Planning your move to Microsoft Defender portal for all Microsoft Sentinel customers
In November 2023, Microsoft announced our strategy to unify security operations by bringing the best of XDR and SIEM together. Our first step was bringing Microsoft Sentinel into the Microsoft Defender portal, giving teams a single, comprehensive view of incidents, reducing queue management, enriching threat intel, streamlining response and enabling SOC teams to take advantage of Gen AI in their day-to-day workflow. Since then, considerable progress has been made with thousands of customers using this new unified experience; to enhance the value customers gain when using Sentinel in the Defender portal, multi-tenancy and multi-workspace support was added to help customers with more sophisticated deployments. Our mission is to unify security operations by bringing all your data, workflows, and people together to unlock new capabilities and drive better security outcomes. As a strong example of this, last year we added extended posture management, delivering powerful posture insights to the SOC team. This integration helps build a closed-loop feedback system between your pre- and post-breach efforts. Exposure Management is just one example. By bringing everything together, we can take full advantage of AI and automation to shift from a reactive to predictive SOC that anticipates threats and proactively takes action to defend against them. Beyond Exposure Management, Microsoft has been constantly innovating in the Defender experience, adding not just SIEM but also Security Copilot. The Sentinel experience within the Defender portal is the focus of our innovation energy and where we will continue to add advanced Sentinel capabilities going forward. Onboarding to the new unified experience is easy and doesn’t require a typical migration. Just a few clicks and permissions. Customers can continue to use Sentinel in the Azure portal while it is available even after choosing to transition. Today, we’re announcing that we are moving to the next phase of the transition with a target to retire the Azure portal for Microsoft Sentinel by July 1, 2026. Customers not yet using the Defender portal should plan their transition accordingly. “Really amazing to see that coming, because cross querying with tables in one UI is really cool! Amazing, big step forward to the unified [Defender] portal.” Glueckkanja AG “The biggest benefit of a unified security operations solution (Microsoft Sentinel + Microsoft Defender XDR) has been the ability to combine data in Defender XDR with logs from third party security tools. Another advantage developed has been to eliminate the need to switch between Defender XDR and Microsoft Sentinel portals, now having a single pane of glass, which the team has been wanting for some years.” Robel Kidane, Group Information Security Manager, Renishaw PLC Delivering the SOC of the future Unifying threat protection, exposure management and security analytics capabilities in one pane of glass not only streamlines the user experience, but also enables Sentinel customers to realize security outcomes more efficiently: Analyst efficiency: A single portal reduces context switching, simplifies workflows, reduces training overhead, and improves team agility. Integrated insights: SOC-focused case management, threat intelligence, incident correlation, advanced hunting, exposure management, and a prioritized incident queue enriched with business and sensitivity context—enabling faster, more informed detection and response across all products. SOC optimization: Security controls that can be adjusted as threats and business priorities change to control costs and provide better coverage and utilization of data, thus maximizing ROI from the SIEM. Accelerated response: AI-driven detection and response which reduces mean time to respond (MTTR) by 30%, increases security response efficiency by 60%, and enables embedded Gen AI and agentic workflows. What’s next: Preparing for the retirement of the Sentinel Experience in the Azure Portal Microsoft is committed to supporting every single customer in making that transition over the next 12 months. Beginning July 1, 2026, Sentinel users will be automatically redirected to the Defender portal. After helping thousands of customers smoothly make the transition, we recommend that security teams begin planning their migration and change management now to ensure continuity and avoid disruption. While the technical process is very straightforward, we have found that early preparation allows time for workflow validation, training, and process alignment to take full advantage of the new capabilities and experience. Tips for a Successful Migration to Microsoft Defender 1. Leverage Microsoft’s help: Leverage Microsoft documentation, instructional videos, guidance, and in-product support to help you be successful. A good starting point is the documentation on Microsoft Learn. 2. Plan early: Engage stakeholders early including SOC and IT Security leads, MSSPs, and compliance teams to align on timing, training and organizational needs. Make sure you have an actionable timeline and agreement in the organization around when you can prioritize this transition to ensure access to the full potential of the new experience. 3. Prepare your environment: Plan and design your environment thoroughly. This includes understanding the prerequisites for onboarding Microsoft Sentinel workspaces, reviewing and deciding on access controls, and planning the architecture of your tenant and workspace. Proper planning will ensure a smooth transition and help avoid any disruptions to your security operations. 4. Leverage Advanced Threat Detection: The Defender portal offers enhanced threat detection capabilities with advanced AI and machine learning for Microsoft Sentinel. Make sure to leverage these features for faster and more accurate threat detection and response. This will help you identify and address critical threats promptly, improving your overall security posture. 5. Utilize Unified Hunting and Incident Management: Take advantage of the enhanced hunting, incident, and investigation capabilities in Microsoft Defender. This provides a comprehensive view for more efficient threat detection and response. By consolidating all security incidents, alerts, and investigations into a single unified interface, you can streamline your operations and improve efficiency. 6. Optimize Cost and Data Management The Defender portal offers cost and data optimization features, such as SOC Optimization and Summary Rules. Make sure to utilize these features to optimize your data management, reduce costs, and increase coverage and SIEM ROI. This will help you manage your security operations more effectively and efficiently. Unleash the full potential of your Security team The unified SecOps experience available in the Defender portal is designed to support the evolving needs of modern SOCs. The Defender portal is not just a new home for Microsoft Sentinel - it’s a foundation for integrated, AI-driven security operations. We’re committed to helping you make this transition smoothly and confidently. If you haven’t already joined the thousands of security organizations that have done so, now is the time to begin. Resources AI-Powered Security Operations Platform | Microsoft Security Microsoft Sentinel in the Microsoft Defender portal | Microsoft Learn Shifting your Microsoft Sentinel Environment to the Defender Portal | Microsoft Learn Microsoft Sentinel is now in Defender | YouTubeMigrate Sentinel to Defender - Why It Is a Security Architecture Decision, Not Just a Portal Change
Microsoft will retire the Sentinel experience in Azure on March 31, 2027. Most of the conversation around this transition focuses on cost optimization and portal consolidation. That framing undersells what is actually happening. The unified Defender portal is not a new interface for the same capabilities. It is the platform foundation for a fundamentally different SOC operating model — one built on a 2-tier data architecture, graph-based investigation, and AI agents that can hunt, enrich, and respond at machine speed. Partners who understand this will help customers build security programs that match how attackers actually operate. This document covers four things: What the unified experience delivers — the security capabilities that do not exist in standalone Sentinel and why they matter against today’s threats. What the transition really involves - is not data migration, but it is a data architecture project that changes how telemetry flows, where it lives, and who queries it. Where the partner opportunity lives — a structured progression from professional services (transactional, transition execution, and advisory) to ongoing managed security services. Why does the unified experience win competitively — factual capability advantages that give partners a defensible position against third-party SIEM alternatives. The Bigger Picture: Preparing for the Agentic SOC Before getting into transition mechanics, partners need to understand where the industry is headed — because the platform decisions made during this transition will determine whether a customer’s SOC is ready for what comes next. The security industry is moving from human-driven, alert-centric workflows to an operating model built on three pillars: Intellectual Property — the detection logic, hunting hypotheses, response playbooks, and domain expertise that differentiate one security team from another. Human Orchestration — the judgment, context, and decision-making that humans bring to complex incidents. Humans set strategy, validate findings, and make containment decisions. They do not manually triage every alert. AI Agents - built agents that execute repeatable work: enriching incidents, hunting across months of telemetry, validating security posture, drafting response actions, and flagging anomalies for human review. The SOC of 2027 will not be scaled by hiring more analysts. It will be scaled by deploying agents that encode institutional knowledge into automated workflows — orchestrated by humans who focus on the decisions that require