Welcome to the Microsoft Security Community!
We have moved! Registering for webinars is now easier than ever—you can add any session directly to your calendar with a single click using the link below. Please visit: https://securitycommunity.microsoft.com/VirtualEvents/ to sign up for future webinars!What’s new in Microsoft Defender XDR at Secure 2025
Protecting your organization against cybersecurity threats is more challenging than ever before. As part of our 2025 Microsoft Secure cybersecurity conference announcements, we’re sharing new product features that spotlight our AI-first, end-to-end security innovations designed to help - including autonomous AI agents in the Security Operations Center (SOC), as well as automatic detection and response capabilities. We also share information on how you can expand your protection by bringing data security and collaboration tools closer to the SOC. Read on to learn more about how these capabilities can help your organization stay ahead of today’s advanced threat actors. Expanding AI-Driven Capabilities for Smarter SOC Operations Introducing Microsoft Security Copilot’s Security Alert Triage Agent (previously named Phishing Triage Agent) Today, we are excited to introduce Security Copilot agents, a major step in bringing AI-driven automation to Microsoft Security solutions. As part of this, we’re unveiling our newest innovation in Microsoft Defender: the Security Alert Triage Agent. Acting as a force multiplier for SOC analysts, it streamlines the triage of user-submitted phishing incidents by autonomously identifying and resolving false positives, typically cleaning out over 95% of submissions. This allows teams to focus on the remaining incidents – those that pose the most critical threats. Phishing submissions are among the highest-volume alerts that security teams handle daily, and our data shows that at least 9 in 10 reported emails turn out to be harmless bulk mail or spam. As a result, security teams must sift through hundreds of these incidents weekly, often spending up to 30 minutes per case determining whether it represents a real threat. This manual triage effort not only adds operational strain but also delays the response to actual phishing attacks, potentially impacting protection levels. The Security Alert Triage Agent transforms this process by leveraging advanced LLM-driven analysis to conduct sophisticated assessments –such as examining the semantic content of emails– to autonomously determine whether an incident is a genuine phishing attempt or a false alarm. By intelligently cutting through the noise, the agent alleviates the burden on SOC teams, allowing them to focus on high-priority threats. Figure 1. A phishing incident triaged by the Security Copilot Security Alert Triage Agent To help analysts gain trust in its decision-making, the agent provides natural language explanations for its classifications, along with a visual representation of its reasoning process. This transparency enables security teams to understand why an incident was classified in a certain way, making it easier to validate verdicts. Analysts can also provide feedback in plain language, allowing the agent to learn from these interactions, refine its accuracy, and adapt to the organization’s unique threat landscape. Over time, this continuous feedback loop fine-tunes the agent’s behavior, aligning it more closely with organizational nuances and reducing the need for manual verification. The Security Copilot Security Alert Triage Agent is designed to transform SOC operations with autonomous, AI-driven capabilities. As phishing threats grow increasingly sophisticated and SOC analysts face mounting demands, this agent alleviates the burden of repetitive tasks, allowing teams to shift their focus to proactive security measures that strengthen the organization’s overall defense. Note: The Phishing Triage Agent has since been expanded and is now called the Security Alert Triage Agent. Learn more at aka.ms/SATA Security Copilot Enriched Incident Summaries and Suggested Prompts Security Copilot Incident Summaries in Microsoft Defender now feature key enrichments, including related threat intelligence and asset risk –enhancements driven by customer feedback. Additionally, we are introducing suggested prompts following incident summaries, giving analysts quick access to common follow-up questions for deeper context on devices, users, threat intelligence, and more. This marks a step towards a more interactive experience, moving beyond predefined inputs to a more dynamic, conversational workflow. Read more about Microsoft Security Copilot agent announcements here. New protection across Microsoft Defender XDR workloads To strengthen core protection across Microsoft Defender XDR workloads, we're introducing new capabilities while building upon existing integrations for enhanced protection. This ensures a more comprehensive and seamless defense against evolving threats. Introducing collaboration security for Microsoft Teams Email remains a prevalent entry point for attackers. But the fast adoption of collaboration tools like Microsoft Teams has opened new attack surfaces for cybercriminals. Our advancements within Defender for Office 365 allow organizations to continue to protect users in Microsoft Teams against phishing and other emerging cyberthreats with inline protection against malicious URLs, safe attachments, brand impersonation protection, and more. And to ensure seamless investigation and response at the incident level, everything is centralized across our SOC workflows in the unified security operations platform. Read the announcement here. Introducing Microsoft Purview Data Security Investigations for the SOC Understanding the extent of the data that has been impacted to better prioritize incidents has been a challenge for security teams. As data remains the main target for attackers it’s critical to dismantle silos between security and data security teams to enhance response times. At Microsoft, we’ve made significant investments in bringing SOC and data security teams closer together by integrating