Anthropic Claude Purview Data Connector showing all users as Guests..
It appears this connector is not mapping fields properly causing internal users to be mapped as "guests", and since prompts/data isn't maintained for guest users the connector is effectively not gathering anything but noise. Unlike the other data connectors, one cannot create field mappings. Also the app being named using the guid of Microsoft's own "dataassessments" service principal I don't think is intended either. Has anybody else experienced this? See below for an example.Extend Microsoft Purview data protection to AWS Bedrock agents for cross-cloud AI governance
Organizations are moving fast with AI, and many of those AI workloads are not staying in one cloud. A team might use Microsoft 365 and Microsoft Purview for governance and in addition to Microsoft Foundry they may still choose to run an AI agent on AWS Bedrock or on the Google Cloud Platform. The technical challenge is straightforward: how do you keep one consistent set of data security, governance, and compliance controls when the agent itself runs outside Microsoft Azure? This is where Microsoft Purview becomes the central policy engine for your data estate. In this post, we show why that matters and then walk through a practical example: an expense approval agent running on Amazon Bedrock, protected by Microsoft Purview Data Loss Prevention (DLP) policies. ExpenseApprovalAgent" details of the Agent blade Why Purview should be the central policy engine Most organizations do not want separate policy stacks for every cloud, every model endpoint, and every app team. That leads to duplicated controls, inconsistent enforcement, and audit gaps. The better model is to separate where workloads run from where policy decisions are made. That is the value proposition for Microsoft Purview in cross-cloud AI scenarios. Purview gives you: A consistent policy layer for sensitive information types such as credit card numbers, Social Security numbers, financial data, and other regulated content. A governance plane that can extend beyond Microsoft-hosted workloads into multi-cloud environments. A compliance framework with auditability, policy traceability, and a familiar operational model for security and compliance teams. A way to apply data-aware controls to AI interactions, not just to storage locations. In practical terms, that means the same organization that already trusts Purview to govern Exchange, SharePoint, Teams, and Copilot can use Purview to govern prompts and responses in a Bedrock-based agent as well. The key architectural shift is this: your app does not need to invent its own data policy engine. It can call Purview at the points where risk exists. What this Bedrock agent demonstrates The sample solution in this blog is a cross-cloud AI pattern: The frontend is a single-page browser-based chat app. Users authenticate with Microsoft Entra ID via MSAL. The backend runs in AWS Lambda. The model is Amazon Bedrock using Nova 2 Lite. Microsoft Purview evaluates prompts and model responses for DLP policy violations. This matters because it proves a broader point: Microsoft Purview can govern AI interactions even when the model and compute are not running in Azure. The core architecture As shown above the end-to-end flow follows this pattern: A user signs in through Microsoft Entra ID from the frontend. The frontend sends the user's access token and prompt to an API endpoint in AWS. The Lambda function exchanges that token using the On-Behalf-Of flow so Purview can evaluate under the signed-in user's identity. Purview scans the full prompt for sensitive information before the model is called. If the prompt is allowed, the Lambda function sends the request to Amazon Bedrock. Purview scans the model response before it is returned to the user. The frontend shows the result along with a Purview evaluation badge. That gives you two strong governance controls: In-line data loss prevention enforcement, which can block risky requests before they ever reach the model. Response-time enforcement, which can stop sensitive data from being returned even if a model generates it. The implementation also uses the user's identity for policy evaluation. That is important because governance decisions should reflect who is asking, not just what application is running. Why this pattern is useful for security, governance, and compliance teams There are three reasons this pattern is worth paying attention to. First, it aligns policy with risk rather than with