judgment. This transformation requires a platform that provides three things: Deep telemetry — agents need months of queryable data to analyze behavioral patterns, build baselines, and detect slow-moving threats. The Sentinel data lake provides this at a cost point that makes long-retention feasible. Relationship context — agents need to understand how entities connect. Which accounts share credentials? What is the blast radius of a compromised service principle? What is the attack path from a phished user to domain admin? Sentinel Graph provides this. Extensibility — partners and customers need to build and deploy their own agents without waiting for Microsoft to ship them. The MCP framework and Copilot agent architecture provide this. None of these exist in Azure experience for Sentinel. All three ship with the Defender experience. The urgency goes beyond the March 2027 deadline. Organizations are deploying AI agents, copilots, and autonomous workflows across their businesses — and every one of those creates a new attack surface. Prompt injection, data poisoning, agent hijacking, cross-plugin exploitation — these are not theoretical risks. They are in the wild today. Defending against AI-powered attacks requires a security platform that is itself AI Agent-ready. The new experience in Defender unlocks this experience. What Unified SIEM and XDR Actually Delivers The original framing — “single pane of glass for SIEM and XDR” — is accurate but insufficient. Here is what the unified platform delivers that standalone Sentinel does not. Cross-Domain Incident Correlation The Defender correlation engine does not just group alerts by time proximity. It builds multi-stage incident graphs that link identity compromise to lateral movement to data exfiltration across SIEM and XDR telemetry — automatically. Consider a token theft chain: an infostealer harvests browser session cookies (endpoint telemetry), the attacker replays the token from a foreign IP (Entra ID sign-in logs), creates a mailbox forwarding rule (Exchange audit logs), and begins exfiltrating data (DLP alerts). In standalone Sentinel, these are four separate alerts in four different tables. In the unified platform, they are one correlated incident with a visual attack timeline. 2-Tier Data Architecture The Sentinel data lake introduces a second storage tier that changes the economics and capabilities of security telemetry: Analytics Tier Data Lake Purpose Real-time detection rules, SOAR, alerting Hunting, forensics, behavioral analysis, AI agent queries Latency Sub-5-minute query and alerting Minutes to hours acceptable Cost ~$4.30/GB PAYG ingestion (~$2.96 at 100 GB/day commitment) ~$0.05/GB ingestion + $0.10/GB data processing (at least 20x cheaper) Retention 90 days default (expensive to extend) Up to 12 years at low cost Best for High-signal, low-volume sources High-volume, investigation-critical sources The architecture decision is not “which tier is cheaper.” It is “which tier gives me the right detection capability for each data source.” Analytics tier candidates: Entra ID sign-in logs, Azure activity, audit logs, EDR alerts, PAM events, Defender for Identity alerts, email threat detections. These need sub-5-minute alerting. Data lake candidates: Raw firewall session logs, full DNS query streams, proxy request logs, Sysmon process events, NSG flow logs. These drive hunting and forensic analysis over weeks or months. Dual-ingest sources: Some sources need both tiers. Entra ID sign-in logs are the canonical example — analytics tier for real-time password spray detection, Data Lake for graph-based blast radius analysis across months of authentication history. Implementation is straightforward: a single Data Collection Rule (DCR) transformation handles the split. One collection point, two routing destinations. The right framing: “Right data in the right tier = better detections AND lower cost.” Cost savings are a side effect of good security architecture, not the goal. Sentinel Graph Sentinel graph enables SOC teams and AI agents to answer questions that flat log queries cannot: What is the blast radius of this compromised account? Which service principals share credentials with the breached identity? What is the attack path from this phished user to domain admin? Which entities are connected to this suspicious IP across all telemetry sources? Graph-based investigation turns isolated alerts into context-rich intelligence. It is the difference between knowing “this account was compromised” and understanding “this account has access to 47 service principals, 3 of which have written access to production Key Vault.” Security Copilot Integration Security Copilot embedded in the defender portal helps analysts summarize incidents, generate hunting queries, explain attacker behavior, and draft response actions. For complex multi-stage incidents, it reduces the time from “I see an alert” to “I understand the full scope” from hours to minutes. With free SCUs available with Microsoft 365 E5, teams can apply AI to the highest-effort investigation work without adding incremental cost. MCP and the Agent Framework The Model Context Protocol (MCP) and Copilot agent architecture let partners and customers build purpose-built security agents. A concrete example: an MCP-enabled agent can automatically enrich a phishing incident by querying email metadata, checking the sender against threat intelligence, pulling the user’s recent sign-in patterns, correlating with Sentinel Graph for lateral risk, and drafting a containment recommendation — in under 60 seconds. This is where partner intellectual property becomes competitive advantage. The agent framework is the mechanism for encoding proprietary detection logic, response playbooks, and domain expertise into automated workflows that run at machine speed. Security Store Security Store allows partners to evolve from one‑time transition projects into repeatable, scalable offerings—supporting professional services, managed services, and agent‑based IP that align with the customer’s unified SecOps operating model As part of the transition, the Microsoft Security Store becomes the extension layer for the Defender —allowing partners to deliver differentiated agents, SaaS, and security services natively within Defender and Sentinel, instead of building and integrating in isolation The 4 Investigation Surfaces: A Customer Maturity Ladder The Sentinel Data Lake exposes four distinct investigation surfaces, each representing a step toward the Agentic SOC — and a partner service opportunity: Surface Capability Maturity Level Partner Opportunity KQL Query Ad-hoc hunting, forensic investigation Basic — “we can query” Hunting query libraries; KQL training Graph Analytics Blast radius, attack paths, entity relationships Intermediate — “we understand relationships” Graph investigation training; attack path workshops Notebooks (PySpark) Statistical analysis, behavioral baselines, ML models Advanced — “we predict behaviors” Custom notebook development; anomaly scoring Agent/MCP Access Autonomous hunting, triage, response at machine speed Agentic SOC — “we automate” Custom agent development; MCP integration The customer who starts with “help us hunt better” ends up at “build us agents that hunt autonomously.” That is the progression from professional services to managed services. What the Transition Actually Involves It is not a data migration — customers’ underlying log data and analytics remain in their existing Log Analytics workspaces. That is important for partners to communicate clearly. But partners should not set the expectation that nothing changes except the URL. Microsoft’s official transition guide documents significant operational changes — including automation rules and playbooks, analytics rule, RBAC restructuring to the new unified model (URBAC), API schema changes that break ServiceNow and Jira integrations, analytics rule transitions where the Fusion engine is replaced by the Defender XDR correlation engine, and data policy shifts for regulated industries. Most customers cannot navigate this complexity without professional help. Important: Transitioning to the Defender portal has no extra cost - estimate the billing with the new Sentinel Cost Estimator Optimizing the unified platform means making deliberate changes: Adding dual-ingest for critical sources that need both real-time detection and long-horizon hunting. Moving high-volume telemetry to the Data Lake — enabling hunting at scale that was previously cost-prohibitive. Retiring redundant data copies where Defender XDR already provides the investigation capability. Updating RBAC, automation, and integrations for the unified portal’s consolidated schema and permission structure. Training analysts on new investigation workflows, Sentinel Graph navigation, and Copilot-assisted triage. Threat Coverage: The Detection Gap Most Organizations Do Not Know They Have This transition is an opportunity to quantify detection maturity — and most organizations will not like what they find. Based on real-world breach analysis — infostealers, business email compromise, human-operated ransomware, cloud identity abuse, vulnerability exploitation, nation-state espionage, and other prevalent threat categories — organizations running standalone Sentinel with default configurations typically have significant detection gaps. Those gaps cluster in three areas: Cross-domain correlation gaps — attacks that span identity, endpoint, email, and cloud workloads. These require the Defender correlation engine because no single log source tells the complete story. Long-retention hunting gaps — threats like command-and-control beaconing and slow data exfiltration that unfold over weeks or months. Analytics-tier retention at 90 days is too expensive to extend and too short for historical pattern analysis. Graph-based analysis gaps — lateral movement, blast radius assessment, and attack path analysis that require understanding entity relationships rather than flat log queries. The unified platform with proper log source coverage across Microsoft-native sources can materially close these gaps — but only if the transition includes a detection coverage