Introducing Microsoft Sentinel graph (Public Preview)
Security is being reengineered for the AI era—moving beyond static, rulebound controls and after-the-fact response toward platform-led, machine-speed defense. The challenge is clear: fragmented tools, sprawling signals, and legacy architectures that can’t match the velocity and scale of modern attacks. What’s needed is an AI-ready, data-first foundation—one that turns telemetry into a security graph, standardizes access for agents, and coordinates autonomous actions while keeping humans in command of strategy and high-impact investigations. Security teams already center operations on their SIEM for end-to-end visibility, and we’re advancing that foundation by evolving Microsoft Sentinel into both the SIEM and the platform for agentic defense—connecting analytics and context across ecosystems. And today, we announced the general availability of Sentinel data lake and introduced new preview platform capabilities that are built on Sentinel data lake (Figure 1), so protection accelerates to machine speed while analysts do their best work. We are excited to announce the public preview of Microsoft Sentinel graph, a deeply connected map of your digital estate across endpoints, cloud, email, identity, SaaS apps, and enriched with our threat intelligence. Sentinel graph, a core capability of the Sentinel platform, enables Defenders and Agentic AI to connect the dots and bring deep context quickly, enabling modern defense across pre-breach and post-breach. Starting today, we are delivering new graph-based analytics and interactive visualization capabilities across Microsoft Defender and Microsoft Purview. Attackers think in graphs. For a long time, defenders have been limited to querying and analyzing data in lists forcing them to think in silos. With Sentinel graph, Defenders and AI can quickly reveal relationships, traversable digital paths to understand blast radius, privilege escalation, and anomalies across large, cloud-scale data sets, deriving deep contextual insight across their digital estate, SOC teams and their AI Agents can stay proactive and resilient. With Sentinel graph-powered experiences in Defender and Purview, defenders can now reason over assets, identities, activities, and threat intelligence to accelerate detection, hunting, investigation, and response. Incident graph in Defender. The incident graph in the Microsoft Defender portal is now enriched with ability to analyze blast radius of the active attack. During an incident investigation, the blast radius analysis quickly evaluates and visualizes the vulnerable paths an attacker could take from a compromise entity to a critical asset. This allows SOC teams to effectively prioritize and focus their attack mitigation and response saving critical time and limiting impact. Hunting graph in Defender. Threat hunting often requires connecting disparate pieces of data to uncover hidden paths that attackers exploit to reach your crown jewels. With the new hunting graph, analysts can visually traverse the complex web of relationships between users, devices, and other entities to reveal privileged access paths to critical assets. This graph-powered exploration transforms threat hunting into a proactive mission, enabling SOC teams to surface vulnerabilities and intercept attacks before they gain momentum. This approach shifts security operations from reactive alert handling to proactive threat hunting, enabling teams to identify vulnerabilities and stop attacks before they escalate. Data risk graph in Purview Insider Risk Management (IRM). Investigating data leaks and insider risks is challenging when information is scattered across multiple sources. The data risk graph in IRM offers a unified view across SharePoint and OneDrive, connecting users, assets, and activities. Investigators can see not just what data was leaked, but also the full blast radius of risky user activity. This context helps data security teams triage alerts, understand the impact of incidents, and take targeted actions to prevent future leaks. Data risk graph in Purview Data Security Investigation (DSI). To truly understand a data breach, you need to follow the trail—tracking files and their activities across every tool and source. The data risk graph does this by automatically combining unified audit logs, Entra audit logs, and threat intelligence, providing an invaluable insight. With the power of the data risk graph, data security teams can pinpoint sensitive data access and movement, map potential exfiltration paths, and visualize the users and activities linked to risky files, all in one view. Getting started Microsoft Defender If you already have the Sentinel data lake, the required graph will be auto provisioned when you login into the Defender portal; hunting graph and incident graph experience will appear in the Defender portal. New to data lake? Use the Sentinel data lake onboarding flow to provision the data lake and graph. Microsoft Purview Follow the Sentinel data lake onboarding flow to provision the data lake and graph. In Purview Insider Risk Management (IRM), follow the instructions here. In Purview Data Security Investigation (DSI), follow the instructions here. Reference links Watch Microsoft Secure Microsoft Secure news blog Data lake blog MCP server blog ISV blog Security Store blog Copilot blog Microsoft Sentinel—AI-Powered Cloud SIEM | Microsoft SecurityIntroducing Microsoft Security Store
Security is being reengineered for the AI era—moving beyond static, rulebound controls and after-the-fact response toward platform-led, machine-speed defense. We recognize that defending against modern threats requires the full strength of an ecosystem, combining our unique expertise and shared threat intelligence. But with so many options out there, it’s tough for security professionals to cut through the noise, and even tougher to navigate long procurement cycles and stitch together tools and data before seeing meaningful improvements. That’s why we built Microsoft Security Store - a storefront designed for security professionals to discover, buy, and deploy security SaaS solutions and AI agents from our ecosystem partners such as Darktrace, Illumio, and BlueVoyant. Security SaaS solutions and AI agents on Security Store integrate with Microsoft Security products, including Sentinel platform, to enhance end-to-end protection. These integrated solutions and agents collaborate intelligently, sharing insights and leveraging AI to enhance critical security tasks like triage, threat hunting, and access management. In Security Store, you can: Buy with confidence – Explore solutions and agents that are validated to integrate with Microsoft Security products, so you know they’ll work in your environment. Listings are organized to make it easy for security professionals to find what’s relevant to their needs. For example, you can filter solutions based on how they integrate with your existing Microsoft Security products. You can also browse listings based on their NIST Cybersecurity Framework functions, covering everything from network security to compliance automation — helping you quickly identify which solutions strengthen the areas that matter most to your security posture. Simplify purchasing – Buy solutions and agents with your existing Microsoft billing account without any additional payment setup. For Azure benefit-eligible offers, eligible purchases contribute to your cloud consumption commitments. You can also purchase negotiated deals through private offers. Accelerate time to value – Deploy agents and their dependencies in just a few steps and start getting value from AI in minutes. Partners offer ready-to-use AI agents that can triage alerts at scale, analyze and retrieve investigation insights in real time, and surface posture and detection gaps with actionable recommendations. A rich ecosystem of solutions and AI agents to elevate security posture In Security Store, you’ll find solutions covering every corner of cybersecurity—threat protection, data security and governance, identity and device management, and more. To give you a flavor of what is available, here are some of the exciting solutions on the store: Darktrace’s ActiveAI Security SaaS solution