Enterprise Cybersecurity in the Age of AI: Why Legacy Security Is Failing as Attackers Move Faster
Cybersecurity has always been an asymmetric game. But with the rise of AI‑enabled attacks, that imbalance has widened dramatically. Microsoft Threat Intelligence and Microsoft Defender Security Research have publicly reported a clear shift in how attackers operate: AI is now being embedded across the entire attack lifecycle. Threat actors are using it to accelerate reconnaissance, generate highly targeted phishing at scale, automate infrastructure, and adapt their techniques in real time - reducing the time and effort required to move from initial access to impact. In recent months, Microsoft has documented AI‑enabled phishing campaigns abusing legitimate authentication mechanisms - including OAuth and device‑code flows - to compromise enterprise accounts at scale. These campaigns rely on automation, dynamic code generation, and highly personalised lures, rather than on stealing passwords or exploiting traditional vulnerabilities. Meanwhile, many large enterprises are still defending themselves with security controls designed for a very different threat model - one rooted in predictability, static signatures, and trusted perimeters. These approaches were built to stop repeatable attacks, not adversaries that continuously adapt and blend into normal business activity. The result is a dangerous gap: highly adaptive attackers versus static, legacy defences. Below are some of the most common outdated security practices still widely used by enterprises today - and why they are no longer sufficient against modern, AI‑driven threats. 1. Signature‑Based Antivirus Traditional antivirus solutions rely on known signatures and hashes, assuming malware looks the same each time it is deployed. AI has completely broken that assumption. Modern malware families now automatically mutate their code, generate new variants on execution, and adapt behaviour based on the environment they encounter. Microsoft Threat Intelligence has observed multiple actors using AI‑assisted tooling to rapidly rewrite payload components during development and testing, making each deployment look subtly different. In this model, there is no stable signature to detect. By the time a pattern exists, the attacker has already iterated past it. Signature‑based detection is not just slow - it is structurally mismatched to how modern threats operate. What to adopt instead Shift from artifact‑based detection to behavior‑based endpoint protection: EDR/XDR platforms that analyse process behaviour, memory activity, and execution chains Machine‑learning models trained on what attackers do, not what binaries look like Continuous monitoring with automated response, not one‑time blocking 2. Firewalls Many enterprises still rely on firewalls that enforce static allow/deny rules based on ports and IP addresses. That approach worked when applications were predictable and networks were clearly segmented. Today, traffic is encrypted, cloud‑based, API‑driven, and deeply intertwined with legitimate SaaS and identity services. Recent AI‑assisted phishing campaigns abusing legitimate OAuth and device‑code authentication flows illustrate this perfectly. From a network perspective, everything looks allowed: HTTPS traffic to trusted identity providers. There is no suspicious port, no malicious domain, no obvious anomaly - yet the attacker successfully hijacks the authentication process itself. What to adopt instead Move from perimeter controls to identity‑ and context‑aware network security: Application‑aware firewalls with behavioural and risk‑based inspection Integration with identity signals (user, device, location, risk score) Continuous evaluation of sessions, not one‑time allow/deny decisions In modern environments, identity is the new control plane. 3. Single‑Factor Authentication Despite years of guidance, single‑factor passwords remain common - especially for legacy applications, VPN access, and service accounts. AI‑powered credential abuse changes the economics of these attacks entirely. Threat actors now operate credential‑stuffing and phishing campaigns that adapt lures in real time, testing millions of combinations with minimal cost. In multiple Microsoft‑observed campaigns, attackers didn’t brute‑force access broadly. Instead, they used AI to identify which compromised identities were financially or operationally valuable - executives, payroll, procurement - and focused only on those accounts. What to adopt instead Replace static authentication with phishing‑resistant, risk‑based identity controls: Phishing‑resistant MFA (hardware‑backed or passkeys) Conditional access based on user behaviour, device health, and risk Continuous authentication instead of a single login event 4. VPN‑Centric Security VPNs were designed to extend the corporate network to remote users, based on the assumption that “inside” meant trustworthy. That assumption no longer holds. AI‑assisted attacks increasingly exploit VPN access post‑compromise. Once credentials are obtained, automation is used to map internal resources, identify privilege escalation paths, and move laterally - often without triggering traditional alerts. In parallel, Microsoft has observed nation‑state actors using AI to create highly convincing fake employee personas, complete with AI‑generated resumes, consistent communication styles, and synthetic media, allowing them to pass hiring and onboarding processes and gain long‑term, trusted access. In these scenarios, VPN access is not breached - it is granted. What to adopt instead Transition from network trust to Zero Trust access models: Identity‑based access to applications, not networks Least‑privilege, per‑app/user/service access instead of broad internal connectivity Continuous verification using behavioural signals In modern enterprises, access should be explicit, scoped, and continuously re‑evaluated. 