How much of your Microsoft 365 environment can you actually see at once?
Not how many users you have. Not how many sites, teams, apps or flows you have. How much of it can you actually see connected together? I've been working across Microsoft 365 environments for a while, and I kept running into the same thing. There is no shortage of information. If anything, there is probably too much of it. Users, groups, permissions, SharePoint, Teams, Power Apps, Power Automate, Power BI, Dataverse, OneDrive, Exchange, Intune, licensing, configuration... It's all there. But when you're actually trying to understand how everything fits together, it can be a different story. You open one blade. Find something. Open another. Cross-check it. Go back. Open something else. Before long, you're jumping between different parts of the tenant trying to build the bigger picture in your head. And if you're working with larger environments, that gets difficult pretty quickly. The information isn't necessarily missing. The relationships between the information are what can be difficult to see. That got me thinking about a slightly different question: Instead of "where do I find this information?" "Show me what's connected to this." That's where VisibleState started. Start anywhere. Follow the connections. Imagine starting with a single user. Instead of seeing that user simply as a record with a list of properties, imagine being able to explore the relationships around them: User → Groups → SharePoint → Teams → Power Apps → Power Automate → Power BI → Dataverse → OneDrive → Exchange → Intune Then the questions become different: What does this user have access to? Is that access direct or coming through a group? What resources are connected to them? What depends on something they're associated with? Which licenses are involved? Are there relationships that look unusual? If something changes, what else might be affected? Those questions aren't necessarily about finding another piece of information. They're about putting information that already exists into context. A report can tell you that something exists. A connected view helps you understand what it is connected to. Illustrative example below — not a real customer environment. I'm not suggesting Microsoft 365 doesn't already give us this information Quite the opposite. Microsoft 365 already gives administrators an incredible amount of information and tooling. The thing I've been thinking about is what happens when you want to look across those boundaries. Sometimes I don't want another export. I don't want another list. I don't necessarily want another dashboard. I want to start with something I'm looking at and ask: "What's connected to this?" And then keep following the trail. That's the idea I'm exploring with VisibleState. The interesting part is what happens when you change the viewpoint The same relationships can be useful for completely different reasons. For example: Administrators may want to understand access, permissions and dependencies. Security and governance teams may want to find unusual relationships or exceptions. Compliance teams may need to understand who can access something and why. People managing multiple environments may want a consistent way to understand what's there without rebuilding the picture manually every time. Leadership may not need to see the graph at all. They may simply want to know what's important, what's exposed and what could be affected. It's still the same underlying environment. You're just looking at it from a different angle. And that's where I think things get interesting. Where I'm at with it VisibleState started as something I was building to make my own work easier. I was spending a lot of time investigating environments, tracing access and putting information together for reports. The individual tasks weren't necessarily difficult. It was the jumping between different places and reconstructing the bigger picture that took the time. So I started building something that would let me approach the environment through the relationships instead. It's grown quite a bit from where it started, and I'm continuing to build it. I'm not posting this as a product launch, and I'm not looking for people to sign up. I'm genuinely interested in whether the problem I'm seeing is familiar to other people working with Microsoft 365. So I'm curious... If you could start with any object in your Microsoft 365 environment and immediately see what it's connected to, where would you start? Would it be: Users and access Groups and permissions SharePoint and Teams Power Apps, Power Automate, Power BI and Dataverse Licensing and resources Governance and unusual relationships Something completely different Maybe you've already got a good way of doing this. Maybe you still find yourself jumping between different services and piecing things together manually. Or maybe I'm looking at the problem from the wrong direction. What's the one relationship in your Microsoft 365 environment that you wish you could see instantly?Authorization and Governance for AI Agents: Runtime Authorization Beyond Identity at Scale
Designing Authorization‑Aware AI Agents at Scale Enforcing Runtime RBAC + ABAC with Approval Injection (JIT) Microsoft Entra Agent Identity enables organizations to govern and manage AI agent identities in Copilot Studio, improving visibility and identity-level control. However, as enterprises deploy multiple autonomous AI agents, identity and OAuth permissions alone cannot answer a more critical question: “Should this action be executed now, by this agent, for this user, under the current business and regulatory context?” This post introduces a reusable Authorization Fabric—combining a Policy Enforcement Point (PEP) and Policy Decision Point (PDP)—implemented as a Microsoft Entra‑protected endpoint using Azure Functions/App Service authentication. Every AI agent (Copilot Studio or AI Foundry/Semantic Kernel) calls this fabric before tool execution, receiving a deterministic runtime decision: ALLOW / DENY / REQUIRE_APPROVAL / MASK Who this is for Anyone building AI agents (Copilot Studio, AI Foundry/Semantic Kernel) that call tools, workflows, or APIs Organizations scaling to multiple agents and needing consistent runtime controls Teams operating in regulated or security‑sensitive environments, where decisions must be deterministic and auditable Why a V2? Identity is necessary—runtime authorization is missing Entra Agent Identity (preview) integrates Copilot Studio agents with Microsoft Entra so that newly created agents automatically get an Entra agent identity, manageable in the Entra admin center, and identity activity is logged in Entra. That solves who the agent is and improves identity governance visibility. But multi-agent deployments introduce a new risk class: Autonomous execution sprawl — many agents, operating with delegated privileges, invoking the same backends independently. OAuth and API permissions answer “can the agent call this API?” They do not answer “should the agent execute this action under business policy, compliance constraints, data boundaries, and approval thresholds?” This is where a runtime authorization decision plane becomes essential. The pattern: Microsoft Entra‑Protected Authorization Fabric (PEP + PDP) Instead of embedding RBAC logic independently inside every agent, use a shared fabric: PEP (Policy Enforcement Point): Gatekeeper invoked before any tool/action PDP (Policy Decision Point): Evaluates RBAC + ABAC + approval policies Decision output: ALLOW / DENY / REQUIRE_APPROVAL / MASK This Authorization Fabric functions as a shared enterprise control plane, decoupling authorization logic from individual agents and enforcing policies consistently across all autonomous execution paths. Architecture (POC reference architecture) Use a single runtime decision plane that sits between agents and tools. What’s important here Every agent (Copilot Studio or AI Foundry/SK) calls the Authorization Fabric API first The fabric is a protected endpoint (Microsoft Entra‑protected endpoint required) Tools (Graph/ERP/CRM/custom APIs) are invoked only after an ALLOW decision (or approval) Trust boundaries enforced by this architecture Agents never call business tools directly without a prior authorization decision The Authorization Fabric validates caller identity via Microsoft Entra Authorization decisions are centralized, consistent, and auditable Approval workflows act as a runtime “break-glass” control for high-impact actions This ensures identity, intent, and execution are independently enforced, rather than implicitly trusted. Runtime flow (Decision → Approval → Execution) Here is the runtime sequence as a simple flow (you can keep your Mermaid diagram too). ```mermaid flowchart TD START(["START"]) --> S1["[1] User Request"] S1 --> S2["[2] Agent Extracts Intent\n(action, resource, attributes)"] S2 --> S3["[3] Call /authorize\n(Entra protected)"] S3 --> S4 subgraph S4["[4] PDP Evaluation"] ABAC["ABAC: Tenant · Region · Data Sensitivity"] RBAC["RBAC: Entitlement Check"] Threshold["Approval Threshold"] ABAC --> RBAC --> Threshold end S4 --> Decision{"[5] Decision?"