integrates with Microsoft Security to extend self-learning AI across a customer's entire digital estate, helping detect anomalies and stop novel attacks before they spread. The Darktrace Email Analysis Agent helps SOC teams triage and threat hunt suspicious emails by automating detection of risky attachments, links, and user behaviors using Darktrace Self-Learning AI, integrated with Microsoft Defender and Security Copilot. This unified approach highlights anomalous properties and indicators of compromise, enabling proactive threat hunting and faster, more accurate response. Illumio for Microsoft Sentinel combines Illumio Insights with Microsoft Sentinel data lake and Security Copilot to enhance detection and response to cyber threats. It fuses data from Illumio and all the other sources feeding into Sentinel to deliver a unified view of threats across millions of workloads. AI-driven breach containment from Illumio gives SOC analysts, incident responders, and threat hunters unified visibility into lateral traffic threats and attack paths across hybrid and multi-cloud environments, to reduce alert fatigue, prioritize threat investigation, and instantly isolate workloads. Netskope’s Security Service Edge (SSE) platform integrates with Microsoft M365, Defender, Sentinel, Entra and Purview for identity-driven, label-aware protection across cloud, web, and private apps. Netskope's inline controls (SWG, CASB, ZTNA) and advanced DLP, with Entra signals and Conditional Access, provide real-time, context-rich policies based on user, device, and risk. Telemetry and incidents flow into Defender and Sentinel for automated enrichment and response, ensuring unified visibility, faster investigations, and consistent Zero Trust protection for cloud, data, and AI everywhere. PERFORMANTA Email Analysis Agent automates deep investigations into email threats, analyzing metadata (headers, indicators, attachments) against threat intelligence to expose phishing attempts. Complementing this, the IAM Supervisor Agent triages identity risks by scrutinizing user activity for signs of credential theft, privilege misuse, or unusual behavior. These agents deliver unified, evidence-backed reports directly to you, providing instant clarity and slashing incident response time. Tanium Autonomous Endpoint Management (AEM) pairs realtime endpoint visibility with AI-driven automation to keep IT environments healthy and secure at scale. Tanium is integrated with the Microsoft Security suite—including Microsoft Sentinel, Defender for Endpoint, Entra ID, Intune, and Security Copilot. Tanium streams current state telemetry into Microsoft’s security and AI platforms and lets analysts pivot from investigation to remediation without tool switching. Tanium even executes remediation actions from the Sentinel console. The Tanium Security Triage Agent accelerates alert triage, enabling security teams to make swift, informed decisions using Tanium Threat Response alerts and real-time endpoint data. Walkthrough of Microsoft Security Store Now that you’ve seen the types of solutions available in Security Store, let’s walk through how to find the right one for your organization. You can get started by going to the Microsoft Security Store portal. From there, you can search and browse solutions that integrate with Microsoft Security products, including a dedicated section for AI agents—all in one place. If you are using Microsoft Security Copilot, you can also open the store from within Security Copilot to find AI agents - read more here. Solutions are grouped by how they align with industry frameworks like NIST CSF 2.0, making it easier to see which areas of security each one supports. You can also filter by integration type—e.g., Defender, Sentinel, Entra, or Purview—and by compliance certifications to narrow results to what fits your environment. To explore a solution, click into its detail page to view descriptions, screenshots, integration details, and pricing. For AI agents, you’ll also see the tasks they perform, the inputs they require, and the outputs they produce —so you know what to expect before you deploy. Every listing goes through a review process that includes partner verification, security scans on code packages stored in a secure registry to protect against malware, and validation that integrations with Microsoft Security products work as intended. Customers with the right permissions can purchase agents and SaaS solutions directly through Security Store. The process is simple: choose a partner solution or AI agent and complete the purchase in just a few clicks using your existing Microsoft billing account—no new payment setup required. Qualifying SaaS purchases also count toward your Microsoft Azure Consumption Commitment (MACC), helping accelerate budget approvals while adding the security capabilities your organization needs. Security and IT admins can deploy solutions directly from Security Store in just a few steps through a guided experience. The deployment process automatically provisions the resources each solution needs—such as Security Copilot agents and Microsoft Sentinel data lake notebook jobs—so you don’t have to do so manually. Agents are deployed into Security Copilot, which is built with security in mind, providing controls like granular agent permissions and audit trails, giving admins visibility and governance. Once deployment is complete, your agent is ready to configure and use so you can start applying AI to expand detection coverage, respond faster, and improve operational efficiency. Security and IT admins can view and manage all purchased solutions from the “My Solutions” page and easily navigate to Microsoft Cost Management tools to track spending and manage subscriptions. Partners: grow your business with Microsoft For security partners, Security Store opens a powerful new channel to reach customers, monetize differentiated solutions, and grow with Microsoft. We will showcase select solutions across relevant Microsoft Security experiences, starting with Security Copilot, so your offerings appear in the right context for the right audience. You can monetize both SaaS solutions and AI agents through built-in commerce capabilities, while tapping into Microsoft’s go-to-market incentives. For agent builders, it’s even simpler—we handle the entire commerce lifecycle, including billing and entitlement, so you don’t have to build any infrastructure. You focus on embedding your security expertise into the agent, and we take care of the rest to deliver a seamless purchase experience for customers. Security Store is built on top of Microsoft Marketplace, which means partners publish their solution or agent through the Microsoft Partner Center - the central hub for managing all marketplace offers. From there, create or update your offer with details about how your solution integrates with Microsoft Security so customers can easily discover it in Security Store. Next, upload your deployable package to the Security Store registry, which is encrypted for protection. Then define your license model, terms, and pricing so customers know exactly what to expect. Before your offer goes live, it goes through certification checks that include malware and virus scans, schema validation, and solution validation. These steps help give customers confidence that your solutions meet Microsoft’s integration standards. Get started today By creating a storefront optimized for security professionals, we are making it simple to find, buy, and deploy solutions and AI agents that work together. Microsoft Security Store helps you put the right AI‑powered tools in place so your team can focus on what matters most—defending against attackers with speed and confidence. Get started today by visiting Microsoft Security Store. If you’re a partner looking to grow your business with Microsoft, start by visiting Microsoft Security Store - Partner with Microsoft to become a partner. Partners can list their solution or agent if their solution has a qualifying integration with Microsoft Security products, such as a Sentinel connector or Security Copilot agent, or another qualifying MISA solution integration. You can learn more about qualifying integrations and the listing process in our documentation here.