5. Treating Unencrypted Data as “Low‑Risk” It is still common to find sensitive data stored unencrypted in older databases, file shares, and backups. In an AI‑driven threat landscape, data discovery is no longer manual or slow. After compromise, attackers increasingly use AI as an on‑demand analyst - summarizing directory structures, classifying stolen datasets, and prioritizing what matters most for impact or monetization. Unencrypted data dramatically lowers the cost and consequence of breach activity, turning what could have been a limited incident into a full‑scale exposure. What to adopt instead Shift from passive data storage to data‑centric security: Encryption by default, both at rest and in transit Data classification and sensitivity labeling built into platforms Access controls tied to data sensitivity, not just system location Begin preparing for post‑quantum cryptography (PQC) as part of long‑term data protection and crypto‑agility strategy 6. Intrusion Detection Systems (IDS) Built on Known Patterns Traditional IDS platforms look for known indicators of compromise - assuming attackers reuse the same tools and techniques. AI‑driven attacks deliberately avoid that assumption. Microsoft Threat Intelligence reports actors using large language models to quickly analyse publicly disclosed vulnerabilities, understand exploitation paths, and compress the time between disclosure and weaponization. This isn’t about zero‑days - it’s about speed. What once took days or weeks now takes hours. Legacy IDS platforms often fail silently in these scenarios, detecting only what they already know how to recognize. What to adopt instead Move from static detection to adaptive, correlation‑based threat detection: Graph‑based XDR platforms correlating signals across identity, endpoint, email, cloud, and network Anomaly detection that focuses on deviation from normal behaviour Automated investigation and response to match attacker speed Closing Thought: Security Is a Journey, Not a Destination AI is not a future cybersecurity problem. It is a current force multiplier for attackers - and it is exposing the limits of legacy security architectures faster than many organisations are willing to admit. A realistic security strategy starts with an uncomfortable but necessary acknowledgement: no organisation can be 100% secure. Intrusions will happen. Credentials will be compromised. Controls will be tested. The difference between a resilient enterprise and a vulnerable one is not the absence of incidents, but how effectively risk is managed when they occur. In mature organisations, this means assuming breach and designing for containment. Strong access controls limit blast radius. Least privilege and conditional access reduce what an attacker can reach. Data Loss Prevention (DLP) ensures that even when access is misused, sensitive data cannot be freely exfiltrated. Just as importantly, leaders understand the business consequences of compromise - which data matters most, which systems are critical, and which risks are acceptable versus existential. As a cybersecurity architect, I see this moment as a unique opportunity. AI adoption does not have to repeat the mistakes of earlier technology waves, where innovation moved fast and security followed years later. AI gives organisations the chance to introduce a new class of service while embedding security from day one - designing access, data boundaries, monitoring, and governance into the platform before it becomes business‑critical. When security is built in upfront, enterprises don’t just reduce risk - they gain confidence to move faster and truly leverage AI’s value. Security, especially in the age of AI, is not about preventing every intrusion. It is about controlling impact, preserving trust, and maintaining operational continuity in a world where attackers move faster than ever. In the age of AI, standing still is the same as falling behind. References: Inside an AI‑enabled device code phishing campaign | Microsoft Security Blog AI as tradecraft: How threat actors operationalize AI | Microsoft Security Blog Detecting and analyzing prompt abuse in AI tools | Microsoft Security Blog Post-Quantum Cryptography | CSRC Microsoft Digital Defense Report 2025 | MicrosoftAuthorization 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.Microsoft Purview Data Quality Thresholds: More Control, More Trust
What Are Data Quality Thresholds? A data quality threshold defines the minimum acceptable score for a rule to pass. Instead of applying a single fixed standard across all data, organizations can now set expectations that align with business context and criticality. For example: An email column may require 99% completeness A product description column may only require 85% completeness Financial or regulatory data may require 100% accuracy With customizable thresholds, quality expectations become more meaningful and business-aligned. Why Does This Matter? Previously, using a single hardcoded threshold could lead to misleading quality scores. Critical data might appear “healthy” even when it didn’t meet business standards. With Data Quality Thresholds, you can: Define rule-level expectations Align quality scores with business risk Increase trust in DQ reporting Improve governance decision-making Data Asset-Level Quality Threshold Users can define data quality thresholds at the data asset level to measure how suitable a dataset is for specific business use cases. This allows organizations to quantify the overall health and fitness of a data asset before it is used in analytics, reporting, or data products. If the measured data quality score falls below the predefined threshold, the system can trigger notifications to the data asset owner or steward, prompting them to take corrective actions. It is important to note that not all data assets are equally critical. Therefore, thresholds should be context-driven and use-case specific. Example Scenario A marketing dataset used for campaign analysis may tolerate a lower quality threshold (e.g., 80%), since minor inconsistencies may not significantly impact insights. However, a financial reporting dataset used for regulatory filings may require a very high threshold (e.g., 98–100%), as even small errors can lead to compliance risks. Data Quality Rule-Level Threshold Thresholds can also be defined at the individual rule level, particularly for rules applied to specific columns. This provides more granular control and ensures that critical data elements are held to higher standards. Not all attributes have the same importance, so