} Decision -->|"ALLOW"| Exec["Execute Tool / API"] Decision -->|"MASK"| Masked["Execute with Masked Data"] Decision -->|"DENY"| Block["Block Request"] Decision -->|"REQUIRE_APPROVAL"| Approve{"[6] Approval Flow"} Approve -->|"Approved"| Exec Approve -->|"Rejected"| Block Exec --> Audit["[7] Audit & Telemetry"] Masked --> Audit Block --> Audit Audit --> ENDNODE(["END"]) style START fill:#4A90D9,stroke:#333,color:#fff style ENDNODE fill:#4A90D9,stroke:#333,color:#fff style S1 fill:#5B5FC7,stroke:#333,color:#fff style S2 fill:#5B5FC7,stroke:#333,color:#fff style S3 fill:#E8A838,stroke:#333,color:#fff style S4 fill:#FFF3E0,stroke:#E8A838,stroke-width:2px style ABAC fill:#FCE4B2,stroke:#999 style RBAC fill:#FCE4B2,stroke:#999 style Threshold fill:#FCE4B2,stroke:#999 style Decision fill:#fff,stroke:#333 style Exec fill:#2ECC71,stroke:#333,color:#fff style Masked fill:#27AE60,stroke:#333,color:#fff style Block fill:#C0392B,stroke:#333,color:#fff style Approve fill:#F39C12,stroke:#333,color:#fff style Audit fill:#3498DB,stroke:#333,color:#fff ``` Design principle: No tool execution occurs until the Authorization Fabric returns ALLOW or REQUIRE_APPROVAL is satisfied via an approval workflow. Where Power Automate fits (important for readers) In most Copilot Studio implementations, Agents calls Power Automate (agent flows), is the practical integration layer that calls enterprise services and APIs. Copilot Studio supports “agent flows” as a way to extend agent capabilities with low-code workflows. For this pattern, Power Automate typically: acquires/uses the right identity context for the call (depending on your tenant setup), and calls the /authorize endpoint of the Authorization Fabric, returns the decision payload to the agent for branching. Copilot Studio also supports calling REST endpoints directly using the HTTP Request node, including passing headers such as Authorization: Bearer <token>. Protected endpoint only: Securing the Authorization Fabric with Microsoft Entra For this V2 pattern, the Authorization Fabric must be protected using Microsoft Entra‑protected endpoint on Azure Functions/App Service (built‑in auth). Microsoft Learn provides the configuration guidance for enabling Microsoft Entra as the authentication provider for Azure App Service / Azure Functions. Step 1 — Create the Authorization Fabric API (Azure Function) Expose an authorization endpoint: HTTP Step 2 — Enable Microsoft Entra‑protected endpoint on the Function App In Azure Portal: Function App → Authentication Add identity provider → Microsoft Choose Workforce configuration (enterprise tenant) Set Require authentication for all requests This ensures the Authorization Fabric is not callable without a valid Entra token. Step 3 — Optional hardening (recommended) Depending on enterprise posture, layer: IP restrictions / Private endpoints APIM in front of the Function for rate limiting, request normalization, centralized logging (For a POC, keep it minimal—add hardening incrementally.) Externalizing policy (so governance scales) To make this pattern reusable across multiple agents, policies should not be hardcoded inside each agent. Instead, store policy definitions in a central policy store such as Cosmos DB (or equivalent configuration store), and have the PDP load/evaluate policies at runtime. Why this matters: Policy changes apply across all agents instantly (no agent republish) Central governance + versioning + rollback becomes possible Audit and reporting become consistent across environments (For the POC, a single JSON document per policy pack in Cosmos DB is sufficient. For production, add versioning and staged rollout.) Store one PolicyPack JSON document per environment (dev/test/prod). Include version, effectiveFrom, priority for safe rollout/rollback. Minimal decision contract (standard request / response) To keep the fabric reusable across agents, standardize the request payload. Request payload (example) Decision response (deterministic) Example scenario (1 minute to understand) Scenario: A user asks a Finance agent to create a Purchase Order for 70,000. Even if the user has API permission and the agent can technically call the ERP API, runtime policy should return: REQUIRE_APPROVAL (threshold exceeded) trigger an approval workflow execute only after approval is granted This is the difference between API access and authorized business execution. Sample Policy Model (RBAC + ABAC + Approval) This POC policy model intentionally stays simple while demonstrating both coarse and fine-grained governance. 1) Coarse‑grained RBAC (roles → actions) FinanceAnalyst CreatePO up to 50,000 ViewVendor FinanceManager CreatePO up to 100,000 and/or approve higher spend 2) Fine‑grained ABAC (conditions at runtime) ABAC evaluates context such as region, classification, tenant boundary, and risk: 3) Approval injection (Agent‑level JIT execution) For higher-risk/high-impact actions, the fabric returns REQUIRE_APPROVAL rather than hard deny (when appropriate): How policies should be evaluated (deterministic order) To ensure predictable and auditable behavior, evaluate in a deterministic order: Tenant isolation & residency (ABAC hard deny first) Classification rules (deny or mask) RBAC entitlement validation Threshold/risk evaluation Approval injection (JIT step-up) This prevents approval workflows from bypassing foundational security boundaries such as tenant isolation or data sovereignty. Copilot Studio integration (enforcing runtime authorization) Copilot Studio can call external REST APIs using the HTTP Request node, including passing headers such as Authorization: Bearer <token> and binding response schema for branching logic. Copilot Studio also supports using flows with agents (“agent flows”) to extend capabilities and orchestrate actions. Option A (Recommended): Copilot Studio → Agent Flow (Power Automate) → Authorization Fabric Why: Flows are a practical place to handle token acquisition patterns, approval orchestration, and standardized logging. Topic flow: Extract user intent + parameters Call an agent flow that: calls /authorize returns decision payload Branch in the topic: If ALLOW → proceed to tool call If REQUIRE_APPROVAL → trigger approval flow; proceed only if approved If DENY → stop and explain policy reason Important: Tool execution must never be reachable through an alternate topic path that bypasses the authorization check. Option B: Direct HTTP Request node to Authorization Fabric Use the Send HTTP request node to call the authorization endpoint and branch using the response schema. This approach is clean, but token acquisition and secure secretless authentication are often simpler when handled via a managed integration layer (flow + connector). AI Foundry / Semantic Kernel integration (tool invocation gate) For Foundry/SK agents, the integration point is before tool execution. Semantic Kernel supports Azure AI agent patterns and tool integration, making it a natural place to enforce a pre-tool authorization check. Pseudo-pattern: Agent extracts intent + context Calls Authorization Fabric Enforces decision Executes tool only when allowed (or after approval) Telemetry & audit (what Security Architects will ask for) Even the best policy engine is incomplete without audit trails. At minimum, log: agentId, userUPN, action, resource decision + reason + policyIds approval outcome (if any) correlationId for downstream tool execution Why it matters: you now have a defensible answer to: “Why did an autonomous agent execute this action?” Security signal bonus: Denials, unusual approval rates, and repeated policy mismatches can also indicate prompt injection attempts, mis-scoped agents, or governance drift. What this enables (and why it scales) With a shared Authorization Fabric: Avoid duplicating authorization logic across agents Standardize decisions across Copilot Studio + Foundry agents Update governance once (policy change) and apply everywhere Make autonomy safer without blocking productivity Closing: Identity gets you who. Runtime authorization gets you whether/when/how. Copilot Studio can automatically create Entra agent identities (preview), improving identity governance and visibility for agents. But safe autonomy requires a runtime decision plane. Securing that plane as an Entra-protected endpoint is foundational for enterprise deployments. In enterprise environments, autonomous execution without runtime authorization is equivalent to privileged access without PIM—powerful, fast, and operationally risky.I built a free, open-source M365 security assessment tool - looking for feedback
