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 Below are some of the most common outdated security practices still widely used by enterprises today - and why they are no longer sufficient against modern, AI‑driven threats. 1.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 | MicrosoftRegistration Open: Community-Led Purview Lightning Talks
Get ready for an electrifying event! The Microsoft Security Community proudly presents Purview Lightning Talks; an action-packed series featuring your fellow Microsoft users, partners and passionate Microsoft Security community members of all sorts. Each 3-12 minute talk cuts straight to the chase, delivering expert insights, real-world use cases, and even a few game-changing tips and tricks. Don’t miss this opportunity to learn, connect, and be inspired! Secure your spot now for the big day: April 30th at 8am Redmond Time. 💙 See agenda details below and follow this blog post (sign in and click the "follow" heart in the upper right) to receive notifications. We have more speaker details and community connection information coming soon! AGENDA The Day Offboarding Exposed Infinite Retention - Nikki Chapple nikkichapple A real-world discovery of orphaned OneDrives and retention debt caused by retain-only policies, and how Adaptive Scopes help prevent it. Topic: Data Lifecycle Management Securing Data in the Age of AI - Julio Cesar Goncalves Vasconcelos How Microsoft Purview enables organizations to accelerate AI adoption while maintaining security, compliance, and transparency. Topic: Purview for AI What’s In My Compliance Manager Toolbox - Jerrad Dahlager j-dahl7 A practical walkthrough of using Compliance Manager to map controls, track improvements, and simplify multi-framework compliance. Topic: Compliance Manager Why You Should Create Your Own Sensitive Information Types (SITs) - Niels Jakobsen Niels_Jakobsen An in-depth analysis of why built-in SITs are not one-size-fits-all, and how to tailor them for real enterprise needs. Topic: Information Protection Beyond eDiscovery – Purview DSI for Security Investigation - Susantha Silva How to turn DLP alerts and Insider Risk signals into structured data investigations without jumping between portals. Topic: Data Security (DSI) Four Labels Max for Daily Use: Which Ones & Why? - Romain Dalle Romain DALLE A minimalist sensitivity labeling baseline designed for real-world adoption and usability. Topic: Information Protection Elevating Purview DLP with a Real-World Use Case - Victor Wingsing vicwingsing Hardening Purview DLP beyond default configurations to close real-world data loss gaps. Topic: Data Loss Prevention (DLP) Stop, Think, Protect: Data Security in Real Life with Purview - Oliver Sahlmann Oliver Sahlmann A traffic-light approach showing how simple labels and DLP policies still deliver meaningful protection. Topic: Data Security The Purview Label Engine: Automated Classification & Documentation - Michael Kirst Neshva MichaelKirst1970 A scalable framework for rolling out Microsoft Purview labels across global, multilingual enterprises. Topic: Information Protection Data-Driven Endpoint DLP with Advanced Hunting - Tatu Seppälä tatuseppala Using KQL queries and usage patterns to refine endpoint DLP policies based on real behavior. Topic: Data Loss Prevention (DLP) Improving Discovery, Trust, and Reuse of Analytics with Purview Data Products - Craig Wyndowe CraigWyndowe How Purview Governance Domains and Data Products create a trusted, reusable analytics ecosystem. Topic: Data Governance From Zero to First Signal: Insider Risk Management Prerequisites That Matter - Sathish Veerapandian Sathish Veerapandian A focused look at the configurations required for Insider Risk Management to actually generate alerts. Topic: Insider Risk Management The Purview Hack No One Talks About: Container Sensitivity Labels - Nikki Chapple nikkichapple How container sensitivity labels instantly fix oversharing for Teams, Groups, and SharePoint sites. Topic: Information Protection Using Purview to Prevent Oversharing with AI Services - Viktor Hedberg headburgh How Information Protection and DLP prevent Copilot and AI services from exposing sensitive data. Topic: Information Protection & DLP How I Helped Customers Understand Their AI Usage (and Protect Data) - Bram de Jager Bram de Jager Exposing risky AI usage patterns and protecting sensitive data entered into public AI tools. Topic: Data Security Posture Management for AI Bulk Sensitivity Label Removal with Microsoft Purview Information Protection (MPIP) - Zak Hepler A practical demo on safely removing sensitivity labels at scale from SharePoint libraries. Topic: Information Protection Does M365 Support eDiscovery? (Mythbusting) - Julian Kusenberg Leprechaun91 A myth-busting session separating perception from reality in Microsoft 365 eDiscovery. Topic: eDiscoveryAuthorization 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.Announcing GA: Advanced Resource Sets in Microsoft Purview Unified Catalog
The Microsoft Purview product team is constantly listening to customer feedback about the data governance challenges that slow teams down. One of the most persistent pain points — understanding the true shape of large-scale data lakes where thousands of files represent a single logical dataset — has driven a highly requested capability. We are pleased to announce that Advanced Resource Sets are now generally available for all Microsoft Purview Unified Catalog customers. The Problem It Solves Anyone managing a modern data lake knows the clutter: a single partitioned dataset like a daily transaction log might manifest as hundreds or thousands of individual files in Azure Data Lake Storage or Amazon S3. Without intelligent grouping, each of those files appears as a separate asset in the catalog. The result is a flood of noise — a catalog that technically contains your data estate but makes it nearly impossible to reason about it at a logical level. Data stewards end up buried in meaningless entries. Analysts searching for "the transactions table" find thousands of file-level hits instead of one clean, actionable asset. Governance efforts stall because nobody can agree on what the estate looks like. Advanced Resource Sets directly address this by grouping those physically separate but logically related files into a single, representative catalog asset — giving your teams a clean, meaningful view of the data landscape. What Advanced Resource Sets Actually Do The standard resource set capability in Purview already groups files using naming pattern heuristics. Advanced Resource Sets go significantly further, and this is where it gets interesting. Custom pattern configuration allows data curators to define precisely how partitioned datasets should be grouped — whether that is by date partition, region, environment, or any other dimension embedded in your file naming conventions. You are no longer relying solely on out-of-the-box heuristics. Partition schema surfacing means Purview now extracts and displays the partition dimensions themselves as metadata on the resource set asset. Instead of knowing only that "a resource set called transactions exists," your teams can see "that resource set is partitioned by year, month, and region." That is the difference between a data inventory and a genuinely useful data catalog. Accurate asset counts ensure that your catalog's asset metrics reflect logical datasets rather than