Registration Open: Community-Led Purview Lightning Talks
Get ready for an electrifying event! The Microsoft Security Community proudly presents Purview Lightning Talks; an action-packed series featuring your fellow Microsoft users, partners and passionate Microsoft Security community members of all sorts. Each 3-12 minute talk cuts straight to the chase, delivering expert insights, real-world use cases, and even a few game-changing tips and tricks. Don’t miss this opportunity to learn, connect, and be inspired! Secure your spot now for the big day: April 30th at 8am Redmond Time. See agenda details below and follow this blog post (sign in and click the "follow" heart in the upper right) to receive notifications. ❗UPDATE❗This event is expected to last around 2 hours and 15 minutes, due to the incredible number of community sessions that were submitted! 💖 Please see the timing table below broken out into sections of four talks each, and plan to arrive 10 minutes before the section that interests you, OR stay for the whole time! Speakers will be available in the chat to answer your questions; please ask your questions during their session. Spillover Q&A forum links will also be shared. The full session recording will be indexed and posted to Microsoft Security Community YouTube within 24 hours after the event. Bookmark this page or follow this blog post for updates! Agenda Legend ↩️ Data Lifecycle Management 🔐 Information Protection 🚫 Data Loss Prevention (DLP) 🦾 Data Security Posture Management (DSPM) for AI 🤖 Purview for AI 👁️ Insider Risk Management (IRM) 🔍 eDiscovery 📊 Governance 🗒️ Compliance Manager 🛡️ Data Security All times are listed in US Pacific/Redmond Time. Session lengths are rounded to the nearest minute. AGENDA Section 1 - approximately 8:00 am - 8:43 am ↩️ The Day Offboarding Exposed Infinite Retention — Nikki Chapple Length: 10 minutes | Topic: Data Lifecycle Management A routine Purview request led to an unexpected discovery: more than 9,000 orphaned OneDrives and thousands of inactive mailboxes still storing content long after employees had left. This talk explains how a retain-only policy created hidden retention debt and how Adaptive Scopes can help organisations separate active users from leavers to avoid similar pitfalls. 🔐 The Purview Label Engine: Automated Classification, Translation, and co-Documentation for Enterprise Tenants — Michael Kirst-Neshva Length: 12 minutes | Topic: Information Protection Global enterprises face the challenge of implementing uniform data protection standards across borders and languages. In this talk, I’ll present a framework that makes Microsoft Purview labels truly scalable. Discover how to roll out parent and child label logics automatically, manage priorities with a single click, and generate instant compliance documentation for every business unit. 🗒️ What's In My Compliance Manager Toolbox: A Cloud Security Architect's Perspective — Jerrad Dahlager Length: 8 minutes | Topic: Compliance Manager A practical walkthrough of how I use Compliance Manager across real client engagements to map controls, track improvement actions, and simplify multi-framework compliance. No theory, just what works in the field. 🛡️ Stop, Think, Protect: Data Security in Real Life with Purview — Oliver Sahlmann Length: 8 minutes | Topic: Data Security With simple labels and matching DLP policies, Purview offers a practical and accessible way to approach data security. This lightning talk uses a real-life traffic light concept to show how a low barrier to adoption can still drive meaningful protection and awareness. Section 2 - approximately 8:44 am - 9:15 am 🔐 Using Purview to prevent oversharing with AI services — Viktor Hedberg Length: 10 minutes | Topic: Information Protection In this day and age, AI is the big thing. However, Copilot has access to everything you can access, including potentially sensitive data. In this session we will look at how to prevent Copilot to access highly sensitive data, using Information Protection. 🦾 How I Helped My Customers Understand their AI Usage (and protect their sensitive data) — Bram de Jager Length: 5 minutes | Topic: Data Security Posture Management (DSPM) for AI As AI tools explode across the web, many organizations still have no idea what’s actually happening in the browser—where employees type prompts, paste sensitive data, or visit public AI sites outside corporate governance. In this lightning talk, I’ll share how I helped customers shine a light on this issue. We’ll explore how Purview Data Security Posture Management (DSPM) can reveal which AI tools employees use, what types of data they input, and where sensitive information may leak through prompts. I’ll walk through real customer scenario where we detected risky AI usage patterns—such as employees pasting confidential documents into public chatbots. 🔐 Four Labels Max for Daily Use: Which Ones & Why? — Romain Dalle Length: 8 minutes | Topic: Information Protection Sensitivity labels are one of the most critical parts of a Purview Risk and compliance deployment, if not the most critical, because it directly impacts how end-users and business units should allow or restrict themselves to share their business data, internally and externally, on a daily basis. Labels have not other options than being precise, meaningful, and balanced in terms of embedded data security. Setting the right taxonomy is core to success, and is everything but a one-time project. 🚫 Data-driven Endpoint DLP Solution with Advanced Hunting — Tatu Seppälä Length: 8 minutes | Topic: Data Loss Prevention (DLP) This lightning talk shows you how to use KQL queries in advanced hunting to easily build initial sensitive service domain groups for authorized and unauthorized domains based on your organization's usage patterns. The same approach can be used for numerous other similar solution refinement and design purposes. Section 3 - approximately 9:16 am - 9:46 am 🔐 The Purview Hack No One Talks About: Container Sensitivity Labels That Fix Oversharing Fast — Nikki Chapple Length: 10 minutes | Topic: Information Protection Most organizations tackle oversharing with manual fixes, but the fastest solution is often overlooked. In this lightning talk, I show how container sensitivity labels automatically apply the right sharing and collaboration controls, ensuring every new Group, Team or SharePoint site starts secure by default. 