thresholds should reflect business criticality. Example Scenarios Email vs. Gender (Customer Contact Data) A completeness rule for a customer’s email address should have a higher threshold (e.g., 95–100%), since missing or invalid email addresses directly impact communication and engagement. In contrast, a gender attribute may have a lower threshold (e.g., 70–80%), as it is often less critical for most use cases. Billing Address vs. CRM Address A billing address is highly critical because it directly impacts: Invoice generation Tax calculations Timely delivery of invoices Therefore, the threshold for billing address quality should be very high (e.g., 98–100%). On the other hand, a CRM address used for general customer profiling may have a lower threshold, as occasional inaccuracies may not significantly affect business operations. The Impact By enabling flexible, context-aware scoring, Data Quality Thresholds help organizations move beyond generic quality checks and toward business-driven data quality management. Summary Data Quality Thresholds define the minimum acceptable score for data quality rules, allowing organizations to move beyond a one-size-fits-all approach and align quality expectations with business context and criticality. Instead of using fixed thresholds, organizations can set custom thresholds based on how important the data is. For example, financial data may require near-perfect accuracy, while less critical fields can tolerate lower thresholds. Thresholds can be applied at two levels: Data Asset Level: Measures the overall fitness of a dataset for a specific use case. Critical datasets (e.g., financial reporting) require higher thresholds than less critical ones (e.g., marketing analytics). Rule Level: Applies to individual columns or rules, ensuring that critical attributes (e.g., email, billing address) have stricter quality requirements than less important ones. This approach improves: Alignment with business risk and priorities Trust in data quality reporting Governance decision-making Focus on high-impact data issues Overall, data quality thresholds enable more meaningful, context-aware, and business-driven data quality management, helping organizations prioritize what matters most and build confidence in their data.
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 type
Building Secure, Enterprise Ready AI Agents with Purview SDK and Agent Framework
At Microsoft Ignite, we announced the public preview of Purview integration with the Agent Framework SDK—making it easier to build AI agents that are secure, compliant, and enterprise‑ready from day one. AI agents are quickly moving from demos to production. They reason over enterprise data, collaborate with other agents, and take real actions. As that happens, one thing becomes non‑negotiable: Governance has to be built in. That’s where Purview SDK comes in. Agentic AI Changes the Security Model Traditional apps expose risks at the UI or API layer. AI agents are different. Agents can: Process sensitive enterprise data in prompts and responses Collaborate with other agents across workflows Act autonomously on behalf of users Without built‑in controls, even a well‑designed agent can create compliance gaps. Purview SDK brings Microsoft’s enterprise data security and compliance directly into the agent runtime, so governance travels with the agent—not after it. What You Get with Purview SDK + Agent Framework This integration delivers a few key things developers and enterprises care about most: Inline Data Protection Evaluate prompts and responses against Data Loss Prevention (DLP) policies in real time. Content can be allowed or blocked automatically. Built‑In Governance Send AI interactions to Purview for audit, eDiscovery, communication compliance, and lifecycle management—without custom plumbing. Enterprise‑Ready by Design Ship agents that meet enterprise security expectations from the start, not as a follow‑up project. All of this is done natively through Agent Framework middleware, so governance feels like part of the platform—not an add‑on. How Enforcement Works (Quickly) When an agent runs: Prompts and responses flow through the Agent Framework pipeline Purview SDK evaluates content against configured policies A decision is returned: allow, redact, or block Governance signals are logged for audit and compliance This same model works for: User‑to‑agent interactions Agent‑to‑agent communication Multi‑agent workflows Try It: Add Purview SDK in Minutes Here’s a minimal Python example using Agent Framework: That’s it! From that point on: Prompts and responses are evaluated against Purview policies setup within the enterprise tenant Sensitive data can be automatically blocked Interactions are logged for governance and audit Designed for Real Agent Systems Most production AI apps aren’t single‑agent systems. Purview SDK supports: Agent‑level enforcement for fine‑grained control Workflow‑level enforcement across orchestration steps Agent‑to‑agent governance to protect data as agents collaborate This makes it a natural fit for enterprise‑scale, multi‑agent architectures. Get Started Today You can start experimenting right away: Try the Purview SDK with Agent Framework Follow the Microsoft Learn docs to configure Purview SDK with Agent Framework. Explore the GitHub samples See examples of policy‑enforced agents in Python and .NET. Secure AI, Without Slowing It Down AI agents are quickly becoming production systems—not experiments. By integrating Purview SDK directly into the Agent Framework, Microsoft is making governance a default capability, not a deployment blocker. Build intelligent agents. Protect sensitive data. Scale with confidence.Monitor logical disk space through Intune
Hi All, We have a requirement to monitor low disk space, particularly on devices with less than 1GB of available space. We were considering creating a custom compliance policy, but this would lead to blocking access to company resources as soon as the device becomes non-compliant. Therefore, we were wondering if there are any other automated methods we could use to monitor the logical disk space (primarily the C drive) using Intune or Microsoft Graph. Thanks in advance, DilanLearn more about Microsoft Security Communities.