assessment, not just a portal cutover. Partners should use MITRE ATT&CK as the common framework for measuring detection maturity. Map existing detections to ATT&CK tactics and techniques before and after transition — a measurable, defensible improvement that justifies advisory fees and ongoing managed services. Partner Opportunity: Professional Services to Managed Services This transition creates a structured progression for all partner types — from professional services that build trust and surface findings, to managed security services that deliver ongoing value. The key insight most partners miss: do not jump from “transition assessment” to “managed services pitch.” Customers are not ready for that conversation until they have experienced the value of professional services. The bridge engagement — whether transactional, transition execution, or advisory — builds trust, demonstrates the expertise, and surfaces the findings that make the managed services conversation a logical next step. Professional Services (transactional + transition execution + advisory) → Managed Security Services (MSSP) The USX transition is the ideal professional services entry point because it combines a mandatory deadline (March 2027) with genuine technical complexity (analytics rule, automation behavioral changes, RBAC restructuring, API schema shifts) that most customers cannot navigate alone. Every engagement produces findings — detection gaps, automation fragility, staffing shortfalls — that are the most credible possible evidence for managed services. Professional Services Transactional Partners Offer Customer Value Key Deliverables Transition Readiness Assessment Risk-mitigated transition with clear scope Sentinel deployment inventory; Defender portal compatibility check; transition roadmap with timeline; MITRE ATT&CK detection coverage baseline Transition Execution and Enablement Accelerated time-to-value, minimal disruption Workspace onboarding; RBAC and automation updates; Dual-portal testing and validation; SOC team training on unified workflows Security Posture and Detection Optimization Better detections and lower cost Data ingestion and tiering strategy; Dual-ingest implementation for critical sources; Detection coverage gap analysis; Automation and Copilot/MCP recommendations Advisory Partners Offer Customer Value Key Deliverables Executive and Strategy Advisory Leadership alignment on why this transition matters Unified SecOps vision and business case; Zero Trust and SOC modernization alignment; Stakeholder alignment across security, IT, and leadership Architecture and Design Advisory Future-ready architecture optimized for the Agentic SOC Target-state 2-tier data architecture; Dual-ingest routing decisions mapped to MITRE tactics; RBAC, retention, and access model design Detection Coverage and Gap Analysis Measurable detection maturity improvement Current-state MITRE ATT&CK coverage mapping; Gap analysis against 24 threat patterns; Detection improvement roadmap with priority recommendations SOC Operating Model Advisory Smooth analyst adoption with clear ownership Redesigned SOC workflows for unified portal; Incident triage and investigation playbooks; RACI for detection engineering, hunting, and platform ops Agentic SOC Readiness Preparation for AI-driven security operations MCP and agent architecture assessment; Custom agent development roadmap; IP + Human Orchestration + Agent operating model design Cost, Licensing and Value Advisory Transparent cost impact with strong business case Current vs. future cost analysis; Data tiering optimization recommendations; TCO and ROI modeling for leadership The conversion to managed services is evidence-based. Every professional services engagement produces findings — detection gaps, automation fragility, staffing shortfalls. Those findings are the most credible possible case for ongoing managed services. Managed Security Services The unified platform changes the managed security conversation. Partners are no longer selling “we watch your alerts 24/7.” They are selling an operating model where proprietary AI agents handle the repeatable work — enrichment, hunting, posture validation, response drafting — and human experts focus on the decisions that require judgment. This is where the competitive moat forms. The formula: IP + Human Orchestration + AI Agents = differentiated managed security. The unified platform enables this through: Multi-tenancy — the built-in multitenant portal eliminates the need for third-party management layers. Sentinel Data Lake — agents can query months of customer telemetry for behavioral analysis without cost constraints. Sentinel Graph — agents can traverse entity relationships to assess blast radius and map attack paths. MCP extensibility — partners can build agents that integrate with proprietary tools and customer-specific systems. Partners who build proprietary agents encoding their detection logic into the MCP framework will differentiate from partners who rely on out-of-box capabilities. The Securing AI Opportunity Organizations are deploying AI agents, copilots, and autonomous workflows across their businesses at an accelerating pace. Every AI deployment creates a new attack surface — prompt injection, data poisoning, agent hijacking, cross-plugin exploitation, unauthorized data access through agentic workflows. These are not theoretical risks. They are in the wild today. Partners who can help customers secure their AI deployments while also using AI to strengthen their SOC will command premium positioning. This requires a security platform that is itself AI Agent-ready — one that can deploy defensive agents at the same pace organizations deploy business AI. The unified Defender portal is that platform. Partners who position USX as “preparing your SOC for AI-driven security operations” will differentiate from partners who position it as “moving to a new portal.” Cost and Operational Benefits Better security architecture also costs less. This is not a contradiction — it is the natural result of putting the right data in the right tier. Benefit How It Works Eliminate low-value ingestion Identify and remove log sources that are never used for detections, investigations, or hunting. Immediately lowers analytics-tier costs without impacting security outcomes. Right-size analytics rules Disable unused rules, consolidate overlapping detections, and remove automation that does not reduce SOC effort. Pay only for processing that delivers measurable security value. Avoid SIEM/XDR duplication Many threats can be investigated directly in Defender XDR without duplicating telemetry into Sentinel. Stop re-ingesting data that Defender already provides. Tier data by detection need Store high-volume, hunt-oriented telemetry in the Data Lake at at least 20x lower cost. Promote only high-signal sources to the analytics tier. Full data fidelity preserved in both tiers. Reduce operational overhead Unified SIEM+XDR workflows in a single portal reduce tool switching, accelerate investigations, simplify analyst onboarding, and enable SOC teams to scale without proportional headcount increases. Improve detection quality The Defender correlation engine produces higher-fidelity incidents with fewer false positives. SOC teams spend less time triaging noise and more time on real threats. Competitive Positioning Partners need defensible talking points when customers evaluate third-party SIEM alternatives. The following advantages are factual, sourced from Microsoft’s transition documentation and platform capabilities — not marketing claims. No extra cost for transitioning — even for non-E5 customers. Third-party SIEM migrations involve licensing, data migration, detection rewrite, and integration rebuild costs. Native cross-domain correlation across Sentinel + Defender products into multi-stage incident graphs. Third-party SIEMs receive Microsoft logs as flat events — they lack the internal signal context, entity resolution, and product-specific intelligence that powers cross-domain correlation. Custom detections across SIEM + XDR — query both Sentinel and Defender XDR tables without ingesting Defender data into Sentinel. Eliminates redundant ingestion cost. Alert tuning extends to Sentinel — previously Defender-only capability, now applicable to Sentinel analytics rules. Net-new noise reduction. Unified entity pages — consolidated user, device, and IP address pages with data from both Sentinel and Defender XDR, plus global search across SIEM and XDR. Third-party SIEMs provide entity views from ingested data only. Built-in multi-tenancy for MSSPs — multitenant portal manages incidents, alerts, and hunting across tenants without third-party management layers. Try out the new GDAP capabilities in Defender portal. Industry validation: Microsoft’s SIEM+XDR platform has been recognized as a Leader by both Forrester (Security Analytics Platforms, 2025) and Gartner (SIEM Magic Quadrant, 2025). Summary: What Partners Should Take Away Topic Key Message Framing USX is a security architecture transformation, not a portal transition. Lead with detection capability, not cost savings. Platform foundation Sentinel Data Lake + Sentinel Graph + MCP/Agent Framework = the platform for the Agentic SOC. 4 investigation surfaces KQL → Graph → Notebooks → Agent/MCP. A maturity ladder from “we can query” to “we automate at machine speed.” Architecture 2-tier data model (analytics + Data Lake) with dual-ingest for critical sources. Cost savings are a side effect of good architecture. Transition complexity Analytics rules and automation rules. API schema changes. RBAC restructuring. Most customers need professional help. Partner engagement model Professional Services (transactional + transition execution + advisory) → Managed Services (MSSP). Competitive positioning No extra cost. Native correlation. Cross-domain detections. Built-in multi-tenancy. Capabilities third-party SIEMs cannot replicate. Partner differentiation IP + Human Orchestration + AI Agents. Partners who build proprietary agents on MCP have competitive advantage. Timeline March 31, 2027. Start now — phased transition with one telemetry domain first, then scale.