🔍 Does M365 Support eDiscovery? — Julian Kusenberg Length: 11 minutes | Topic: eDiscovery A myth-busting session that separates perception from reality when it comes to Microsoft 365 eDiscovery capabilities. 📊 Improving Discovery, Trust, and Reuse of Analytics with Purview Data Products — Craig Wyndowe Length: 5 minutes | Topic: Governance This talk shows how bringing Power BI and Fabric assets into Microsoft Purview Governance Domains and Data Products creates a single, trusted view of enterprise analytics. By connecting reports, semantic models, and underlying data with shared metadata, ownership, and business context, organizations can make existing assets easy to discover and safe to reuse. 🔐 Why You Should Create Your Own Sensitive Information Types (SITs) — Niels Jakobsen Length: 5 minutes | Topic: Information Protection An in depth analysis of why Microsoft SITs are not one-size-fits-all, and how to create your own using what Microsoft has already built for you. Section 4 - approximately 9:47 am-10:30 am 👁️ From Zero to First Signal: Insider Risk Management Prerequisites That Actually Matter — Sathish Veerapandian Length: 8 minutes | Topic: Insider Risk Management (IRM) A focused live demo showing the real world prerequisites required for Microsoft Purview Insider Risk Management to work effectively. This session highlights the critical Entra ID, Intune, Microsoft Defender for Endpoint, and Purview DLP configurations that must be in place before creating IRM policies. 🤖 Securing data in the age of AI — Júlio César Gonçalves Vasconcelos Length: 11 minutes | Topic: Purview for AI AI will transform business as we know it; but without proper governance, it can introduce serious risks. We’ll show you how Microsoft Purview enables organizations to accelerate AI adoption while maintaining security, compliance, and transparency. 🔍 Beyond eDiscovery - Purview DSI for Security Investigation — Susantha Silva Length: 11 minutes | Topic: eDiscovery Most people hear “Microsoft Purview” and immediately think compliance, eDiscovery, or legal holds. But this session highlights Data Security Investigations, showing how DSI lets you take a DLP alert or insider risk signal and turn it into a structured investigation. 🚫 Elevating Purview DLP with a real world use case — Victor Wingsing Length: 14 minutes | Topic: Data Loss Prevention (DLP) Learn how I hardened Microsoft Purview DLP beyond out of the box defaults—closing real world data loss gaps, tuning policies to actual user behavior, and turning noisy alerts into protection that really blocks exfiltration. - Quick Closing/ Resource SharingAccelerate Your Security Copilot Readiness with Our Global Technical Workshop Series
The Security Copilot team delivers free, hands-on virtual technical workshops for practitioners looking to build AI-for-Security expertise across Microsoft Entra, Intune, Purview, and Threat Protection. These sessions help you onboard, configure, and operationalize Security Copilot—including working with agents—in real-world scenarios. Offered year-round across multiple time zones, they’re led by Microsoft engineering experts and focused on 100% technical, scenario-driven learning through demos, labs, and live Q&A. These workshops are ideal for Security Architects & Engineers, SOC Analysts, Identity & Access Management Engineers, Endpoint & Device Admins, Compliance & Risk Practitioners, Partner Technical Consultants and Customer technical teams adopting AI powered defense. Register now! Below is the schedule of global live deliveries as well as recorded versions of all Security Copilot Virtual Workshops. Join a live workshop: Start building Security Copilot skills—choose the product area and time zone that works best for you. Please take note of pre-requisites for each workshop in the registration page. Please note at the moment we are not able to accept participants from Russia, China and North Korea. Security Copilot Virtual Workshop: Copilot in Defender North America time zone June 24, 2026 at 8:00-9:30 AM (PST) - register here July 22, 2026 at 8:00-9:30 AM (PST) - register here August 19, 2026 at 8:00-9:30 AM (PST) - register here September 16, 2026 at 8:00-9:30 AM (PST) - register here Asia Pacific time zone June 24, 2026 - register here July 23, 2026 - register here August 20, 2026 - register here September 17, 2026 - register here Security Copilot Virtual Workshop: Copilot in Entra North America time zone June 17, 2026 at 8:00-9:30 AM (PST) - register here July 15, 2026 at 8:00-9:30 AM (PST) - register here August 14, 2026 at 8:00-9:30 AM (PST) - register here Asia Pacific time zone June 18, 2026 - register here Security Copilot Virtual Workshop: Copilot in Intune North America time zone June 3, 2026 at 8:00-9:30 AM (PST) - register here July 1, 2026 at 8:00-9:30 AM (PST) - register here July 29, 2026 at 8:00-9:30 AM (PST) -register here August 26, 2026 at 8:00-9:30 AM (PST) -register here September 23, 2026 at 8:00-9:30 AM (PST) -register here Asia Pacific time zone June 4, 2026 - register here July 2, 2026 - register here July 30, 2026 -register here August 27, 2026 -register here Security Copilot Virtual Workshop: Copilot in Purview North America time zone June 10, 2026 at 8:00-9:30 AM (PST) - register here July 8, 2026 at 8:00-9:30 AM (PST) - register here August 5, 2026 at 8:00-9:30 AM (PST) -register here September 2, 2026 at 8:00-9:30 AM (PST) -register here Asia Pacific time zone June 11, 2026 - register here July 9, 2026 -register here August 6, 2026 -register here September 3, 2026 -register here October 1, 2026 -register here Can't join live? No problem! Access the recordings and workshop guides Copilot in Defender workshop recording Workshop guide Copilot in Purview workshop recording Workshop guide Copilot in Entra workshop recording Workshop guide Copilot in Intune workshop recording Workshop guide Learn and Engage with the Microsoft Security Community Log in and follow this Microsoft Security Community Blog and post/ interact in the Microsoft Security Community discussion spaces. 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.. Learn about the Microsoft MVP Program. Join the Microsoft Security Community LinkedIn and the Microsoft Entra Community LinkedIn
Why “Data in Switzerland” Is Not Enough