In the last five years, Microsoft has increased the emphasis on community programs – specifically within the security, compliance, and management space. These communities fall into two categories: Public and Private (or NDA only). In this blog, we will share a breakdown of each community and how to join.Secure and govern AI apps and agents with Microsoft Purview
The Microsoft Purview family is here to help you secure and govern data across third party IaaS and Saas, multi-platform data environment, while helping you meet compliance requirements you may be subject to. Purview brings simplicity with a comprehensive set of solutions built on a platform of shared capabilities, that helps keep your most important asset, data, safe. With the introduction of AI technology, Purview also expanded its data coverage to include discovering, protecting, and governing the interactions of AI apps and agents, such as Microsoft Copilots like Microsoft 365 Copilot and Security Copilot, Enterprise built AI apps like Chat GPT enterprise, and other consumer AI apps like DeepSeek, accessed through the browser. To help you view, investigate interactions with all those AI apps, and to create and manage policies to secure and govern them in one centralized place, we have launched Purview Data Security Posture Management (DSPM) for AI. You can learn more about DSPM for AI here with short video walkthroughs: Learn how Microsoft Purview Data Security Posture Management (DSPM) for AI provides data security and compliance protections for Copilots and other generative AI apps | Microsoft Learn Purview capabilities for AI apps and agents To understand our current set of capabilities within Purview to discover, protect, and govern various AI apps and agents, please refer to our Learn doc here: Microsoft Purview data security and compliance protections for Microsoft 365 Copilot and other generative AI apps | Microsoft Learn Here is a quick reference guide for the capabilities available today: Note that currently, DLP for Copilot and adhering to sensitivity label are currently designed to protect content in Microsoft 365. Thus, Security Copilot and Copilot in Fabric, along with Copilot studio custom agents that do not use Microsoft 365 as a content source, do not have these features available. Please see list of AI sites supported by Microsoft Purview DSPM for AI here Conclusion Microsoft Purview can help you discover, protect, and govern the prompts and responses from AI applications in Microsoft Copilot experiences, Enterprise AI apps, and other AI apps through its data security and data compliance solutions, while allowing you to view, investigate, and manage interactions in one centralized place in DSPM for AI. Follow up reading Check out the deployment guides for DSPM for AI How to deploy DSPM for AI - https://aka.ms/DSPMforAI/deploy How to use DSPM for AI data risk assessment to address oversharing - https://aka.ms/dspmforai/oversharing Address oversharing concerns with Microsoft 365 blueprint - aka.ms/Copilot/Oversharing Explore the Purview SDK Microsoft Purview SDK Public Preview | Microsoft Community Hub (blog) Microsoft Purview documentation - purview-sdk | Microsoft Learn Build secure and compliant AI applications with Microsoft Purview (video) References for DSPM for AI 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 Block Users From Sharing Sensitive Information to Unmanaged AI Apps Via Edge on Managed Devices (preview) | Microsoft Learn as part of Scenario 7 of Create and deploy a data loss prevention policy | 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 Explore the roadmap for DSPM for AI Public roadmap for DSPM for AI - Microsoft 365 Roadmap | Microsoft 365PMPurEmpowering organizations with integrated data security: What’s new in Microsoft Purview
Today, data moves across clouds, apps, and devices at an unprecedented speed, often outside the visibility of siloed legacy tools. The rise of autonomous agents, generative AI, and distributed data ecosystems means that traditional perimeter-based security models are no longer sufficient. Even though companies are spending more than $213 billion globally, they still face several persistent security challenges: Fragmented tools don’t integrate together well and leave customers lacking full visibility of their data security risks The growing use of AI in the workplace is creating new data risks for companies to manage The shortage of skilled cybersecurity professionals is making it difficult to accomplish data security objectives Microsoft is a global leader in cloud, productivity, and security solutions. Microsoft Purview benefits from this breadth of offerings, integrating seamlessly across Microsoft 365, Azure, Microsoft Fabric, and other Microsoft platforms — while also working in harmony with complementary security tools. Unlike fragmented point solutions, Purview delivers an end-to-end data security platform built into the productivity and collaboration tools organizations already rely on. This deep understanding of data within Microsoft environments, combined with continually improving external data risk detections, allows customers to simplify their security stack, increase visibility, and act on data risks more quickly. At Ignite, we’re introducing the next generation of data security — delivering advanced protection and operational efficiency, so security teams can move at business speed while maintaining control of their data. Go beyond visibility into action, across your data estate