One SOC, Many Tenants: Centralizing Microsoft Sentinel with Azure Lighthouse
Most large organizations don’t live in a single Microsoft Entra ID tenant. Acquisitions, regulatory separation, sovereignty mandates, and mission boundaries all multiply tenants over time. For a security operations team, that sprawl creates one hard question: how do you run a single security operations center (SOC) with one pane of glass, without copying every tenant’s logs into a central bucket and inheriting the compliance risk that comes with it? Azure Lighthouse provides delegated resource management for this scenario. It lets authorized operators in a central hub tenant work with scoped resources in spoke tenants from their own tenant. Paired with Microsoft Sentinel, it supports cross-tenant visibility while source logs remain stored in each spoke workspace. Group-based RBAC and Privileged Identity Management (PIM) can help govern privileged access. This practical blueprint applies least privilege to that pattern: one Sentinel deployment in the hub, delegated read access to the Log Analytics workspaces in each spoke, and RBAC and PIM controls that support auditability. The delegation is scoped to the workspaces the SOC needs. Before you begin: prerequisites Gather the identifiers and identity groups below before you touch any tenant. The consistency you establish here is what makes the per-spoke procedure repeatable across an entire estate. Collect tenant and workspace identifiers Hub CSOC tenant ID and hub Sentinel workspace resource ID. For each spoke: tenant ID, subscription ID, workspace resource group, and workspace resource ID. Create hub security groups in Microsoft Entra ID SOC-Readers, the required core query-access group. SOC-Responders, an optional group for response-specific actions. SOC-Admins, an optional and tightly controlled group. Define the minimum RBAC baseline Assign Log Analytics Reader to SOC-Readers at the spoke workspace or resource-group scope. Add elevated roles only when a documented use case requires them. Register the required resource providers Lighthouse and Sentinel both depend on resource providers being registered before delegation will work. In every spoke subscription, register Microsoft.ManagedServices so the delegation can be created. And because Sentinel lives only in the hub, register Microsoft.SecurityInsights and Microsoft.OperationalInsights on at least one subscription in the hub tenant. That last step is easy to overlook, and skipping it quietly blocks cross-tenant operations. What you’ll find here The architecture, and why data residency makes hub-and-spoke the right call. Prerequisites, including the resource providers teams most often forget to register. A repeatable, per-spoke delegation procedure. Validation queries that prove the access path actually works. A complete RBAC assignment matrix and PIM activation policy. Troubleshooting for the errors you’ll actually hit. Why hub-and-spoke for a cross-tenant SOC In this model, Microsoft Sentinel is deployed once, in the hub (CSOC) tenant. Each spoke tenant keeps its own Log Analytics workspace, where its logs are collected and retained. Azure Lighthouse connects the two: the spoke delegates scoped access to the hub, and authorized hub analysts query spoke workspaces from their own tenant. The source logs remain stored in the spoke workspace, while query results are returned across tenant boundaries to authorized users and services. Figure 1. The hub runs Sentinel and queries each spoke’s Log Analytics workspace through a scoped Azure Lighthouse delegation; logs never leave the spoke tenant. That separation is the entire point, and it lines up with the advantages Microsoft calls out for centralized cross-tenant management: Workspace ownership and source-log storage remain with each spoke tenant. Source telemetry remains stored in the configured spoke workspace and region, subject to the service configuration and authorized query access. Separate workspaces help maintain tenant isolation between spokes. Cross-tenant detection and hunting can query spoke workspaces without centralizing the underlying source logs; authorized query results are returned across tenant boundaries. Ingestion and retention costs are billed to the tenant that generates the data, not to the hub. What this runbook delivers The objective is a centralized CSOC with scoped, governed access across spoke tenants from a single Sentinel instance. Hub SOC teams query spoke Log Analytics workspaces through delegation, run cross-tenant analytics, and create and manage incidents centrally, while spoke logs stay in spoke workspaces and tenant isolation is preserved. Just as important is how that access is granted. The target state is least-privilege by construction: RBAC assignments carry only the permissions the SOC needs, privileged roles are PIM-governed rather than standing, and every delegation is scoped to the specific resources in play. When the build is complete, the operating model is both active and auditable. Configuring the delegation (per spoke tenant) Repeat the three steps below for each spoke. For fleets larger than a handful of tenants, capture the same authorization in an Azure Lighthouse ARM template and deploy it per spoke, so the scope and role assignments stay identical across the estate. Step 1: Register the provider in the spoke subscription Register Microsoft.ManagedServices in the spoke subscription. Confirm the provider registration state is Registered before continuing. Step 2: Create the Lighthouse delegation from spoke to hub Open Azure Lighthouse in the spoke tenant. Create a delegation, or offer, and set the managing tenant to the hub CSOC tenant ID. Add an authorization with the principal set to the hub SOC-Readers group and the role set to Log Analytics Reader. Set the scope to the workspace resource group (preferred) or to the individual workspace. Prefer group-based assignments and avoid direct user assignments. Step 3: Repeat across all spoke tenants Apply the same pattern and naming convention every time. Document any scope or role exceptions for security review. Validating the delegation Three checks confirm the delegation is wired correctly: visibility, query access, and incident generation. Run them in order, because each one depends on the check before it. Figure 2. The three validation checks run in sequence — visibility, then cross-tenant query, then incident generation — each building on the one before. Confirm delegated visibility From the hub context, verify that each spoke appears under Azure Lighthouse delegated resources. Confirm that the expected principals and scopes are listed. Run a cross-tenant query from hub Sentinel Run a simple take query to verify that the access path resolves. Run a data query against a known active table to confirm you can see ingestion. Confirm the access path resolves: workspace("/subscriptions/<spoke-sub-id>/resourceGroups/<spoke-rg>/providers/Microsoft.OperationalInsights/workspaces/<spoke-ws>") | take 1 Check that a known table is receiving data: workspace("/subscriptions/<spoke-sub-id>/resourceGroups/<spoke-rg>/providers/Microsoft.OperationalInsights/workspaces/<spoke-ws>").Heartbeat | where TimeGenerated > ago(24h) | summarize Events = count() Verify incident generation in hub Sentinel Create a temporary scheduled analytics rule that uses cross-tenant query logic. Trigger the test condition and confirm that the incident is created in the hub. Standardizing new-spoke onboarding Turn the procedure into a checklist so every new spoke is onboarded the same way and nothing slips. Resource provider registered. Delegation deployed with the hub as managing tenant. Roles assigned to hub SOC groups. Cross-tenant query test passed. Hub incident-generation test passed. Access-review owner assigned. Security and governance Delegation connects the hub to scoped spoke resources; governance helps control that access. Three controls do most of the work. Enforce privileged identity controls Make privileged groups PIM-eligible rather than permanently assigned. Require MFA, approval, justification, and time-bound activation. Maintain separation of duties Keep SOC monitoring, content engineering, and platform administration in separate roles. Review delegated access on a recurring governance cadence. Manage exceptions with formal controls Document every elevated-access and broad-scope delegation exception. Require security-architecture approval for any non-standard scope. Role and RBAC assignment matrix The tables below translate those principles into concrete assignments: first the hub-local roles, then the delegated spoke roles, and finally the PIM activation policy