Moving from Residency to Control in Microsoft 365 Every conversation about data sovereignty in regulated industries tends to start the same way: “We use Multi-Geo. The data stays in Switzerland.” It’s the right starting point. Microsoft 365 Multi-Geo allows organizations to place selected workloads - SharePoint sites, OneDrive accounts, Teams data, or Exchange mailboxes - into specific regions, including Switzerland, while maintaining a single global tenant. This makes it possible to align sensitive data with regulatory or customer requirements without fragmenting the overall environment. But it only answers one question: Where is the data stored? It does not answer who accessed the data, from where, under which conditions, or what happened after access. That is where the real problem begins. A scenario that happens every day A Swiss engineering firm stores sensitive project documentation in Switzerland using Multi-Geo. An external contractor - working from an unmanaged device outside Switzerland - is granted access to review a file. The document opens. The data is now on a screen in an unknown location, on a device with no compliance posture, in a session with no restrictions. From the platform’s perspective, residency was enforced. From a sovereignty perspective, control was lost the moment access was granted without conditions. The file never left Switzerland. But sovereignty did. Residency is static. Control is not. The moment a document is opened, storage location stops being the relevant boundary. The file is no longer just “in Switzerland.” It moves instantly across endpoints and browsers, collaboration tools like Teams, external users and partners, and increasingly AI-driven contexts. The infrastructure remains unchanged. The data does not. From the platform’s perspective, everything is working as designed - access was granted, residency was enforced - and control was lost. Most “data in Switzerland” strategies fail at exactly this moment: when the data is used. The shift: from location to conditions If data sovereignty is the goal, the question must change. Not “Where is the data stored?” but: Under which conditions can data be accessed and used? This shift fundamentally changes the architecture. Control must be applied across three distinct layers - and all three must be connected. Layer 1: Access is conditional, not static Conditional Access extends control beyond authentication and turns it into continuous evaluation. Access decisions can depend on: Device compliance Location (geo-restriction) Identity and risk signals Multi-Geo ensures data is placed correctly. Conditional Access ensures it is reachable only under defined conditions. The two must work together - residency without access governance is an incomplete control. Layer 2: The session is the real risk surface Even with strict access controls, risk remains. A session is an exposure surface by design. During an active session, data is viewed, copied, shared, processed by applications, and connected to AI prompts. The gap does not appear at storage or authentication. It appears during active usage - inside the session. This is the layer most architectures do not explicitly address. Controls must extend into the session itself: limiting data transfer and replication, restricting interaction patterns, and enforcing policies in real time. Access is no longer a one-time event. It becomes continuously governed. This becomes even more critical as AI assistants consume content across SharePoint, Teams, Exchange, and other Microsoft 365 services. The question is no longer only where the source document resides - but whether the AI interaction itself is governed by the same access and protection controls as direct access. Layer 3: The document becomes the control point The most durable control does not sit in the network or in the session. It sits in the data itself. In regulated industries, organizations often arrive at this architecture having first evaluated sovereign or national encryption solutions. The decision to rely on native Microsoft 365 Purview encryption rather than a separate layer comes down to integration: AES-256 protection operating natively at file, user, and SharePoint level - including geo-based access restrictions - without an additional system to maintain. When protection is applied directly to the document through Microsoft Purview: Sensitivity labels define classification - automatically assigned based on content Encryption enforces access - AES-256, bound to the file itself IRM controls usage - view, copy, print, share, and presentation rights DLP governs movement across services - preventing data from leaving defined boundaries Dynamic watermarking tracks exposure - applied on open, view, or print At that point, access is enforced by the file, usage restrictions travel with it, and control persists regardless of location. The document becomes the perimeter. Platform control: limiting provider access One dimension often overlooked in sovereignty discussions is platform access itself. Even a perfectly configured tenant is only as sovereign as the controls placed on the operator. Customer Lockbox ensures that even Microsoft support cannot access customer data without explicit, logged, time-bound approval. Every access request is visible, auditable, and subject to customer veto. Data control applies not only to users - but also to the platform operating the service. Enforcement requires an integrated architecture Most organizations already have the required capabilities: Multi-Geo, Conditional Access, session control, Purview (labels, encryption, DLP, IRM), and monitoring. The issue is not capability. It is fragmentation. In practice, fragmentation looks like this: residency is configured in one project, Conditional Access policies are managed by a different team, and Purview labels were applied during a compliance initiative that never connected to the access layer. The tools exist. The signals do not flow between them. When designed as a single architecture: Data is placed intentionally - residency aligned to regulatory requirements Access is governed by context - device, location, and identity evaluated continuously Usage is controlled dynamically - session-level restrictions enforced in real time Protection is embedded in the document - encryption and IRM travel with the file Signals are connected across the platform - monitoring feeds access policy, not just audit logs “Data in Switzerland” becomes not just a statement - but an enforceable system property. Closing thought Placing data in Switzerland is the right first step. Multi-Geo makes it possible, even in global environments. But residency alone is not control. Data residency answers where information is stored. Data sovereignty requires proving who can access it, under which conditions, and what controls remain in place after access is granted. In Microsoft 365, sovereignty is no longer defined by geography alone. It is defined by the ability to enforce control wherever the data travels.Migrate 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.State Explosion Security Problem in AI-Era Software Supply Chains