I work as an IT consultant, and a good chunk of my time is spent assessing Microsoft 365 environments for small and mid-sized businesses. Every engagement started the same way: connect to five different PowerShell modules, run dozens of commands across Entra ID, Exchange Online, Defender, SharePoint, and Teams, manually compare each setting against CIS benchmarks, then spend hours assembling everything into a report the client could actually read. The tools that automate this either cost thousands per year, require standing up Azure infrastructure just to run, or only cover one service area. I wanted something simpler: one command that connects, assesses, and produces a client-ready deliverable. So I built it. What M365 Assess does https://github.com/Daren9m/M365-Assess is a PowerShell-based security assessment tool that runs against a Microsoft 365 tenant and produces a comprehensive set of reports. Here is what you get from a single run: 57 automated security checks aligned to the CIS Microsoft 365 Foundations Benchmark v6.0.1, covering Entra ID, Exchange Online, Defender for Office 365, SharePoint Online, and Teams 12 compliance frameworks mapped simultaneously -- every finding is cross-referenced against NIST 800-53, NIST CSF 2.0, ISO 27001:2022, SOC 2, HIPAA, PCI DSS v4.0.1, CMMC 2.0, CISA SCuBA, and DISA STIG (plus CIS profiles for E3 L1/L2 and E5 L1/L2) 20+ CSV exports covering users, mailboxes, MFA status, admin roles, conditional access policies, mail flow rules, device compliance, and more A self-contained HTML report with an executive summary, severity badges, sortable tables, and a compliance overview dashboard -- no external dependencies, fully base64-encoded, just open it in any browser or email it directly The entire assessment is read-only. It never modifies tenant settings. Only Get-* cmdlets are used. A few things I'm proud of Real-time progress in the console. As the assessment runs, you see each check complete with live status indicators and timing. No staring at a blank terminal wondering if it hung. The HTML report is a single file. Logos, backgrounds, fonts -- everything is embedded. You can email the report as an attachment and it renders perfectly. It supports dark mode (auto-detects system preference), and all tables are sortable by clicking column headers. Compliance framework mapping. This was the feature that took the most work. The compliance overview shows coverage percentages across all 12 frameworks, with drill-down to individual controls. Each finding links back to its CIS control ID and maps to every applicable framework control. Pass/Fail detail tables. Each security check shows the CIS control reference, what was checked, what the expected value is, what the actual value is, and a clear Pass/Fail/Warning status. Findings include remediation descriptions to help prioritize fixes. Quick start If you want to try it out, it takes about 5 minutes to get running: # Install prerequisites (if you don't have them already) Install-Module Microsoft.Graph, ExchangeOnlineManagement -Scope CurrentUser Clone and run git clone https://github.com/Daren9m/M365-Assess.git cd M365-Assess .\Invoke-M365Assessment.ps1 The interactive wizard walks you through selecting assessment sections, entering your tenant ID, and choosing an authentication method (interactive browser login, certificate-based, or pre-existing connections). Results land in a timestamped folder with all CSVs and the HTML report. Requires PowerShell 7.x and runs on Windows (macOS and Linux are experimental -- I would love help testing those platforms). Cloud support M365 Assess works with: Commercial (global) tenants GCC, GCC High, and DoD environments If you work in government cloud, the tool handles the different endpoint URIs automatically. What is next This is actively maintained and I have a roadmap of improvements: More automated checks -- 140 CIS v6.0.1 controls are tracked in the registry, with 57 automated today. Expanding coverage is the top priority. Remediation commands -- PowerShell snippets and portal steps for each finding, so you can fix issues directly from the report. XLSX compliance matrix -- A spreadsheet export for audit teams who need to work in Excel. Standalone report regeneration -- Re-run the report from existing CSV data without re-assessing the tenant. I would love your feedback I have been building this for my own consulting work, but I think it could be useful to the broader community. If you try it, I would genuinely appreciate hearing: What checks should I prioritize next? Which security controls matter most in your environment? What compliance frameworks are most requested by your clients or auditors? How does the report land with non-technical stakeholders? Is the executive summary useful, or does it need work? macOS/Linux users -- does it run? What breaks? I have tested it on macOS, but not extensively. Bug reports, feature requests, and contributions are all welcome on GitHub. Repository: https://github.com/Daren9m/M365-Assess License: MIT (free for commercial and personal use) Runtime: PowerShell 7.x Thanks for reading. Happy to answer any questions in the comments.The Advantages of Premium Cases in Purview eDiscovery
Capacity & Scale Feature Description Advantage over E3 Enhanced Limits Supports significantly higher limits, including eDiscovery case count and export volume. For example, up to 50,000 cases and 5 TB per search in E5 (versus 10,000 cases and 2 TB in E3). Handles large investigations without splitting into multiple cases or searches. E3’s lower limits would force breaking up big jobs, adding overhead and risk of errors. E5’s higher capacity means fewer workarounds and seamless handling of large-scale litigation. Tenant-Wide eDiscovery Process and Holds Reports (Preview) Provides a central dashboard of all eDiscovery activities and eDiscovery holds across the tenant. Compliance and IT teams get at-a-glance status of ongoing jobs and active holds. Improves oversight and management efficiency for eDiscovery. E3 lacks centralized reporting, making it harder to track many cases. E5’s reporting gives better visibility into operations, which is crucial for heavy workloads and tight deadlines. Expanded Hold Capacity Each legal hold in E5 can encompass up to 2,000 mailboxes and 2,000 sites in one policy. E3 holds are limited to 1,000 mailboxes or 100 sites per policy. Enables placing very large custodian sets on hold with a single action. In E3, exceeding hold limits means juggling multiple policies for one case, increasing complexity. E5 simplifies hold management by consolidating more custodians per hold, reducing admin burden. Search & Collection Feature Description Advantage over E3 Advanced Search Filters Offers richer search criteria beyond keywords. You can filter by sensitive info types (credit cards, SSNs), specific message IDs, or sensitivity labels on documents. This helps pinpoint relevant sensitive content directly. Enables more precise and speedy discovery of critical data. In E3, finding the same info might require complex keyword strings or separate tools (with a higher chance of missing items). E5’s advanced filters mean faster, targeted searches for things like confidential data or GDPR content. Data Source Sync Allows you to refresh custodians’ data sources in a search or hold to catch updates to locations. For example, if a custodian adds a new OneDrive, E5 will detect and prompt you to include it. Ensures no content location is overlooked as the case evolves. E3 has no easy way to know if data moved or new sites were created, potentially leaving gaps. E5’s sync provides complete and defensible collection by keeping holds/searches up-to-date. Cloud Attachment Collection (Hyper-linked Documents) Automatically collects the content of files shared via cloud links (OneDrive/SharePoint) in emails or chats. E5 can retrieve the actual document (and its versions) that was linked, even pulling the specific version that was shared at the time if the version shared feature is enabled. Preserves evidence that E3 would miss. E3 eDiscovery does not fetch linked file content. It would only show a hyperlink, making it difficult to return the associated file. E5 ensures linked documents (with version history) are collected, so the full context of communications is retained. Conversation Threading (Chats & Email) Reconstructs conversations in a threaded view for Microsoft Teams chats and email chains. Reviewers can see messages in context (like a chat transcript or email thread) rather than as isolated items. Greatly improves contextual understanding. E3 exports chats as separate messages with no threading, making it hard to follow the story. E5’s threaded view lets reviewers grasp the full conversation at a glance, reducing confusion and ensuring nothing is interpreted out of context. Custodian & Hold Management Feature Description Advantage over E3 Case-Level Custodian Management Provides a dedicated tab to manage custodians (people) within each case. You add custodians once and can easily apply holds or searches to all their data without re-entering their information each time. Streamlines hold setup and ensures clarity on who is in the case. E3 has no concept of custodians. You must manually input email or site addresses for each search/hold. E5’s approach saves time, reduces errors, and gives a clear view of all people involved in the matter. Bulk Custodian Import Supports importing up to 1,000 custodians at once from a list into a case. Useful for large investigations (e.g., adding an entire department as custodians in one go). Dramatically faster setup for big cases. In E3, adding hundreds of people means typing or pasting each individually, which is time-consuming and error prone. E5’s bulk import means quick, one-time