raw file counts — giving leadership and governance teams a truthful picture of the data estate's scale. Getting Started — Simpler Than You Might Expect Enabling Advanced Resource Sets requires no additional connectors or infrastructure changes. The feature is activated and configured directly within the Microsoft Purview Governance Portal. At a high level: Sign in with an account that has Data Curator role in the default domain. Open Account settings in Microsoft Purview. Use the toggle to enable or disable Advanced resource sets. Define custom pattern rules by going to Data Map -> Source Management -> Pattern Rules Trigger a rescan (or allow scheduled scans to run). Purview will re-evaluate existing assets and collapse file-level entries into properly grouped resource sets with partition schema metadata attached. What You Can Do With It Once configured, Advanced Resource Sets surface in the Unified Catalog alongside all other scanned assets — but now at the right level of abstraction for your data consumers and governance teams. Data discoverability improves immediately. Analysts searching the catalog find logical datasets, not file fragments. They can evaluate partition coverage, understand data freshness based on partition metadata, and make confident decisions about whether an asset meets their needs before requesting access. Governance accuracy follows naturally. Data owners can apply classifications, sensitivity labels, and glossary terms to a single representative asset rather than chasing down hundreds of file-level entries. Ready to enable Advanced Resource Sets in your environment? Head to the Microsoft Purview Portal, navigate to account settings. Full documentation is available at Microsoft Learn: Manage resource sets.Authorization and Identity Governance Inside AI Agents
Designing Authorization‑Aware AI Agents Enforcing Microsoft Entra ID RBAC in Copilot Studio As AI agents move from experimentation to enterprise execution, authorization becomes the defining line between innovation and risk. AI agents are rapidly evolving from experimental assistants into enterprise operators—retrieving user data, triggering workflows, and invoking protected APIs. While many early implementations rely on prompt‑level instructions to control access, regulated enterprise environments require authorization to be enforced by identity systems, not language models. This article presents a production‑ready, identity‑first architecture for building authorization‑aware AI agents using Copilot Studio, Power Automate, Microsoft Entra ID, and Microsoft Graph, ensuring every agent action executes strictly within the requesting user’s permissions. Why Prompt‑Level Security Is Not Enough Large Language Models interpret intent—they do not enforce policy. Even the most carefully written prompts cannot: Validate Microsoft Entra ID group or role membership Reliably distinguish delegated user identity from application identity Enforce deterministic access decisions Produce auditable authorization outcomes Relying on prompts for authorization introduces silent security failures, over‑privileged access, and compliance gaps—particularly in Financial Services, Healthcare, and other regulated industries. Authorization is not a reasoning problem. It is an identity enforcement problem. Common Authorization Anti‑Patterns in AI Agents The following patterns frequently appear in early AI agent implementations and should be avoided in enterprise environments: Hard‑coded role or group checks embedded in prompts Trusting group names passed as plain‑text parameters Using application permissions for user‑initiated actions Skipping verification of the user’s Entra ID identity Lacking an auditable authorization decision point These approaches may work in demos, but they do not survive security reviews, compliance audits, or real‑world misuse scenarios. Authorization‑Aware Agent Architecture In an authorization‑aware design, the agent never decides access. Authorization is enforced externally, by identity‑aware workflows that sit outside the language model’s reasoning boundary. High‑Level Flow The Copilot Studio agent receives a user request The agent passes the User Principal Name (UPN) and intended action A Power Automate flow validates permissions using Microsoft Entra ID via Microsoft Graph Only authorized requests are allowed to proceed Unauthorized requests fail fast with a deterministic outcome Authorization‑aware Copilot Studio architecture enforces Entra ID RBAC before executing any business action. The agent orchestrates intent. Identity systems enforce access. Enforcing Entra ID RBAC with Microsoft Graph Power Automate acts as the authorization enforcement layer: Resolve user identity from the supplied UPN Retrieve group or role memberships using Microsoft Graph Normalize and compare memberships against approved RBAC groups Explicitly deny execution when authorization fails This keeps authorization logic: Centralized Deterministic Auditable Independent of the AI model Reference Implementation: Power Automate RBAC Enforcement Flow The following import‑ready Power Automate cloud flow demonstrates a secure RBAC enforcement pattern for Copilot Studio agents. It validates Microsoft Entra ID group membership before allowing any business action. Scenario Trigger: User‑initiated agent action Identity model: Delegated user identity Input: userUPN, requestedAction Outcome: Authorized or denied based on Entra ID RBAC { "$schema": "https://schema.management.azure.com/providers/Microsoft.Logic/schemas/2016-06-01/workflowdefinition.json#", "contentVersion": "1.0.0.0", "triggers": { "Copilot_Request": { "type": "Request", "kind": "Http", "inputs": { "schema": { "type": "object", "properties": { "userUPN": { "type": "string" }, "requestedAction": { "type": "string" } }, "required": [ "userUPN" ] } } } }, "actions": { "Get_User_Groups": { "type": "Http", "inputs": { "method": "GET", "uri": "https://graph.microsoft.com/v1.0/users/@{triggerBody()?['userUPN']}/memberOf?$select=displayName", "authentication": { "type": "ManagedServiceIdentity" } } }, "Normalize_Group_Names": { "type": "Select", "inputs": { "from": "@body('Get_User_Groups')?['value']", "select": { "groupName": "@toLower(item()?['displayName'])" } }, "runAfter": { "Get_User_Groups": [ "Succeeded" ] } }, "Check_Authorization": { "type": "Condition", "expression": "@contains(body('Normalize_Group_Names'), 'ai-authorized-users')", "runAfter": { "Normalize_Group_Names": [ "Succeeded" ] }, "actions": { "Authorized_Action": { "type": "Compose", "inputs": "User authorized via Entra ID RBAC" } }, "else": { "actions": { "Access_Denied": { "type": "Terminate", "inputs": { "status": "Failed", "message": "Access denied. User not authorized via Entra ID RBAC." } } } } } } } This pattern enforces authorization outside the agent, aligns with Zero Trust principles, and creates a clear audit boundary suitable for enterprise and regulated environments. Flow Diagram: Agent Integrated with RBAC Authorization Flow and Sample Prompt Execution: Delegated vs Application Permissions Scenario Recommended Permission Model User‑initiated agent actions Delegated permissions Background or system automation Application permissions Using delegated permissions ensures agent execution remains strictly within the requesting user’s identity boundary. Auditing and Compliance Benefits Deterministic and explainable authorization decisions Centralized enforcement aligned with identity governance Clear audit trails for security and compliance reviews Readiness for SOC, ISO, PCI, and FSI assessments Enterprise Security Takeaways Authorization belongs in Microsoft Entra ID, not prompts AI agents must respect enterprise identity boundaries Copilot Studio + Power Automate + Microsoft Graph enable secure‑by‑design AI agents By treating AI agents as first‑class enterprise actors and enforcing authorization at the identity layer, organizations can scale AI adoption with confidence, trust, and compliance.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.Security as the core primitive - Securing AI agents and apps