Introduction To see why this problem scales so quickly, start with the smallest possible change: a single line of code. In modern software, even a tiny edit is rarely just a local modification. It can change execution flow, introduce a new dependency, expose sensitive data, or quietly shift the purpose of the package itself. What looks trivial in a diff can create a materially different security outcome. That is why supply chain defenders cannot afford to treat small code changes as small security events. How a Single Line Changes Package Intent Every software package exists in a particular state at a particular moment in time. Imagine a benign version — State X — that behaves exactly as intended. Now add one line of code. That small edit can shift the package into a new state with different behavior and, potentially, a very different risk profile. The security issue is not the added line by itself. It is the fact that the package now has to be interpreted differently. A tiny diff can change the role of the entire component, which means defenders have to reason about the resulting behavior, not just the textual change. That is why file-level scanning breaks down so quickly. A change in one file can alter the behavior of the entire package because software semantics emerge from how components interact. Security systems therefore need to analyze packages as composed systems, not as a series of isolated file edits. Why the whole package matters This matters even more in modern supply chain attacks, where malicious intent is rarely concentrated in one obvious file. More often, the behavior is distributed across several files that look harmless when viewed independently. File A defines an encoded string constant. Looks like a config value. File B provides a decode function. Looks like a utility. File C (setup.py / postinstall) imports both, decodes, and executes. Viewed independently, each file may appear benign. No single file has to trigger a clear signature, rule, or heuristic. The malicious behavior only becomes visible when you reconstruct how the files interact as a system. Any scanner that evaluates files one by one without rebuilding that interaction is likely to miss the real behavior. Why every change demands re-analysis Every meaningful state change — a commit, pull request, version bump, or package publish — can alter the semantics of the software. That means defenders cannot stop at diff inspection or lightweight pattern matching. The real question is not only what changed, but what the software now does. Quantifying the problem The scale of the problem becomes clearer when you look at how many software state changes occur across the ecosystem every day: GitHub alone recorded nearly 1 billion commits in 2025, merged an average of 43.2 million pull requests per month, and now hosts roughly 630 million repositories. In 2026, GitHub was projected to reach roughly 38 million commits per day. npm has grown to well over 2 million packages, making JavaScript one of the largest public package ecosystems. PyPI published more than 130,000 new projects in 2025 and more than 3.9 million new files in the same year. NuGet serves package downloads at massive operational scale, with recent weekly totals in the 5 to 6 billion range. Maven Central indexed more than 20 million packages and published more than 3.2 million packages in 2025. Taken together, these ecosystems are generating an enormous stream of new software states. Some numbers describe repositories, some describe publishes, and some describe downloads, but they all point to the same reality: the scale of software movement is already massive before you even account for the acceleration from AI-assisted development. The number of state changes is already enormous, and AI-assisted development is increasing it even further. The result is not just more code, but more package states that may require meaningful security interpretation. Why the math breaks traditional scanning Assume a single semantic package analysis takes 30 seconds, which is a reasonable range for LLM-based inference. Scanning 50,000 packages would require roughly 1.5 million seconds of compute time per day — about 417 hours. But the ecosystem only gives defenders 24 hours before the next wave of packages arrives. Without aggressive parallelism and purpose-built infrastructure, backlog becomes inevitable. The scanning bottleneck This leaves modern scanning systems with a fundamental bottleneck: Heuristic and signature-based scanners are fast. They can match known patterns in milliseconds and work well for familiar malware families or repeated behaviors. Some systems also use emulation or detonation, but these approaches still struggle to deliver deep reasoning at ecosystem scale. That makes them easier to bypass with novel, well-structured, or AI-generated code that behaves maliciously without resembling previously known samples. LLM-based semantic analysis can reason about intent. It can follow behavior across files, recognize obfuscated exfiltration paths, and explain why a package is suspicious even when the code appears ordinary at first glance. The tradeoff is cost, latency, and trust: inference takes seconds rather than milliseconds, and a single package may require multiple reasoning passes. At ecosystem scale, that becomes a serious infrastructure challenge. Neither approach is sufficient on its own. Heuristics provide speed without deep understanding, while semantic models provide understanding without inherent scale. Closing the gap requires systems that combine both: package-level reasoning