high-value UK identities- specifically CEOs, Finance Directors, and Procurement leads. The Risk: Static passwords are now the primary weak link in UK supply chain security. The C-Suite Pivot: Mandate Phishing‑resistant MFA (Passkeys or hardware security keys). Implement Conditional Access policies that evaluate risk dynamically at the moment of access, not just at login. Legacy Security vs. AI‑Era Reality 4. The Inherent Risk of VPN-Centric Security VPNs were built on a flawed assumption: that anyone "inside" the network is trustworthy. In 2026, this logic is a liability. AI-assisted attackers now use automation to map internal networks and identify escalation paths the moment they gain VPN access. Furthermore, Microsoft has tracked nation-state actors using AI to create synthetic employee identities- complete with fake resumes and deepfake communication. In these scenarios, VPN access isn't "hacked"; it is legally granted to a fraudster. The Risk: A compromised VPN gives an attacker the "keys to the kingdom." The C-Suite Pivot: Transition to Zero Trust Architecture (ZTA). Access must be explicit, scoped to the specific application, and continuously re‑evaluated using behavioural signals. 5. Data: The High-Velocity Target Sensitive data sitting unencrypted in legacy databases or backups is a ticking time bomb. In the AI era, data discovery is no longer a slow, manual process for a hacker. Attackers now use AI to instantly analyse your directory structures, classify your files, and prioritise high-value data for theft. Unencrypted data significantly increases your "blast radius," turning a containable incident into a catastrophic board-level crisis. The Risk: Beyond the technical breach, unencrypted data leads to massive UK GDPR fines and irreparable brand damage. The C-Suite Pivot: Adopt Data-Centric Security. Implement encryption by default, classify data while adding sensitivity labels and start board-level discussions regarding post‑quantum cryptography (PQC) to future-proof your most sensitive assets. 6. The Failure of Static IDS Traditional Intrusion Detection Systems (IDS) rely on known indicators of compromise - assuming attackers reuse the same tools and techniques. AI‑driven attacks deliberately avoid that assumption. Threat actors are now using Large Language Models (LLMs) to weaponize newly disclosed vulnerabilities within hours. While your team waits for a "known pattern" to be updated in your system, the attacker is already using a custom, AI-generated exploit. The Risk: Your team is defending against yesterday's news while the attacker is moving at machine speed. The C-Suite Pivot: Invest in Adaptive Threat Detection. Move toward Graph‑based XDR platforms that correlate signals across email, endpoint, and cloud to automate investigation and response before the damage spreads. From Static Security to Continuous Security Closing Thought: Security Is a Journey, Not a Destination For UK enterprises, the shift toward adaptive cybersecurity is no longer optional - it is increasingly driven by regulatory expectation, board oversight, and accountability for operational resilience. Recent UK cyber resilience reforms and evolving regulatory frameworks signal a clear direction of travel: cybersecurity is now a board‑level responsibility, not a back‑office technical concern. Directors and executive leaders are expected to demonstrate effective governance, risk ownership, and preparedness for cyber disruption - particularly as AI reshapes the threat landscape. AI is not a future cybersecurity problem. It is a current force multiplier for attackers, exposing the limits of legacy enterprise security architectures faster than many organisations are willing to admit. The uncomfortable truth for boards in 2026 is that no enterprise is 100% secure. Intrusions are inevitable. Credentials will be compromised. Controls will be tested. The difference between a resilient enterprise and a vulnerable one is not the absence of incidents, but how risk is managed when they occur. In mature organisations, this means assuming breach and designing for containment: Access controls that limit blast radius Least privilege and conditional access restricting attackers to the smallest possible scope if an identity is compromised Data‑centric security using automated classification and encryption, ensuring that even when access is misused, sensitive data cannot be freely exfiltrated As a Senior Enterprise Cybersecurity Architect, I see this moment as a unique opportunity. AI adoption does not have to repeat the mistakes of earlier technology waves, where innovation moved fast and security followed years later. We now have a rare chance to embed security from day one - designing identity controls, data boundaries, automated monitoring, and governance before AI systems become