This week at Microsoft Ignite, we shared our vision for Microsoft security -- In the agentic era, security must be ambient and autonomous, like the AI it protects. It must be woven into and around everything we build—from silicon to OS, to agents, apps, data, platforms, and clouds—and throughout everything we do. In this blog, we are going to dive deeper into many of the new innovations we are introducing this week to secure AI agents and apps. As I spend time with our customers and partners, there are four consistent themes that have emerged as core security challenges to secure AI workloads. These are: preventing agent sprawl and access to resources, protecting against data oversharing and data leaks, defending against new AI threats and vulnerabilities, and adhering to evolving regulations. Addressing these challenges holistically requires a coordinated effort across IT, developers, and security leaders, not just within security teams and to enable this, we are introducing several new innovations: Microsoft Agent 365 for IT, Foundry Control Plane in Microsoft Foundry for developers, and the Security Dashboard for AI for security leaders. In addition, we are releasing several new purpose-built capabilities to protect and govern AI apps and agents across Microsoft Defender, Microsoft Entra, and Microsoft Purview. Observability at every layer of the stack To facilitate the organization-wide effort that it takes to secure and govern AI agents and apps – IT, developers, and security leaders need observability (security, management, and monitoring) at every level. IT teams need to enable the development and deployment of any agent in their environment. To ensure the responsible and secure deployment of agents into an organization, IT needs a unified agent registry, the ability to assign an identity to every agent, manage the agent’s access to data and resources, and manage the agent’s entire lifecycle. In addition, IT needs to be able to assign access to common productivity and collaboration tools, such as email and file storage, and be able to observe their entire agent estate for risks such as over-permissioned agents. Development teams need to build and test agents, apply security and compliance controls by default, and ensure AI models are evaluated for safety guardrails and security vulnerabilities. Post deployment, development teams must observe agents to ensure they are staying on task, accessing applications and data sources appropriately, and operating within their cost and performance expectations. Security & compliance teams must ensure overall security of their AI estate, including their AI infrastructure, platforms, data, apps, and agents. They need comprehensive visibility into all their security risks- including agent sprawl and resource access, data oversharing and leaks, AI threats and vulnerabilities, and complying with global regulations. They want to address these risks by extending their existing security investments that they are already invested in and familiar with, rather than using siloed or bolt-on tools. These teams can be most effective in delivering trustworthy AI to their organizations if security is natively integrated into the tools and platforms that they use every day, and if those tools and platforms share consistent security primitives such as agent identities from Entra; data security and compliance controls from Purview; and security posture, detections, and protections from Defender. With the new capabilities being released today, we are delivering observability at every layer of the AI stack, meeting IT, developers, and security teams where they are in the tools they already use to innovate with confidence. For IT Teams - Introducing Microsoft Agent 365, the control plane for agents, now in preview The best infrastructure for managing your agents is the one you already use to manage your users. With Agent 365, organizations can extend familiar tools and policies to confidently deploy and secure agents, without reinventing the wheel. By using the same trusted Microsoft 365 infrastructure, productivity apps, and protections, organizations can now apply consistent and familiar governance and security controls that are purpose-built to protect against agent-specific threats and risks. gement and governance of agents across organizations Microsoft Agent 365 delivers a unified agent Registry, Access Control, Visualization, Interoperability, and Security capabilities for your organization. These capabilities work together to help organizations manage agents and drive business value. The Registry powered by the Entra provides a complete and unified inventory of all the agents deployed and used in your organization including both Microsoft and third-party agents. Access Control allows you to limit the access privileges of your agents to only the resources that they need and protect their access to resources in real time. Visualization gives organizations the ability to see what matters most and gain insights through a unified dashboard, advanced analytics, and role-based reporting. Interop allows agents to access organizational data through Work IQ for added context, and to integrate with Microsoft 365 apps such as Outlook, Word, and Excel so they can create and collaborate alongside users. Security enables the proactive detection of vulnerabilities and misconfigurations, protects against common attacks such as prompt injections, prevents agents from processing or leaking sensitive data, and gives organizations the ability to audit agent interactions, assess compliance readiness and policy violations, and recommend controls for evolving regulatory requirements. Microsoft Agent 365 also includes the Agent 365 SDK, part of Microsoft Agent Framework, which empowers developers and ISVs to build agents on their own AI stack. The SDK enables agents to automatically inherit Microsoft's security and governance protections, such as identity controls, data security policies, and compliance capabilities, without the need for custom integration. For more details on Agent 365, read the blog here. For Developers - Introducing Microsoft Foundry Control Plane to observe, secure and manage agents, now in preview Developers are moving fast to bring agents into production, but operating them at scale introduces new challenges and responsibilities. Agents can access tools, take actions, and make decisions in real time, which means development teams must ensure that every agent behaves safely, securely, and consistently. Today, developers need to work across multiple disparate tools to get a holistic picture of the cybersecurity and safety risks that their agents may have. Once they understand the risk, they then need a unified and simplified way to monitor and