with the latency and throughput needed for production supply chains. Heuristics often miss novel attacks, while LLM-based approaches remain too slow to apply inline at large scale. That gap between understanding and throughput is where supply chain malware can persist. What needs to change Closing that gap will require a different class of supply chain security systems. Detonation can help in some cases, but it is too slow and expensive to apply inline to every package state change. What is needed is a system that can: Analyze entire packages as a unit — not individual files. The intent lives in the interaction between files, not within any single one. Run semantic analysis at data-plane speed — every package, every version, on the hot path, with latency low enough for inline enforcement. Not async advisories. Not CI-time checks. Inline, before delivery. Handle the state explosion — millions of state changes per day, each requiring full re-analysis. This is an infrastructure problem as much as a security problem: rate limiting, backpressure, connection pooling, regional failover, model versioning — the same hard distributed systems problems, with security stakes. Maintain high accuracy under evasion — attackers deliberately use encoding, string splitting, dynamic imports, polyglot files, and similar techniques to reduce detection quality. The scanner must continue to classify packages accurately even when the code is designed to obscure intent. The Latency-Accuracy Tradeoff: Malware Detection as an ML Problem At cloud scale, malware detection is governed by a hard tradeoff between latency, accuracy, throughput, and cost. The fastest detectors are typically shallow: signatures, heuristics, and lightweight models can make decisions in milliseconds, but they often miss novel, compositional, or intent-level attacks. Deeper semantic analysis can improve recall and resilience against evasion, but it also increases inference time, compute cost, and operational complexity. As a result, defenders cannot optimize for accuracy in isolation; they must deliver strong detection quality within strict performance constraints. This makes malware detection not just a cybersecurity problem, but a machine learning and distributed systems problem. In modern software supply chains, AI-assisted development increases the number of package states and enables attackers to generate variants at high speed, expanding the space defenders must reason over. The challenge is therefore to build detection architectures that preserve semantic depth while remaining fast enough for inline use at global scale. The gap between the rate of software change and the capacity to analyze it is widening. That gap is the attack surface. If defenders cannot inspect software at the speed it is being produced and published, attackers will continue to exploit the delay. What the industry needs now is a cloud-scale malware analysis capability that can deliver low latency, low cost, high accuracy, and the flexibility to meet different operational requirements , such as SLAs, false-positive tolerance, and enforcement policies , without compromising on package-level semantic analysis.Security Dashboard for AI: 3 Ways CISOs Drive Impact Today
AI is reshaping the enterprise and, with it, the threat landscape. Today's organizations face new threats with AI agents that modify configurations, execute workflows, and access data without direct human oversight. As a result, the gap between AI adoption and AI governance is widening, and CISOs face growing challenges to maintain visibility, control, and compliance across an increasingly complex ecosystem. As AI becomes embedded across the enterprise, CISOs face four key challenges: Scale without visibility: Over 75% of enterprises surveyed by PWC report they are already adopting AI agents. ¹ At the same time, over 80% of security teams surveyed by Nokod report visibility gaps into the applications and AI agents created within their organization. ² Rapid AI proliferation and evolving regulations make unified visibility across AI platforms, apps, and agents critical for CISOs. Fragmentation: Organizations rely on multiple siloed tools for AI asset visibility, making oversight fragmented and inefficient. According to Gartner’s 2024 survey of 162 enterprises, organizations use 45 cybersecurity tools on average. Expanding AI risk: AI proliferation is rapidly increasing the attack and risk surface, with the surge of AI-generated identities. By 2027, 4 out of 5 organizations will face phishing attacks powered by AI-generated synthetic identities, according to IDC. ³ This makes it harder for CISOs to track emerging threats, unmanaged assets, and shifting risk patterns. Overload: Alert fatigue is now a top challenge, with organizations now receiving an average of 2,992 security alerts daily, yet 63% go unaddressed. ⁴ Increasing AI risk without a way to prioritize what matters most compounds pressure on CISOs. In conversations between Microsoft and CISOs, one common need emerged: a single place to view integrated AI risk across the enterprise. To address these growing challenges, we are excited to provide CISOs with the Security Dashboard for AI, which recently became generally available. This unified dashboard aggregates posture and real-time risk signals from Microsoft Defender, Entra, and Purview into one unified, executive-level view of AI posture, risk, and inventory across agents, apps, and platforms. The Security Dashboard for AI helps CISOs: Gain unified AI risk visibility: Discover AI agents and applications and continuously monitor posture across the environment Prioritize critical risks: Correlate signals across identity, data, and threat protection to surface the most urgent issues Drive risk mitigations: Investigate activity and take action to help reduce exposure across the AI ecosystem The dashboard is capable of aggregating and surfacing AI risks from across Microsoft Defender, Entra, Purview - including Microsoft 365 Copilot, Microsoft Copilot Studio agents, and Microsoft Foundry applications and agents as well as cross-platform AI risks with Microsoft network-based or SDK-enabled integrations, and MCP servers. This supports comprehensive visibility and control, regardless of where applications and agents are built. As you activate Microsoft Security for AI capabilities, you can gain richer visibility into different aspects of your AI risk posture. Figure 1: Security Dashboard for AI in browser Getting Started with the Security Dashboard for AI The Security Dashboard for AI is provided at no additional cost to customers already using Defender, Entra, and/or Purview to protect their AI innovation. Based on how early adopter CISOs are using the dashboard, here are three ways you can start leveraging the dashboard today. 