business‑critical. When security is built in upfront, enterprises don’t just reduce risk - they gain the confidence to move faster and unlock AI’s value safely. Security is no longer a “department”. In the age of AI, it is a continuous business function - essential to preserving trust and maintaining operational continuity as attackers move at machine speed. References: Inside an AI‑enabled device code phishing campaign | Microsoft Security Blog AI as tradecraft: How threat actors operationalize AI | Microsoft Security Blog Detecting and analyzing prompt abuse in AI tools | Microsoft Security Blog Post-Quantum Cryptography | CSRC Microsoft Digital Defense Report 2025 | Microsoft https://www.ncsc.gov.uk/news/government-adopt-passkey-technology-digital-servicesAnnouncing public preview of custom graphs in Microsoft Sentinel
Security attacks span identities, devices, resources, and activity, making it critical to understand how these elements connect to expose real risk. In November, we shared how Sentinel graph brings these signals together into a relationship-aware view to help uncover hidden security risks. We’re excited to announce the public preview of custom graphs in Sentinel, available starting April 1 st . Custom graphs let defenders model relationships that are unique to their organization, then run graph analytics to surface blast radius, attack paths, privilege chains, chokepoints, and anomalies that are difficult to spot in tables alone. In this post, we’ll cover what custom graphs are, how they work, and how to get started so the entire team can use them. Custom graphs Security data is inherently connected: a sign-in leads to a token, a token touches a workload, a workload accesses data, and data movement triggers new activity. Graphs represent these relationships as nodes (entities) and edges (relationships), helping you answer questions like: “Who received the phishing email, who clicked, and which clicks were allowed by the proxy?” or “Show me users who exported notebooks, staged files in storage, then uploaded data to personal cloud storage- the full, three‑phase exfiltration chain through one identity.” With custom graphs, security teams can build, query, and visualize tailored security graphs using data from the Sentinel data lake and non-Microsoft sources, powered by Fabric. By uncovering hidden patterns and attack paths, graphs provide the relationship context needed to surface real risk. This context strengthens AI‑powered agent experiences, speeds investigations, clarifies blast radius, and helps teams move from noisy, disconnected alerts to confident decisions. In the words of our preview customers: “We ingested our Databricks management-plane telemetry into the Sentinel data lake and built a custom security graph. Without writing a single detection rule, the graph surfaced unusual patterns of activity and overprivileged access that we escalated for investigation. We didn't know what we were looking for, the graph surfaced the risk for us by revealing anomalous activity patterns and unusual access combinations driven by relationships, not alerts.” – SVP, Security Solutions | Financial Services organization Use cases Sentinel graph offers embedded, Microsoft managed, security graphs in Defender and Microsoft Purview experiences to help you at every stage of defense, from pre-breach to post-breach and across assets, activities, and threat intelligence. See here for more details. The new custom graph capability gives you full control to create your own graphs combining data from Microsoft sources, non-Microsoft sources, and federated sources in the Sentinel data lake. With custom graphs you can: Understand blast radius – Trace phishing campaigns, malware spread, OAuth abuse, or privilege escalation paths across identities, devices, apps, and data, without stitching together dozens of tables. Reconstruct real attack chains – Model multi-step attacker behavior (MITRE techniques, lateral movement, before/after malware) as connected sequences so investigations are complete and explainable, not a set of partial pivots. Reconstruct these chains from historical data in the Sentinel data lake. Figure 2: Drill into which specific MITRE techniques each IP is executing and in which tactic category Spot hidden risks and anomalies – Detect structural outliers like users with unusually broad access, anomalous email exfiltration, or dangerous permission combinations that are invisible in flat logs. Figure 3: OAuth consent chain – a single compromised user consented four dangerous permissions Creating custom graph Using the Sentinel VS Code extension, you can generate graphs to validate hunting hypotheses, such as understanding attack paths and blast radius of a phishing campaign, reconstructing multi‑step attack chains, and identifying structurally unusual or high‑risk