hosting location. The compute might run in Lambda and the model might be in Bedrock, but Purview still remains the policy decision point. Second, it improves operational clarity. Security teams do not have to learn a different governance toolchain for each AI stack. They can keep using Purview concepts, policy models, and audit workflows. Third, it supports real-world adoption. Most large enterprises are hybrid and multi-cloud already. A governance pattern that only works for one vendor's runtime is not enough. Policy definition in Purview Two polices are needed to enforce DLP-a collection policy for Enterprise AI Apps and a DLP policy Collection policy 2. DLP policy Follow the steps outlined here to create the DLP policy for Enterprise AI Apps. Sample provided: purview-api-samples/DLPforCustomAIApps at main · microsoft/purview-api-samples To replicate this scenario, follow this link to the official GitHub repo: purview-api-samples/AWSBedrock at main · microsoft/purview-api-samples Once deployed, you will have: An AWS Lambda function that calls Amazon Bedrock. A browser frontend that authenticates with Microsoft Entra ID. Microsoft Purview evaluating both prompts and responses. A demo flow where safe prompts succeed and sensitive prompts are blocked. With the App and agent deployed, now comes the moment when the architectural value becomes clear. The model runtime is AWS Bedrock, but the policy decision is still coming from Microsoft Purview. Below screenshot shows the prompt containing sensitive information being blocked based on the policy evaluation by Purview. Minimal code integration requirements using the SDK Below is the code needed to perform the integration between Purview and Bedrock to perform the in and outbound inspection of content destined to and from the Bedrock model. Results of Purview’s verdict presented to user in the App UI Review governance evidence in Purview Data Security Posture Management Summary The bigger story here is not just that Microsoft Purview can protect an Amazon Bedrock agent. It is that organizations can centralize data security, governance, and compliance policy even while their AI architecture becomes more distributed across multiple clouds. That is the operational win. Developers keep the freedom to choose the best runtime and model platform. Security and compliance teams keep a central policy engine they already understand and trust. AI applications can be multi-cloud, but your data protection model does not have to be fragmented. Additional resources Configure Microsoft Purview - purview-sdk | Microsoft Learn Microsoft Purview Developer Platform Documentation - purview-sdk | Microsoft Learn
Purview DLP Behaviours in Outlook Desktop
We are currently testing Microsoft Purview DLP policies for user awareness, where sensitive information shared externally triggers a policy tip, with override allowed (justification options enabled) and no blocking action configured. We are observing the following behaviours in Outlook Desktop: Inconsistent policy tip display (across Outlook Desktop Windows clients) – For some users, the policy tip renders correctly, while for others it appears with duplicated/stacked lines of text. This is occurring across users with similar configurations. Override without justification – Users are able to click “Send Anyway/Confirm and send” without selecting any justification option (e.g. business justification, manager approval, etc.), which bypasses the intended control. New Outlook: Classic Outlook: This has been observed on Outlook Desktop (Microsoft 365 Apps), including: Version 2602 (Build 19725.20170 Click-to-Run) Version 2602 (Build 16.0.19725.20126 MSO) Has anyone experienced similar behaviour with DLP policy tips or override enforcement in Outlook Desktop? Keen to understand if this is a known issue or if there are any recommended fixes or workarounds.Enhancements to Device Status API & Logged-In User Email in Endpoint DLP