manage their entire agent fleet and apply controls and guardrails as needed. Microsoft Foundry provides a unified platform for developers to build, evaluate and deploy AI apps and agents in a responsible way. Today we are excited to announce that Foundry Control Plane is available in preview. This enables developers to observe, secure, and manage their agent fleets with built-in security, and centralized governance controls. With this unified approach, developers can now identify risks and correlate disparate signals across their models, agents, and tools; enforce consistent policies and quality gates; and continuously monitor task adherence and runtime risks. Foundry Control Plane is deeply integrated with Microsoft’s security portfolio to provide a ‘secure by design’ foundation for developers. With Microsoft Entra, developers can ensure an agent identity (Agent ID) and access controls are built into every agent, mitigating the risk of unmanaged agents and over permissioned resources. With Microsoft Defender built in, developers gain contextualized alerts and posture recommendations for agents directly within the Foundry Control Plane. This integration proactively prevents configuration and access risks, while also defending agents from runtime threats in real time. Microsoft Purview’s native integration into Foundry Control Plane makes it easy to enable data security and compliance for every Foundry-built application or agent. This allows Purview to discover data security and compliance risks and apply policies to prevent user prompts and AI responses from safety and policy violations. In addition, agent interactions can be logged and searched for compliance and legal audits. This integration of the shared security capabilities, including identity and access, data security and compliance, and threat protection and posture ensures that security is not an afterthought; it’s embedded at every stage of the agent lifecycle, enabling you to start secure and stay secure. For more details, read the blog. For Security Teams - Introducing Security Dashboard for AI - unified risk visibility for CISOs and AI risk leaders, coming soon AI proliferation in the enterprise, combined with the emergence of AI governance committees and evolving AI regulations, leaves CISOs and AI risk leaders needing a clear view of their AI risks, such as data leaks, model vulnerabilities, misconfigurations, and unethical agent actions across their entire AI estate, spanning AI platforms, apps, and agents. 90% of security professionals, including CISOs, report that their responsibilities have expanded to include data governance and AI oversight within the past year. 1 At the same time, 86% of risk managers say disconnected data and systems lead to duplicated efforts and gaps in risk coverage. 2 To address these needs, we are excited to introduce the Security Dashboard for AI. This serves as a unified dashboard that aggregates posture and real-time risk signals from Microsoft Defender, Microsoft Entra, and Microsoft Purview. This unified dashboard allows CISOs and AI risk leaders to discover agents and AI apps, track AI posture and drift, and correlate risk signals to investigate and act across their entire AI ecosystem. For example, you can see your full AI inventory and get visibility into a quarantined agent, flagged for high data risk due to oversharing sensitive information in Purview. The dashboard then correlates that signal with identity insights from Entra and threat protection alerts from Defender to provide a complete picture of exposure. From there, you can delegate tasks to the appropriate teams to enforce policies and remediate issues quickly. With the Security Dashboard for AI, CISOs and risk leaders gain a clear, consolidated view of AI risks across agents, apps, and platforms—eliminating fragmented visibility, disconnected posture insights, and governance gaps as AI adoption scales. Best of all, there’s nothing new to buy. If you’re already using Microsoft security products to secure AI, you’re already a Security Dashboard for AI customer. Figure 5: Security Dashboard for AI provides CISOs and AI risk leaders with a unified view of their AI risk by bringing together their AI inventory, AI risk, and security recommendations to strengthen overall posture Together, these innovations deliver observability and security across IT, development, and security teams, powered by Microsoft’s shared security capabilities. With Microsoft Agent 365, IT teams can manage and secure agents alongside users. Foundry Control Plane gives developers unified governance and lifecycle controls for agent fleets. Security Dashboard for AI provides CISOs and AI risk leaders with a consolidated view of AI risks across platforms, apps, and agents. Added innovation to secure and govern your AI workloads In addition to the IT, developer, and security leader-focused innovations outlined above, we continue to accelerate our pace of innovation in Microsoft Entra, Microsoft Purview, and Microsoft Defender to address the most pressing needs for securing and governing your AI workloads. These needs are: Manage agent sprawl and resource access e.g. managing agent identity, access to resources, and permissions lifecycle at scale Prevent data oversharing and leaks e.g. protecting sensitive information shared in prompts, responses, and agent interactions Defend against shadow AI, new threats, and vulnerabilities e.g. managing unsanctioned applications, preventing prompt injection attacks, and detecting AI supply chain vulnerabilities Enable AI governance for regulatory compliance e.g. ensuring AI development, operations, and usage comply with evolving global regulations and frameworks Manage agent sprawl and resource access 76% of business leaders expect employees to manage agents within the next 2–3 years. 3 Widespread adoption of agents is driving the need for visibility and control, which includes the need for a unified registry, agent identities, lifecycle governance, and secure access to resources. Today, Microsoft Entra provides robust identity protection and secure access for applications and users. However, organizations lack a unified way to manage, govern, and protect agents in the same way they manage their users. Organizations need a purpose-built identity and access framework for agents. Introducing Microsoft Entra Agent ID, now in preview Microsoft Entra Agent ID offers enterprise-grade capabilities that enable organizations to prevent agent sprawl and protect agent identities and their access to resources. These new purpose-built capabilities enable organizations to: Register and manage agents: Get a complete inventory of the agent fleet and ensure all new agents are created with an identity built-in and are automatically protected by organization policies to accelerate adoption. Govern agent identities and lifecycle: Keep the agent fleet under control with lifecycle management and IT-defined guardrails for both agents and people who create and manage them. Protect agent access to resources: Reduce risk of breaches, block risky agents, and prevent agent access to malicious resources with conditional access and traffic inspection. Agents built in Microsoft Copilot Studio, Microsoft Foundry, and Security Copilot get an Entra Agent ID built-in at creation. Developers can also adopt Entra Agent ID for agents they build through Microsoft Agent Framework, Microsoft Agent 365 SDK, or Microsoft Entra Agent ID SDK. Read the Microsoft Entra blog to learn more. Prevent data oversharing and leaks Data security is more complex than ever. Information Security Media Group (ISMG) reports that 80% of leaders cite leakage of sensitive data as their top concern. 