1. Manage Daily AI Risk Beyond reporting, you must stay hands-on with AI risks, scanning for emerging issues, verifying asset governance, and delegating remediations. The Security Dashboard for AI consolidates daily operations into a single pane of glass, surfacing critical alerts, unmanaged assets, and emerging risks. Use the dashboard as a daily AI risk radar, enabling rapid triage and ensuring you focus on the most urgent threats. Scan and triage daily AI risk: Start each day by identifying and prioritizing the highest-risk AI exposures. Risks are prioritized on severity reported by underlying security tools, helping you focus on the most critical exposures. Track AI asset inventory and monitor agent sprawl: Use the Inventory page to gain comprehensive visibility into all AI assets. Identify newly registered assets to mitigate the risk of shadow or unmanaged IT and surface inactive agents to proactively monitor and control agent sprawl. Delegate tasks for remediation: Move from insight to action by delegating tasks to your security team with easy click delegation. Delegation routes ownership via email or Microsoft Teams with notifications, due date, and ownership tracking. Delegate actions to specific roles such as global admin and AI administrator, without granting full access to underlying tools. Figure 2: Security Dashboard for AI risk page 2. Guide Briefings with Security Teams You require up-to-date intelligence to guide conversations with Security Teams about what is happening across the AI estate. The Security Dashboard for AI helps you anchor discussions in specific risks, trends, and ownership gaps surfaced in the data. The dashboard becomes a conversation driver, helping you ask the right questions about risk and security posture, to help ensure you and your team are triaging the right priorities. Because the dashboard consolidates signals from Defender, Entra, and Purview, both CISO and security teams operate from the same facts, enabling more outcome-driven discussions and faster prioritization, so you can shift the conversations from status updates to targeted action planning. Prioritize top AI Risk: Use the dashboard to help you prioritize the AI risk that matters the most. In preparation for team meetings, use Microsoft Security Copilot to explore AI risks, agent activity, and security recommendations via prompts to strengthen your AI security posture. With your team, take a closer look at risk vectors like data leakage, oversharing and unethical behavior, and discuss what actions need to be taken. Review Security Recommendations: Create a routine with your security team to review the recommended Microsoft security actions and track your progress over time. Across regular team check‑ins, review what has been addressed, what remains open, and which actions require follow‑up so you are prepared to respond to regulatory, audit, or executive questions with up‑to‑date metrics. Figure 3: Security Dashboard for AI inventory page Figure 4: Security Dashboard for AI delegation 3. Executive Reporting Reporting to the board on AI security posture has historically meant weeks of manual data gathering across multiple tools. The Security Dashboard for AI streamlines the data collection process with a single source of truth for AI risk, enabling confident, data-backed insights for your board presentations and conversations. Early adopters confirm the value and are using it for quarterly executive briefings. Prepare for Board Discussions: Use the dashboard to help get the right insights at the right altitude to help you prepare for discussions with your board. The Overview page aggregates identity, data security, and threat protection signals from Defender, Entra, and Purview into an AI risk scorecard with risk factors. The embedded Security Copilot AI-powered insights provide suggested prompts with risk assessments, summaries, and recommendations to help you prioritize what matters most. Extend Observability to Executive Stakeholders: Authorize AI risk follow‑ups to the appropriate security, identity, or governance owners using Microsoft Teams or email. Distribute visibility across GRC lead, AI governance, and IT leaders, while maintaining executive‑level oversight. Figure 5: Security Dashboard for AI Copilot prompt gallery Next Steps The Security Dashboard for AI helps CISOs manage AI risk faster, more confidently and more collaboratively with their team. Defender, Entra, and Purview signals are surfaced in a single pane of glass, providing observability across your AI estate. Drive faster triage, use data to support board-level discussions about AI risk, and enable coordinated action with integrated insights, recommendations, and delegation to help accelerate remediation across existing security workflows. The Security Dashboard for AI is generally available now. If your organization uses Microsoft Defender, Entra, and/or Purview, you already have access, no additional licensing is required. Visit ai.security.microsoft.com to access the dashboard directly, or navigate to it from the Defender, Entra, or Purview portals. Learn more about the Security Dashboard for AI on the MS Learn page and the Security Dashboard for AI Security Blog. Discover new features in the Security Dashboard for AI such as the Security Reader role, new delegation flow, and new identity risk section here. ¹AI agent survey. PwC, May 2025 ²Security Teams Taking on Expanded AI Data Responsibilities. Bedrock Data, March 2025 ³IDC FutureScape: Worldwide Security and Trust 2026 Predictions, November 2025 ⁴2026 State of Threat Detection and Response Report. Vectra AI, February 2026Security 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, 2026
Unsanctioned cloud apps generates constant alerts