setup for large custodian lists, ensuring no one is missed. “Explore & Add” Custodian Sources Provides an intelligent way to discover related data sources for a given custodian. For example, it can list Teams, SharePoint sites, or groups the person is part of, and let you add those to the case. Helps capture all relevant locations for each person. In E3, you might overlook a Teams channel or group mailbox a custodian was involved in. E5’s explore feature surfaces those connections, improving completeness of your holds and searches by including collaboration spaces that might otherwise be missed. In-Place Review & Analytics Feature Description Advantage over E3 Advanced Indexing and OCR Automatically re-indexes content that was partially indexed or had errors and performs OCR (Optical Character Recognition) on images to extract text. This means files with images or previously unsearchable formats become searchable in E5. Ensures “no stone is left unturned.” E3 would flag such content as “unindexed” (meaning you know a file exists but not what’s inside it). With E5, far more data is searchable, even text inside images or scanned PDFs, reducing the amount of partially indexed content and the chance of missing critical evidence due to format issues. In-Place Review Sets Lets you create a review set of collected data in the cloud. Review sets offer contextual review of conversations, powerful query and filtering capabilities, and query reports for additional insights. Pre-review culling is possible in E5. E3 has no in-product review capability. You must export everything to an outside tool for examination. E5’s review sets allow the team to filter out irrelevant data and focus on what matters before exporting. This reduces the volume (and cost) of data sent for attorney review and keeps data in a secure, auditable environment during analysis. Tagging and Metadata Filters Enables applying tags (labels like “Responsive,” “Privileged,” “Personal Data”) to documents and emails in a review set, and filtering by these tags or other metadata fields. Improves organization and review workflow. E3 cannot tag items in-place, so keeping track of important documents is harder. In E5, tagging allows systematic categorization for quick retrieval (e.g., find all items tagged Highly Relevant instantly). These tags also carry over on export, so any work done during review isn’t lost when handing off to external counsel. Email Threading and Analytics Automatically identifies and stitches together email threads, showing only the last inclusive email that contains the entire conversation. Earlier duplicate emails in the chain are noted and can be skipped. Cuts down review volume and improves context. E3 reviewers would see every single email (even if content repeats across replies). This saves review time and ensures attorneys see the full discussion in one place rather than piecemeal. Conversation View Displays collected Teams (and other chat) messages in a conversation format in a review set, similar to how one would view a chat in the app, instead of individual out-of-context messages. Makes reviewing chat evidence much easier. In E3, chat messages are isolated, forcing reviewers to manually piece together who said what when. E5’s conversational view provides full context at a glance, so nothing is misunderstood or missed in chat-based communications. Near-Duplicate Detection Finds and groups nearly identical documents (e.g. multiple versions of a file or emails with only slight differences). Reviewers are informed which items are alike. Saves time and ensures consistency. E3 requires manually spotting similar files. E5 can let a reviewer examine one version and then quickly tag all its close duplicates the same way. This speeds up review and ensures similar content is handled uniformly (no conflicting judgments on essentially the same document). Themes (Topic Analytics) Uses analytics to cluster documents by themes/topics. For example, it might reveal a group of emails all discussing “Project X” or detect an unusual theme (like frequent mentions of “resignation”). Uncovers hidden patterns that simple keyword searches in E3 might miss. This insight helps investigators spot important threads of discussion or issues they weren’t explicitly searching for, leading to a more thorough understanding of the data set. It adds a layer of proactive insight absent in E3. Global Deduplication Automatically de-duplicates exact copies of emails or files across all custodians using review sets. Each unique item is retained once for review, with duplicates noted. Prevents redundant review work. In E3, the same email stored in five mailboxes would appear five times and could be reviewed and tagged inconsistently by different people. E5’s deduplication means reviewers spend time only on unique content improving efficiency and ensuring consistency in treatment of identical items. Export & Integration Feature Description Advantage over E3 Guest Reviewer Access Allows secure, read-only external access to a review set for outside experts (like outside counsel). Guest reviewers can be invited to review and tag documents in your E5 case via secure Azure AD access (with MFA), without data leaving the tenant. Enables collaboration with outside counsel without exporting data. E3 cannot extend access to external users. You’d have to export files and send them out, which is slower and riskier. E5 keeps the data in-place and governed, letting external reviewers work more efficiently while your organization retains control and visibility. Import External Data Supports ingestion of data from outside M365 into eDiscovery. You can load files like PST emails, PDFs, or documents from file shares into an E5 review set, maintaining custodians’ identity and metadata. Brings all relevant data under one roof. E3 cannot handle content beyond Exchange/SharePoint/Teams, so any non-M365 data would be reviewed separately. E5’s ingestion means even file server or third-party data can be included in the case, making your eDiscovery truly comprehensive and eliminating blind spots between different systems. Rich Export with Metadata Exports include a detailed load file with extensive metadata from the review (custodian info, email thread indices, attachment names, message IDs, tags applied, etc.). This is in addition to the actual content files. Simplifies downstream processing and preserves review decisions. E3’s export is basic (limited metadata), often requiring additional data processing in third-party tools. E5’s comprehensive load file means that all important context (including tags like “Privileged” that your team applied) travels with the exported data, so external reviewers immediately see those cues. This saves time and prevents rework. MIP Search and Decryption Integration Can automatically decrypt protected content (encrypted by Microsoft Information Protection, e.g. with sensitivity labels/Azure RMS) during eDiscovery. Encrypted emails and documents are made readable and searchable when added to a review set. Ensures encrypted files aren’t “invisible” in your investigation. E3 often cannot search or preview MIP-protected emails/docs until they’re manually decrypted after export (if at all). E5 seamlessly includes these encrypted items in search results and review, so you don’t miss evidence that was simply locked behind encryption. Insider Risk Management Escalation Integrates with Microsoft Insider Risk Management (IRM) alerts. With E5, if an insider risk policy flags a user (e.g., for a potential data theft), you can one-click escalate to create an eDiscovery case that automatically targets that user’s content around the incident. Enables a fast, seamless response to insider threats. E3 has no IRM at all, so there’s no such trigger. In E5, the moment a high-risk activity is detected, the legal team can immediately jump into collecting and reviewing the related data. This tight integration means quicker investigations and potentially mitigating issues before they escalate. Communication Compliance Escalation Ties into Communication Compliance (E5’s internal communications monitoring for policy violations). If a serious policy violation is found (e.g., harassment in Teams chats or inappropriate sharing of sensitive info), it can be escalated directly into an eDiscovery case for further investigation. Offers proactive discovery of misconduct. E3 lacks built-in communication monitoring, so issues may go unnoticed until too late. With E5, compliance officers can swiftly pivot from detecting a problem to launching a full eDiscovery inquiry, ensuring faster and more thorough handling of incidents like HR violations or data leaks. Graph API & Automation Fully supports the Microsoft Graph API for eDiscovery. This means eDiscovery tasks (case creation, adding custodians, running searches, exporting data) can be automated or integrated into other applications via scripting/programming without additional cost. While API support is supported for E3, the E3 export API is a metered solution. E5 allows organizations to streamline eDiscovery workflows – for example, auto-create a case and hold when HR flags an employee exit, or integrate with third-party legal management tools without additional cost. Teams and Copilot Interactions Purge Provides an incident response capability to search and purge Teams chats or Microsoft 365 Copilot interactions if sensitive information was shared. Authorized investigators can directly delete up to 100 Teams chat messages (across participant mailboxes) in one go via the eDiscovery interface (leveraging Graph API) when necessary to contain a data leak. Allows quick containment of spills that E3 cannot do. E3’s content search can purge emails but cannot delete Teams messages or Copilot content. With E5, if confidential data pops up in a Teams chat, compliance can not only find it but also bulk-delete those messages from user mailboxes to mitigate further exposure. This capability is crucial for responding to internal data mishandling in real time.Collecting Microsoft 365 Copilot Data with Microsoft Purview eDiscovery