4 In addition to data security and compliance risks of generative AI (GenAI) apps, agents introduces new data risks such as unsupervised data access, highlighting the need to protect all types of corporate data, whether it is accessed by employees or agents. To mitigate these risks, we are introducing new Microsoft Purview data security and compliance capabilities for Microsoft 365 Copilot and for agents and AI apps built with Copilot Studio and Microsoft Foundry, providing unified protection, visibility, and control for users, AI Apps, and Agents. New Microsoft Purview controls safeguard Microsoft 365 Copilot with real-time protection and bulk remediation of oversharing risks Microsoft Purview and Microsoft 365 Copilot deliver a fully integrated solution for protecting sensitive data in AI workflows. Based on ongoing customer feedback, we’re introducing new capabilities to deliver real-time protection for sensitive data in M365 Copilot and accelerated remediation of oversharing risks: Data risk assessments: Previously, admins could monitor oversharing risks such as SharePoint sites with unprotected sensitive data. Now, they can perform item-level investigations and bulk remediation for overshared files in SharePoint and OneDrive to quickly reduce oversharing exposure. Data Loss Prevention (DLP) for M365 Copilot: DLP previously excluded files with sensitivity labels from Copilot processing. Now in preview, DLP also prevents prompts that include sensitive data from being processed in M365 Copilot, Copilot Chat, and Copilot agents, and prevents Copilot from using sensitive data in prompts for web grounding. Priority cleanup for M365 Copilot assets: Many organizations have org-wide policies to retain or delete data. Priority cleanup, now generally available, lets admins delete assets that are frequently processed by Copilot, such as meeting transcripts and recordings, on an independent schedule from the org-wide policies while maintaining regulatory compliance. On-demand classification for meeting transcripts: Purview can now detect sensitive information in meeting transcripts on-demand. This enables data security admins to apply DLP policies and enforce Priority cleanup based on the sensitive information detected. & bulk remediation Read the full Data Security blog to learn more. Introducing new Microsoft Purview data security capabilities for agents and apps built with Copilot Studio and Microsoft Foundry, now in preview Microsoft Purview now extends the same data security and compliance for users and Copilots to agents and apps. These new capabilities are: Enhanced Data Security Posture Management: A centralized DSPM dashboard that provides observability, risk assessment, and guided remediation across users, AI apps, and agents. Insider Risk Management (IRM) for Agents: Uniquely designed for agents, using dedicated behavioral analytics, Purview dynamically assigns risk levels to agents based on their risky handing of sensitive data and enables admins to apply conditional policies based on that risk level. Sensitive data protection with Azure AI Search: Azure AI Search enables fast, AI-driven retrieval across large document collections, essential for building AI Apps. When apps or agents use Azure AI Search to index or retrieve data, Purview sensitivity labels are preserved in the search index, ensuring that any sensitive information remains protected under the organization’s data security & compliance policies. For more information on preventing data oversharing and data leaks - Learn how Purview protects and governs agents in the Data Security and Compliance for Agents blog. Defend against shadow AI, new threats, and vulnerabilities AI workloads are subject to new AI-specific threats like prompt injections attacks, model poisoning, and data exfiltration of AI generated content. Although security admins and SOC analysts have similar tasks when securing agents, the attack methods and surfaces differ significantly. To help customers defend against these novel attacks, we are introducing new capabilities in Microsoft Defender that deliver end-to-end protection, from security posture management to runtime defense. Introducing Security Posture Management for agents, now in preview As organizations adopt AI agents to automate critical workflows, they become high-value targets and potential points of compromise, creating a critical need to ensure agents are hardened, compliant, and resilient by preventing misconfigurations and safeguarding against adversarial manipulation. Security Posture Management for agents in Microsoft Defender now provides an agent inventory for security teams across Microsoft Foundry and Copilot Studio agents. Here, analysts can assess the overall security posture of an agent, easily implement security recommendations, and identify vulnerabilities such as misconfigurations and excessive permissions, all aligned to the MITRE ATT&CK framework. Additionally, the new agent attack path analysis visualizes how an agent’s weak security posture can create broader organizational risk, so you can quickly limit exposure and prevent lateral movement. Introducing Threat Protection for agents, now in preview Attack techniques and attack surfaces for agents are fundamentally different from other assets in your environment. That’s why Defender is delivering purpose-built protections and detections to help defend against them. Defender is introducing runtime protection for Copilot Studio agents that automatically block prompt injection attacks in real time. In addition, we are announcing agent-specific threat detections for Copilot Studio and Microsoft Foundry agents coming soon. Defender automatically correlates these alerts with Microsoft’s industry-leading threat intelligence and cross-domain security signals to deliver richer, contextualized alerts and security incident views for the SOC analyst. Defender’s risk and threat signals are natively integrated into the new Microsoft Foundry Control Plane, giving development teams full observability and the ability to act directly from within their familiar environment. Finally, security analysts will be able to hunt across all agent telemetry in the Advanced Hunting experience in Defender, and the new Agent 365 SDK extends Defender’s visibility and hunting capabilities to third-party agents, starting with Genspark and Kasisto, giving security teams even more coverage across their AI landscape. To learn more about how you can harden the security posture of your agents and defend against threats, read the Microsoft Defender blog. Enable AI governance for regulatory compliance Global AI regulations like the EU AI Act and NIST AI RMF are evolving rapidly; yet, according to ISMG, 55% of leaders report lacking clarity on current and future AI regulatory requirements. 