Java MIP SDK 1.17.154: commitAsync() TemplateNotFoundError (C# OK; Java fails Win & Ubuntu)
TL;DR Java SDK 1.17.154: calling setLabel() then commitAsync() fails with TemplateNotFoundError (TemplateId=2ea3c830-...). Same label/code works on Java 1.16.x and C# 1.17.154. Policy cache cleared, templates/labels verified, token/tenant checked—issue persists. Environment SDK (Java): 1.16.x (OK), 1.17.154 (FAIL) SDK (C#): 1.17.154 (OK) OS (Java): Windows 10/11 (win32 build), Ubuntu 20.04 / 22.04 / 24.04 Java: Corretto/OpenJDK 17.0.16 (x64) Service/Tenant: Microsoft Purview Information Protection Auth: (e.g., user delegated token / app-only token) Code Snippet (Java) // Label apply options LabelingOptions labelingOptions = new LabelingOptions(); labelingOptions.setAssignmentMethod(AssignmentMethod.PRIVILEGED); labelingOptions.setDowngradeJustified(true); labelingOptions.setJustificationMessage("Label Apply"); // Get label Label label = fileEngine.getLabelById(labelId); // Apply label (no explicit template handling) fileHandler.setLabel(label, labelingOptions, new ProtectionSettings()); // Commit File workFile = new File(domainFolder, UidUtil.makeUid()); CompletableFuture<Boolean> commitFuture = fileHandler.commitAsync(workFile.getAbsolutePath()); commitFuture.get(); // <-- Throws TemplateNotFoundError on 1.17.154 Stack trace excerpt: Caused by: com.microsoft.informationprotection.internal.gen.Error: TemplateNotFoundError: Could not find template with id: 2ea3c830-5a0e-4eea-b48b-c72186d453c0, BadInputError.Code=General, CorrelationId=42ffaad4-3a0f-4986-ba9d-b5a79c5fd076 (ProtectionEngine), CorrelationId=16819f70-e419-473f-9895-c756f3dd5e4b (FileHandler) at com.microsoft.informationprotection.internal.gen.SdkWrapperJNI.SwigDirector_FileHandler_Observer_OnCommitFailure(SdkWrapperJNI.java:2688) Expected Behavior setLabel() should apply the label (and its protection) and commit successfully, as it does in Java 1.16 and C# 1.17.154. Actual Behavior commitAsync() fails with TemplateNotFoundError for the GUID referenced by the label’s ApplyProtectionAction. What I’ve Tried Policy/cache refresh: Deleted %LOCALAPPDATA%\Microsoft\MSIP\ / ~/.mip/, reloaded engine. Template/label verification: Confirmed existence and publish scope in Purview portal & via PowerShell/Graph. Label actions check: policyEngine.getLabelActions(labelId) shows an ApplyProtectionAction with that GUID. Token/tenant sanity check: Correct scopes and same tenant. Rollback test: Java 1.16 works; C# 1.17.154 works. Questions Any breaking change in Java 1.17 regarding how protection templates are resolved during setLabel()? Is this a known issue specific to Java SDK 1.17.154 (win32 & Ubuntu 20/22/24 builds)? Should we now explicitly use ProtectionDescriptor / SetProtection() in Java? Can someone review the service logs using the CorrelationIds above? Happy to provide additional logs, PowerShell/Graph queries, or action dumps if needed. Thanks!New Microsoft Purview Deployment Blueprint | Lightweight guide to mitigate data leakage
We’re excited to share our latest Data Security deployment blueprint: “Lightweight guide to mitigate data leakage”—a practical resource designed to help organizations quickly enable core data security features across their Microsoft 365 estate with minimal setup. The blueprint follows a good / better / best model that maps protections to your licensing. “Good” highlights foundational features included in Business Premium SKUs, while “Better” and “Best” layer in advanced E5 Compliance capabilities, such as auto-labeling, Endpoint DLP, insider risk signals and much more. With the new E5 Compliance Add-On for Business Premium, this guide shows how organizations can capture quick wins today while building toward stronger, long-term security practices. This blueprint is designed for IT administrators, security teams, and compliance stakeholders tasked with protecting sensitive data – and it’s equally valuable for Microsoft partners and consultants supporting customers on their data security journey. Whether you’re enabling basic safeguards or advancing towards automated protection, this guide provides clear, actionable steps to strengthen your data security posture. Ready to get started? Visit our Purview deployment blueprint page or jump straight to the direct PPT link for a step-by-step walkthrough. Securing your data doesn’t have to be complex – this lightweight blueprint makes it achievable for organizations of any size.Retired: The Data Loss Prevention Ninja Training is here!
August 2025: New Ninja training can be found at https://aka.ms/DLPNinja RETIRED July 2025: Under Construction for new hosting location The Microsoft Purview Data Loss Prevention Ninja Training is here! We are very excited and pleased to announce this rendition of the Ninja Training Series. With all the other training out there, our team has been working diligently to get this content out there. There are several videos and resources out there and the overall purpose of the Microsoft Purview Data Loss Prevention Ninja training is to help you master this realm. We aim to get you up-to-date links to the community blogs, training videos, Interactive Guides, learning paths, and any other relevant documentation. To make it easier for you to start and advance your knowledge gradually without throwing you in deep waters, we split content in each offering into three levels: beginner, intermediate, and advanced. Please find the Microsoft Purview Information Protection Ninja Training here. In addition, after each section, there will be a knowledge check based on the training material you’d have just finished! Since there’s a lot of content, the goal of these knowledge checks is to help you determine if you were able to get a few of the major key takeaways. There’ll be a fun certificate issued at the end of the training: Disclaimer: This is NOT an official Microsoft certification and only acts as a way of recognizing your participation in this training content. Lastly, this training will be updated one to two times a year to ensure you all have the latest and greatest material! If there's any topic you'd like for us to include and/or have any thoughts on this training, please let us know what you think below in the comments! Legend/Acronyms (D) Microsoft Documentation (V) Video (B) Blog (P) PDF (S) Site (SBD) Scenario Based Demo (Video) (DAG) Deployment Acceleration Guide MIP Microsoft Information Protection (old terminology for Microsoft Purview Information Protection) AIP Azure Information Protection ULC Unified Labeling Client SIT Sensitive Information Type RBAC Role-based access control eDLP Endpoint DLP OME Office 365 Message Encryption EDM Exact Data Match DLP Data Loss Prevention SPO SharePoint Online OCR Optical character recognition MCAS Microsoft Cloud App Security (old terminology for Microsoft Defender for Cloud Apps) TC Trainable Classifiers ODSP OneDrive SharePoint EXO Exchange Online Microsoft Purview Data Loss Prevention (DLP) Microsoft’s DLP solution provides a broad range of capabilities to address the modern workplace and the unique challenges represented by these very different scenarios. One of the key investment areas is in providing a unified and comprehensive solution across the many different kinds of environments and services where sensitive data is stored, used or shared. This includes platforms native to Microsoft and also non-Microsoft services and apps. Beginner Training Public forums to contact the overall information protection team Yammer Tech Community Introducing Microsoft Purview (V) In this video, hear from Microsoft executives on this new product family and our vision for the future of data governance. Introduction to Microsoft Purview Data Loss Prevention? (V) In this video, you’ll find an overview on Microsoft Purview Data Loss Prevention. Quick overview on new Exchange DLP Predicates (V) This video provides a quick walk through on creating an Exchange DLP policy and a soft focus on the new predicates and actions. Microsoft Purview Information Protection Framework (D) Check out the above documentation to see how Microsoft Purview Information Protection uses 3 pillars to deploy an information protection solution. Protect Data with Zero Trust (LP) Zero Trust isn't a tool or product, it's an essential security strategy, with data at its core. Here, you'll learn how to identify and protect your data using a Zero Trust approach. Learn about data loss prevention (D) Learn about DLP basics and Microsoft Unified DLP and why it’s uniquely positioned to protect your data in the cloud. How to secure your data with Microsoft Security (V) The above video is a quick summary on how to protect your data. Microsoft Purview Information Protection and Data Loss Prevention Roadmap (S) Please check out the above site on the latest items on our public roadmap. Microsoft Purview Information Protection support for PDF and GitHub (V) and Ignite Conversation (V) The above videos walk through announcements regarding support for PDF and GitHub Microsoft Defender