Many customers today lack a comprehensive view of how to holistically address data security risks and properly manage their data security posture. To help customers strengthen data security across their data estate, we are excited to announce the new, enhanced Microsoft Purview Data Security Posture Management (DSPM). This new AI-powered DSPM experience unifies current Purview DSPM and DSPM for AI capabilities to create a central entry point for data security insights and controls, from which organizations can take action to continually improve their data security posture and prioritize risks. The new capabilities in the enhanced DSPM experience are: Outcome-Based workflows: Choose a data security objective and see related metrics, risk patterns, a recommended action plan and its impact - going from insight to action. Expanded coverage and remediation on Data Risk Assessments: Conduct item-level analysis with new remediation actions like bulk disabling of overshared SharePoint links. Out-of-box posture reports: Uncover data protection gaps and track security posture improvements with out-of-box reports that provide rich context on label usage, auto-labeling effectiveness, posture drift through label transitions, and DLP policy activities. AI Observability: Surface an organization’s agent inventory with assigned agent risk level and agent posture metrics based on agentic interactions with the organization’s data. New Security Copilot Agent: Accelerate the discovery and analysis of sensitive data to uncover hidden risks across files, emails, and messages. Gain visibility of non-Microsoft data within your data estate: Enable a unified view of data risks by gaining visibility into Salesforce, Snowflake, Google Cloud Platform, and Databricks – available through integrations with external partners via Microsoft Sentinel. These DSPM enhancements will be available in Public Preview within the upcoming weeks. Learn more in our blog dedicated to the announcement of the new Microsoft Purview DSPM. Together, these innovations reflect a larger shift: data security is no longer about silos—it’s about unified visibility and control everywhere data lives and having a comprehensive understanding of the data estate to detect and prevent data risks. Organizations trust Microsoft for their productivity and security platforms, but their footprint spans across third-party data environments too. That’s why Purview continues to expand protection beyond Microsoft environments. In addition to bringing in 3rd party data into DSPM, we are also expanding auto-labeling to three new Data Map sources, adding to the data sources we previously announced. Currently in public preview, the new sources include Snowflake, SQL Server, and Amazon S3. Once connected to Purview, admins gain an “at-a-glance” view of all data sources and can automatically apply sensitivity labels, enforcing consistent security policies without manual effort. This helps organizations discover sensitive information at scale, reduce the risk of data exposure, and ensure safer AI adoption all while simplifying governance through centralized policy management and visibility across their entire data estate. Enable AI adoption and prevent data oversharing As organizations adopt more autonomous agents, new risks emerge, such as unsupervised data access and creation, cascading agent interactions, and unclear data activity accountability. Besides AI Observability in DSPM providing details on the inventory and risk level of the agents, Purview is expanding its industry-leading data security and compliance capabilities to secure and govern agents that inherit users’ policies and controls, as well as agents that have their own unique IDs, policies, and controls. This includes agent types across Microsoft 365 Copilot, Copilot Studio, Microsoft Foundry, and third-party platforms. Key enhancements include: Extension of Purview Information Protection and Data Loss Prevention policies to autonomous agents: Scope autonomous agents with an Agent ID into Purview policies that work for users across Microsoft 365 apps, including Exchange, SharePoint, and Teams. Microsoft Purview Insider Risk Management for Agents: With dedicated indicators and behavioral analytics to flag specific risky agent activities, enable proactive investigation by assigning risk levels to each agent. Extension of Purview data compliance capabilities to agent interactions: Microsoft Purview Communication Compliance, Data Lifecycle Management, Audit, and eDiscovery extend to agent interactions, supporting responsible use, secure retention, and agentic accountability. Purview SDK embedded in Agent Framework SDK: Purview SDK embedded in Agent Framework SDK enables developers to integrate enterprise-grade security, compliance, and governance into AI agents. It delivers automatic data classification, prevents sensitive data leaks and oversharing, and provides visibility and control for regulatory compliance, empowering secure adoption of AI agents in complex environments. Purview integration with Foundry: Purview is now enabled within Foundry, allowing Foundry admins to activate Microsoft Purview on their subscription. Once enabled, interaction data from all apps and agents flows into Purview for centralized compliance, governance, and posture management of AI data. Azure AI Search honors Purview labels and policies: Azure AI Search now ingests Microsoft Purview sensitivity labels and enforces corresponding protection policies through built-in