that governs both. Hub (CSOC) tenant: local assignments Spoke (service) tenant: delegated via Azure Lighthouse PIM activation requirements Key design rules Minimum privilege governs spoke delegation. Log Analytics Reader covers every cross-tenant detection and query operation, so Owner and broad Contributor at subscription scope have no place in a SOC delegation. Sentinel Responder in a spoke is rarely needed. It matters only when analysts must acknowledge, close, or act on spoke-level resources directly, and in a hub-only Sentinel model spoke incidents don’t exist, so the role usually isn’t required. Separation of duties is strict. Content engineers don’t get responder rights, responders don’t get content-deployment rights, and platform admins sit apart from both monitoring and detection engineering. Routine privileged roles are eligible and time-bound. SOC responders, hunters, content engineers, and platform admins use PIM rather than permanent assignment. Emergency-access accounts are the exception and should follow Microsoft Entra emergency-access guidance, including monitoring and regular validation. Troubleshooting common issues Most problems fall into three buckets, and each has a short diagnostic path. Design notes and what’s next By default, Sentinel stays enabled in the hub only, unless a spoke-specific requirement is approved. Spoke tenants remain data-source focused and don’t generate local Sentinel incidents; detection, incident management, and automation all live in hub Sentinel, giving you one place to build content and one place to respond. A centralized SOC does not require centralizing every source workspace. With scoped Azure Lighthouse delegation and PIM-governed access, a CSOC can query a multitenant estate while source logs remain stored in their spoke workspaces. Further reading Manage Microsoft Sentinel workspaces at scale (Azure Lighthouse) Manage multiple tenants in Microsoft Sentinel as an MSSP Extend Microsoft Sentinel across workspaces and tenantsMCP safety & evaluation with the Agent 365 CLI & Agent Governance Toolkit
Co Author: JiteshThakur AI agents are useful because they can act. They call tools, query databases, send messages, and hand work to other agents. That same freedom creates a problem: access control can tell you which service an agent may reach, but it does not always tell you whether a particular action is sensible, safe, or permitted. MCP is how most agents now act. Two Control Points: This post examines two control points that address different parts of the MCP lifecycle. Agent 365 CLI evaluates the MCP server before an agent uses it. Agent Governance Toolkit (AGT) governs sensitive tool calls while the agent runs. One improves what the agent sees. The other governs what the agent does. The Agent 365 CLI is a cross-platform command-line tool for Agent 365 applications on Azure. Its evaluation command examines MCP tool definitions and scores their quality. AGT evaluates actions against policy and records each decision. Together, these tools support a practical model: evaluate the server first, then provide proper scaffolding for the developer to test this in a dry run. Agent 365 CLI: Score an MCP server from the command line: The Agent 365 CLI can evaluate an MCP server against research-based practices for production readiness. The result is more useful than a simple pass or fail. The evaluation gives you: A score for each tool name, description, and parameter schema; A prioritized list of improvements; An overall maturity score for the server; and Local output that you can use early in development. This report turns a vague question, "Is this MCP server ready?", into a concrete list of work. The evaluate command a365 develop-mcp evaluate --server-url <server-url> [--auth-token <auth-token>] [options] The command reads the tool schemas from the server. It then produces guidance for names, descriptions, parameters, and schema structure. A local coding-agent CLI scores the semantic checks. You can use GitHub Copilot CLI or Claude Code under your account and AI subscription. The command does not send tool-schema data to Microsoft. Prerequisites: Install the following software: Agent 365 CLI; Node.js 18 or later for GitHub Copilot CLI; and A supported coding-agent CLI for semantic scoring. For example, install GitHub Copilot CLI with this command: powershell npm install -g @github/copilot This bring-your-own-LLM model keeps the scoring step in your local development environment. It is useful when model calls must remain inside an approved deployment. How the evaluation works The command runs a five-step pipeline and logs progress as it goes. Fig 1: MCP Evaluation using Agent 365 Cli Connect to the MCP server and collect its tool schemas. Generate an evaluation checklist in the output directory. Score the semantic checks with the selected coding agent. Calculate the maturity level and action priorities. Write the JSON and HTML reports. The evaluation contains two types of checks: Deterministic checks use exact rules in the CLI. For example, a tool name cannot be empty. Semantic checks use a coding agent to score clarity and meaning. Each result includes a reason for the score. Examples Set the authentication token in an environment variable. Then evaluate an authenticated server and write the artifacts to a subfolder. powershell $env:A365_MCP_AUTH_TOKEN = "<bearer-token>" a365 develop-mcp evaluate --server-url "https://my-mcp-server.contoso.com/mcp" --output-dir "./eval" Use a specific scoring engine with the `--eval-engine` option: powershell a365 develop-mcp evaluate --server-url "http://localhost:5000/mcp" --eval-engine claude-code Scenario: Evaluate a malicious MCP server For this demonstration, we hosted a deliberately malicious MCP server at `http://127.0.0.1:8124/`. It exposes tools that demonstrate tool poisoning, credential leakage, prompt injection, schema mismatch, sandbox escape, and other attacks. The server is intentionally unsafe and is for demonstration only. Fig 2: Setting up a test MCP server for evaluation We ran the evaluation in two steps. First, we generated the checklist without automatic semantic scoring: a365 develop-mcp evaluate --server-url "http://127.0.0.1:8124/" --eval-engine none Fig 3: Agent365CLI MCP Evaluation The command wrote the checklist and a semantic-evaluation prompt to the output directory. It also displayed the next steps. Second, we gave the prompt and checklist to a coding agent. The agent completed each unscored semantic check with a Boolean score and a short reason. After we saved the completed checklist, we ran the command again to generate the report: a365 develop-mcp evaluate --server-url "http://127.0.0.1:8124/" --output-dir "C:\temp\MaliciousMCP" Fig 4: Creating the report with Agent365 CLI MCP Evaluate command Understanding the evaluation report Open `<server-name>_eval_report.html` from the output directory. The report contains: The overall score from 0 to 100; The maturity level from 0 to 4; Scores for each tool and quality category; and A prioritized action list for the next maturity level. Fig 5: MCP Evaluation Report In our demonstration, the server scored 86.0 and reached Level 3: Optimized for AI. That strong overall score did not mean that every tool was safe or clear. The report found 58 action items, including one critical item and 33 high-priority items. That contrast matters. A server can have valid schemas and consistent names while still exposing misleading or dangerous tools. Fig 6: MCP Evaluation Report - Tool-By-Tool Detail What to look for Read the per-tool results before the overall score. A single weak tool can create more risk than the server average suggests. Focus on these report sections: Tool names: Can an agent select the correct tool from its name? Tool descriptions: Does each description explain the purpose and correct use? Parameter names: Do the names identify the data that the tool requires? Parameter descriptions: Do they explain the format, type, and constraints? Schema structure: Are the schemas valid and processable? Action items: Which changes have the highest effect on tool selection and use? The command processes static tool schemas from `tools/list`. It does not process runtime payloads, end-user data, or personal data. The command keeps the `--auth-token` value in memory. It sends the value only in the HTTP `Authorization` header. It does not write the token to disk or give it to the coding agent. AGT: Put governance in the execution path: Microsoft's open-source Agent Governance Toolkit (AGT) evaluates an action before execution. It adds identity and policy context, records the decision, and can send risky work for approval. This can