for Cloud Apps integration (D) Please visit the above documentation to learn more about how Microsoft Purview Information Protection integrates with Microsoft Defender for Cloud Apps Trainable Classifiers (D) Check out the documentation to create custom trainable classifiers. Retrain a classifier in content explorer (D) The above documentation shows you how to improve the performance of custom trainable classifiers by providing them more feedback. Explain data loss prevention reporting capabilities (LP) The above learning path walks you through reporting in the Microsoft Purview Compliance Portal. Review and analyze data loss prevention reports (LP) The above learning path walks you through analyzing reports in the Microsoft Purview Compliance Portal. Beginner Knowledge Check Intermediate Training Microsoft Compliance Extension for Chrome (B) aka Microsoft Purview Extension (D) Please check out the above blog and Microsoft Doc to understand what we’re doing to expand our DLP capabilities to Chrome. Microsoft Purview extension for Firefox (D) The above documentation details procedures to roll out the Microsoft Purview extension for Firefox. Data Loss Prevention and Endpoint DLP (V) This video details how Microsoft approaches information protection across Files, emails, Teams, endpoints and others. How DLP works between the Compliance portal and Exchange admin center (D) You can create a data loss prevention (DLP) policy in two different admin centers; the above document walks through the differences and similarities. Data Loss Prevention across endpoints, apps, & services | Microsoft Purview (V) This video walks you through how to protect sensitive data everywhere you create, view, and access information with one Data Loss Prevention policy in Microsoft Purview. Data Loss Prevention Policy Tips Reference Guide (D) and Quick Overview (V) Please check out the above documentation and short video on where we support policy tips. Create a DLP Policy for Microsoft 365 Online Services (IG) Please use the above interactive guide to see how to create DLP policies. Apply Microsoft Purview Endpoint DLP to Devices (IG) Please use the above interactive guide to see how to create Endpoint DLP policies. Sites for testing documentation (S) The above site details locations where you can get sample data. Scope of DLP Protection for Microsoft Teams (D) The above documentation walks through how DLP protection is applied differently to Teams entities. Manage DLP alerts in the Microsoft Purview compliance portal (LP) The above learning path walks you through managing DLP alerts. Endpoint activities you can monitor and best practices (LP) The above learning path walks you through Endpoint DLP activities and best practices. Troubleshoot and Manage Microsoft Purview Data Loss Prevention for your Endpoint Devices (B) The above blog goes through a quick guide to troubleshooting Endpoint DLP. Microsoft Purview DLP Interactive Guides (IG) Please visit the above home page to see the latest interactive guides walking you through DLP. Learn how to investigate Microsoft Purview Data Loss Prevention alerts in Microsoft 365 Defender (B) This blog is a step-by-step guided walkthrough of the Microsoft 365 Defender Analyst experience for Microsoft Purview Data Loss Prevention (DLP) incident management. Intermediate Knowledge Check Advanced Training Microsoft Defender for Cloud Apps and Data Loss Preventions (D) Please check out the documentation above detailing how the integration to Microsoft Defender for Cloud Apps further enhances your data loss prevention plan. Power BI: Learn about centralized data loss prevention policies (V) This video highlights DLP capabilities with Power BI. Take a unified and comprehensive approach to prevent data exfiltration with Microsoft (V) This video helps show how we can help you prevent unauthorized sharing, use, and transfer of sensitive information across your applications, services, endpoints, and on-premises file shares – all from a single place. Onboard macOS devices into Microsoft 365 (D), capability announcement (B), and additional screengrabs (B) Please use the documentation above to deploy macOS devices into Endpoint DLP and check out the blog to see a few screengrabs on how the user experience. Troubleshooting Guides (D) Resolve issues that affect DLP policy tips Changes to a data loss prevention policy don't take effect in Outlook 2013 in Microsoft 365 DLP policy tips in Security and Compliance Center don't work in OWA/Outlook How to troubleshoot data loss prevention policy tips in Exchange Online Protection in Microsoft 365 Please check out the below documentation to find guides on common issues. Securing data in an AI-first world with Microsoft Purview (B) The above blog details some new updates on AI with Microsoft Purview. Common questions on Microsoft Purview Data Loss Prevention for endpoints (B) This guide covers the top-of-mind FAQs on Microsoft Purview Data Loss Prevention for endpoints (referred to as Endpoint DLP in the blog). Guidance for investigating Microsoft Purview Data Loss Prevention incidents (B) This blog provides guidance for choosing the best investigation experience suited for your organization when using Microsoft Purview Data Loss Prevention. Data Loss Prevention: From on-premises to cloud (PDF) This whitepaper focuses on why you should move to cloud-native data loss prevention. The Microsoft Purview DLP Migration Assistant for Symantec (IG) Follow the above IG to get guidance on migrating from Symantec to Microsoft Purview DLP. Migrating from Windows Information Protection to Microsoft Purview (B) The above blog gives guidance on how to migrate from WIP to the Microsoft Purview stack. Insider Risk in Conditional Access | Microsoft Entra + Microsoft Purview Adaptive Protection (V) The above video goes through how to protect your organization from insider threats with Microsoft Entra's Conditional Access and Adaptive Protection in Microsoft Purview. Please check out this link for a blog with more details. (B) Protect sensitive data throughout its Copilot journey (B) The above details how the native integration enables organizations to leverage the power of GenAI when working with sensitive data as Copilot can understand and honor the controls such as encryption and provide comprehensive visibility into usage. Protect at the speed and scale of AI with Copilot for Security in Microsoft Purview (B) The above blog details the embedded experiences of Copilot for Security in Microsoft Purview (Communication Compliance, Data Loss Prevention, Insider Risk Management, and eDiscovery. Strengthen protection to mitigate data overexposure in GenAI tools with data classification/labeling (B) The blog above goes into detail on OCR, its cost, and how it goes into the AI Realm with Microsoft Purview Information Protection and Data Loss Prevention. Microsoft Purview Exact Data Match (EDM) support for multi-token corroborative evidence (B) The above blog goes into the new feature that improves the accuracy and effectiveness of EDM detection. Advanced Knowledge Check Once you’ve finished the training and the knowledge checks, please go to our attestation portal to generate your certificate; you'll see it in your inbox within 3-5 business days (Coming Soon). We hope you enjoy this training!Sensitivity Auto-labelling via Document Property
Why is this needed? Sensitivity labels are generally relevant within an organisation only. If a file is labelled within one environment and then moved to another environment, sensitivity label content markings may be visible, but by default, the applied sensitivity label will not be understood. This can lead to scenarios where information that has been generated externally is not adequately protected. My favourite analogy for these scenarios is to consider the parallels between receiving sensitive information and unpacking groceries. When unpacking groceries, you might sit your grocery bag on a counter or on the floor next to the pantry. You’ll likely then unpack each item, take a look at it and then decide where to place it. Without looking at an item to determine its correct location, you might place it in the wrong location. Porridge might be safe from the kids on the bottom shelf. If you place items that need to be protected, such as chocolate, on the bottom shelf, it’s not likely to last very long. So, I affectionately refer to information that hasn’t been evaluated as ‘porridge’, as until it has been checked, it will end up on the bottom shelf of the pantry where it is quite accessible. Label-based security controls, such as Data Loss Prevention (DLP) policies using conditions of ‘content contains sensitivity label’ will not apply to these items. To ensure the security of any contained sensitive information, we should look for potential clues to its sensitivity and then utilize these clues to ensure that the contained information is adequately protected - We take a closer look at the ‘porridge’, determine whether it’s an item that needs protection