5 As enterprises adopt AI, they must ensure that their AI innovation aligns with global regulations and standards to avoid costly compliance gaps. Introducing new Microsoft Purview Compliance Manager capabilities to stay ahead of evolving AI regulations, now in preview Today, Purview Compliance Manager provides over 300 pre-built assessments for common industry, regional, and global standards and regulations. However, the pace of change for new AI regulations requires controls to be continuously re-evaluated and updated so that organizations can adapt to ongoing changes in regulations and stay compliant. To address this need, Compliance Manager now includes AI-powered regulatory templates. AI-powered regulatory templates enable real-time ingestion and analysis of global regulatory documents, allowing compliance teams to quickly adapt to changes as they happen. As regulations evolve, the updated regulatory documents can be uploaded to Compliance Manager, and the new requirements are automatically mapped to applicable recommended actions to implement controls across Microsoft Defender, Microsoft Entra, Microsoft Purview, Microsoft 365, and Microsoft Foundry. Automated actions by Compliance Manager further streamline governance, reduce manual workload, and strengthen regulatory accountability. Introducing expanded Microsoft Purview compliance capabilities for agents and AI apps now in preview Microsoft Purview now extends its compliance capabilities across agent-generated interactions, ensuring responsible use and regulatory alignment as AI becomes deeply embedded across business processes. New capabilities include expanded coverage for: Audit: Surface agent interactions, lifecycle events, and data usage with Purview Audit. Unified audit logs across user and agent activities, paired with traceability for every agent using an Entra Agent ID, support investigation, anomaly detection, and regulatory reporting. Communication Compliance: Detect prompts sent to agents and agent-generated responses containing inappropriate, unethical, or risky language, including attempts to manipulate agents into bypassing policies, generating risky content, or producing noncompliant outputs. When issues arise, data security admins get full context, including the prompt, the agent’s output, and relevant metadata, so they can investigate and take corrective action Data Lifecycle Management: Apply retention and deletion policies to agent-generated content and communication flows to automate lifecycle controls and reduce regulatory risk. Read about Microsoft Purview data security for agents to learn more. Finally, we are extending our data security, threat protection, and identity access capabilities to third-party apps and agents via the network. Advancing Microsoft Entra Internet Access Secure Web + AI Gateway - extend runtime protections to the network, now in preview Microsoft Entra Internet Access, part of the Microsoft Entra Suite, has new capabilities to secure access to and usage of GenAI at the network level, marking a transition from Secure Web Gateway to Secure Web and AI Gateway. Enterprises can accelerate GenAI adoption while maintaining compliance and reducing risk, empowering employees to experiment with new AI tools safely. The new capabilities include: Prompt injection protection which blocks malicious prompts in real time by extending Azure AI Prompt Shields to the network layer. Network file filtering which extends Microsoft Purview to inspect files in transit and prevents regulated or confidential data from being uploaded to unsanctioned AI services. Shadow AI Detection that provides visibility into unsanctioned AI applications through Cloud Application Analytics and Defender for Cloud Apps risk scoring, empowering security teams to monitor usage trends, apply Conditional Access, or block high-risk apps instantly. Unsanctioned MCP server blocking prevents access to MCP servers from unauthorized agents. With these controls, you can accelerate GenAI adoption while maintaining compliance and reducing risk, so employees can experiment with new AI tools safely. Read the Microsoft Entra blog to learn more. As AI transforms the enterprise, security must evolve to meet new challenges—spanning agent sprawl, data protection, emerging threats, and regulatory compliance. Our approach is to empower IT, developers, and security leaders with purpose-built innovations like Agent 365, Foundry Control Plane, and the Security Dashboard for AI. These solutions bring observability, governance, and protection to every layer of the AI stack, leveraging familiar tools and integrated controls across Microsoft Defender, Microsoft Entra, and Microsoft Purview. The future of security is ambient, autonomous, and deeply woven into the fabric of how we build, deploy, and govern AI systems. Explore additional resources Learn more about Security for AI solutions on our webpage Learn more about Microsoft Agent 365 Learn more about Microsoft Entra Agent ID Get started with Microsoft 365 Copilot Get started with Microsoft Copilot Studio Get started with Microsoft Foundry Get started with Microsoft Defender for Cloud Get started with Microsoft Entra Get started with Microsoft Purview Get started with Microsoft Purview Compliance Manager Sign up for a free Microsoft 365 E5 Security Trial and Microsoft Purview Trial 1 Bedrock Security, 2025 Data Security Confidence Index, published Mar 17, 2025. 2 AuditBoard & Ascend2, Connected Risk Report 2024; as cited by MIT Sloan Management Review, Spring 2025. 3 KPMG AI Quarterly Pulse Survey | Q3 2025. September 2025. n= 130 U.S.-based C-suite and business leaders representing organizations with annual revenue of $1 billion or more 4 First Annual Generative AI study: Business Rewards vs. Security Risks, , Q3 2023, ISMG, N=400 5 First Annual Generative AI study: Business Rewards vs. Security Risks, Q3 2023, ISMG, N=400
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.Strengthening your Security Posture with Microsoft Security Store Innovations at RSAC 2026
Security teams are facing more threats, more complexity, and more pressure to act quickly - without increasing risk or operational overhead. What matters is being able to find the right capability, deploy it safely, and use it where security work already happens. Microsoft Security Store was built with that goal in mind. It provides a single, trusted place to discover, purchase, and deploy Microsoft and partner-built security agents and solutions that extend Microsoft Security - helping you improve protection across SOC, identity, and data protection workflows. Today, the Security Store includes 75+ security agents and 115+ solutions from Microsoft and trusted partners - each designed to integrate directly into Microsoft Security experiences and meet enterprise security requirements. At RSAC 2026, we’re announcing capabilities that make it easier to turn security intent into action- by improving how you discover agents, how quickly you can put them to use, and how effectively you can apply them across workflows to achieve your security outcomes. Meet the Next Generation of Security Agents Security agents are becoming part of day-to-day operations for many teams - helping automate investigations, enrich signals, and reduce manual effort across common security tasks. Since Security Store became generally available, Microsoft and our partners have continued to expand the set of agents that integrate directly with Microsoft Defender, Sentinel, Entra, Purview, Intune and Security Copilot. Some of the notable partner-built agents available through Security Store include: XBOW Continuous Penetration Testing Agent XBOW’s penetration testing agents perform pen-tests, analyzes findings, and correlates those findings with a customer’s Microsoft