When I mark a cloud app as unsanctioned it created a URL based indicator to block the site. However, it also by default enables the Generate Alert option on the indictor. This causes my SOC to bet inundated with garbage alerts. Now normally if I'm just unsanctioning one Cloud App a could go and turn of the alert. However, I use cloud app policy that will identify any new Cloud Apps in an entire category and then unsanction it. But it enables Generate Alert on the URL indicator. Then if someone accesses that new one the generate alert kicks off. I don't want to have to go into every new app and untick generate alert manually that's just too time consuming. Is there a way to change the default behaviour when adding an indicator to not enable the generate alert? Of is there some other way to do this? I could consider using power automate or something but I'd rather the default behaviour be the fix as automation can break. I don't have time to babysit it.Security Copilot Agents in Defender XDR: where things actually stand
With RSAC 2026 behind us and the E5 inclusion now rolling out between April 20 and June 30, anyone planning SOC workflows or sitting on a capacity budget needs to get a clear picture of what is GA, what is preview, and what was just announced. The marketing pages tend to blur those lines. This is my sober look at the current state, with the operational details that matter for adoption decisions. What is actually shipping right now The Phishing Triage Agent is GA. It only handles user-reported phish through Defender for Office 365 P2, but for most SOCs that is a meaningful chunk of the L1 queue. Verdicts come with a natural-language rationale rather than just a label, which is the part that determines whether analysts will trust it. The agent learns from analyst confirmations and overrides, so the feedback loop matters more than the initial setup. There is a setup detail that is easy to miss: the agent will not classify alerts that have already been suppressed by alert tuning. The built-in rule "Auto-Resolve - Email reported by user as malware or phish" needs to be off, and any custom tuning rules that touch this alert type need review. If you skip this, the agent runs on an empty queue and you wonder why nothing is happening. The Threat Intelligence Briefing Agent is also GA. It produces tenant-tailored intel briefings on a regular cadence. Useful, but lower operational impact than the triage agents. Copilot Chat in Defender went GA with the April 2026 update. Conversational Q&A inside the portal, grounded in your incident and entity data. This is the lowest-risk way to get value out of Security Copilot and probably where most teams should start. Public preview, worth watching The Dynamic Threat Detection Agent is the most technically interesting one. It runs continuously in the Defender backend, correlates across Defender and Sentinel telemetry, generates its own hypotheses, and emits a dynamic alert when the evidence converges. Detection source on the alert is Security Copilot. Each alert includes the structured fields (severity, MITRE techniques, remediation) plus a narrative explaining the reasoning. For EU tenants the residency point is worth confirming with whoever owns data protection in your org: the service runs region-local, so customer data and required telemetry stay inside the designated geographic boundary. During public preview it is enabled by default for eligible customers and is free. At GA, currently targeted for late 2026, it transitions to the SCU consumption model and can be disabled. The Threat Hunting Agent is also in public preview. Natural language to KQL with guided hunting. Lower stakes, but useful for teams without deep KQL expertise on hand. Announced at RSAC, still preview Two agents got the headlines in March: The Security Alert Triage Agent extends the agentic triage approach beyond phishing into identity and cloud alerts. The longer-term direction is consolidating phishing, identity, and cloud triage under a single agent. Rollout is from April 2026, in preview. The Security Analyst Agent is the multi-step investigation agent. Deeper context across Defender and Sentinel, prioritised findings, transparent reasoning trace. Preview since March 26. Both look promising on paper, but Microsoft's history of preview features that take a long time to mature is well-documented. I would not plan production workflows around either of them yet. What you actually get with the E5 inclusion This is the licensing change most people are dealing with right now. Security Copilot has been part of the E5 product terms since January 1, 2026. Tenant rollout is phased between April 20 and June 30, 2026, with a 7-day notification before activation. The numbers: 400 SCUs per month for every 1,000 paid user licenses Capped at 10,000 SCUs per month, which you hit at around 25,000 seats Linear scaling below that, so a 3,000-seat tenant gets 1,200 SCUs per month No rollover, the pool resets monthly What is included: chat, promptbooks, agentic scenarios across Defender, Entra, Intune, Purview, and the standalone portal. Agent Builder and the Graph APIs are in. If you also run Sentinel, the included SCUs apply to Security Copilot scenarios there. What is not included: Sentinel data lake compute and storage. Those still run through Azure on the regular meters. Beyond the included pool you pay 6 USD per SCU pay-as-you-go, with 30 days notice before that mode kicks in. Practical things worth knowing before activation A few details that are easy to miss in the docs: Under System > Settings > Copilot in Defender > Preferences, switch from Auto-generate to Generate on demand. Auto-generate will burn SCUs on incidents nobody is going to look at. Generate on demand gives you direct control. In the Security Copilot portal workspace settings, check the data storage location and the data sharing toggle. Data sharing is on by default, which means Microsoft uses interaction data for product improvement. If your compliance position does not allow that, change it before agents start running. Changing it requires the Capacity Contributor role. Agent runs are not equivalent to the same number of analyst chat prompts. A triage agent processing fifty alerts in one run consumes meaningfully more SCUs than fifty manual prompts on the same data. If you have a high-volume phishing pipeline, model that out before you flip the switch broadly. The usage dashboard in the Security Copilot portal breaks down consumption by day, user, and