Copilot Data Collection Reference Table Data Type Storage Location Item Class Collection Strategy Copilot Prompts (user questions sent to M365 Copilot) Exchange Online: Hidden folder in the user's mailbox. Compliance copies stored similar to Teams chats, but with unique item classes. IPM.SkypeTeams.Message.Copilot.<AppName> (e.g., .Word, .Excel, .Outlook, .BizChat). Additional AI-related classes may also apply: IPM.SkypeTeams.Message.ConnectedAIApp*, IPM.SkypeTeams.Message.CloudAIApp*, IPM.SkypeTeams.Message.TeamCopilot*, IPM.SkypeTeams.TeamCopilot* 1. Add the user's Exchange mailbox as a data source to the search. 2. In the condition builder you can optionally filter the search to only return Copilot prompts by adding a condition of "Item class contains any of Copilot activity". This automatically applies all relevant M365 Copilot item classes as a condition of the search. 3. Add any further additional conditions such as date range or keywords to narrow results as required. You can also use the Item Class condition to exclude M365 Copilot interactions from your collections when targeting a user’s mailbox. Notes: · Additional item classes may be added. The item class condition will be updated accordingly. Copilot Responses (AI-generated answers) Exchange Online: The same hidden folder in the user's mailbox as prompts. The same IPM.SkypeTeams.Message.Copilot.<AppName> pattern as prompts The same collection strategy used for prompts. Copilot Memories (personalized saved information Copilot "remembers") Exchange Online: Hidden CopilotMemory subfolder within the user's mailbox contacts. Stored as contact entries separate from prompts and responses. IPM.Contact Each memory item appears as a contact card within Exchange, which is distinct from the message-based item classes used for prompts/responses. 1. Add the user's Exchange mailbox as a data source to the search. 2. In the condition builder you can optionally filter the search to only return Contacts by adding a condition of "Item class contains any of Contacts". Notes: · Copilot memories will not be preserved under a legal hold or retention policy. · This will return both Copilot memories stored in contacts as well as traditional contacts from the user’s Exchange mailbox. Copilot Pages (AI-generated, user-editable documents) SharePoint Online: Stored in a user-owned SharePoint embedded container (shared with Loop workspace content and Copilot Notebooks). File format is .page. Not stored in the user's mailbox. N/A These are SharePoint files (not Exchange items), so no item class applies. Identify them in search results by the .page file extension. 1. Add the custodian’s SharePoint embedded site URL as a data source to the search. Alternatively, tenant-wide searches of all SPO sites will include all SharePoint Embedded containers 2. Optionally use the condition builder with conditions such as date range, keywords or file type to further filter results returned Facilitator agent interactions in a Team meeting chat Exchange Online: Hidden folder in all meeting attendees’ mailboxes. Compliance copies stored as Teams chats IPM.SkypeTeams.Message 1. Add the user's Exchange mailbox as a data source to the search. 2. In the condition builder you can optionally filter the search to only return Copilot prompts by adding a condition of "Item class contains any of Instant messages". 3. Add any further additional conditions such as date range or keywords to narrow results as required. Facilitator agent meeting notes (loop) SharePoint Online: Facilitator meeting notes are stored as a .loop file in a OneDrive folder titled Meetings of the user who initiated Facilitator in Teams N/A These are SharePoint files (not Exchange items), so no item class applies. Identify them in search results by the .loop file extension. 1. Add the user's OneDrive URL as a data source to the search. 2. In the condition builder you can optionally filter the search to only return loop files by adding a condition of "File type equals any of loop". 3. Add any further additional conditions such as date range or keywords to narrow results as required. Notes: · With eDiscovery premium enabled cases you can follow the standard workflow for collecting Team meeting messages and select to include cloud attachments in your collection. This will automatically pull into the export or review set any Facilitator agent meeting notes. Facilitator created word/loop documents SharePoint Online: When the facilitator agent is asked to create a word or loop document during a meeting they are stored in the requesters OneDrive in a folder called N/A These are SharePoint files (not Exchange items), so no item class applies. Identify them in search results by the .loop file extension. 1. Add the user's OneDrive URL as a data source to the search. 2. In the condition builder you can optionally filter the search to only return loop and doc files by adding a condition of "File type equals any of loop, docx". 3. Add any further additional conditions such as date range or keywords to narrow results as required. Notes: · With eDiscovery premium enabled cases you can follow the standard workflow for collecting Team meeting messages and select to include cloud attachments in your collection. This will automatically pull into the export or review set any Facilitator generated loop or word documents. Facilitator generated and assigned tasks Exchange Online: When the facilitator agent creates and assigns a task to an individual, it is created as a to-do item in the assigned individual's Exchange Mailbox IPM.Task 1. Add the user's Exchange mailbox as a data source to the search. 2. In the condition builder you can optionally filter the search to only return Tasks by adding a condition of "Item class contains any of Tasks". 3. Add any further additional conditions such as date range or keywords to narrow results as required. Application-Specific Item Classes for Prompts & Responses For more granular filtering by Copilot application, the following item class values can be used in KQL queries: Application Context Item Class Value Microsoft Copilot Chat (BizChat / Teams) IPM.SkypeTeams.Message.Copilot.BizChat Copilot in Excel IPM.SkypeTeams.Message.Copilot.Excel Copilot in Loop IPM.SkypeTeams.Message.Copilot.Loop Copilot in Outlook IPM.SkypeTeams.Message.Copilot.Outlook Copilot in PowerPoint IPM.SkypeTeams.Message.Copilot.PowerPoint Copilot in Teams IPM.SkypeTeams.Message.Copilot.Teams Copilot in Whiteboard IPM.SkypeTeams.Message.Copilot.Whiteboard Copilot in Word IPM.SkypeTeams.Message.Copilot.Word To target all Copilot applications at once, use the wildcard query ItemClass:IPM.SkypeTeams.Message.Copilot.*. For a wider list of AI data sources, see the following link: https://learn.microsoft.com/en-us/purview/edisc-search-copilot-data#data-sources-for-ai-data Important Notes for eDiscovery Practitioners Excluding Copilot Data from Broader Searches Because Copilot prompts and responses reside in the same Exchange mailbox as emails and Teams chats, they will appear in broad mailbox searches unless explicitly filtered out. To exclude Copilot items, use the condition "Item Class Contains none of Copilot activity" in the condition builder, or add (-ItemClass:IPM.SkypeTeams.Message.Copilot.