Microsoft Defender XDR and Microsoft Purview. We are continuing to build upon the rich set of capabilities and today, we are excited to announce that Microsoft Purview Data Security Investigations (DSI) can be initiated from the incident graph in Defender XDR. Ensuring robust data security within the SOC has always been important, as it helps protect sensitive information from breaches and unauthorized access. Data Security Investigations significantly accelerates the process of analyzing incident related data such as emails, files, and messages. With AI-powered deep content analysis, DSI reveals the key security and sensitive data risks. This integration allows analysts to further analyze the data involved in the incident, learn which data is at risk of compromise, and take action to respond and mitigate the incident faster, to keep the organization’s data protected. Read the announcement here. Figure 2. An incident that shows the ability to launch a data security investigation. OAuth app insights are now available in Exposure Management In recent years, we’ve witnessed a substantial surge in attackers exploiting OAuth applications to gain access to critical data in business applications like Microsoft Teams, SharePoint, and Outlook. To address this threat, Microsoft Defender for Cloud Apps is now integrating OAuth apps and their connections into Microsoft Security Exposure Management, enhancing both attack path and attack surface map experiences. Additionally, we are introducing a unified application inventory to consolidate all app interactions into a single location. This will address the following use cases: Visualize and remediate attack paths that attackers could potentially exploit using high-privilege OAuth apps to access M365 SaaS applications or sensitive Azure resources. Investigate OAuth applications and their connections to the broader ecosystem in Attack Surface Map and Advanced Hunting. Explore OAuth application characteristics and actionable insights to reduce risk from our new unified application inventory. Figure 3. An attack path infused with OAuth app insights Read the latest announcement here AI & TI are critical for effective detection & response To effectively combat emerging threats, AI has become critical in enabling faster detection and response. By combining this with the latest threat analytics, security teams can quickly pinpoint emerging risks and respond in real-time, providing organizations with proactive protection against sophisticated attacks. Disrupt more attacks with automatic attack disruption In this era of multi-stage, multi-domain attacks, the SOC need solutions that enable both speed and scale when responding to threats. That’s where automatic attack disruption comes in—a self-defense capability that dynamically pivots to anticipate and block an attacker’s next move using multi-domain signals, the latest TI, and AI models. We’ve made significant advancements in attack disruption, such as threat intelligence-based disruption announced at Ignite, expansion to OAuth apps, and more. Today, we are thrilled to share our next innovation in attack disruption—the ability to disrupt more attacks through a self-learning architecture that enables much earlier and much broader disruption. At its core, this technology monitors a vast array of signals, ranging from raw telemetry data to alerts and incidents across Extended Detection and Response (XDR) and Security Information and Event Management (SIEM) systems. This extensive range of data sources provides an unparalleled view of your security environment, helping to ensure potential threats do not go unnoticed. What sets this innovation apart is its ability learn from historical events and previously seen attack types to identify and disrupt new attacks. By recognizing similar patterns across data and stitching them together into a contextual sequence, it processes information through machine learning models and enables disruption to stop the attack much earlier in the attack sequence, stopping significantly more attacks in volume and variety. Comprehensive Threat Analytics are now available across all Threat Intelligence reports Organizations can now leverage the full suite of Threat Analytics features (related incidents, impacted assets, endpoints exposure, recommended actions) on all Microsoft Threat Intelligence reports. Previously only available for a limited set of threats, these features are now available for all threats Microsoft has published in Microsoft Defender Threat Intelligence (MDTI), offering comprehensive insights and actionable intelligence to help you ensure your security measures are robust and responsive. Some of these key features include: IOCs with historical hunting: Access IOCs after expiration to investigate past threats and aid in remediation and proactive hunting. MITRE TTPs: Build detections based on threat techniques, going beyond IOCs to block and alert on specific tactics. Targeted Industries: Filter threats by industry, aligning security efforts with sector-specific challenges. We’re proud of our new AI-first innovations that strengthen security protections for our customers and help us further our pledge to customers and our community to prioritize cyber safety above all else. Learn more about the innovations designed to help your organization protect data, defend against cyber threats, and stay compliant. Join Microsoft leaders online at Microsoft Secure on April 9. We hope you’ll also join us in San Francisco from April 27th-May 1 st 2025 at the RSA Conference 2025 to learn more. At the conference, we’ll share live, hands-on demos and theatre sessions all week at the Microsoft booth at Moscone Center. Secure your spot today.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-services
MDO query of EmailEvents is not accepted in the flow which is why causing the badgateway error
When used the following MDO query of EmailEvents it is working in the Defender control panel but when applied through 'Advanced Hunting' action in Power automate application given bad gateway error. Is this query supported in this application?