1. The Real‑World Problem Endpoint DLP analyst Faced (What Was Missing Earlier) Before the introduction of the Device Status API enhancements and logged‑in user visibility, Endpoint DLP teams consistently struggled in below discussed areas: Device Visibility Was Fragmented and Manual - Customers repeatedly told us: We know some devices are unhealthy, but we don’t know who owns them. We export the onboarding table to Excel every week just to understand drift. By the time we detect a policy issue, the user is already blocked or impacted. In practice, this meant: Device onboarding views were static snapshots, not operationally actionable. Admins relied on manual Excel exports to track onboarding, drift, and health. Reporting pipelines were brittle and always out of date. 2. Device Status API: Why Customers Asked for This (Beyond “Reporting”) The Hidden Cost of Excel‑Driven Operations as earlier, customers had to: Export device onboarding data manually. Rebuild dashboards every time they needed updated insight. Repeat this process weekly or even daily for compliance and SOC reviews. This approach failed at scale and created blind spots during incidents. When a device policy sync failed or appeared unhealthy, admins had no real‑time, view to answer basic questions like: Is this device configured correctly? Is the OS or Defender version lagging? Is the issue widespread or isolated? 3. What the Improvement Unlocks (New Operational Reality) From Static Views to Continuous Monitoring with the Device Status API: Device health, configuration status, policy sync state, OS version, and Defender version become query able signals Customers can power custom reporting and Advanced Hunting queries that are always current SOC and Endpoint teams finally share a single source of device truth This fundamentally changes how customers monitor Endpoint DLP not as a setup task, but as a living control plane. The Device Status API directly addresses this gap by making device‑level status continuously available through Advanced Hunting, allowing customers to build living dashboards instead of static reports. 4. The Old Workflow (Customer Pain) Historically, when a device showed: Policy Sync Failed Unhealthy Configuration mismatch Admins had to: Leave the Purview console Open Microsoft Defender for Endpoint or Intune Correlate device IDs or names Identify the user Start remediation This context‑switching cost time, accuracy, and confidence. 5. The New Reality: User Context Where It Matters Admins can now see who is logged in directly on the device onboarding page, aligning Windows with the macOS experience like: Immediate user context during device issues. Faster outreach and remediation. One unified investigative surface. What used to require three portals and multiple teams now happens in One Place. 6. When Customers Actually Needed This Data (But Didn’t Have It) This improvement wasn’t driven by curiosity it was driven by failure points in production. Some of the common customer scenarios listed below: Scenario Before Now / After Improvement Scenario 1: Quarterly Compliance Reviews Teams exported Excel files days before audits, resulting in stale data. Auditors questioned the reliability of reports. Advanced Hunting queries power live compliance dashboards. Reports are defensible because the data is always current. Scenario 2: Incident Post‑Mortems Teams struggled to answer whether devices were healthy at the time of the incident or if policies were enforced versus just configured. Reviews relied on assumptions. Device status, policy sync state, and OS/Defender versions are query able facts. Incident reviews shift from guesswork to evidence‑based analysis. Scenario 3: Silent Policy Drift Devices drifted due to OS updates, sensor lag, or configuration changes. Issues surfaced only after a DLP violation occurred. Policy drift becomes detectable before enforcement failures. Endpoint DLP acts as a reliability signal rather than a last‑line alarm. 7. New enhancement on Device Status API Device status API provides admins with access to device level information to integrate onboarded device information to custom reporting or use in advanced hunting queries. It has helped admins track down users associated with devices instead reaching out to Entra, on-premises Active Directory, or Intune team. During troubleshooting, if a device is not receiving policies on time, the device API allows quick identification of the device owner and assists in enabling always-on diagnostics or collecting logs directly from the device via Purview console. 8. Steps to capture User UPN Admin can find the device status by login to Security.microsoft.com as security admin. Click on Investigation and responses > Hunting > Advanced hunting. Device data can be found under DLPInfo JSON Column in the Deviceinfo table 3. Once we run above or any custom query as per requirement, you would see below as response. 4. Click on the loggedonuser field and expand the right-side information and look for DLPUPN under inspect record. 