and if so, move it to a higher shelf in the pantry so that it’s out of reach for the kids. Effective use of Purview revolves around the use of ‘know your data’ strategies. We should be using as many methods as possible to try to determine the sensitivity of items. This can include the use of Sensitive Information Types (SITs) containing keyword or pattern-based classifiers, trainable classifiers, Exact Data Match, Document fingerprinting, etc. Matching items via SITs present in the items content can be problematic due to false positives. Keywords like ‘Sensitive’ or ‘Protected’ may be mentioned out of context, such as when referring to a classification or an environment. When classifications have been stamped via a property, it allows us to match via context rather than content. We don’t need to guess at an item’s sensitivity if another system has already established what the item’s classification is. These methods are much less prone to false positives. Why isn’t everyone doing this? Document properties are often not considered in Purview deployments. SharePoint metadata management seems to be a dying artform and most compliance or security resources completing Purview configurations don’t have this skill set. There’s also a lack of understanding of the relevance of checking for item properties. Microsoft haven’t helped as the documentation in this space is somewhat lacking and needs to be unpicked via some aligning DLP guidance (Create a DLP policy to protect documents with FCI or other properties). Many of these configurations will also be tied to regional requirements. Document properties being used by systems where I’m from, in Australia, will likely be very different to those used in other parts of the world. In the following sections, we’ll take a look at applicable use cases and walk through how to enable these configurations. Scenarios for use Labelling via document property isn’t for everyone. If your organisation is new to classification or you don’t have external partners that you collaborate with at higher sensitivity levels, then this likely isn’t for you. For those that collaborate heavily and have a shared classification framework, as is often seen across government, this is a must! This approach will also be highly relevant to multi-tenant organisations or conglomerates where information is regularly shared between environments. The following scenarios are examples of where this configuration will be relevant: 1. Migrating from 3 rd party classification tools If an item has been previously stamped by a 3 rd party classification tool, then evaluating its applied document properties will provide a clear picture of its security classification. These properties can then be used in service-based auto-labelling policies to effectively transition items from 3 rd party tools to Microsoft Purview sensitivity labels. As labels are applied to items, they will be brought into scope of label-based controls. 2. Detecting data spill Data spill is a term that is used to define situations where information that is of a higher than permitted security classification land in an environment. Consider a Microsoft 365 tenant that is approved for the storage of Official information but Top Secret files are uploaded to it. Document properties that align with higher than permitted classifications provide us with an almost guaranteed method of identifying spilled items. Pairing this document property with an auto-labelling policy allows for the application of encryption to lock unauthorized users out of the items. Tools like Content Explorer and eDiscovery can then be used to easily perform cleanup activities. If using document properties and auto-labelling for this purpose, keep in mind that you’ll need to create sensitivity labels for higher than permitted classifications in order to catch spilled items. These labels won’t impact usability as you won’t publish them to users. You will, however, need to publish them to a single user or break glass account so that they’re not ignored by auto-labelling. 3. Blocking access by AI tools If your organization was concerned about items with certain properties applied being accessed by generative AI tools, such as Copilot, you could use Auto-labelling to apply a sensitivity label that restricts EXTRACT permissions. You can find some information on this at Microsoft 365 Copilot data protection architecture | Microsoft Learn. This should be relevant for spilled data, but might also be useful in situations where there are certain records that have been marked via properties and which should not be Copilot accessible. 4. External Microsoft Purview Configurations Sensitivity labels are relevant internally only. A label, in its raw form, is essentially a piece of metadata with an ID (or GUID) that we stamp on pieces of information. These GUIDs are understood by your tenant only. If an item marked with a GUID shows up in another Microsoft 365 tenant, the GUID won’t correspond with any of that tenant’s labels or label-based controls. The art in Microsoft Purview lies in interpreting the sensitivity of items based on content markings and other identifiers, so that data security can be maintained. Document properties applied by Purview, such as ClassificationContentMarkingHeaderText are not relevant to a specific tenant, which makes them portable. We can use these properties to help maintain classifications as items move between environments. 5. Utilizing metadata applied by Records Management solutions Some EDRMS, Records or Content Management solutions will apply properties to items. If an item has been previously managed and then stamped with properties, potentially including a security classification, via one of these systems, we could use this information to inform sensitivity label application. 6. 3 rd party classification tools used externally Even if your organisation hasn’t been using 3rd party classification tools, you should consider that partner organisations, such as other Government departments, might be. Evaluating the properties applied by external organisations to items that you receive will allow you to extend protections to these items. If classification tools like Janus or Titus are used in your geography/industry, then you may want to consider checking for their properties. Regarding the use of auto-classification tools Some organisations, particularly those in Government, will have organisational policies that prevent the use of automatic classification capabilities. These policies are intended to ensure that each item is assessed by an actual person for risk of disclosure rather than via an automated service that could be prone to error. However, when auto-labelling is used to interpret and honour existing classifications, we are lowering rather than raising the risk profile. If the item’s existing classification (applied via property) is ignored, the item will be treated as porridge and is likely to be at risk. If auto-labelling is able to identify a high-risk item and apply the relevant label, it will then be within scope of Purview’s data security controls, including label-based DLP, groups and sites data out of place alerting, and potentially even item encryption. The outcome is that, through the use of auto-labelling, we are able to significantly reduce risk of inappropriate or unintended disclosure. Configuration Process Setting up document property-based auto-labelling is fairly straightforward. We need to setup a managed property and then utilize it an auto-labelling policy. Below, I've split this process into 6 steps: Step 1 – Prepare your files In order to make use of document properties, an item with the properties applied will first need to be indexed by SharePoint. SharePoint will record the properties as ‘crawled properties’, which we’ll then need to convert into ‘managed properties’ to make them useful. If you already have items with the relevant properties stored in SharePoint, then they are likely already indexed. If not, you’ll need to upload or create an item or items with the properties applied. For testing, you’ll want to create a file with each property/value combination so that you can confirm that your auto-labelling policies are all working correctly. This could require quite a few files depending on the number of properties you’re looking for. To kick off your crawled property generation though, you could create or upload a single file with the correct properties applied. For example: In the above, I’ve created properties for ClassificationContentMarkingHeaderText and ClassificationContentMarkingFooterText, which you’ll often see applied by Purview when an item has a sensitivity label content marking applied to it. I’ve also included properties to help identify items classified via JanusSeal, Titus and Objective. Step 2 – Index the files After creating or uploading your file, we then need SharePoint to index it. This should happen fairly quickly depending on the size of your environment. I'd expect to wait sometime between 10 minutes