be used by developers during the build time for dynamic evaluation of the MCP server. AGT lets developers put part of that intent into the execution path. Remote tools still need secure implementations, sandboxes need hard boundaries, and audit records need appropriate storage and access controls. You do not need to replace your agent framework to use it. What sits in the decision path? AGT wraps the tools that an agent already uses. You can start to govern a tool with two lines of Python: python from agentmesh.governance import govern safe_tool = govern(my_tool, policy="policy.yaml") On each call, `safe_tool` evaluates the configured policy. An allowed action reaches the original tool. A denied action raises `GovernanceDenied` and creates a decision record. This wrapper model reduces the cost of adoption. Teams can add governance to an existing agent stack without rebuilding it. AGT supports Python, TypeScript, .NET, Rust, and Go. Its documented integrations include popular agent frameworks, MCP, and A2A. Teams can also adopt AGT in stages. A team can begin with policy checks and audit records. It can add identity, approvals, sandboxing, and operational controls as risk increases. Each control answers a different question: Policy: Is this action allowed? Identity and trust: Which agent made the request? Runtime controls: What limits apply to execution? Audit evidence: Why did AGT allow or deny the action? A low-risk assistant can need only a deny rule and basic logging. An agent that moves money or changes production systems needs stronger controls. Fig 7: AGT Architecture Scenario: Govern the same malicious MCP server For this scenario demonstration, we used AGT Python packages as an MCP gateway. The gateway sat between an agent and the same malicious server from the earlier evaluation. This setup let us examine both control points against one target. The Agent 365 CLI examined the server's static tool definitions. The AGT gateway examined real requests and responses for the developer during its testing. Fig 8: AGT findings at runtime In the policy interface, a developer can edit runtime limits and detection rules. The developer can also validate the policy against sample tool metadata, save a revision, and activate it with a recorded reason. Fig 9: AGT control coverage The control-coverage view shows which AGT capabilities are active in the gateway. It also links each capability to package checks and end-to-end evidence. In our demonstration, we included the following controls: Tool metadata poisoning detection; Tool change and rug-pull detection; Dangerous argument blocking; Tool-response content scanning; Per-client tool-call budgets; and A redacted decision audit trail. You can build your detection & input security by reading more about it here. The gateway detected malicious content. For one blocked `tools/list` request, it recorded the findings. The important result was not only that AGT blocked the request. It also preserved the matched evidence, affected tool locations, policy modes, and request context. Fig 10: Example detection via AGT The dashboard then summarized block-mode findings, leading risk drivers, and tools that required review. This evidence can help a team prioritize policy changes and investigate repeated attacks. Fig 11: Sample AGT metrics AGT does not require this UI, gateway, or architecture. Its structured decisions can feed an admin console, SIEM, incident workflow, or approval queue. AGT also includes an Agent Compliance package with mappings for OWASP and other controls. These mappings give developers and governance teams a common record of applied controls. Teams do not need to reconstruct the agent's behavior after an incident. Check Compliance - Agent Governance Toolkit for more information. Conclusion: MCP safety needs controls before and during execution. The Agent 365 CLI improves the MCP interface before deployment. It exposes unclear tool definitions, scores server maturity, and turns quality gaps into prioritized work. While AGT is implemented at the build phase, it provides developers the ability to test policy, identity, execution context & preserve evidence for allowed or denied decisions. Neither tool replaces secure server code, strong sandbox boundaries, or protected audit storage. Instead, they make those controls easier to evaluate and explain. Start with one MCP server and one consequential tool call. Evaluate the server with the Agent 365 CLI. Then put an AGT policy around the action that carries the most risk. While these controls help secure the build phase of an agent, once agents move into production, runtime controls become essential. Agent365 provides those controls at runtime. With thanks to Ashik Kuppil for his inputs and collaboration on this post.Microsoft Security Community Spotlight: Marcel Graewer
Globally, Marcel shares practical detection engineering insights on Microsoft Sentinel and Microsoft Defender XDR through forums and blog posts. Locally, he represents his employer in the IT-Security group of the Microsoft Business User Forum, where German companies using Microsoft technologies exchange real-world experience and expertise. The work Marcel values most is helping people enter the IT field. In Germany, "Fachinformatiker" is a recognized IT profession learned through a multi-year apprenticeship, and he is proud to have trained apprentices. He also serves as an examiner for the IHK (the German Chamber of Industry and Commerce), evaluating the final exams of these IT apprentices. This commitment also led him to support younger learners by teaching school cybersecurity classes and participating in Girls’ Day, where he introduced female students to the field. “I do this because most people don’t get an honest view of security work until much later in their education—if they see it at all. Showing someone early that this field is creative, varied, and genuinely interesting can change their path. Being part of that, even for a few people, means more to me than anything that fits neatly on a CV.” Let’s hear more from Marcel about his Microsoft Security Community and product paths. All responses to questions are direct quotes from Marcel. What do you find most rewarding about being a member of the Microsoft Security Community? The most rewarding part for me is how practical the exchange is. Microsoft security tooling moves fast - Microsoft Sentinel, Microsoft Defender XDR and Microsoft Security Copilot all change month to month- and no single person keeps up with all of it alone. The community is where that gap gets closed. When I read how someone else tuned a detection in their environment, or when someone responds to something I posted with a problem I hadn't considered, my own work gets better. It's a feedback loop you don't get from documentation. The other part I value is that it works in both directions: I started as a reader, learning from people more experienced than me, and now I'm at a point where I can give some of that back. Watching that shift happen has been genuinely motivating. How long have you been working with Microsoft Security Products? Over ten years! My way into Microsoft security ran through infrastructure rather than security itself. I started out administering Active Directory and VMware environments, the on-premises world, and that is where I first understood identity, endpoints and the quiet attack surface they create. At the time, security was something layered on top of infrastructure. What changed everything was the shift to the cloud. As the environments I worked in moved into Microsoft Azure and Microsoft 365, the old separation between "running things" and "securing things" stopped making sense. In a cloud-first world, the identity is the perimeter, the sign-in log is the crime scene, and the telemetry that used to be scattered across servers suddenly lives in one place you could actually query. That was the moment Microsoft's security stack became less of a product set and more of a working environment for me. As I moved from running infrastructure into roles centered on defending it, first leading IT infrastructure and security as a team lead, then as an IT Security Expert, and now as IT Security Manager focused on architecture and incident response in an Azure and M365 environment, Sentinel and Defender XDR went from tools I knew of to tools I work in every day. The infrastructure background turned out to be an advantage rather than a detour. Detection engineering makes far more sense once you have run the Active Directory and the endpoints that generate the very signals you are now writing detections against, and cloud security makes far more sense once you have felt