Defender MDO permissions broken (again)
Defender wasn't letting me approve pending AIR remediation options, something I do every day, with my usual custom RBAC role checked out. Nor could I move or delete emails. I also had Security Operator checked out. I checked out Security Admin and tried again, no dice. It wasn't until I checked out Global Admin until I got the permissions I needed.Authorization and Governance for AI Agents: Runtime Authorization Beyond Identity at Scale
Designing Authorization‑Aware AI Agents at Scale Enforcing Runtime RBAC + ABAC with Approval Injection (JIT) Microsoft Entra Agent Identity enables organizations to govern and manage AI agent identities in Copilot Studio, improving visibility and identity-level control. However, as enterprises deploy multiple autonomous AI agents, identity and OAuth permissions alone cannot answer a more critical question: “Should this action be executed now, by this agent, for this user, under the current business and regulatory context?” This post introduces a reusable Authorization Fabric—combining a Policy Enforcement Point (PEP) and Policy Decision Point (PDP)—implemented as a Microsoft Entra‑protected endpoint using Azure Functions/App Service authentication. Every AI agent (Copilot Studio or AI Foundry/Semantic Kernel) calls this fabric before tool execution, receiving a deterministic runtime decision: ALLOW / DENY / REQUIRE_APPROVAL / MASK Who this is for Anyone building AI agents (Copilot Studio, AI Foundry/Semantic Kernel) that call tools, workflows, or APIs Organizations scaling to multiple agents and needing consistent runtime controls Teams operating in regulated or security‑sensitive environments, where decisions must be deterministic and auditable Why a V2? Identity is necessary—runtime authorization is missing Entra Agent Identity (preview) integrates Copilot Studio agents with Microsoft Entra so that newly created agents automatically get an Entra agent identity, manageable in the Entra admin center, and identity activity is logged in Entra. That solves who the agent is and improves identity governance visibility. But multi-agent deployments introduce a new risk class: Autonomous execution sprawl — many agents, operating with delegated privileges, invoking the same backends independently. OAuth and API permissions answer “can the agent call this API?” They do not answer “should the agent execute this action under business policy, compliance constraints, data boundaries, and approval thresholds?” This is where a runtime authorization decision plane becomes essential. The pattern: Microsoft Entra‑Protected Authorization Fabric (PEP + PDP) Instead of embedding RBAC logic independently inside every agent, use a shared fabric: PEP (Policy Enforcement Point): Gatekeeper invoked before any tool/action PDP (Policy Decision Point): Evaluates RBAC + ABAC + approval policies Decision output: ALLOW / DENY / REQUIRE_APPROVAL / MASK This Authorization Fabric functions as a shared enterprise control plane, decoupling authorization logic from individual agents and enforcing policies consistently across all autonomous execution paths. Architecture (POC reference architecture) Use a single runtime decision plane that sits between agents and tools. What’s important here Every agent (Copilot Studio or AI Foundry/SK) calls the Authorization Fabric API first The fabric is a protected endpoint (Microsoft Entra‑protected endpoint required) Tools (Graph/ERP/CRM/custom APIs) are invoked only after an ALLOW decision (or approval) Trust boundaries enforced by this architecture Agents never call business tools directly without a prior authorization decision The Authorization Fabric validates caller identity via Microsoft Entra Authorization decisions are centralized, consistent, and auditable Approval workflows act as a runtime “break-glass” control for high-impact actions This ensures identity, intent, and execution are independently enforced, rather than implicitly trusted. Runtime flow (Decision → Approval → Execution) Here is the runtime sequence as a simple flow (you can keep your Mermaid diagram too). ```mermaid flowchart TD START(["START"]) --> S1["[1] User Request"] S1 --> S2["[2] Agent Extracts Intent\n(action, resource, attributes)"] S2 --> S3["[3] Call /authorize\n(Entra protected)"] S3 --> S4 subgraph S4["[4] PDP Evaluation"] ABAC["ABAC: Tenant · Region · Data Sensitivity"] RBAC["RBAC: Entitlement Check"] Threshold["Approval Threshold"] ABAC --> RBAC --> Threshold end S4 --> Decision{"[5] Decision?"