behavior, making it accessible to your team and AI agents. Once persisted via a schedule job, you can access these custom graphs from the ready-to-use section in the graphs section in the Defender portal. Figure 4: Use AI-assisted vibe coding in Visual Studio Code to create tailored security graphs powered by Sentinel data lake and Fabric Graphs experience in the Microsoft Defender portal After creating your custom graphs, you can access them in the Graphs section of the Microsoft Defender portal under Sentinel. From there, you can perform interactive, graph-based investigations, for example, using a graph built for phishing analysis to quickly evaluate the impact of a recent incident, profile the attacker, and trace paths across Microsoft telemetry and third-party data. The graph experience lets you run Graph Query Language (GQL) queries, view the graph schema, visualize results, see results in a table, and interactively traverse to the next hop with a single click. Figure 5: Query, visualize, and traverse custom graphs with the new graph experience in Sentinel Billing Custom graph API usage for creating graph and querying graph is billed according to the Sentinel graph meter. Get started To use custom graphs, you’ll need Microsoft Sentinel data lake enabled in your tenant, since the lake provides the scalable, open-format foundation that custom graphs build on. Use the Sentinel data lake onboarding flow to provision the data lake if it isn’t already enabled. Ensure the required connectors are configured to populate your data lake. See Manage data tiers and retention in Microsoft Sentinel | Microsoft Learn. Create and persist a custom graph. See Get started with custom graphs in Microsoft Sentinel (preview) | Microsoft Learn. Run adhoc graph queries and visualize graph results. See Visualize custom graphs in Microsoft Sentinel graph (preview) | Microsoft Learn. [Optional] Schedule jobs to write graph query results to the lake tier and analytics tier using notebooks. See Exploring and interacting with lake data using Jupyter Notebooks - Microsoft Security | Microsoft Learn. Learn more Earlier posts (Sentinel graph general availability) RSAC 2026 announcement roundup Custom graphs documentation Custom graph billingSecure AI Workloads in Azure: Join Our Next Azure Decoded Session on April 8th
AI introduces new risks—like prompt injection, data leakage, and model misuse—which means security teams need visibility and guardrails that extend beyond traditional cloud controls. In our next Azure Decoded session, we’ll focus on securing AI workloads in Azure with Microsoft Defender for Cloud. Register now for the Azure Decoded session on April 8th at 12 PM PST. Bringing AI security architecture to life with Azure Decoded In the Lockdown AI workloads with Microsoft Defender for Cloud session, we move from concepts to implementation and show how these protections appear in the platform. We’ll walk through where Microsoft Defender for Cloud fits into an end-to-end AI security strategy—and how discovery, posture management, and runtime protection work together to secure AI workloads built on Azure. You’ll also see how to connect the dots across the workflow—so signals from AI resources, identity, and data controls roll up into actionable recommendations and alerts. Enable and scope the AI workloads protections in Defender for Cloud Use the Data & AI security dashboard to understand coverage and priority risks Review posture findings (CSPM) and translate them into remediation steps Investigate runtime detections (CWP) and see how they map into Microsoft Defender XDR Our goal isn’t theory for theory’s sake. It’s to help you see how AI security shows up in real architecture and real workflows—so you can apply it confidently in your own environment. Who is this session for? We built this session for practitioners who are actively working with AI in Azure, including: Developers building AI applications and agents Security engineers responsible for protecting AI workloads Cloud architects designing enterprise‑ready AI solutions If you’re balancing innovation with security and governance, this session is designed to help you translate AI security concepts into concrete steps in Azure. Before you join: Familiarity with core Azure concepts (subscriptions, resource groups, identity, networking) is helpful. You don’t need to be a machine learning expert—the focus is on securing the cloud resources and workflows that power AI solutions. From AI security concepts to platform protections If you’d like to get the most out of the session, start with the Microsoft Learn module Protect AI workloads with Microsoft Defender for Cloud. It introduces the building blocks of AI workloads in Azure and the security considerations that come with them. In the module, you’ll learn how to: Identify