*) in KQL. Some eDiscovery managers run separate searches, one for Copilot data and one for other communications, to keep collections distinct. Copilot Memories: Retention & Hold Limitations Purview retention policies and eDiscovery holds do not currently apply to Copilot memory items. Memory items remain until a user deletes them or an admin explicitly removes them via eDiscovery or Graph API. Additionally, deleting a Copilot prompt and response does not delete any memory derived from that conversation. Memories must be removed separately if required. Copilot Pages: Do Not Treat Like Prompts/Responses Copilot Pages are not stored in Exchange mailboxes. Searching only a custodian’s mailbox will not return Copilot Pages. Treat Copilot Pages the same way as you do for SharePoint content in your existing eDiscovery workflow. For collections, keyword searches will generate hits on text content within the .page file if either the SharePoint Embedded URL is included in the search or the search is a tenant-wide search of all SharePoint sites Be aware that full-text search within .page files in Purview eDiscovery review sets is not currently available. Instead you can use filters such as Subject/Title or Native File Type to locate Copilot Pages in your review set and review the content. When an eDiscovery hold is placed on a custodian’s mailbox, it does not automatically extend to the SharePoint Embedded site where the Copilot Pages are stored. Instead, ensure the hold policy includes the URL for the user-owned SharePoint Embedded site that contains the Copilot Page(s) that must be preserved. Audit Logs vs. eDiscovery for Copilot Content Audit logs record that a Copilot interaction occurred (time, user, workload context) but do not include the actual prompt or response text. To retrieve the substance of Copilot interactions, use Purview eDiscovery searches against the mailbox. Copilot Prompts and Responses: HTML Transcription Copilot prompts and responses are stored as individual messages within the user’s mailbox. When collecting Copilot interactions, enabling the “Organize conversations into HTML transcripts” premium option will convert these individual messages into HTML transcripts making for easier review and linkage between the user’s original prompt and the Copilot responses. Copilot Prompts and Responses: Contextual prompts and responses When using the Keywords condition as part of your collection in eDiscovery, it will only return items that match the keywords included in the query. This means that you may only return a part of the Copilot interaction. If using keywords in your collection query you can enable the “Include full conversation for Copilot, Teams and Viva Engage messages” premium option. This will include in the export or review set any prompts or responses from the Copilot interaction within a 12-hour window before and after each responsive item. This means that you are able to see the full context of the prompt or response that was responsive to search. Collecting Referenced Documents (Cloud Attachments) Copilot responses may reference or summarize SharePoint/OneDrive files. When collecting Copilot interactions, enabling the "Access links (cloud attachments) in messages" premium option will additionally collect the files referenced in the prompt or response and include them in the export package. This provides full evidentiary context but can significantly increase export size and processing time so consider if collecting these artifacts are relevant to the investigation. If so, look to use additional conditions such as date to effectively manage volumes or reduce the number of custodians in the collection. Facilitator agent in Microsoft Teams Meetings The Facilitator agent in Microsoft Teams is an AI-powered assistant (included with Microsoft 365 Copilot) that enhances meeting productivity by generating real-time notes, summarizing key decisions, and managing action items. It acts as an active participant, allowing for collaborative editing of notes and answering chat questions during calls. As the Facilitator works within the context of Microsoft Teams meetings (scheduled private meetings only) your existing workflows for collecting Microsoft Teams meetings chat should be used. In addition, enabling the "Access links (cloud attachments) in messages" premium setting will automatically collect any meeting note (loop) or loop or word documents created by the Facilitator agent. Copilot Retention Reference Table Data Type Microsoft Purview Retention Policy Location/Scope Copilot prompts and responses Microsoft Copilot experiences Copilot Memories (personalized saved information Copilot "remembers") Not supported Copilot Pages (AI-generated, user-editable documents) SharePoint classic and communications sites (Static Scopes only) Facilitator interactions in a Team meeting Teams chats Facilitator meeting notes (loop) OneDrive Accounts Facilitator created word/loop documents OneDrive Accounts Facilitator generated and assigned tasks Exchange mailboxes (Tasks with end dates only)
Optimizing OneDrive Retention Policies with Administrative Units and Adaptive Scopes
A special thank you note to Ashwini_Anand for contributing to the content of this blog. In today's digital landscape, efficient data retention management is a critical priority for organizations of all sizes. Organizations can optimize their OneDrive retention policies, ensuring efficient and compliant data management tailored to their unique user base and licensing arrangements. Scenario: Contoso Org encountered a distinct challenge - managing data retention for their diverse user base of 200,000 employees, which includes 80,000 users with F3 licenses and 120,000 users with E3 and E5 licenses. As per Microsoft licensing, F3 users are allocated only 2 GB of OneDrive storage, whereas E3 and E5 users are provided with a much larger allocation of 5 TB. This difference required creating separate retention policies for these users' groups. The challenge was further complicated by the fact that retention policies utilize the same storage for preserving deleted data. If a unified retention policy were applied to all users such as retaining data for 6 years before deletion - F3 users’ OneDrive storage could potentially fill up within a year or less (depending on usage patterns). This would leave F3 users unable to delete or save new files, severely disrupting productivity and data management. To address this, it is essential to create a separate retention policy for E3 and E5 users, ensuring that the policy applies only to these users and excludes F3 users. This blog will discuss the process of designing and implementing such a policy for the large user base based on separate licenses, ensuring efficient data management and uninterrupted productivity. Challenges with Retention Policy Configuration for large organizations 1. Adaptive Scope Adaptive scopes in Microsoft Purview allow you to dynamically target policies based on specific attributes or properties such as department, location, email address, custom Exchange attributes etc. Refer the link to get the list of supported attributes: Adaptive scopes | Microsoft Learn. Limitation: Although Adaptive scopes can filter by user properties, Contoso, being a large organization, had already utilized all 15 custom attributes for various purposes. Additionally, user attributes also couldn’t be used to segregate users based on licenses. This made it challenging to repurpose any attribute for our filter criteria to apply the retention policy to a specific set of users. Furthermore, refinable strings used in SharePoint do not work for OneDrive sites. 2. Static Scope Static scope refers to manually selected locations (e.g., specific users, mailboxes, or sites) where the policy is applied. The scope remains fixed and does not automatically adjust. Limitation: Static scope allows the inclusion or exclusion of mailboxes and sites but is limited to 100 sites and 1000 mailboxes, making it challenging to utilize for large organizations. Proposed Solution: Administrative Units with Adaptive Scope To address the above challenges, it required utilizing Administrative Units (Admin Units - is a container within an organization that can hold users, groups, or devices. It helps us to manage and organize users within an organization more efficiently, especially in large or complex environments) with Adaptive Scopes for creation of a retention policy targeting E3 and E5 licensed users. This approach allows organizations to selectively apply retention policies based on user licenses, enhancing both efficiency and governance. Prerequisites For Administrative unit - Microsoft Entra ID P1 license For Retention policy - Refer to the link: Microsoft 365 guidance for security & compliance - Service Descriptions | Microsoft Learn Configuration Steps Step 1: Create Administrative Unit: Navigate to Microsoft Entra Admin Center https://entra.microsoft.com/#home Click on ‘Identity’ and then click on ‘Show more’ Expand ‘Roles & admins’ Proceed to ‘Admin units’ -> Add. Figure 1: Create an Administrative unit and enter the name and description Define a name for the Administrative unit. Click on ‘Next: Assign roles’ No role assignment required, click on 'Next: Review + create’) Click on ‘Create’. To get more information about creating administrative unit, refer this link: Create or delete administrative units - Microsoft Entra ID | Microsoft Learn Step 2: Update Dynamic Membership: Select the Administrative Unit which is created in Step1. Navigate to ‘Properties’ Choose ‘Dynamic User’ for Membership type. Click on ‘Add a dynamic query’ for Dynamic user members. Click on ‘Edit' for Rule syntax In order to include E3 and E5 licensed users who are using OneDrive, you need to include SharePoint Online Service Plan 2 enabled users. Use the query below in the code snippet to define the dynamic membership. user.assignedPlans -any (assignedPlan.servicePlanId -eq "5dbe027f-2339-4123-9542-606e4d348a72" -and assignedPlan.capabilityStatus -eq "Enabled") 7. Click on 'Save' to update the Dynamic membership rules 8. Click on 'Save' to update the Administrative unit changes. 