indexers (SharePoint, OneLake, Azure Blob, ADLS Gen2). This ensures secure, policy-aligned search over enterprise data, enabling agentic RAG scenarios where only authorized documents are returned or sent to LLMs, preventing oversharing and aligning with enterprise data protection standards. Extension of Purview Data Loss Prevention policies to Copilot Mode in Edge for Business: This week, Microsoft Edge for Business introduced Copilot Mode, transforming the browser into a proactive, agentic partner. This is AI-assisted browsing will honor the user’s existing DLP protections, such as endpoint DLP policies that prevent pasting to sensitive service domains, or summarizing sensitive page content. Learn more in our blog dedicated to the announcements of Microsoft Purview for Agents. New capabilities in Microsoft Purview, now in public preview, to help prevent data oversharing and leakage through AI include: Expansion of Microsoft Purview Data Loss Prevention (DLP) for Microsoft 365 Copilot: Previously, we introduced DLP for Microsoft 365 Copilot to prevent labeled files & emails from being used as grounding data for responses, therefore reducing the risk of oversharing. Today, we are expanding DLP for Microsoft 365 Copilot to safeguard prompts containing sensitive data. This real-time control helps organizations mitigate data leakage and oversharing risks by preventing Microsoft 365 Copilot, Copilot Chat, and Microsoft 365 Copilot agents from returning a response when prompts contain sensitive data or using that sensitive data for grounding in Microsoft 365 or the web. For example, if a user searches, “Can you tell me more about my customer based on their address: 1234 Main Street,” Copilot will both inform the user that organizational policies prevent it from responding to their prompt, as well as block any web queries to Bing for “1234 Main Street.” Enhancements to inline data protection in Edge for Business: Earlier this year, we introduced inline data protection in Edge for Business to prevent sensitive data from being leaked to unmanaged consumer AI apps, starting with ChatGPT, Google Gemini, and DeepSeek. We are not only making this capability generally available for the initial set of AI apps, but also expanding the capability to 30+ new apps in public preview and supporting file upload activity in addition to text. This addresses potential data leakage that can occur when employees send organizational files or data to consumer AI apps for help with work-related tasks, such as document creation or code reviews. Inline data protection for the network: For user activity outside of the browser, we are also enabling inline data protection at the network layer. Earlier this year, we introduced integrations with supported secure service edge (SSE) providers to detect when sensitive data is shared to unmanaged cloud locations, such as consumer AI apps or personal cloud storage, even if sharing occurs outside of the Edge browser. In addition to the discovery of sensitive data, these integrations now support protection controls that block sensitive data from leaving a user device and reaching an unmanaged cloud service or application. These capabilities are now generally available through the Netskope and iboss integrations, and inline data discovery is available in public preview through the Palo Alto Networks integration. Extension of Purview protection to on-device AI: Purview DLP policies now extend to the Recall experience in Copilot+ PC devices to prevent sensitive organizational data from being undesirably captured and retained. Admins can now block Recall snapshots based on sensitivity label or the presence of Purview sensitive information types (SITs) in a document open on the device, or simply honor and display the sensitivity labels of content captured in the Recall snapshot library. For example, a DLP policy can be configured to prevent recall from taking snapshots of any documents labeled “Highly Confidential,” or a product design file that contains intellectual property. Learn more in the Windows IT Pro blog. Best-in-class data security for Microsoft environments Microsoft Purview sets the standard for data security within its own ecosystem. Organizations benefit from unified security policies and seamless compliance controls that are purpose-built for Microsoft environments, ensuring sensitive data remains secure without compromising productivity. We also are constantly investing in expanding protections and controls to Microsoft collaboration tools including SharePoint, Teams, Fabric, Azure and across Microsoft 365. On-demand classification adds meeting transcript coverage and new enhancements: To help organizations protect sensitive data sitting in data-at-rest, on-demand classification now extends to meeting transcripts, enabling the discovery and classification of sensitive information shared in existing recorded meeting transcripts. Once classified, admins can set up DLP or Data Lifecycle Management (DLM) policies to properly protect and retain this data according to organizational policies. This is now generally available, empowering organizations to strengthen data security, streamline compliance, and ensure even sensitive information in data-at-rest is discovered, protected, and governed more effectively. In addition, on-demand classification for endpoints is also generally available, giving organizations even broader coverage across their data estate. New usage posture and consumption reports: We’re introducing new