the layers that make up AI workloads in Azure Understand AI-specific risks, including prompt injection, data leakage, and model misuse Use Microsoft Foundry guardrails and observability to monitor and constrain model behavior See how Defender for Cloud, Microsoft Purview, and Microsoft Entra ID work together for defense in depth and governance Think of this as your foundation: it connects AI workload architecture to the controls you’ll configure in Azure, so you can protect inputs and outputs, maintain visibility, and apply governance without slowing delivery. Catch up on the previous Azure Decoded session If you missed the previous Azure Decoded session—or want a refresher—you can watch it on demand on YouTube: ▶️ Watch the previous Azure Decoded session on YouTube It’s a helpful refresher and sets the stage for the April 8 discussion. Turn learning into hands-on skills If you want to move the show, you can do this in your environment. The Microsoft Applied Skills credential, Secure AI Solutions in the Cloud, is a great next step after the Azure Decoded session. You will: Scope and enable protections for AI-related resources and workloads in Azure Validate coverage and prioritize risks using the Data & AI security dashboard Find and remediate posture gaps (CSPM) that increase exposure for AI workloads Investigate runtime detections (CWP) and understand what they mean in the context of AI workload behavior Triage AI-related alerts and incidents in Microsoft Defender XDR and decide on next steps Get started 1️⃣ Register for Azure Decoded: Lock Down AI Workloads with Microsoft Defender for Cloud 2️⃣ Watch the previous Azure Decoded session before April 8th (optional refresher) 3️⃣ Earn the Microsoft Applied Skills: Secure AI Solutions in the Cloud credential to showcase skills. The goal is to leave with something reusable: a practical sequence you can apply to new projects to confirm coverage, reduce posture gaps, and respond quickly when Defender signals suspicious activity tied to AI workloads.AI Security in Azure with Microsoft Defender for Cloud: Learn the How, Join the Session
As organizations accelerate AI adoption, securing AI workloads has become a top priority. Unlike traditional cloud applications, AI systems introduce new risks—such as prompt injection, data leakage, and model misuse—that require a more integrated approach to security and governance. To help developers and security teams understand and address these challenges, we are hosting Azure Decoded: Kickstart AI Security with Microsoft Defender for Cloud, a live session on March 18 th at 12 PM PST focused on securing AI workloads built with Microsoft Foundry and Azure AI services. From AI Security Concepts to Platform Protections A strong foundation for this session starts with the Microsoft Learn module Understand how Microsoft Defender for Cloud supports AI security and governance in Azure. This training introduces how AI workloads are structured in Azure and why they require a different security model than traditional applications. In the module, learners explore: The layers that make up AI workloads in Azure Security risks unique to AI, including prompt injection, data leakage, and model misuse How Microsoft Foundry provides guardrails and observability for AI models How Microsoft Defender for Cloud works with Microsoft Purview and Microsoft Entra ID to deliver a unified, defense‑in‑depth security and governance strategy for AI Together, these services help organizations protect model inputs and outputs, maintain visibility, and enforce governance across AI workloads in Azure. Bringing AI Security Architecture to Life with Azure Decoded The Azure Decoded: Kickstart AI Security with Microsoft Defender for Cloud session on March 18 th builds on these concepts by connecting them to real‑world architecture and platform decisions. Attendees learn how Microsoft Defender for Cloud fits into a broader AI security strategy and how Microsoft Foundry helps apply guardrails, visibility, and governance across AI workloads. This session is designed for: Developers building AI applications and agents on Azure Security engineers responsible for protecting AI workloads Cloud architects designing enterprise‑ready AI solutions By combining conceptual understanding with platform‑level security discussions, the session helps teams design AI solutions that are not only innovative—but also secure, governed, and trustworthy. Be sure to register so you do not miss out. Start Your AI Security Journey AI security is evolving quickly, and it requires both architectural understanding and practical platform knowledge. Start by exploring how Microsoft Defender for Cloud supports AI security and governance in Azure, then join the Azure Decoded session to see how these principles come together in real‑world AI workloads.