9. Open the Administrative Unit and click on the 'Users' tab to check if users have started to populate. Note: It may take some time to replicate all users, depending on the size of your organization. Please wait for minutes and then check again. Step 3: Create Adaptive Scope under Purview Portal: Access https://purview.microsoft.com Navigate to ‘Settings’ Expand ‘Roles & scopes’ and click on ‘Adaptive scopes’ Create a new adaptive scope, providing ‘Name’ and ‘Description’. Proceed to select the Administrative unit which was created earlier. (It takes time for the Admin/Administrative Unit to become visible. Please wait for some time if it does not appear immediately.) Click on ‘Add’ and ‘Next’ Select ‘Users’ and 'Next' Once the Admin unit is selected, we need to specify the criteria which allows to select users within the Admin unit (this is the second level of filtering available). However, in this case since we needed to select all users of the admin unit, hence the below criteria was used. Click 'Add attribute' and form the below query. Email addresses is not equal to $null Note: You can apply any other filter if you need to select a subset of users within the Admin Unit based on your business use case. Click on ‘Next’ Review and ‘Submit’ the adaptive scope. Step 4: Create Retention Policy using Adaptive Scope: Access to the portal https://purview.microsoft.com/datalifecyclemanagement/overview Navigate to ‘Policies’ and then go to ‘Retention Policies’. Create a ‘New Retention policy’, providing a ‘Name’ and ‘Description’. Click on "Next", there is no need to add Admin units here as its already defined in Adaptive scope. Figure 9: Select the 'Admin Units' as Full directory 6. Choose ‘Adaptive’ and click on ‘Next’. Click on ‘Add scopes’ and Select the previously created Adaptive scope. Under Location, select OneDrive. Figure 11: Select the Adaptive scope and location at this point. 8. Click on ‘Next’ to proceed and select the desired retention settings. 9. Click Next and Finish Outcome By implementing Admin Units with adaptive scopes, organizations can effectively overcome challenges associated with applying OneDrive retention policies for distinguished and large set of users. This approach facilitates the dynamic addition of required users, eliminating the need for custom attributes and manual user management. Users are dynamically added or removed from the policy based on license status, ensuring seamless compliance management. FAQ: Why is it important to differentiate retention policies based on user licensing tiers? It is important to differentiate retention policies based on user licensing tiers to ensure that each user group has policies tailored to their specific needs and constraints, avoiding issues such as storage limitations for users with lower-tier licenses like F3. How many Exchange custom attributes are typically available? There are typically 15 Exchange custom attributes available, which can limit scalability when dealing with a large user base. What challenge does Adaptive Scoping face when including a large number of OneDrive sites? Adaptive Scoping faces the challenge of including a large number of OneDrive sites due to limitations in the number of custom attributes allowed. While these custom attributes help in categorizing and managing OneDrive sites, the finite number of attributes available can restrict scalability and flexibility. Why are refinable strings a limitation for Adaptive Scoping in OneDrive? Refinable strings are a limitation for Adaptive Scoping in OneDrive because their usage is restricted to SharePoint only. What are the limitations of Static Scoping for OneDrive sites? Static Scoping for OneDrive sites is limited by the strict limit of including or excluding only 100 sites, making it usage limited for larger environments. Do we need any licenses to create an administrative unit with dynamic membership? Yes, a Microsoft Entra ID P1 license is required for all members of the group.Select the 'Adaptive' retention policy typeFigure 10: Select the 'Adaptive' retention policy typeDisable incessant nagware popups
I don't know about everyone else, but I am sick and tired of the nagware pop ups in Word, Excel, PowerPoint, Outlook, etc. Every single product harasses me with pop ups trying to tell me "hey, did you know this feature was here?", "you can do this if you click that", "let me hold your hand through using products you've used for decades even though you don't want daddy Microslop to do that". This is a prime example. I keep getting the same ones again and again and again and everything I've read indicates they should only appear once. But they don't. They keep coming back like a psychotic stalker ex who wants alimony even though you were never married. How do I get this nagware to stop?!
Email alerts for sharepoint action "DownloadedFile"
Hi, We are trying setup alerts for files being downloaded on specific executive sharepoint sites. We have created a custom alert policy in security.microsoft.com for that specific site and did few test downloads. We have not received any notifications for these activities. Tried searching the audit logs manually and there are no entries for these activities. Is there any specific setting that needs to be turned on in order to trigger the alert policy? We have activated "Reporting" in Site collection features. Thanks in advance!!How to deploy Microsoft Purview DSPM for AI to secure your AI apps
Microsoft Purview Data Security Posture Management (DSPM for AI) is designed to enhance data security for the following AI applications: Microsoft Copilot experiences, including Microsoft 365 Copilot. Enterprise AI apps, including ChatGPT enterprise integration. Other AI apps, including all other AI applications like ChatGPT consumer, Microsoft Copilot, DeepSeek, and Google Gemini, accessed through the browser. In this blog, we will dive into the different policies and reporting we have to discover, protect and govern these three types of AI applications. Prerequisites Please refer to the prerequisites for DSPM for AI in the Microsoft Learn Docs. Login to the Purview portal To begin, start by logging into Microsoft 365 Purview portal with your admin credentials: In the Microsoft Purview portal, go to the Home page. Find DSPM for AI under solutions. 1. Securing Microsoft 365 Copilot Be sure to check out our blog on How to use the DSPM for AI data assessment report to help you address oversharing concerns when you deploy Microsoft 365 Copilot. Discover potential data security risks in Microsoft 365 Copilot interactions In the Overview tab of DSPM for AI, start with the tasks in “Get Started” and Activate Purview Audit if you have not yet activated it in your tenant to get insights into user interactions with Microsoft Copilot experiences In the Recommendations tab, review the recommendations that are under “Not Started”. Create the following data discovery policy to discover sensitive information in AI interactions by clicking into it. Detect risky interactions in AI apps - This public preview Purview Insider Risk Management policy helps calculate user risk by detecting risky prompts and responses in Microsoft 365 Copilot experiences. Click here to learn more about Risky AI usage policy. With the policies to discover sensitive information in Microsoft Copilot experiences in place, head back to the Reports tab of DSPM for AI to discover any AI interactions that may be risky, with the option to filter to Microsoft Copilot Experiences, and review the following for Microsoft Copilot experiences: Total interactions over time (Microsoft Copilot) Sensitive interactions per AI app Top unethical AI interactions Top sensitivity labels references in Microsoft 365 Copilot Insider Risk severity Insider risk severity per AI app Potential risky AI usage Protect sensitive data in Microsoft 365 Copilot interactions From the Reports tab, click on “View details” for each of the report graphs to view detailed activities in the Activity Explorer. Using available filters, filter the results to view activities from Microsoft Copilot experiences based on different Activity type, AI app category and App type, Scope, which support administrative units for DSPM for AI, and more. Then drill down to each activity to view details including the capability to view prompts and response with the right permissions. To protect the sensitive data in interactions for Microsoft 365 Copilot, review the Not Started policies in the Recommendations tab and create these policies: Information Protection Policy for Sensitivity Labels - This option creates default sensitivity labels and sensitivity label policies. If you've already configured sensitivity labels and their policies, this configuration is skipped. Protect sensitive data referenced in Microsoft 365 Copilot - This guides you through the process of creating a Purview Data Loss Prevention (DLP) policy to restrict the processing of content with specific sensitivity labels in Copilot interactions. Click here to learn more about Data Loss Prevention for Microsoft 365 Copilot. Protect sensitive data referenced in Copilot responses - Sensitivity