} Decision -->|"ALLOW"| Exec["Execute Tool / API"] Decision -->|"MASK"| Masked["Execute with Masked Data"] Decision -->|"DENY"| Block["Block Request"] Decision -->|"REQUIRE_APPROVAL"| Approve{"[6] Approval Flow"} Approve -->|"Approved"| Exec Approve -->|"Rejected"| Block Exec --> Audit["[7] Audit & Telemetry"] Masked --> Audit Block --> Audit Audit --> ENDNODE(["END"]) style START fill:#4A90D9,stroke:#333,color:#fff style ENDNODE fill:#4A90D9,stroke:#333,color:#fff style S1 fill:#5B5FC7,stroke:#333,color:#fff style S2 fill:#5B5FC7,stroke:#333,color:#fff style S3 fill:#E8A838,stroke:#333,color:#fff style S4 fill:#FFF3E0,stroke:#E8A838,stroke-width:2px style ABAC fill:#FCE4B2,stroke:#999 style RBAC fill:#FCE4B2,stroke:#999 style Threshold fill:#FCE4B2,stroke:#999 style Decision fill:#fff,stroke:#333 style Exec fill:#2ECC71,stroke:#333,color:#fff style Masked fill:#27AE60,stroke:#333,color:#fff style Block fill:#C0392B,stroke:#333,color:#fff style Approve fill:#F39C12,stroke:#333,color:#fff style Audit fill:#3498DB,stroke:#333,color:#fff ``` Design principle: No tool execution occurs until the Authorization Fabric returns ALLOW or REQUIRE_APPROVAL is satisfied via an approval workflow. Where Power Automate fits (important for readers) In most Copilot Studio implementations, Agents calls Power Automate (agent flows), is the practical integration layer that calls enterprise services and APIs. Copilot Studio supports “agent flows” as a way to extend agent capabilities with low-code workflows. For this pattern, Power Automate typically: acquires/uses the right identity context for the call (depending on your tenant setup), and calls the /authorize endpoint of the Authorization Fabric, returns the decision payload to the agent for branching. Copilot Studio also supports calling REST endpoints directly using the HTTP Request node, including passing headers such as Authorization: Bearer <token>. Protected endpoint only: Securing the Authorization Fabric with Microsoft Entra For this V2 pattern, the Authorization Fabric must be protected using Microsoft Entra‑protected endpoint on Azure Functions/App Service (built‑in auth). Microsoft Learn provides the configuration guidance for enabling Microsoft Entra as the authentication provider for Azure App Service / Azure Functions. Step 1 — Create the Authorization Fabric API (Azure Function) Expose an authorization endpoint: HTTP Step 2 — Enable Microsoft Entra‑protected endpoint on the Function App In Azure Portal: Function App → Authentication Add identity provider → Microsoft Choose Workforce configuration (enterprise tenant) Set Require authentication for all requests This ensures the Authorization Fabric is not callable without a valid Entra token. Step 3 — Optional hardening (recommended) Depending on enterprise posture, layer: IP restrictions / Private endpoints APIM in front of the Function for rate limiting, request normalization, centralized logging (For a POC, keep it minimal—add hardening incrementally.) Externalizing policy (so governance scales) To make this pattern reusable across multiple agents, policies should not be hardcoded inside each agent. Instead, store policy definitions in a central policy store such as Cosmos DB (or equivalent configuration store), and have the PDP load/evaluate policies at runtime. Why this matters: Policy changes apply across all agents instantly (no agent republish) Central governance + versioning + rollback becomes possible Audit and reporting become consistent across environments (For the POC, a single JSON document per policy pack in Cosmos DB is sufficient. For production, add versioning and staged rollout.) Store one PolicyPack JSON document per environment (dev/test/prod). Include version, effectiveFrom, priority for safe rollout/rollback. Minimal decision contract (standard request / response) To keep the fabric reusable across agents, standardize the request payload. Request payload (example) Decision response (deterministic) Example scenario (1 minute to understand) Scenario: A user asks a Finance agent to create a Purchase Order for 70,000. Even if the user has API permission and the agent can technically call the ERP API, runtime policy should return: REQUIRE_APPROVAL (threshold exceeded) trigger an approval workflow execute only after approval is granted This is the difference between API access and authorized business execution. Sample Policy Model (RBAC + ABAC + Approval) This POC policy model intentionally stays simple while demonstrating both coarse and fine-grained governance. 