Artificial Intelligence & Security
Understanding Artificial Intelligence Artificial intelligence (AI) is a computational system that perform human‑intelligence tasks, learning, reasoning, problem‑solving, perception, and language understanding by leveraging algorithmic and statistical methods to analyse data and make informed decisions. Artificial Intelligence (AI) can also be abbreviated as is the simulation of human intelligence through machines programmed to learn, reason, and act. It blends statistics, machine learning, and robotics to deliver following outcomes: Prediction: The application of statistical modelling and machine learning techniques to anticipate future outcomes, such as detecting fraudulent transactions. Automation: The utilisation of robotics and artificial intelligence to streamline and execute routine processes, exemplified by automated invoice processing. Augmentation: The enhancement of human decision-making and operational capabilities through AI-driven tools, for instance, AI-assisted sales enablement. Artificial Intelligence: Core Capabilities and Market Outlook Key capabilities of AI include: Data-driven decision-making: Analysing large datasets to generate actionable insights and optimise outcomes. Anomaly detection: Identifying irregular patterns or deviations in data for risk mitigation and quality assurance. Visual interpretation: Processing and understanding visual inputs such as images and videos for applications like computer vision. Natural language understanding: Comprehending and interpreting human language to enable accurate information extraction and contextual responses. Conversational engagement: Facilitating human-like interactions through chatbots, virtual assistants, and dialogue systems. With the exponential growth of data, ML learning models and computing power. AI is advancing much faster and as According to industry analyst reports breakthroughs in deep learning and neural network architectures have enabled highly sophisticated applications across diverse sectors, including healthcare, finance, manufacturing, and retail. The global AI market is on a trajectory of significant expansion, projected to increase nearly 5X by 2030, from $391 billion in 2025 to $1.81 trillion. This growth corresponds to a compound annual growth rate (CAGR) of 35.9% during the forecast period. These projections are estimates and subject to change as per rapid growth and advancement in the AI Era. AI and Cloud Synergy AI, and cloud computing form a powerful technological mixture. Digital assistants are offering scalable, cloud-powered intelligence. Cloud platforms such as Azure provide pre-trained models and services, enabling businesses to deploy AI solutions efficiently. Core AI Workloads Capabilities Machine Learning Machine learning (ML) underpins most AI systems by enabling models to learn from historical and real-time data to make predictions, classifications, and recommendations. These models adapt over time as they are exposed to new data, improving accuracy and robustness. Example use cases: Credit risk scoring in banking, demand forecasting in retail, and predictive maintenance in manufacturing. Anomaly Detection Anomaly detection techniques identify deviations from expected patterns in data, systems, or processes. This capability is critical for risk management and operational resilience, as it enables early detection of fraud, security breaches, or equipment failures. Example use cases: Fraud detection in financial transactions, network intrusion monitoring in cybersecurity, and quality control in industrial production. Natural Language Processing (NLP) NLP focuses on enabling machines to understand, interpret, and generate human language in both text and speech formats. This capability powers a wide range of applications that require contextual comprehension and semantic accuracy. Example use cases: Sentiment analysis for customer feedback, document summarisation for legal and compliance teams, and multilingual translation for global operations. Principles of Responsible AI To ensure ethical and trustworthy AI, organisations must embrace: Reliability & Safety Privacy & Security Inclusiveness Fairness Transparency Accountability These principles are embedded in frameworks like the Responsible-AI-Standard and reinforced by governance models such as Microsoft AI Governance Framework. Responsible AI Principles and Approach | Microsoft AI AI and Security AI introduces both opportunities and risks. A responsible approach to AI security involves three dimensions: Risk Mitigation: It Is addressing threats from immature or malicious AI applications. Security Applications: These are used to enhance AI security and public safety. Governance Systems: Establishing frameworks to manage AI risks and ensure safe development. Security Risks and Opportunities Due to AI Transformation AI’s transformative nature brings new challenges: Cybersecurity: This brings the opportunities and advancement to track, detect and act against Vulnerabilities in infrastructure and learning models. Data Security: This helps the tool and solutions such as Microsoft Purview to prevent data security by performing assessments, creating Data loss prevention policies applying sensitivity labels. Information Security: The biggest risk is securing the information and due to the AI era of transformation securing IS using various AI security frameworks. These concerns are echoed in The Crucial Role of Data Security