the limits of the on-premises model it replaced. The part that keeps me engaged is that none of this stands still. The cloud security landscape changes constantly, the work is never quite finished, and that is exactly what I like about it. What Microsoft Security features or products have provided the most impact? The single biggest impact for me comes from Microsoft Sentinel as a cloud-native SIEM and SOAR platform. The move away from a self-hosted SIEM matters more than it first appears. A traditional SIEM is itself a piece of infrastructure that has to be sized, hosted, patched, and scaled, and that effort constantly competes with the actual security work. Microsoft Sentinel removes that layer. There is no platform estate to keep alive and no capacity planning for the SIEM itself, which frees attention for what actually matters: getting the right telemetry in and getting detection and response right. What I value most is how naturally Sentinel fits into modern, cloud-first environments. When the landscape you are protecting already lives in Azure and Microsoft 365, a security platform that lives in the same place removes an entire class of integration friction. The other strength is the breadth of data onboarding. With a traditional SIEM, connecting a new log source was often a small project of its own, with connectors to build and parsers to maintain. With Sentinel, that friction is largely gone. Whether a source sits on-premises, in another cloud or in a third-party product, getting it in is straightforward, and the platform still provides the integration depth that genuinely matters rather than a shallow connection. Microsoft Sentinel handles almost anything you point it at. Equally important is that SIEM and SOAR are not two separate platforms here. The orchestration and automation layer is built into the same solution, so response playbooks run on the same data that the detections are built on. For architecture, that is a real advantage: detection and response are designed as one system rather than stitched together afterwards. The central telemetry layer is one of the few decisions that is genuinely hard to reverse later, and Sentinel makes that an easy one to defend. What advice do you have for others who would like to get involved in the Microsoft Community? My advice is to start before you feel ready. I read Microsoft Tech Community (forums) for years before I posted anything myself, always with the feeling that I needed more experience first, that I would just be adding noise. That was the wrong instinct. The moment I actually started contributing, the feedback I got back made my own work better, and I realised the bar for being useful is far lower than it looks from the outside. You do not need to be the leading expert on a topic. You need a real problem you have worked through and the willingness to write down how you solved it. Someone else is stuck on exactly that problem right now. Start small, stay consistent, and treat the community as an exchange rather than a stage. Consistency matters more than any single brilliant post. Alles rund um sein Buch (All About His Book) Last year, I published "Die neue Realität der Cybersecurity" (2025). It tackles a question every security team is dealing with right now: “Where does AI genuinely strengthen security architecture and incident response, and where is it just noise?” Rather than staying abstract, the book takes the practitioner's side of that question, looking at how AI actually changes the work of designing defensible systems and responding to incidents, and where the limits and risks really are. It is written for the people doing the work, security architects, IR practitioners and the leaders who have to make decisions about AI without the marketing gloss. If that question is on your desk too, it is worth a look. Connect with Marcel Microsoft Tech Community: @marcel_graewer Linkedin: https://www.linkedin.com/in/mgraewer/ Github: https://github.com/bifrost0x Blogs: graewer.com and magra-sec.de Book: Die neue Realität der Cybersecurity (ISBN: 9783695708833) Marcel Graewer is currently an IT-Security Manager at Festool Group and holds the CISSP certification. Outside of work, he is happiest when experimenting with technology on his own terms. He runs a Proxmox-based homelab with a range of self-hosted services and Docker containers, using it as both a playground and a testing ground. It gives him space to break things, learn, and explore without the constraints of formal change processes. He also spends time on Hack The Box and TryHackMe, believing that staying sharp on the offensive side makes him a stronger defender. Away from the keyboard, his life is refreshingly analog. He and his family, including two children, live in an old house that always seems to have one more project waiting. Between the homelab and the house, there is never a shortage of things to fix, and that suits him just fine. Learn and Engage with the Microsoft Security Community Log in and follow this Microsoft Security Community Blog. Follow = Click the heart in the upper right when you're logged in 🤍. Join the Microsoft Security Community and be notified of upcoming events, product feedback surveys, and more. Get early access to Microsoft Security products and provide feedback to engineers by joining the Microsoft Security Advisors. Join the Microsoft Security Community LinkedIn Group and follow the Microsoft Entra Community on LinkedInSecurity Dashboard for AI - Now Generally Available
AI proliferation in the enterprise, combined with the emergence of AI governance committees and evolving AI regulations, leaves CISOs and AI risk leaders needing a clear view of their AI risks, such as data leaks, model vulnerabilities, misconfigurations, and unethical agent actions across their entire AI estate, spanning AI platforms, apps, and agents. 53% of security professionals say their current AI risk management needs improvement, presenting an opportunity to better identify, assess and manage risk effectively. 1 At the same time, 86% of leaders prefer integrated platforms over fragmented tools, citing better visibility, fewer alerts and improved efficiency. 2 To address these needs, we are excited to announce the Security Dashboard for AI, previously announced at Microsoft Ignite, is now generally available. This unified dashboard aggregates posture and real-time risk signals from Microsoft Defender, Microsoft Entra, and Microsoft Purview - enabling users to see left-to-right across purpose-built security tools from within a single pane of glass. The dashboard equips CISOs and AI risk leaders with a governance tool to discover agents and AI apps, track AI posture and drift, and correlate risk signals to investigate and act across their entire AI ecosystem. Security teams can continue using the tools they trust while empowering security leaders to govern and collaborate effectively. Gain Unified AI Risk Visibility Consolidating risk signals from across purpose-built tools can simplify AI asset visibility and oversight, increase security teams’ efficiency, and reduce the opportunity for human error. The Security Dashboard for AI provides leaders with unified AI risk visibility by aggregating security, identity, and data risk across Defender, Entra, Purview into a single interactive dashboard experience. The Overview tab of the dashboard provides users with an AI risk scorecard, providing immediate visibility to where there may be risks for security teams to address. It also assesses an organization's implementation of Microsoft security for AI capabilities and provides recommendations for improving AI security posture. The dashboard also features an AI inventory with comprehensive views to support AI assets discovery, risk assessments, and remediation actions for broad coverage of AI agents, models, MCP servers, and applications. The dashboard provides coverage for all Microsoft AI solutions supported by Entra, Defender and Purview—including Microsoft 365 Copilot, Microsoft Copilot Studio agents, and Microsoft Foundry applications and agents—as well as third-party AI models, applications, and agents, such as Google Gemini, OpenAI ChatGPT, and MCP servers. This supports comprehensive visibility and control, regardless of where applications and agents are built. Prioritize Critical Risk with Security Copilots AI-Powered Insights Risk leaders must do more than just recognize existing risks—they also need to determine which ones pose the greatest threat to their business. The dashboard provides a consolidated view of AI-related security risks and leverages Security Copilot’s AI-powered insights to help find the most critical risks within an