and 24 hrs. If you're not in a hurry, then I'd recommend just checking back the next day. You'll know when this has been completed when you head into SharePoint Admin > Search > Managed Search Schema > Crawled Properties and can find your newly indexed properties: Step 3 – Configure managed properties Next, the properties need to be configured as managed properties. To do this, go to SharePoint Admin > More features > Search > Managed Search Schema > Managed Properties. Create a new managed property and give it a name. Note that there are some character restrictions in naming, but you should be able to get it close to your document property name. Set the property’s type to text, select queryable and retrievable. Under ‘mappings to crawled properties’, choose add mapping, search for and select the property indexed from the file property. Note that the crawled property will have the same name as your document property, so there’s no need to browse through all of them: Repeat this so that you have a managed property for each document property that you want to look for. Step 4 – Configure Auto-labelling policies Next up, create some auto-labelling policies. You’ll need one for each label that you want to apply, not one per property as you can check multiple properties within the one auto-labelling policy. - From within Purview, head to Information Protection > Policies > Auto-labelling policies. - Create a new policy using the custom policy template. - Give your policy an appropriate name (e.g. Label PROTECTED via property). - Select the label that you want to apply (e.g. PROTECTED). - Select SharePoint based services (SharePoint and OneDrive). - Name your auto-labelling rules appropriately (e.g. SPO – Contains PROTECTED property) - Enter your conditions as a long string with property and value separated via a colon and multiple entries separated with a comma. For example: ClassificationContentMarkingHeaderText:PROTECTED,ClassificationContentMarkingFooterText:PROTECTED,Objective-Classification:PROTECTED,PMDisplay:PROTECTED,TitusSEC:PROTECTED Note that the properties that you are referencing are the Managed Property rather than the document property. This will be relevant if your managed property ended up having a different name due to character restrictions. After pasting in your string into the UI, the resultant rule should look something like this: When done, you can either leave your policy in simulation mode or save it and then turn it on from the auto-labelling policies screen. Just be aware of any potential impacts, such as accidently locking users out by automatically deploying a label with encryption configuration. You can reduce any potential impact by targeting your auto-labelling policy at a site or set of sites initially and then expanding its scope after testing. Step 5 - Test Testing your configuration will be as easy as uploading or creating a set of files with the relevant document properties in place. Once uploaded, you’ll need to give SharePoint some time to index the items and then the auto-labelling policy some time to apply sensitivity labels to them. To confirm label application, you can head to the document library where your test files are located and enable the sensitivity column. Files that have been auto-labelled will have their label listed: You could also check for auto-labelling activity in Purview via Activity explorer: Step 6 – Expand into DLP If you’ve spent the time setting up managed properties, then you really should consider capitalizing on them in your DLP configurations. DLP policy conditions can be configured in the same manner that we configured Auto-labelling in Step 3 above. The document property also gives us an anchor for DLP conditions that is independent of an item’s sensitivity label. You may wish to consider the following: DLP policies blocking external sharing of items with certain properties applied. This might be handy for situations where auto-labelling hasn’t yet labelled an item. DLP policies blocking the external sharing of items where the applied sensitivity label doesn’t match the applied document property. This could provide an indication of risky label downgrade. You could extend such policies into Insider Risk Management (IRM) by creating IRM policies that are aligned with the above DLP policies. This will allow for document properties to be considered in user risk calculation, which can inform controls like Adaptive Protection. Here's an example of a policy from the DLP rule summary screen that shows conditions of item contains a label or one of our configured document properties: Thanks for reading and I hope this article has been of use. If you have any questions or feedback, please feel free to reach out.From Traditional Security to AI-Driven Cyber Resilience: Microsoft’s Approach to Securing AI
By Chirag Mehta, Vice President and Principal Analyst - Constellation Research AI is changing the way organizations work. It helps teams write code, detect fraud, automate workflows, and make complex decisions faster than ever before. But as AI adoption increases, so do the risks, many of which traditional security tools were not designed to address. Cybersecurity leaders are starting to see that AI security is not just another layer of defense. It is becoming essential to building trust, ensuring resilience, and maintaining business continuity. Earlier this year, after many conversations with CISOs and CIOs, I saw a clear need to bring more attention to this topic. That led to my report on AI Security, which explores how AI-specific vulnerabilities differ from traditional cybersecurity risks and why securing AI systems calls for a more intentional approach. Why AI Changes the Security Landscape AI systems do not behave like traditional software. They learn from data instead of following pre-defined logic. This makes them powerful, but also vulnerable. For example, an AI model can: Misinterpret input in ways that humans cannot easily detect Be tricked into producing harmful or unintended responses through crafted prompts Leak sensitive training data in its outputs Take actions that go against business policies or legal requirements These are not coding flaws. They are risks that originate from how AI systems process information and act on it. These risks become more serious with agentic AI. These systems act on behalf of humans, interact with other software, and sometimes with other AI agents. They can make decisions, initiate actions, and change configurations. If one is compromised, the consequences can spread quickly. A key challenge is that many organizations still rely on traditional defenses to secure AI systems. While those tools remain necessary, they are no longer enough. AI introduces new risks across every layer of the stack, including data, networks, endpoints, applications, and cloud infrastructure. As I explained in my report, the security focus must shift from defending the perimeter to governing the behavior of AI systems, the data they use, and the decisions they make. The Shift Toward AI-Aware Cyber Resilience Cyber resilience is the ability to withstand, adapt to, and recover from attacks. Meeting that standard today requires understanding how AI is developed, deployed, and used by employees, customers, and partners. To get there, organizations must answer questions such as: Where is our sensitive data going, and is it being used safely to train models? What non-human identities, such as AI agents, are accessing systems and data? Can we detect when an AI system is being misused or manipulated? Are we in compliance with new AI regulations and data usage rules? Let’s look at how Microsoft has evolved its mature security portfolio to help protect AI workloads and support this shift toward resilience. Microsoft’s Approach to Secure AI Microsoft has taken a holistic and integrated approach to AI security. Rather than creating entirely new tools, it is extending existing products already used by millions to support AI workloads. These features span identity, data, endpoint, and cloud protection. 1. Microsoft Defender: Treating AI Workloads as Endpoints AI models and applications are emerging as a new class of infrastructure that needs visibility and protection. Defender for Cloud secures AI workloads across Azure and other cloud platforms such as AWS and GCP by monitoring model deployments and detecting vulnerabilities. Defender for Cloud Apps extends protection to AI-enabled apps running at the edge Defender for APIs supports AI systems that use APIs, which are often exposed to risks such as prompt injection or model manipulation Additionally, Microsoft has launched tools to support AI red-teaming, content safety, and continuous evaluation capabilities to ensure agents operate safely and as intended. This allows teams identify and remediate risks such as jailbreaks or prompt injection before models are deployed. 