Posture Management in the AI Era, which highlights insider threats, generative AI risks, and the need for robust data governance. AI in Security Applications AI’s capabilities in data analysis and decision-making enable innovative security solutions: Network Protection: applications include use of AI algorithms for intrusion detection, malware detection, security situational awareness, and threat early warning, etc. Data Management: applications refer to the use of AI technologies to achieve data protection objectives such as hierarchical classification, leak prevention, and leak traceability. Intelligent Security: applications refer to the use of AI technology to upgrade the security field from passive defence toward the intelligent direction, developing of active judgment and timely early warning. Financial Risk Control: applications use AI technology to improve the efficiency and accuracy of credit assessment, risk management, etc., and assisting governments in the regulation of financial transactions. AI Security Management Effective AI security requires: Regulations & Policies: Establish and safety management laws specifically designed to for governance by regulatory authorities and management policies for key application domains of AI and prominent security risks. Standards & Specifications: Industry-wide benchmarks, along with international and domestic standards can be used to support AI safety. Technological Methods: Early detection with Modern set of tools such as Defender for AI can be used to support to detect and mitigate and remediate AI threats. Security Assessments: Organization should use proper tools and platforms for evaluating AI risks and perform assessments regularly using automated tools approach Conclusion AI is transforming how organizations operate, innovate, and secure their environments. As AI capabilities evolve, integrating security and governance considerations from the outset remains critical. By combining responsible AI principles, effective governance, and appropriate security measures, organizations can work toward deploying AI technologies in a manner that supports both innovation and trust. Industry projections suggest continued growth in AI‑related security investments over the coming years, reflecting increased focus on managing AI risks alongside its benefits. These estimates are subject to change and should be interpreted in the context of evolving technologies and regulatory developments. Disclaimer References to Microsoft products and frameworks are for informational purposes only and do not imply endorsement, guarantee, or contractual commitment. Market projections referenced are based on publicly available industry analyses and are subject to change.Microsoft Ignite 2025: Top Security Innovations You Need to Know
🤖 Security & AI -The Big Story This Year 2025 marks a turning point for cybersecurity. Rapid adoption of AI across enterprises has unlocked innovation but introduced new risks. AI agents are now part of everyday workflows-automating tasks and interacting with sensitive data—creating new attack surfaces that traditional security models cannot fully address. Threat actors are leveraging AI to accelerate attacks, making speed and automation critical for defense. Organizations need solutions that deliver visibility, governance, and proactive risk management for both human and machine identities. Microsoft Ignite 2025 reflects this shift with announcements focused on securing AI at scale, extending Zero Trust principles to AI agents, and embedding intelligent automation into security operations. As a Senior Cybersecurity Solution Architect, I’ve curated the top security announcements from Microsoft Ignite 2025 to help you stay ahead of evolving threats and understand the latest innovations in enterprise security. Agent 365: Control Plane for AI Agents Agent 365 is a centralized platform that gives organizations full visibility, governance, and risk management over AI agents across Microsoft and third-party ecosystems. Why it matters: Unmanaged AI agents can introduce compliance gaps and security risks. Agent 365 ensures full lifecycle control. Key Features: Complete agent registry and discovery Access control and conditional policies Visualization of agent interactions and risk posture Built-in integration with Defender, Entra, and Purview Available via the Frontier Program Microsoft Agent 365: The control plane for AI agents Deep dive blog on Agent 365 Entra Agent ID: Zero Trust for AI Identities Microsoft Entra is the identity and access management suite (covering Azure AD, permissions, and secure access). Entra Agent ID extends Zero Trust identity principles to AI agents, ensuring they are governed like human identities. Why it matters: Unmanaged or over-privileged AI agents can create major security gaps. Agent ID enforces identity governance on AI agents and reduces automation risks. Key Features: Provides unique identities for AI agents Lifecycle governance and sponsorship for agents Conditional access policies applied to agent activity Integrated with open SDKs/APIs for third‑party platforms Microsoft Entra Agent ID Overview Entra Ignite 2025 announcements Public Preview details Security Copilot Expansion Security Copilot is Microsoft’s AI assistant for security teams, now expanded to automate threat hunting, phishing triage, identity risk remediation, and compliance tasks. Why it matters: Security teams face alert fatigue and resource constraints. Copilot accelerates response and reduces manual effort. Key Features: 12 new Microsoft-built agents across Defender, Entra, Intune, and Purview. 