environment. For example, Security Copilot natural language interaction improves agent discovery and categorization, helping leaders identify unmanaged and shadow AI agents to enhance security posture. Furthermore, Security Copilot allows leaders to investigate AI risks and agent activities through prompt-based exploration, putting them in the driver’s seat for additional risk investigation. Drive Risk Mitigation By streamlining risk mitigation recommendations and automated task delegation, organizations can significantly improve the efficiency of their AI risk management processes. This approach can reduce the potential hidden AI risk and accelerate compliance efforts, helping to ensure that risk mitigation is timely and accurate. To address this, the Security Dashboard for AI evaluates how organizations put Microsoft’s AI security features into practice and offers tailored suggestions to strengthen AI security posture. It leverages Microsoft’s productivity tools for immediate action within the practitioner portal, making it easy for administrators to delegate recommendation tasks to designated users. With the Security Dashboard for AI, CISOs and risk leaders gain a clear, consolidated view of AI risks across agents, apps, and platforms—eliminating fragmented visibility, disconnected posture insights, and governance gaps as AI adoption scales. Best of all, the Security Dashboard for AI is included with eligible Microsoft security products customers already use. If an organization is already using Microsoft security products to secure AI, they are already a Security Dashboard for AI customer. Getting Started Existing Microsoft Security customers can start using Security Dashboard for AI today. It is included when a customer has the Microsoft Security products—Defender, Entra and Purview—with no additional licensing required. To begin using the Security Dashboard for AI, visit http://ai.security.microsoft.com or access the dashboard from the Defender, Entra or Purview portals. Learn more about the Security Dashboard for AI at Microsoft Security MS Learn. 1AuditBoard & Ascend2 Research. The Connected Risk Report: Uniting Teams and Insights to Drive Organizational Resilience. AuditBoard, October 2024. 2Microsoft. 2026 Data Security Index: Unifying Data Protection and AI Innovation. Microsoft Security, 2026Why 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-servicesCrawl, Walk, Run: A Practitioner's Guide to AI Maturity in the SOC
Every security operations center is being told to adopt AI. Vendors promise autonomous threat detection, instant incident response, and the end of alert fatigue. The reality is messier. Most SOC teams are still figuring out where AI fits into their existing workflows, and jumping straight to autonomous agents without building foundational trust is a recipe for expensive failure. The Crawl, Walk, Run framework offers a more honest path. It's not a new concept. Cloud migration teams, DevOps organizations, and Zero Trust programs have used it for years. But it maps remarkably well to how security teams should adopt AI. Each phase builds organizational trust, governance maturity, and technical capability that the next phase depends on. Skip a phase and the risk compounds. This guide is written for SOC leaders and practitioners who want a practical, phased approach to AI adoption, not a vendor pitch.Accelerate connectors development using AI agent in Microsoft Sentinel
Today, we’re excited to announce the public preview of a Sentinel connector builder agent, via VS code extension, that helps developers build Microsoft Sentinel codeless connectors faster with low-code and AI-assisted prompts. This new capability brings guided workflows directly into the tooling developers already use, helping accelerate time to value as the Sentinel ecosystem continues to grow. Learn more at Create custom connectors using Sentinel connector AI agent Why this matters As the Microsoft Sentinel ecosystem continues to expand, developers are increasingly tasked with delivering high‑quality, production‑ready connectors at a faster pace, often while working across different cloud platforms and development environments. Building these integrations involves coordinating schemas, configuration artifacts, Azure deployment concepts, and validation steps that provide flexibility and control, but can span multiple tools and workflows. As connector development scales across more partners and scenarios, there is a clear opportunity to better integrate these capabilities into the developer environments teams already rely on. The new Sentinel connector builder agent, using GitHub Copilot in the Sentinel VS code extension, brings more of the connector development lifecycle -- authoring, validation, testing, and deployment into a single, cohesive workflow. By consolidating these common steps, it helps developers move more easily from design to validation and deployment without disrupting established processes. A guided, AI‑assisted workflow inside VS Code The Sentinel connector builder agent for Visual Studio Code is designed to help developers move from API documentation to a working codeless connector more efficiently. The experience begins with an ISVs API documentation. Using GitHub Copilot chat inside VS Code, developers can describe the connector they want to build and point the extension to their API docs, either by URL or inline content. From there, the AI‑guided workflow reads and extracts the relevant details needed to begin building the connector. Open the VS Code chat and set the chat to Agent mode. Prompt the agent using sentinel. When prompted, select /create-connector and select any supported API. For example in Contoso API, enter the prompt as: @sentinel /create-connector Create a connector for Contoso. Here are the API docs: https://contoso-security-api.azurewebsites.net/v0101/api-doc Next, the agent generates the required artifacts such as polling configurations, data collection rules (DCRs), table schemas, and connector definitions, using guided prompts with built‑in validation. This step‑by‑step experience helps ensure configurations remain consistent and aligned as they’re created. Note: During agent evaluation, select Allow responses once to approve changes, or select the option Bypass Approvals in the chat. It might take up to several minutes for the evaluations to finish. As the connector takes shape, developers can validate and test configurations directly within VS Code, including testing API interactions before deployment. Validation of the API data source and polling configuration are surfaced in context, supporting faster iteration without leaving the development environment. When ready, connectors can be deployed directly from VS Code to accessible Microsoft Sentinel workspaces, streamlining the path from development to deployment without requiring manual navigation of the Azure portal. Key capabilities The VS Code connector builder experience includes: AI‑guided connector creation to generate codeless connectors from API documentation using natural language prompts. Support for common authentication methods, including Basic authentication, OAuth 2.0, and API keys. Automated validation to check schemas, cross‑file consistency, and configuration correctness as you build. Built‑in testing to validate polling configurations and API interactions before deployment. One‑click deployment that allows publishing connectors directly to accessible Microsoft Sentinel workspaces from within VS Code. Together, these capabilities support a more efficient path from API documentation to a working Microsoft Sentinel connector. Testimonials As partners begin using the Sentinel connector builder agent, feedback from the community will help shape future enhancements and refinements. Here is what some of our early adopters have to say about the experience: “The connector builder agent accelerated our initial exploration of the codeless connector framework and helped guide our connector design decisions.” -- Rodrigo Rodrigues, Technology Alliance Director “The connector builder agent helped us quickly explore and validate connector options on the codeless connector framework while developing our Sentinel integration.” --Chris Nicosia, Head of Cloud and Tech Partnerships Start building This public preview represents an important step toward simplifying how ISVs build and maintain integrations with Microsoft Sentinel. If you’re ready to get started, the Sentinel connector builder agent is available in public preview for all participants. In the unlikely event that an ISV encounters any issues in building or updating a CCF connector, App Assure is here to help. Reach out to us here.