usage posture and consumption reports, now in public preview. Admins can quickly identify compliance gaps, optimize Purview seat assignments, and understand how consumptive features are driving spend. With granular insights by feature, policy, and user type, admins can analyze usage trends, forecast costs, and toggle consumptive features on and off directly, all from a unified dashboard. The result: stronger compliance, easier cost management, and better alignment of Purview investments to your organization’s needs. Enable DLP and Copilot protection with extended SharePoint permissions: Extended SharePoint permissions, now generally available, make it simple to protect and manage files in SharePoint by allowing library owners to apply a default sensitivity label to an entire document library. When this is enabled, the label is dynamically enforced across all unprotected files in the library, both new and existing, within the library. Downloaded files are automatically encrypted, and access is managed based on SharePoint site membership, giving organizations powerful, scalable access control. With extended SharePoint permissions, teams can consistently apply labels at scale, automate DLP policy enforcement, and confidently deploy Copilot, all without the need for manually labeling files. Whether for internal teams, external partners, or any group where permissions need to be tightly controlled, extended SharePoint permissions streamline protection and compliance in SharePoint. Network file filtering via Entra GSA integration: We are integrating Purview with Microsoft Entra to enable file filtering at the network layer. These filtering controls help prevent sensitive content from being shared to unauthorized services based on properties such as sensitivity labels or presence of Purview sensitive information types (SITs) within the file. For example, Entra admins can now create a file policy to block files containing credit card numbers from passing through the network. Learn more here. Expanded protection scenarios enabled by Purview endpoint DLP: We are introducing several noteworthy enhancements to Purview endpoint DLP to protect an even broader range of exfiltration or leakage scenarios from organizational devices, without hindering user productivity. These enhancements, initially available on Windows devices, include: Extending protection to unsaved files: Files no longer need to be saved to disk to be protected under a DLP policy. With this improvement, unsaved files will undergo a point-in-time evaluation to detect the presence of sensitive data and apply the appropriate protections. Expanded support for removable media: Admins can now prevent data exfiltration to broader list of removable media devices, including iPhones, Android devices, and CD-ROMs. Protection for Outlook attachments downloaded to removable media or network shares: Admins can now prevent exfiltration of email attachments when users attempt to drag and drop them into USB devices, network shares, and other removable media. Expanded capability support for macOS: In addition to the new endpoint DLP protections introduced above, we are also expanding the following capabilities, already available for Windows devices, to devices running on macOS: Expanded file type coverage to 110+ file types, blanket protections for non-Office or PDF file types, addition of “allow” and “off” policy actions, device-based policy scoping to scope policies to specific devices or device groups (or apply exclusions), and integration with Power Automate. Manageability and alert investigation improvements in Purview DLP: Lastly, we are also introducing device manageability and alert investigation improvements in Purview DLP to simplify the day-to-day experience for admins. These improvements include: Reporting and troubleshooting improvements for devices onboarded to endpoint DLP: We are introducing additional tools for admins to build confidence in their Purview DLP protections for endpoint devices. These enhancements, designed to maximize reliability and enable better troubleshooting of potential issues, include near real-time reporting of policy syncs initiated on devices and policy health insights into devices’ compliance status and readiness to receive policies. Enhancements to always-on diagnostics: Earlier this year, we introduced always-on diagnostics to automatically collect logs from Windows endpoint devices, eliminating the need to reproduce issues when submitting an investigation request or raising a support ticket. This capability is expanding so that admins now have on-demand access to diagnostic logs from users’ devices without intervening in their operations. This further streamlines the issue resolution process for DLP admins while minimizing end user disruption. Simplified DLP alert investigation, including easier navigation to crucial alert details in just 1 click, and the ability to aggregate alerts originating from a single user for more streamlined investigation and response. For organizations who manage Purview DLP alerts within their broader incident management process in Microsoft Defender, we are pleased to share that alert severities will now be synced between the Purview portal and the Defender portal. Expanding enterprise-grade data security to small and medium businesses (SMBs): Purview is extending its reach beyond large enterprises by introducing a new add-on for Microsoft 365 Business Premium, bringing advanced data security and compliance capabilities to