30+ partner-built agents available in the Microsoft Security Store. Automates threat hunting, phishing triage, identity risk remediation, and compliance tasks. Included for Microsoft 365 E5 customers at no extra cost. Security Copilot inclusion in Microsoft 365 E5 Security Copilot Ignite blog Security Dashboard for AI A unified dashboard for CISOs and risk leaders to monitor AI risks, aggregate signals from Microsoft security services, and assign tasks via Security Copilot - included at no extra cost. Why it matters: Provides a single pane of glass for AI risk management, improving visibility and decision-making. Key Features: Aggregates signals from Entra, Defender, and Purview Supports natural language queries for risk insights Enables task assignment via Security Copilot Ignite Session: Securing AI at Scale Microsoft Security Blog Microsoft Defender Innovations Microsoft Defender serves as Microsoft’s CNAPP solution, offering comprehensive, AI-driven threat protection that spans endpoints, email, cloud workloads, and SIEM/SOAR integrations. Why It Matters Modern attacks target multi-cloud environments and software supply chains. These innovations provide proactive defense, reduce breach risks before exploitation, and extend protection beyond Microsoft ecosystems-helping organizations secure endpoints, identities, and workloads at scale. Key Features: Predictive Shielding: Proactively hardens attack paths before adversaries pivot. Automatic Attack Disruption: Extended to AWS, Okta, and Proofpoint via Sentinel. Supply Chain Security: Defender for Cloud now integrates with GitHub Advanced Security. What’s new in Microsoft Defender at Ignite Defender for Cloud innovations Global Secure Access & AI Gateway Part of Microsoft Entra’s secure access portfolio, providing secure connectivity and inspection for web and AI traffic. Why it matters: Protects against lateral movement and AI-specific threats while maintaining secure connectivity. Key Features: TLS inspection, URL/file filtering AI Prompt Injection protection Private access for domain controllers to prevent lateral movement attacks. Learn about Secure Web and AI Gateway for agents Microsoft Entra: What’s new in secure access on the AI frontier Purview Enhancements Microsoft Purview is the data governance and compliance platform, ensuring sensitive data is classified, protected, and monitored. Why it matters: Ensures sensitive data remains protected and compliant in AI-driven environments. Key Features: AI Observability: Monitor agent activities and prevent sensitive data leakage. Compliance Guardrails: Communication compliance for AI interactions. Expanded DSPM: Data Security Posture Management for AI workloads. Announcing new Microsoft Purview capabilities to protect GenAI agents Intune Updates Microsoft Intune is a cloud-based endpoint device management solution that secures apps, devices, and data across platforms. It simplifies endpoint security management and accelerates response to device risks using AI. Why it matters: Endpoint security is critical as organizations manage diverse devices in hybrid environments. These updates reduce complexity, speed up remediation, and leverage AI-driven automation-helping security teams stay ahead of evolving threats. Key Features: Security Copilot agents automate policy reviews, device offboarding, and risk-based remediation. Enhanced remote management for Windows Recovery Environment (WinRE). Policy Configuration Agent in Intune lets IT admins create and validate policies with natural language What’s new in Microsoft Intune at Ignite Your guide to Intune at Ignite Closing Thoughts Microsoft Ignite 2025 signals the start of an AI-driven security era. From visibility and governance for AI agents to Zero Trust for machine identities, automation in security operations, and stronger compliance for AI workloads-these innovations empower organizations to anticipate threats, simplify governance, and accelerate secure AI adoption without compromising compliance or control. 📘 Full Coverage: Microsoft Ignite 2025 Book of News
Understand New Sentinel Pricing Model with Sentinel Data Lake Tier
Introduction on Sentinel and its New Pricing Model Microsoft Sentinel is a cloud-native Security Information and Event Management (SIEM) and Security Orchestration, Automation, and Response (SOAR) platform that collects, analyzes, and correlates security data from across your environment to detect threats and automate response. Traditionally, Sentinel stored all ingested data in the Analytics tier (Log Analytics workspace), which is powerful but expensive for high-volume logs. To reduce cost and enable customers to retain all security data without compromise, Microsoft introduced a new dual-tier pricing model consisting of the Analytics tier and the Data Lake tier. The Analytics tier continues to support fast, real-time querying and analytics for core security scenarios, while the new Data Lake tier provides very low-cost storage for long-term retention and high-volume datasets. Customers can now choose where each data type lands—analytics for high-value detections and investigations, and data lake for large or archival types—allowing organizations to significantly lower cost while still retaining all their security data for analytics, compliance, and hunting. Please flow diagram depicts