9. User login details on the Purview onboarding page Admins now can see who is currently logged in on the device onboarding page. This update aligns the Windows experience with macOS, allowing admins to respond quickly if necessary. In the past, when a device displayed a "Policy Sync Failed" or "Unhealthy" status, it was necessary to switch to Microsoft Defender for Endpoint (MDE) or Intune to identify the affected user. With this update, all relevant information is now accessible in a single view, streamlining the process. Benefits - Admins gain faster confirmation of device ownership and user context without extra investigation. It simplifies troubleshooting onboarding or policy issues by surfacing the user alongside other device insights like status and IP. No impact on users or DLP policies occurs, and it's enabled by default with no action required. 10.Steps to find User UPN on Purview admin console Login to Purview.microsoft.com with compliance admin > Select settings > Device onboarding > Select device Final Takeaway: Why This Matters More Than It First Appears "These enhancements evolve Endpoint DLP from a static, deployment‑centric control into a continuously observable, user‑context aware security signal, significantly reducing investigation time, operational overhead, and trust gaps at scale"Microsoft Purview Referential Architecture Diagrams
Microsoft Purview architecture diagrams provide a reference view of how classification, sensitivity labelling, Data Loss Prevention (DLP), Insider Risk Management, and Microsoft 365 Copilot protections work together across Microsoft 365 workloads. They illustrate how organisations can consistently identify, label, and protect sensitive data across endpoints, email, collaboration services, browsers, and AI‑assisted workflows—without prescribing a single deployment model. Classification generates sensitivity signals, labels express organizational protection intent, and DLP enforces that intent in real time across devices, apps, and services. Together, these patterns show how Copilot inherits existing security controls so AI‑generated content remains governed within the same compliance boundaries as organizational data.Endpoint DLP Collection Evidence on Devices
Hello team, I am trying to setup the feature collect evidence when endpoint DLP match. Official feature documentation: https://learn.microsoft.com/en-us/purview/dlp-copy-matched-items-learn https://learn.microsoft.com/en-us/purview/dlp-copy-matched-items-get-started unfortunately, it is not working as described in the official documentation, I opened ticket with Microsoft support and MIcrosoft Service Hub, Unfortunatetly, they don't know how to setup it, or they are unable to solve the issue. Support ticket: TrackingID#26040XXXXXXX9201 Service Hub ticket: https://support.serviceshub.microsoft.com/supportforbusiness/onboarding?origin=/supportforbusiness/create TrackingID#26040XXXXXXXX924 I follow the steps to configure: based on the Microsoft documentation, I should be able to see the evidence in Activity explorer or Purview DLP alert or Defender Alerts/Incidents.
Set Up Endpoint DLP Evidence Collection on your Azure Blob Storage
Endpoint Data Loss Prevention (Endpoint DLP) is part of the Microsoft Purview Data Loss Prevention (DLP) suite of features you can use to discover and protect sensitive items across Microsoft 365 services. Microsoft Endpoint DLP allows you to detect and protect sensitive content across onboarded Windows 10, Windows 11 and macOS devices. Learn more about all of Microsoft's DLP offerings. Before you start setting up the storage, you should review Get started with collecting files that match data loss prevention policies from devices | Microsoft Learn to understand the licensing, permissions, device onboarding and your requirements. Prerequisites Before you begin, ensure the following prerequisites are met: You have an active Azure subscription. You have the necessary permissions to create and configure resources in Azure. You have setup endpoint Data Loss Prevention policy on your devices Configure the Azure Blob Storage You can follow these steps to create an Azure Blob Storage using the Azure portal. For other methods refer to Create a storage account - Azure Storage | Microsoft Learn Sign in to the Azure Storage Accounts with your account credentials. Click on + Create On the Basics tab, provide the essential information for your storage account. After you complete the Basics tab, you can choose to further customize your new storage account, or you accept the default options and proceed. Learn more about azure storage