SMBs. The Microsoft Purview suite for Business Premium brings the same enterprise-grade protection, such as sensitivity labeling, data loss prevention, and compliance management, to organizations with up to 300 users. This enables SMBs to operate with the same level of compliance and data security as large enterprises, all within a simplified, cost-effective experience built for smaller teams. Stepping into the new era of technology with AI-powered data security Globally, there is a shortage of skilled cybersecurity professionals. Simultaneously, the volume of alerts and incidents is ever growing. By infusing AI into data security solutions, admins can scale their impact. By reducing manual workloads, they enhance operational effectiveness and strengthen overall security posture – allowing defenders to stay ahead. In 2025, 82% of organizations have developed plans to use GenAI to fortify their data security programs. With its cutting-edge generative AI-powered investigative capabilities, Microsoft Purview Data Security Investigations (DSI) is transforming and scaling how data security admins analyze incident-related data. Since being released into public preview in April, the product has made a big impact with customers like Toyota Motors North America. "Data Security Investigations eliminates manual work, automating investigations in minutes. It’s designed to handle the scale and complexity of large data sets by correlating user activity with data movement, giving analysts a faster, more efficient path to meaningful insights,” said solution architect Dan Garawecki. This Ignite, we are introducing several new capabilities in DSI, including: DSI integration with DSPM: View proactive, summary insights and launch a Data Security Investigation directly from DSPM. This integration brings the full power of DSI analysis to your fingertips, enabling admins to drill into data risks surfaced in DSPM with speed and precision. Enhancements in DSI AI-powered deep content analysis capabilities: Admins can now add context before AI analysis for higher-quality, more efficient investigations. A new AI-powered natural language search function lets admins locate specific files using keywords, metadata, and embeddings. Vector search and content categorization enhancements allow admins to better identify risky assets. Together, these enhancements equip admins with sharper, faster tools for identifying buried data risks – both proactively and reactively. DSI cost transparency report and in-product estimator: To help customers manage pay-as-you-go billing, DSI is adding a new lightweight in-product cost estimator and transparency report. We are also expanding Security Copilot in Microsoft Purview with AI-powered capabilities that strengthen both the protection and investigation of sensitive data by introducing the Data Security Posture Agent and Data Security Triage Agent. Data Security Posture Agent: Available in preview, the new Data Security Posture Agent uses LLMs to help admins answer “Is this happening?” across thousands of files—delivering fast, intent-driven discovery and risk profiling, even when explicit keywords are absent. Integrated with Purview DSPM, it surfaces actionable insights and improves compliance, helping teams reduce risk and respond to threats before they escalate. Data Security Triage Agent: Alongside this, the Data Security Triage Agent, now generally available, enables analysts to efficiently triage and remediate the most critical alerts, automating incident response and surfacing the threats that matter most. Together, these agentic capabilities convert high-volume signals into consistent, closed-loop action, accelerate investigations and remediation, reduce policy-violation dwell time, and improve audit readiness, all natively integrated within Microsoft 365 and Purview so security teams can scale outcomes without scaling headcount. To make the agents easily accessible and help teams get started more quickly, we are excited to announce that Security Copilot will be available to all Microsoft 365 E5 customers. Rollout starts today for existing Security Copilot customers with Microsoft 365 E5 and will continue in the upcoming months for all Microsoft 365 E5 customers. Customers will receive advanced notice before activation. Learn more: https://aka.ms/SCP-Ignite25 Data security that keeps innovating alongside you As we look ahead, Microsoft Purview remains focused on empowering organizations with scalable solutions that address the evolving challenges of data security. While we deliver best-in-class security for Microsoft, we recognize that today’s organizations rarely operate in a single cloud, many businesses rely on a diverse mix of platforms to power their operations and innovation. That’s why we have been extending Purview’s capabilities beyond Microsoft environments, helping customers protect data across their entire digital estate. In a world where data is the lifeblood of innovation, securing it must be more than a checkbox—it must be a catalyst for progress. As organizations embrace AI, autonomous agents, and increasingly complex digital ecosystems, Microsoft Purview empowers them to move forward with confidence. By unifying visibility, governance, and protection across the entire data estate, Purview transforms security from a fragmented challenge into a strategic advantage. The future of data security isn’t just about defense—it’s about enabling bold, responsible innovation at scale. Let’s build that future together.