scenario. Output quality depends on telemetry quality. Flaky connectors, gaps in log sources, or a high baseline of misconfigured alerts will produce verdicts that match. Connector health monitoring (the SentinelHealth table in Advanced Hunting is a sensible starting point) is a precondition. The agents only improve if analysts feed the override loop. If your team treats the verdicts as background noise rather than confirming or correcting them, the feedback signal is lost and calibration stays where it shipped. That is a process problem, not a product problem, but it determines whether any of this is worth the SCUs. A reasonable adoption order A rough sequence that minimises capacity surprises: Copilot Chat in Defender first. Lowest risk, immediate value through natural language Q&A in the investigation context. Phishing Triage Agent on a controlled subset, with a review cadence in place. Check the built-in tuning rules first. Watch the SCU dashboard for the first month before adding anything else. Let the Dynamic Threat Detection Agent run while it is in public preview, since it is default-on and free anyway. Compare its alerts against existing Sentinel detections. Security Alert Triage Agent for identity and cloud once the phishing baseline is stable. Establish a monthly review covering agent decisions, false-positive rate, SCU cost, and MTTD/MTTR trends. Technically, agentic triage is moving past phishing into identity and cloud, and the Dynamic Threat Detection Agent represents a genuine attempt at the false-negative problem rather than just another rule engine. Lizenziell, the E5 inclusion removes the biggest barrier to adoption that previously existed. The risk is enabling everything at once. Agents that nobody reviews are agents that consume capacity without delivering value, and the SCU dashboard is the only thing that will tell you that is happening. One agent, one use case, a 30-day baseline, then the next one. The order matters more than the speed.Intent‑Aware Static Inspection for Agent and Skill Packages
Where AV helps—and what it may not cover Antivirus engines and traditional code scanners are highly effective at identifying known or suspicious executable content, such as binaries, scripts, or exploit patterns. For YAML‑based agent and skill packages, the situation can be different. These packages are often intentionally minimal to reduce distribution overhead and support faster inference. As a result, a configuration file may appear benign from a malware perspective, yet still introduce risk depending on how instructions are written and interpreted. For example, areas that may warrant closer review include: Instructions that influence how data is accessed, processed, or reused across requests Language that expands scope beyond an agent’s or skill’s stated purpose Requests for sensitive information outside expected or documented workflows Guidance that affects how untrusted or external inputs are handled during inference These scenarios do not necessarily indicate malicious intent, but they highlight cases where traditional scanning alone may not fully capture behavioral risk. What to look for when the “payload” is instructions When you review an agent or skill package, you’re effectively reviewing a compact behavior specification. In instruction‑driven designs—often chosen to keep inference paths fast and simple—the goal is not to analyze complex code, but to understand what behavior the instructions enable. A few practical signals include: Intent drift: the description is narrow, but the instructions encourage broader collection, retention, or escalation Overreach by default: language such as “always,” “for every user,” “across all workspaces,” “keep trying,” or “don’t stop until” Exfiltration pathways: instructions to send outputs to external endpoints, webhooks, or reporting channels not aligned with the stated purpose Credential‑related cues: asking users to provide secrets, tokens, recovery codes, or to authenticate outside expected flows Stealth language: “avoid logging,” “don’t mention this to the user,” “run quietly,” or “hide the reason” Injection susceptibility: treating untrusted text as commands (for example, “follow the user’s pasted script exactly” or “execute whatever is in the ticket”) A better model: intent-aware static inspection One practical way to approach review is to treat the instructions as a compact behavior specification. In many agent and skill designs, this specification is intentionally concise to support low latency, low inference cost, and efficient execution. The goal of inspection is not to second-guess that design choice, but to ensure the enabled behavior matches the stated purpose and expected boundaries. By applying intent-aware static inspection with explicit thresholds, review effort was focused on higher-risk packages. Over a one-month internal evaluation, approximately 400 agent and skill packages were reviewed with 1 observed false positive (< 0.0001%), reflecting high detection accuracy. At the same time, the approach preserves system efficiency, delivering low latency (under 10 seconds for most packages) and consistently low inference cost. A lightweight review workflow model Normalize the package: extract human‑readable fields (descriptions, system prompts, tool instructions, examples) and ignore structural YAML details Summarize intended behavior: describe what the agent or skill is expected to do in plain language, independent of implementation Check for higher‑risk actions: broad data access, external sharing, credential requests, persistence, or stealth behavior Decide with thresholds: route low‑risk, narrowly scoped packages differently from those with broader reach or reuse Keep an audit trail: retain a brief summary of extracted intent and review rationale to support iteration over time Final thoughts YAML‑based agent and skill packages are not inherently risky; they are often chosen precisely because they enable simpler distribution and faster inference. The key consideration is how instruction‑defined behavior aligns with expectations and boundaries as packages evolve and are reused. Combining traditional scanning with lightweight, intent‑aware inspection helps teams preserve the benefits of fast, instruction‑driven systems while improving confidence in how those systems behave in practice.