account properties Once you have provided all the information click on the Networking tab. In network access, select Enable public access from all networks while creating the storage account. Click on Review + create to validate the settings. Once the validation passes, click on Create to create the storage Wait for deployment of the resource to be completed and then click on Go to resource. Once the newly created Blob Storage is opened, on the left panel click on Data Storage -> Containers Click on + Containers. Provide the name and other details and then click on Create Once your container is successfully created, click on it. Assign relevant permissions to the Azure Blob Storage Once the container is created, using Microsoft Entra authorization, you must configure two sets of permissions (role groups) on it: One for the administrators and investigators so they can view and manage evidence One for users who need to upload items to Azure from their devices Best practice is to enforce least privilege for all users, regardless of role. By enforcing least privilege, you ensure that user permissions are limited to only those permissions necessary for their role. We will use portal to create these custom roles. Learn more about custom roles in Azure RBAC Open the container and in the left panel click on Access Control (IAM) Click on the Roles tab. It will open a list of all available roles. Open context menu of Owner role using ellipsis button (…) and click on Clone. Now you can create a custom role. Click on Start from scratch. We have to create two new custom roles. Based on the role you are creating enter basic details like name and description and then click on JSON tab. JSON tab gives you the details of the custom role including the permissions added to that role. For owner role JSON looks like this: Now edit these permissions and replace them with permissions required based on the role: Investigator Role: Copy the permissions available at Permissions on Azure blob for administrators and investigators and paste it in the JSON section. User Role: Copy the permissions available at Permissions on Azure blob for usersand paste it in the JSON section. Once you have created these two new roles, we will assign these roles to relevant users. Click on Role Assignments tab, then on Add + and on Add role assignment. Search for the role and click on it. Then click on Members tab Click on + Select Members. Add the users or user groups you want to add for that role and click on Select Investigator role – Assign this role to users who are administrators and investigators so they can view and manage evidence User role – Assign this role to users who will be under the scope of the DLP policy and from whose devices items will be uploaded to the storage Once you have added the users click on Review+Assign to save the changes. Now we can add this storage to DLP policy. For more information on configuring the Azure Blob Storage access, refer to these articles: How to authorize access to blob data in the Azure portal Assign share-level permissions. Configure storage in your DLP policy Once you have configured the required permissions on the Azure Blob Storage, we will add the storage to DLP endpoint settings. Learn more about configuring DLP policy Open the storage you want to use. In left panel click on Data Storage -> Containers. Then select the container you want to add to DLP settings. Click on the Context Menu (… button) and then Container Properties. Copy the URL Open the Data Loss Prevention Settings. Click on Endpoint Settings and then on Setup evidence collection for file activities on devices. Select Customer Managed Storage option and then click on Add Storage Give the storage name and copy the container URL we copied. Then click on Save. Storage will be added to the list. Storage will be added to the list for use in the policy configuration. You can add up to 10 URLs Now open the DLP endpoint policy configuration for which you want to collect the evidence. Configure your policy using these settings: Make sure that Devices is selected in the location. In Incident reports, toggle Send an alert to admins when a rule match occurs to On. In Incident reports, select Collect original file as evidence for all selected file activities on Endpoint. Select the storage account you want to collect the evidence in for that rule using the dropdown menu. The dropdown menu shows the list of storages configured in the endpoint DLP settings. Select the activities for which you want to copy matched items to Azure storage Save the changes Please reach out to the support team if you face any issues. We hope this guide is helpful and we look forward to your feedback. Thank you, Microsoft Purview Data Loss Prevention Team