Defender detections. XBOW integrates offensive security directly into Microsoft Security workflows by streaming validated, exploitable AppSec findings into Microsoft Sentinel and enabling investigation through XBOW's Copilot agents in Microsoft Defender. With XBOW’s pen-testing agents, offensive security can run continuously to identify which vulnerabilities are actually exploitable, and how to improve posture and detections. Tanium Incident Scoping Agent The Tanium Incident Scoping Agent (In Preview) is bringing real-time endpoint intelligence directly into Microsoft Defender and Microsoft Security Copilot workflows. The agent automatically scopes incidents, identifies impacted devices, and surfaces actionable context in minutes-helping teams move faster from detection to containment. By combining Tanium’s real-time intelligence with Microsoft Security investigations, you can reduce manual effort, accelerate response, and maintain enterprise-grade governance and control. Zscaler In Microsoft Sentinel, the Zscaler ZIA–ZPA Correlation Agent correlates ZIA and ZPA activity for a given user to speed malsite/malware investigations. It highlights suspicious patterns and recommends ZIA/ZPA policy changes to reduce repeat exposure. These agents build on a growing ecosystem of Microsoft and partner capabilities designed to work together, allowing you to extend Microsoft Security with specialized expertise where it has the most impact. Discover and Deploy Agents and Solutions in the Flow of Security Work Security teams work best when they don’t have to switch tools to make decisions. That’s why Security Store is embedded directly into Microsoft Security experiences - so you can discover and evaluate trusted agents and solutions in context, while working in the tools you already use. When Security Store became generally available, we embedded it into Microsoft Defender, allowing SOC teams to discover and deploy trusted Microsoft and partner‑built agents and solutions in the middle of active investigations. Analysts can now automate response, enrich investigations, and resolve threats all within the Defender portal. At RSAC, we’re expanding this approach across identity and data security. Strengthening Identity Security with Security Store in Microsoft Entra Identity has become a primary attack surface - from fraud and automated abuse to privileged access misuse and posture gaps. Security Store is now embedded in Microsoft Entra, allowing identity and security teams to discover and deploy partner solutions and agents directly within identity workflows. For external and verified identity scenarios, Security Store includes partner solutions that integrate with Entra External ID and Entra Verified ID to help protect against fraud, DDoS attacks, and intelligent bot abuse. These solutions, built by partners such as IDEMIA, AU10TIX, TrueCredential, HUMAN Security, Akamai and Arkose Labs help strengthen trust while preserving seamless user experiences. For enterprise identity security, more than 15 agents available through the Entra Security Store provide visibility into privileged activity and identity risk, posture health and trends, and actionable recommendations to improve identity security and overall security score. These agents are built by partners such as glueckkanja, adaQuest, Ontinue, BlueVoyant, Invoke, and Performanta. This allows you to extend Entra with specialized identity security capabilities, without leaving the identity control plane. Extending Data Protection with Security Store in Microsoft Purview Protecting sensitive data requires consistent controls across where data lives and how it moves. Security Store is now embedded in Microsoft Purview, enabling teams responsible for data protection and compliance to discover partner solutions directly within Purview DLP workflows. Through this experience, you can extend Microsoft Purview DLP with partner data security solutions that help protect sensitive data across cloud applications, enterprise browsers, and networks. These include solutions from Microsoft Entra Global Secure Access and partners such as Netskope, Island, iBoss, and Palo Alto Networks. This experience will be available to customers later this month, as reflected on the M365 roadmap. By discovering solutions in context, teams can strengthen data protection without disrupting established compliance workflows. Across Defender, Entra, and Purview, purchases continue to be completed through the Security Store website, ensuring a consistent, secure, and governed transaction experience - while discovery and evaluation happen exactly where teams already work. Outcome-Driven Discovery, with Security Store Advisor As the number of agents and solutions in the Store grow, finding the right fit for your security scenario quickly becomes more important. That’s why we’re introducing the AI‑guided Security Store Advisor, now generally available. You can describe your goal in natural language - such as “investigate suspicious network activity” and receive recommendations aligned to that outcome. Advisor also includes side-by-side comparison views for agents and solutions, helping you review capabilities, integrated services, and deployment requirements more quickly and reduce evaluation time. Security Store Advisor is designed with Responsible AI principles in mind, including transparency and explainability. You can learn more about how Responsible AI is applied in this experience in the Security Store Advisor Responsible AI FAQ. Overall, this outcome‑driven approach reduces time to value, improves solution fit, and helps your team move faster from intent to action. Learning from the Security Community with Ratings and Reviews Security decisions are strongest when informed by real world use cases. This is why we are introducing Security Store ratings and reviews from security professionals who have deployed and used agents and solutions in production environments. These reviews focus on practical considerations such as integration quality, operational impact, and ease of use, helping you learn from peers facing similar security challenges. By sharing feedback, the security community helps raise the bar for quality and enables faster, more informed decisions, so teams can adopt agents and solutions with greater confidence and reduce time to value. Making agents easier to use post deployment Once you’ve deployed your agents, we’re introducing several new capabilities that make it easier to work with your agents in your daily workflows. These updates help you operationalize agents faster and apply automation where it delivers real value. Interactive chat with agents in Microsoft Defender lets SOC analysts ask questions to agents with specialized expertise, such as understanding impacted devices or understanding what vulnerabilities to prioritize directly in the Defender portal. By bringing a conversational experience with agents into the place where analysts do most of their investigation work, analysts can seamlessly work in collaboration with agents to improve security. Logic App triggers for agents enables security teams to include security agents in their automated, repeatable workflows. With this update, organizations can apply agentic automation to a wider variety of security tasks while integrating with their existing tools and workflows to perform tasks like incident triage and access reviews. Product combinations in