new sentinel pricing model: Now let's understand this new pricing model with below scenarios: Scenario 1A (PAY GO) Scenario 1B (Usage Commitment) Scenario 2 (Data Lake Tier Only) Scenario 1A (PAY GO) Requirement Suppose you need to ingest 10 GB of data per day, and you must retain that data for 2 years. However, you will only frequently use, query, and analyze the data for the first 6 months. Solution To optimize cost, you can ingest the data into the Analytics tier and retain it there for the first 6 months, where active querying and investigation happen. After that period, the remaining 18 months of retention can be shifted to the Data Lake tier, which provides low-cost storage for compliance and auditing needs. But you will be charged separately for data lake tier querying and analytics which depicted as Compute (D) in pricing flow diagram. Pricing Flow / Notes The first 10 GB/day ingested into the Analytics tier is free for 31 days under the Analytics logs plan. All data ingested into the Analytics tier is automatically mirrored to the Data Lake tier at no additional ingestion or retention cost. For the first 6 months, you pay only for Analytics tier ingestion and retention, excluding any free capacity. For the next 18 months, you pay only for Data Lake tier retention, which is significantly cheaper. Azure Pricing Calculator Equivalent Assuming no data is queried or analyzed during the 18-month Data Lake tier retention period: Although the Analytics tier retention is set to 6 months, the first 3 months of retention fall under the free retention limit, so retention charges apply only for the remaining 3 months of the analytics retention window. Azure pricing calculator will adjust accordingly. Scenario 1B (Usage Commitment) Now, suppose you are ingesting 100 GB per day. If you follow the same pay-as-you-go pricing model described above, your estimated cost would be approximately $15,204 per month. However, you can reduce this cost by choosing a Commitment Tier, where Analytics tier ingestion is billed at a discounted rate. Note that the discount applies only to Analytics tier ingestion—it does not apply to Analytics tier retention costs or to any Data Lake tier–related charges. Please refer to the pricing flow and the equivalent pricing calculator results shown below. Monthly cost savings: $15,204 – $11,184 = $4,020 per month Now the question is: What happens if your usage reaches 150 GB per day? Will the additional 50 GB be billed at the Pay-As-You-Go rate? No. The entire 150 GB/day will still be billed at the discounted rate associated with the 100 GB/day commitment tier bucket. Azure Pricing Calculator Equivalent (100 GB/ Day) Azure Pricing Calculator Equivalent (150 GB/ Day) Scenario 2 (Data Lake Tier Only) Requirement Suppose you need to store certain audit or compliance logs amounting to 10 GB per day. These logs are not used for querying, analytics, or investigations on a regular basis, but must be retained for 2 years as per your organization’s compliance or forensic policies. Solution Since these logs are not actively analyzed, you should avoid ingesting them into the Analytics tier, which is more expensive and optimized for active querying. Instead, send them directly to the Data Lake tier, where they can be retained cost-effectively for future audit, compliance, or forensic needs. Pricing Flow Because the data is ingested directly into the Data Lake tier, you pay both ingestion and retention costs there for the entire 2-year period. If, at any point in the future, you need to perform advanced analytics, querying, or search, you will incur additional compute charges, based on actual usage. Even with occasional compute charges, the cost remains significantly lower than storing the same data in the Analytics tier. Realized Savings Scenario Cost per Month Scenario 1: 10 GB/day in Analytics tier $1,520.40 Scenario 2: 10 GB/day directly into Data Lake tier $202.20 (without compute) $257.20 (with sample compute price) Savings with no compute activity: $1,520.40 – $202.20 = $1,318.20 per month Savings with some compute activity (sample value): $1,520.40 – $257.20 = $1,263.20 per month Azure calculator equivalent without compute Azure calculator equivalent with Sample Compute Conclusion The combination of the Analytics tier and the Data Lake tier in Microsoft Sentinel enables organizations to optimize cost based on how their security data is used. High-value logs that require frequent querying, real-time analytics, and investigation can be stored in the Analytics tier, which provides powerful search performance and built-in detection capabilities. At the same time, large-volume or infrequently accessed logs—such as audit, compliance, or long-term retention data—can be directed to the Data Lake tier, which offers dramatically lower storage and ingestion costs. Because all Analytics tier data is automatically mirrored to the Data Lake tier at no extra cost, customers can use the Analytics tier only for the period they actively query data, and rely on the Data Lake tier for the remaining retention. This tiered model allows different scenarios—active investigation, archival storage, compliance retention, or large-scale telemetry ingestion—to be handled at the most cost-effective layer, ultimately delivering substantial savings without sacrificing visibility, retention, or future analytical capabilities.Welcome to the Microsoft Security Community!
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