Microsoft 365 E7 & Agent365: From Where You Are to Enterprise AI at Scale
Introduction As organizations move beyond AI experimentation and begin operationalizing agent-based AI workloads, a new set of challenges is emerging governance, visibility, and control. Microsoft’s response to this shift is Microsoft 365 E7, introduced on May 1, 2026. It bundles: Microsoft 365 E5 Microsoft 365 Copilot Microsoft Entra Suite Microsoft Agent 365 This represents Microsoft’s strategic direction toward a human-led, agent-operated enterprise. However, a key pattern is emerging: Many organizations deploy Agent 365 and assume governance is complete. It isn’t. Understanding Agent 365: Control Plane, Not Control Source Agent 365 is not a standalone security solution, it is a control plane for AI agents. It provides: Agent registry and discovery Blueprint governance and lifecycle control Observability across agents Aggregation of signals from Entra, Defender and Purview Simple analogy Agent 365 is like a dashboard in a car It shows status It aggregates signals But it does not generate signals Without identity, data and threat signals → governance visibility is incomplete. The Key Gap: “Enabled” vs “Governed” Agent 365 can be enabled standalone but governance requires: Identity signals (Entra) Threat signals (Defender) Data risk signals (Purview) This gap between “enablement” and “full governance” is where most deployments fall short. Agent 365 Governance Maturity Heatmap The following heatmap summarizes how governance capabilities evolve as you layer the Microsoft stack: Capability Agent 365 on E3 + Defender Suite + Purview Suite + Entra Suite E7 (Full) Agent registry / inventory ✅ Full ✅ ✅ ✅ ✅ Shadow agent discovery ✅ Full ✅ ✅ ✅ ✅ Blueprint governance / kill-switch ✅ Full ✅ ✅ ✅ ✅ First-party agent observability ✅ Full ✅ ✅ ✅ ✅ Conditional Access for agents (P1) ✅ Already in BP/E3 ✅ ✅ ✅ ✅ ID Governance for agents (P1) ✅ Already in BP/E3 ✅ ✅ ✅ ✅ Risk-based CA / ID Protection (P2) ❌ ✅ ❌ ✅ ✅ MDA behavioral risk detection ❌ ✅ ❌ ❌ ✅ Risks column fully populated ⚠️ Entra only ⚠️ Entra + Defender ⚠️ Entra + Purview ⚠️ Entra + Network ✅ All signals Purview DLP for agent interactions ⚠️ Basic only ⚠️ Basic only ✅ Full ⚠️ Basic only ✅ Full DSPM for AI ❌ ❌ ✅ ❌ ✅ Shadow AI discovery (external tools) ❌ ❌ ❌ ✅ ✅ Security Copilot SCUs ❌ ❌ ❌ ❌ ✅ (via E5) 🔍 Interpretation of the Heatmap Key insight: Agent 365 on its own provides visibility and governance scaffolding, but true governance maturity emerges only when identity (Entra) threat (Defender), and data (Purview) signals are combined. Microsoft 365 E7 is the only SKU that delivers all signals, identity, security, compliance and AI governance in a single integrated model. What Works with Agent 365 Alone On Business Premium or E3 + Agent 365, you still get meaningful capabilities: Agent registry (full visibility) Shadow agent discovery Blueprint governance and kill-switch Entra Agent ID (identity registration) Conditional Access for agents (via Entra P1) ID Governance (via Entra P1) First-party agent observability This provides a strong governance foundation, especially for early-stage adoption. What’s Missing Without the Full Stack Without Defender, Purview, and Entra Suite key capabilities are limited: Risk-based Conditional Access (requires Entra P2) Behavioral threat detection (Defender) Data interaction governance (Purview DLP) AI data security posture (DSPM for AI) External shadow AI discovery (Entra Internet Access) Result: You can see agents exist but you cannot fully assess risk, behavior or data exposure. What changes across layers: Layer Added What Improves Defender Threat detection, behavioral risk Purview Data protection, AI data governance Entra Suite Network + identity-level AI control E7 Full integration across all layers Licensing Model: Clarifying Agent 365 Agent 365 licensing is simple but often misunderstood: Licensed per user (not per agent) Covers all agents owned or managed by that user Agents do not need individual licenses This eliminates agent sprawl licensing concerns and anchors governance to the user identity. Upgrade Math by Starting Point This is where architecture meets commercial reality. 