1) Coarse‑grained RBAC (roles → actions) FinanceAnalyst CreatePO up to 50,000 ViewVendor FinanceManager CreatePO up to 100,000 and/or approve higher spend 2) Fine‑grained ABAC (conditions at runtime) ABAC evaluates context such as region, classification, tenant boundary, and risk: 3) Approval injection (Agent‑level JIT execution) For higher-risk/high-impact actions, the fabric returns REQUIRE_APPROVAL rather than hard deny (when appropriate): How policies should be evaluated (deterministic order) To ensure predictable and auditable behavior, evaluate in a deterministic order: Tenant isolation & residency (ABAC hard deny first) Classification rules (deny or mask) RBAC entitlement validation Threshold/risk evaluation Approval injection (JIT step-up) This prevents approval workflows from bypassing foundational security boundaries such as tenant isolation or data sovereignty. Copilot Studio integration (enforcing runtime authorization) Copilot Studio can call external REST APIs using the HTTP Request node, including passing headers such as Authorization: Bearer <token> and binding response schema for branching logic. Copilot Studio also supports using flows with agents (“agent flows”) to extend capabilities and orchestrate actions. Option A (Recommended): Copilot Studio → Agent Flow (Power Automate) → Authorization Fabric Why: Flows are a practical place to handle token acquisition patterns, approval orchestration, and standardized logging. Topic flow: Extract user intent + parameters Call an agent flow that: calls /authorize returns decision payload Branch in the topic: If ALLOW → proceed to tool call If REQUIRE_APPROVAL → trigger approval flow; proceed only if approved If DENY → stop and explain policy reason Important: Tool execution must never be reachable through an alternate topic path that bypasses the authorization check. Option B: Direct HTTP Request node to Authorization Fabric Use the Send HTTP request node to call the authorization endpoint and branch using the response schema. This approach is clean, but token acquisition and secure secretless authentication are often simpler when handled via a managed integration layer (flow + connector). AI Foundry / Semantic Kernel integration (tool invocation gate) For Foundry/SK agents, the integration point is before tool execution. Semantic Kernel supports Azure AI agent patterns and tool integration, making it a natural place to enforce a pre-tool authorization check. Pseudo-pattern: Agent extracts intent + context Calls Authorization Fabric Enforces decision Executes tool only when allowed (or after approval) Telemetry & audit (what Security Architects will ask for) Even the best policy engine is incomplete without audit trails. At minimum, log: agentId, userUPN, action, resource decision + reason + policyIds approval outcome (if any) correlationId for downstream tool execution Why it matters: you now have a defensible answer to: “Why did an autonomous agent execute this action?” Security signal bonus: Denials, unusual approval rates, and repeated policy mismatches can also indicate prompt injection attempts, mis-scoped agents, or governance drift. What this enables (and why it scales) With a shared Authorization Fabric: Avoid duplicating authorization logic across agents Standardize decisions across Copilot Studio + Foundry agents Update governance once (policy change) and apply everywhere Make autonomy safer without blocking productivity Closing: Identity gets you who. Runtime authorization gets you whether/when/how. Copilot Studio can automatically create Entra agent identities (preview), improving identity governance and visibility for agents. But safe autonomy requires a runtime decision plane. Securing that plane as an Entra-protected endpoint is foundational for enterprise deployments. In enterprise environments, autonomous execution without runtime authorization is equivalent to privileged access without PIM—powerful, fast, and operationally risky.Announcing 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 billing
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.