2. Microsoft Entra: Managing Non-Human Identities As organizations roll out more AI agents and copilots, non-human identities are becoming more common. These digital identities need strong oversight. Microsoft Entra helps create and manage identities for AI agents Conditional Access ensures AI agents only access the resources they need, based on real-time signals and context Privileged Identity Management manages, controls, and monitors AI agents access to important resources within an organization 3. Microsoft Purview: Securing Data Used in AI Purview plays an important role in securing both the data that powers AI apps and agents, and the data they generate through interactions. Data discovery and classification helps label sensitive information and track its use Data Loss Prevention policies help prevent leaks or misuse of data in tools such as Copilot or agents built in Azure AI Foundry Insider Risk Management alerts security teams when employees feed sensitive data into AI systems without approval Purview also helps organizations meet transparency and compliance requirements, extending the same policies they already use today to AI workloads, without requiring separate configurations, as regulations like the EU AI Act take effect. Here's a video that explains the above Microsoft security products: Securing AI Is Now a Strategic Priority AI is evolving quickly, and the risks are evolving with it. Traditional tools still matter, but they were not built for systems that learn, adapt, and act independently. They also weren’t designed for the pace and development approaches AI requires, where securing from the first line of code is critical to staying protected at scale. Microsoft is adapting its security portfolio to meet this shift. By strengthening identity, data, and endpoint protections, it is helping customers build a more resilient foundation. Whether you are launching your first AI-powered tool or managing dozens of agents across your organization, the priority is clear. Secure your AI systems before they become a point of weakness. You can read more in my AI Security report and learn how Microsoft is helping organizations secure AI supporting these efforts across its security portfolio.Hacking Made Easy, Patching Made Optional: A Modern Cyber Tragedy
In today’s cyber threat landscape, the tools and techniques required to compromise enterprise environments are no longer confined to highly skilled adversaries or state-sponsored actors. While artificial intelligence is increasingly being used to enhance the sophistication of attacks, the majority of breaches still rely on simple, publicly accessible tools and well-established social engineering tactics. Another major issue is the persistent failure of enterprises to patch common vulnerabilities in a timely manner—despite the availability of fixes and public warnings. This negligence continues to be a key enabler of large-scale breaches, as demonstrated in several recent incidents. The Rise of AI-Enhanced Attacks Attackers are now leveraging AI to increase the credibility and effectiveness of their campaigns. One notable example is the use of deepfake technology—synthetic media generated using AI—to impersonate individuals in video or voice calls. North Korean threat actors, for instance, have been observed using deepfake videos and AI-generated personas to conduct fraudulent job interviews with HR departments at Western technology companies. These scams are designed to gain insider access to corporate systems or to exfiltrate sensitive intellectual property under the guise of legitimate employment. Social Engineering: Still the Most Effective Entry Point And yet, many recent breaches have begun with classic social engineering techniques. In the cases of Coinbase and Marks & Spencer, attackers impersonated employees through phishing or fraudulent communications. Once they had gathered sufficient personal information, they contacted support desks or mobile carriers, convincingly posing as the victims to request password resets or SIM swaps. This impersonation enabled attackers to bypass authentication controls and gain initial access to sensitive systems, which they then leveraged to escalate privileges and move laterally within the network. Threat groups such as Scattered Spider have demonstrated mastery of these techniques, often combining phishing with SIM swap attacks and MFA bypass to infiltrate telecom and cloud infrastructure. Similarly, Solt Thypoon (formerly DEV-0343), linked to North Korean operations, has used AI-generated personas and deepfake content to conduct fraudulent job interviews—gaining insider access under the guise of legitimate employment. These examples underscore the evolving sophistication of social engineering and the need for robust identity verification protocols. Built for Defense, Used for Breach Despite the emergence of AI-driven threats, many of the most successful attacks continue to rely on simple, freely available tools that require minimal technical expertise. These tools are widely used by security professionals for legitimate purposes such as penetration testing, red teaming, and vulnerability assessments. However, they are also routinely abused by attackers to compromise systems Case studies for tools like Nmap, Metasploit, Mimikatz, BloodHound, Cobalt Strike, etc. The dual-use nature of these tools underscores the importance of not only detecting their presence but also understanding the context in which they are being used. From CVE to Compromise While social engineering remains a common entry point, many breaches are ultimately enabled by known vulnerabilities that remain unpatched for extended periods. For example, the MOVEit Transfer vulnerability (CVE-2023-34362) was exploited by the Cl0p ransomware group to compromise hundreds of organizations, despite a patch being available. Similarly, the OpenMetadata vulnerability (CVE-2024-28255, CVE-2024-28847) allowed attackers to gain access to Kubernetes workloads and leverage them for cryptomining activity days after a fix had been issued. Advanced persistent threat groups such as APT29 (also known as Cozy Bear) have historically exploited unpatched systems to maintain long-term access and conduct stealthy operations. Their use of credential harvesting tools like Mimikatz and lateral movement frameworks such as Cobalt Strike highlights the critical importance of timely patch management—not just for ransomware defense, but also for countering nation-state actors. Recommendations To reduce the risk of enterprise breaches stemming from tool misuse, social engineering, and unpatched vulnerabilities, organizations should adopt the following practices: 1. Patch Promptly and Systematically Ensure that software updates and security patches are applied in a timely and consistent manner. This involves automating patch management processes to reduce human error and delay, while prioritizing vulnerabilities based on their exploitability and exposure. Microsoft Intune can be used to enforce update policies across devices, while Windows Autopatch simplifies the deployment of updates for Windows and Microsoft 365 applications. To identify and rank vulnerabilities, Microsoft Defender Vulnerability Management offers risk-based insights that help focus remediation efforts where they matter most. 2. Implement Multi-Factor Authentication (MFA) To mitigate credential-based attacks, MFA should be enforced across all user accounts. Conditional access policies should be configured to adapt authentication requirements based on contextual risk factors such as user behavior, device health, and location. Microsoft Entra Conditional Access allows for dynamic policy enforcement, while Microsoft Entra ID Protection identifies and responds to risky sign-ins. Organizations should also adopt phishing-resistant MFA methods, including FIDO2 security keys and certificate-based authentication, to further reduce exposure. 3. Identity Protection Access Reviews and Least Privilege Enforcement Conducting regular access reviews ensures that users retain only the permissions necessary for their roles. Applying least privilege principles and adopting Microsoft Zero Trust Architecture limits the potential for lateral movement in the event of a compromise. Microsoft Entra Access Reviews automates these processes, while Privileged Identity Management (PIM) provides just-in-time access and approval workflows for elevated roles. Just-in-Time Access and Risk-Based Controls Standing privileges should be minimized to reduce the attack surface. Risk-based conditional access policies can block high-risk sign-ins and enforce additional verification steps. Microsoft Entra ID Protection identifies risky behaviors and applies automated controls, while Conditional Access ensures access decisions are based on real-time risk assessments to block or challenge high-risk authentication attempts. Password Hygiene and Secure Authentication Promoting strong password practices and transitioning to passwordless authentication enhances security and user experience. Microsoft Authenticator supports multi-factor and passwordless sign-ins, while Windows Hello for Business enables biometric authentication using secure hardware-backed credentials. 