📍 Business Premium Starting point: $22/user Step Add-on Total Step 1 Agent 365 ($15) $37 Step 2 Defender + Purview Combo ($15) $52 Step 3 Entra Suite ($12) $64 Step 4 Copilot + Intune Suite ~$95 👉 Full E7 Parity: ~$95/user 👉 E7: $99/user At this stage: Minimal price difference E7 adds Security Copilot + removes 300-user limit ✅ This is where consolidation becomes compelling. 📍 E3 Starting point: $39/user Component Cost E3 Base $39 Agent 365 $15 Defender Suite $12 Purview Suite $12 Entra Suite $12 Intune Suite $10 Copilot $30 Total $130/user 👉 E7: $99/user 💥 Delta: $31/user 💥 ~$74K/year extra for 200 users ✅Use Agent 365 for visibility if needed ✅Avoid building full add-on stack ✅Move to E5 or E7 early 📍 E5 Starting point: $60/user Remaining gaps: Copilot ($30) Entra Suite ($12) Agent 365 ($15) 👉 Total: $117/user 👉 E7: $99/user 💥 Savings: $18/user 💥 ~$108K/year for 500 users ✅ ~15% savings ✅ Simplified licensing ✅ This becomes a strong renewal conversation driver. Architectural Perspective AI governance requires layered architecture: Layer Function Agent 365 Control plane Entra Identity + access Defender Threat detection Purview Data protection Governance is not a feature, it is a system built on continuous signals across identity, security and data. How to Position This in Customer Conversations For Business Premium Start with Agent 365 Add Defender + Purview for maximum value For E3 Avoid incremental add-ons Move to E5/E7 For E5 Position E7 as cost optimization + simplification Final Thought Agent 365 is a foundational capability but it is not a complete solution. On its own, it gives you visibility and a governance layer. But enterprise AI governance is not just about seeing and managing agents it’s about understanding what they’re doing, what they’re accessing and whether they should be doing it at all. A simple way to think about it: Deploying Agent 365 alone is like setting up a badge system in your building you can track who is inside and control access. But without the broader security stack, you still can’t: Detect risky or unusual behavior Protect sensitive data from overexposure Enforce governance consistently across the environment Bottom Line Agent 365 provides the control plane Security and compliance services provide the signals Microsoft 365 E7 brings these together into a unified governance model The Strategic Shift Organizations are moving from: AI as tools → isolated productivity gains AI as systems → integrated workflows and automation AI as governed ecosystems → secure, compliant, and scalable operations Sustainable AI adoption is not defined by capability alone it is defined by how effectively that capability is governed at scale. E7 is not just a licensing evolution it represents a shift to an integrated AI operating model, where governance is embedded by design, not added as an afterthought.
DLP Policy - DSPM Block sensitive info from AI sites
Having issues with this DLP policy not being triggered to block specific SITs from being pasted into ChatGPT, Google Gemine, etc. Spent several hours troubleshooting this issue on Windows 11 VM running in Parallels Desktop. Testing was done in Edge. Troubleshooting\testing done: Built Endpoint DLP policy scoped to Devices and confirmed device is onboarded/visible in Activity Explorer. Created/edited DLP rule to remove sensitivity label dependency and use SIT-based conditions (Credit Card, ABA, SSN, etc.). Set Paste to supported browsers = Block and Upload to restricted cloud service domains = Block in the same rule. Configured Sensitive service domain restrictions and tested priority/order (moved policy/rule to top). Created Sensitive service domain group for AI sites; corrected entries to hostname + prefix wildcard a format (e.g., chatgpt.com + *.chatgpt.com) after wildcard/URL-format constraints were discovered. Validated Target domain = chatgpt.com in Activity Explorer for paste events. Tested multiple SIT payloads (credit card numbers with/without context) and confirmed detection occurs. Confirmed paste events consistently show: Policy = Default Policy, Rule = JIT Fallback Allow Rule, Other matches = 0, Enforcement = Allow (meaning configured rules are not matching the PastedToBrowser activity). Verified Upload enforcement works: “DLP rule matched” events show Block for file upload to ChatGPT/LLM site group—proves domain scoping and endpoint enforcement works for upload. Disabled JIT and retested; paste events still fall back to JIT Fallback Allow Rule with JIT triggered = false. Verified Defender platform prerequisites: AMServiceVersion (Antimalware Client) = 4.18.26020.6 (meets/exceeds requirements).