Security Store make it easier to deploy complete security solutions from a single streamlined flow - whether that includes connectors, SaaS tools, or multiple agents that need to work together. Increasingly, partners are building agents that are adept at using your SaaS security tools and security data to provide intelligent recommendations - this feature helps you deploy them faster with ease. A Growing Ecosystem Focused on Security Outcomes As the Security Store ecosystem continues to expand, you gain access to a broader set of specialized agents and solutions that work together to help defend your environment - extending Microsoft Security with partner innovation in a governed and integrated way. At the same time, Security Store provides partners a clear path to deliver differentiated capabilities directly into Microsoft Security workflows, aligned to how customers evaluate, adopt, and use security solutions. Get Started Visit https://securitystore.microsoft.com/ to discover security agents and solutions that meet your needs and extend your Microsoft Security investments. If you’re a partner, visit https://securitystore.microsoft.com/partners to learn how to list your solution or agent and reach customers where security decisions are made. Where to find us at RSAC 2026? Security Reborn in the Era of AI workshop Get hands‑on guidance on building and deploying Security Copilot agents and publishing them to the Security Store. March 23 | 8:00 AM | The Palace Hotel Register: Security Reborn in the Era of AI | Microsoft Corporate Microsoft Security Store: An Inside Look Join us for a live theater session exploring what’s coming next for Security Store March 26 | 1:00 PM | Microsoft Security Booth #5744 | North Expo Hall Visit us at the Booth Experience Security Store firsthand - test the experience and connect with experts. Microsoft Booth #1843Security Dashboard for AI - Now Generally Available
AI proliferation in the enterprise, combined with the emergence of AI governance committees and evolving AI regulations, leaves CISOs and AI risk leaders needing a clear view of their AI risks, such as data leaks, model vulnerabilities, misconfigurations, and unethical agent actions across their entire AI estate, spanning AI platforms, apps, and agents. 53% of security professionals say their current AI risk management needs improvement, presenting an opportunity to better identify, assess and manage risk effectively. 1 At the same time, 86% of leaders prefer integrated platforms over fragmented tools, citing better visibility, fewer alerts and improved efficiency. 2 To address these needs, we are excited to announce the Security Dashboard for AI, previously announced at Microsoft Ignite, is now generally available. This unified dashboard aggregates posture and real-time risk signals from Microsoft Defender, Microsoft Entra, and Microsoft Purview - enabling users to see left-to-right across purpose-built security tools from within a single pane of glass. The dashboard equips CISOs and AI risk leaders with a governance tool to discover agents and AI apps, track AI posture and drift, and correlate risk signals to investigate and act across their entire AI ecosystem. Security teams can continue using the tools they trust while empowering security leaders to govern and collaborate effectively. Gain Unified AI Risk Visibility Consolidating risk signals from across purpose-built tools can simplify AI asset visibility and oversight, increase security teams’ efficiency, and reduce the opportunity for human error. The Security Dashboard for AI provides leaders with unified AI risk visibility by aggregating security, identity, and data risk across Defender, Entra, Purview into a single interactive dashboard experience. The Overview tab of the dashboard provides users with an AI risk scorecard, providing immediate visibility to where there may be risks for security teams to address. It also assesses an organization's implementation of Microsoft security for AI capabilities and provides recommendations for improving AI security posture. The dashboard also features an AI inventory with comprehensive views to support AI assets discovery, risk assessments, and remediation actions for broad coverage of AI agents, models, MCP servers, and applications. The dashboard provides coverage for all Microsoft AI solutions supported by Entra, Defender and Purview—including Microsoft 365 Copilot, Microsoft Copilot Studio agents, and Microsoft Foundry applications and agents—as well as third-party AI models, applications, and agents, such as Google Gemini, OpenAI ChatGPT, and MCP servers. This supports comprehensive visibility and control, regardless of where applications and agents are built. Prioritize Critical Risk with Security Copilots AI-Powered Insights Risk leaders must do more than just recognize existing risks—they also need to determine which ones pose the greatest threat to their business. The dashboard provides a consolidated view of AI-related security risks and leverages Security Copilot’s AI-powered insights to help find the most critical risks within an environment. For example, Security Copilot natural language interaction improves agent discovery and categorization, helping leaders identify unmanaged and shadow AI agents to enhance security posture. Furthermore, Security Copilot allows leaders to investigate AI risks and agent activities through prompt-based exploration, putting them in the driver’s seat for additional risk investigation. Drive Risk Mitigation By streamlining risk mitigation recommendations and automated task delegation, organizations can significantly improve the efficiency of their AI risk management processes. This approach can reduce the potential hidden AI risk and accelerate compliance efforts, helping to ensure that risk mitigation is timely and accurate. To address this, the Security Dashboard for AI evaluates how organizations put Microsoft’s AI security features into practice and offers tailored suggestions to strengthen AI security posture. It leverages Microsoft’s productivity tools for immediate action within the practitioner portal, making it easy for administrators to delegate recommendation tasks to designated users. With the Security Dashboard for AI, CISOs and risk leaders gain a clear, consolidated view of AI risks across agents, apps, and platforms—eliminating fragmented visibility, disconnected posture insights, and governance gaps as AI adoption scales. Best of all, the Security Dashboard for AI is included with eligible Microsoft security products customers already use. If an organization is already using Microsoft security products to secure AI, they are already a Security Dashboard for AI customer. Getting Started Existing Microsoft Security customers can start using Security Dashboard for AI today. It is included when a customer has the Microsoft Security products—Defender, Entra and Purview—with no additional licensing required. To begin using the Security Dashboard for AI, visit http://ai.security.microsoft.com or access the dashboard from the Defender, Entra or Purview portals. Learn more about the Security Dashboard for AI at Microsoft Security MS Learn. 1AuditBoard & Ascend2 Research. The Connected Risk Report: Uniting Teams and Insights to Drive Organizational Resilience. AuditBoard, October 2024. 2Microsoft. 2026 Data Security Index: Unifying Data Protection and AI Innovation. Microsoft Security, 2026