labels help protect files by controlling user access to data. Microsoft 365 Copilot honors sensitivity labels on files and only shows users files they already have access to in prompts and responses. Use Data assessments to identify potential oversharing risks, including unlabeled files. Stay tuned for an upcoming blog post on using DSPM for AI data assessments! Use Copilot to improve your data security posture - Data Security Posture Management combines deep insights with Security Copilot capabilities to help you identify and address security risks in your org. Once you have created policies from the Recommendations tab, you can go to the Policies tab to review and manage all the policies you have created across your organization to discover and safeguard AI activity in one centralized place, as well as edit the policies or investigate alerts associated with those policies in solution. Note that additional policies not from the Recommendations tab will also appear in the Policies tab when DSPM for AI identifies them as policies to Secure and govern all AI apps. Govern the prompts and responses in Microsoft 365 Copilot interactions Understand and comply with AI regulations by selecting “Guided assistance to AI regulations” in the Recommendations tab and walking through the “Actions to take”. From the Recommendations tab, create a Control unethical behavior in AI Purview Communications Compliance policy to detect sensitive information in prompts and responses and address potentially unethical behavior in Microsoft Copilot experiences and ChatGPT for Enterprise. This policy covers all users and groups in your organization. To retain and/or delete Microsoft 365 Copilot prompts and responses, setup a Data Lifecycle policy by navigating to Microsoft Purview Data Lifecycle Management and find Retention Policies under the Policies header. You can also preserve, collect, analyze, review, and export Microsoft 365 Copilot interactions by creating an eDiscovery case. 2. Securing Enterprise AI apps Please refer to this amazing blog on Unlocking the Power of Microsoft Purview for ChatGPT Enterprise | Microsoft Community Hub for detailed information on how to integrate with ChatGPT for enterprise, the Purview solutions it currently supports through Purview Communication Compliance, Insider Risk Management, eDiscovery, and Data Lifecycle Management. Learn more about the feature also through our public documentation. 3. Securing other AI Microsoft Purview DSPM for AI currently supports the following list of AI sites. Be sure to also check out our blog on the new Microsoft Purview data security controls for the browser & network to secure other AI apps. Discover potential data security risks in prompts sent to other AI apps In the Overview tab of DSPM for AI, go through these three steps in “Get Started” to discover potential data security risk in other AI interactions: Install Microsoft Purview browser extension For Windows users: The Purview extension is not necessary for the enforcement of data loss prevention on the Edge browser but required for Chrome to detect sensitive info pasted or uploaded to AI sites. The extension is also required to detect browsing to other AI sites through an Insider Risk Management policy for both Edge and Chrome browser. Therefore, Purview browser extension is required for both Edge and Chrome in Windows. For MacOS users: The Purview extension is not necessary for the enforcement of data loss prevention on macOS devices, and currently, browsing to other AI sites through Purview Insider Risk Management is not supported on MacOS, therefore, no Purview browser extension is required for MacOS. Extend your insights for data discovery – this one-click collection policy will setup three separate Purview detection policies for other AI apps: Detect sensitive info shared in AI prompts in Edge – a Purview collection policy that detects prompts sent to ChatGPT consumer, Micrsoft Copilot, DeepSeek, and Google Gemini in Microsoft Edge and discovers sensitive information shared in prompt contents. This policy covers all users and groups in your organization in audit mode only. Detect when users visit AI sites – a Purview Insider Risk Management policy that detects when users use a browser to visit AI sites. Detect sensitive info pasted or uploaded to AI sites – a Purview Endpoint Data loss prevention (eDLP) policy that discovers sensitive content pasted or uploaded in Microsoft Edge, Chrome, and Firefox to AI sites. This policy covers all users and groups in your org in audit mode only. With the policies to discover sensitive information in other AI apps in place, head back to the Reports tab of DSPM for AI to discover any AI interactions that may be risky, with the option to filter by Other AI Apps, and review the following for other AI apps: Total interactions over time (other AI apps) Total visits (other AI apps) Sensitive interactions per AI app Insider Risk severity Insider risk severity per AI app Protect sensitive info shared with other AI apps From the Reports tab, click on “View details” for each of the report graphs to view detailed activities in the Activity Explorer. Using available filters, filter the results to view activities based on different Activity type, AI app category and App type, Scope, which support administrative units for DSPM for AI, and more. To protect the sensitive data in interactions for other AI apps, review the Not Started policies in the Recommendations tab and create these policies: Fortify your data security – This will create three policies to manage your data security risks with other AI apps: 1) Block elevated risk users from pasting or uploading sensitive info on AI sites – this will create a Microsoft Purview endpoint data loss prevention (eDLP) policy that uses adaptive protection to give a warn-with-override to elevated risk users attempting to paste or upload sensitive information to other AI apps in Edge, Chrome, and Firefox. This policy covers all users and groups in your org in test mode. Learn more about adaptive protection in Data loss prevention. 2) Block elevated risk users from submitting prompts to AI apps in Microsoft Edge – this will create a Microsoft Purview browser data loss prevention (DLP) policy, and using adaptive protection, this policy will block elevated, moderate, and minor risk users attempting to put information in other AI apps using Microsoft Edge. This integration is built-in to Microsoft Edge. Learn more about adaptive protection in Data loss prevention. 3) Block sensitive info from being sent to AI apps in Microsoft Edge - this will create a Microsoft Purview browser data loss prevention (DLP) policy to detect inline for a selection of common sensitive information types and blocks prompts being sent to AI apps while using Microsoft Edge. This integration is built-in to Microsoft Edge. Once you have created policies from the Recommendations tab, you can go to the Policies tab to review and manage all the policies you have created across your organization to discover and safeguard AI activity in one centralized place, as well as edit the policies or investigate alerts associated with those policies in solution. Note that additional policies not from the Recommendations tab will also appear in the Policies tab when DSPM for AI identifies them as policies to Secure and govern all AI apps. Conclusion Microsoft Purview DSPM for AI can help you discover, protect, and govern the interactions from AI applications in Microsoft Copilot experiences, Enterprise AI apps, and other AI apps. We recommend you review the Reports in DSPM for AI routinely to discover any new interactions that may be of concern, and to create policies to secure and govern those interactions as necessary. We also recommend you utilize the Activity Explorer in DSPM for AI to review different Activity explorer events while users interacting with AI, including the capability to view prompts and response with the right permissions. We will continue to update this blog with new features that become available in DSPM for AI, so be sure to bookmark this page! Follow-up Reading Check out this blog on the details of each recommended policies in DSPM for AI: Microsoft Purview – Data Security Posture Management (DSPM) for AI | Microsoft Community Hub Address oversharing concerns with Microsoft 365 blueprint - aka.ms/Copilot/Oversharing Microsoft Purview data security and compliance protections for Microsoft 365 Copilot and other generative AI apps | Microsoft Learn Considerations for deploying Microsoft Purview AI Hub and data security and compliance protections for Microsoft 365 Copilot and Microsoft Copilot | Microsoft Learn Commonly used properties in Copilot audit logs - Audit logs for Copilot and AI activities | Microsoft Learn Supported AI sites by Microsoft Purview for data security and compliance protections | Microsoft Learn Where Copilot usage data is stored and how you can audit it - Microsoft 365 Copilot data protection and auditing architecture | Microsoft Learn Downloadable whitepaper: Data Security for AI Adoption | Microsoft Public roadmap for DSPM for AI - Microsoft 365 Roadmap | Microsoft 365