4. Deploy SIEM and XDR for Detection and Response A robust detection and response capability is vital for identifying and mitigating threats across endpoints, identities, and cloud environments. Microsoft Sentinel serves as a cloud-native SIEM that aggregates and analyses security data, while Microsoft Defender XDR integrates signals from multiple sources to provide a unified view of threats and automate response actions. 5. Map and Harden Attack Paths Organizations should regularly assess their environments for attack paths such as privilege escalation and lateral movement. Tools like Microsoft Defender for Identity help uncover Lateral Movement Paths, while Microsoft Identity Threat Detection and Response (ITDR) integrates identity signals with threat intelligence to automate response. These capabilities are accessible via the Microsoft Defender portal, which includes an attack path analysis feature for prioritizing multicloud risks. 6. Stay Current with Threat Actor TTPs Monitor the evolving tactics, techniques, and procedures (TTPs) employed by sophisticated threat actors. Understanding these behaviours enables organizations to anticipate attacks and strengthen defenses proactively. Microsoft Defender Threat Intelligence provides detailed profiles of threat actors and maps their activities to the MITRE ATT&CK framework. Complementing this, Microsoft Sentinel allows security teams to hunt for these TTPs across enterprise telemetry and correlate signals to detect emerging threats. 7. Build Organizational Awareness Organizations should train staff to identify phishing, impersonation, and deepfake threats. Simulated attacks help improve response readiness and reduce human error. Use Attack Simulation Training, in Microsoft Defender for Office 365 to run realistic phishing scenarios and assess user vulnerability. Additionally, educate users about consent phishing, where attackers trick individuals into granting access to malicious apps. Conclusion The democratization of offensive security tooling, combined with the persistent failure to patch known vulnerabilities, has significantly lowered the barrier to entry for cyber attackers. Organizations must recognize that the tools used against them are often the same ones available to their own security teams. The key to resilience lies not in avoiding these tools, but in mastering them—using them to simulate attacks, identify weaknesses, and build a proactive defense. Cybersecurity is no longer a matter of if, but when. The question is: will you detect the attacker before they achieve their objective? Will you be able to stop them before reaching your most sensitive data? Additional read: Gartner Predicts 30% of Enterprises Will Consider Identity Verification and Authentication Solutions Unreliable in Isolation Due to AI-Generated Deepfakes by 2026 Cyber security breaches survey 2025 - GOV.UK Jasper Sleet: North Korean remote IT workers’ evolving tactics to infiltrate organizations | Microsoft Security Blog MOVEit Transfer vulnerability Solt Thypoon Scattered Spider SIM swaps Attackers exploiting new critical OpenMetadata vulnerabilities on Kubernetes clusters | Microsoft Security Blog Microsoft Defender Vulnerability Management - Microsoft Defender Vulnerability Management | Microsoft Learn Zero Trust Architecture | NIST tactics, techniques, and procedures (TTP) - Glossary | CSRC https://learn.microsoft.com/en-us/security/zero-trust/deploy/overviewDeep Dive: DLP Incidents, Alerts & Events - Part 2
Alerts Overview Like the Incidents, alerts also provide comprehensive information such as severity, status, category etc. to help users understand and navigate efficiently. In addition to these standard details, the alert view also displays the correlation reason, which is particularly beneficial for security analysts and administrators. The correlation reason explains why an alert is linked to a particular incident or other alerts, helping users trace how different pieces of suspicious activity are connected. By understanding the correlation reason, users can better assess the scope and impact of security issues, streamline investigations, and take more effective remediation actions, ultimately improving the overall security posture of the organisation Alert Details The details page is divided into two sections. The section on the left presents information regarding Impacted Users and Devices (for Endpoints), including details such as the alert story, policy description, matched sensitive information, and its count. It also displays related events with detection time and location (such as Exchange or Endpoint). The right pane lets you view alert status, details, manage the alert, or move it to another incident. It shows evidence (entity details like IP, user, device), policy info, incident details, correlation reasons, plus alert comments, history, and a timeline. Event Details Now that we understand incidents and alerts, let's review events. Events offer detailed information about activity. Below is a brief description of each section for reference. Details Event Details Event ID: A unique identifier assigned to each event. It can be used to associate the event with corresponding DLP rule match activity within the Activity Explorer. Location: Specifies where the activity occurred, such as Exchange, Devices, etc. Time of Activity: Indicates the exact time at which the event took place. Impacted Entities: Hostname: The name of the device where the DLP event occurred. IP Address: The IP address of the device or client involved in the event. Application: The app or service used during the event (e.g., Edge, Outlook, Teams). File Name: The name of the file involved in the DLP violation. File Path: The full directory path where the file was located or accessed. File Extension: The file type suffix (e.g., .docx, .pdf) indicating format. Sha1: A SHA-1 hash value used to uniquely identify the file. Sha256: A SHA-256 hash value offering stronger file identification. File Type: The type of the file (e.g., document, spreadsheet, image). File Size: The size of the file in bytes or megabytes. RMS Encrypted: Indicates whether the file is protected using Rights Management Services. MDATP Device ID: A unique identifier for the device from Microsoft Defender for Endpoint. Client Country: The geographic location of the client device based on IP. Client IP Location: More granular geolocation data derived from the client IP address. Target Domain: The destination domain involved in the data transfer (e.g., external email or cloud service). Evidence File: A copy or reference to the file that triggered the DLP event, used for investigation. Hunt & Monitor: Hunt All Sensitive Content Activity by Device: Displays all sensitive data interactions per device, useful for device-level threat hunting. Activity by User: Shows user-specific actions involving sensitive content, helping identify insider risks. DLP Violations for Last 30 Days: Summarizes all DLP policy violations over the past month for trend analysis and compliance tracking. User & Role Info: Displays the user detail and the role assigned to the user. Policy Details: DLP Policy Matched: The specific DLP policy that was triggered by the event, based on organizational data protection rules. Rule Matched: The exact rule within the DLP policy that was violated (e.g., sharing sensitive data externally). Sensitive Info Types Detected: Lists predefined or custom sensitive information types (e.g., credit card numbers, health records) found in the content. You can click on the SIT to view more information such as count by confidence levels, matched content and surrounding text. Trainable Classifiers Detected: Lists AI-based classifiers that identify sensitive content based on context and user behavior (e.g., source code, resumes). Violating Action: The user action that caused the violation (e.g., copy to USB, email to external domain, print, upload to cloud). Enforcement Mode: Indicates whether the policy is in audit, block/warn, override/warn & bypass mode—defining how the system responds to violations. Override Justification Text: If a user overrides a policy block, this field captures their justification or reason. Sensitive Information Type: A more detailed view of the specific sensitive data detected (e.g., “India Aadhaar Number”, “EU Passport Number”). Source This tab enables users to preview content intended for exfiltration, including files or emails. The Actions tab allows authorised users to download the specified content. Note: Previewing or downloading content requires that the administrator or analyst possesses the appropriate role permissions. Sensitive Info Types Lists predefined or custom sensitive information types found in the content with matched content and surrounding text info. Trainable Classifiers Lists Trainable Classifiers and their details. Metadata Provides the detailed metadata in simple txt format. Hope this helps!
