How Karambit.AI and Microsoft Bring Software Authenticity to 14 Billion Files Per Month
The Problem: Static Analysis Without Context Traditional static analysis treats every file as an island. Scan a binary, match against known signatures, flag what you recognize. The approach is well-understood and increasingly insufficient against modern threats. The fundamental limitation is the absence of context. Without it, a packer is just a packer. A network call is just a network call. An obfuscation routine is just an obfuscation routine. Whether that behavior is normal or anomalous, whether it belongs in this software, in this ecosystem, performing this function, is invisible to tools that evaluate files in isolation. Attackers exploit this gap. They hide malicious behavior inside legitimate software patterns, evolve their techniques between versions, and distribute intent across multiple components so that no single artifact triggers a detection in a context-free scan. Context-Aware Behavior Analysis Context-aware analysis inverts the model. Instead of asking "is this file bad?" it asks: "is this file behaving the way it should, given everything we know about this ecosystem?" This requires building and maintaining behavioral context across multiple dimensions: Ecosystem-level behavioral baselines: Understanding what behaviors are normal across the entire corpus and which should never appear. In a trusted software ecosystem, obfuscated or packed content is itself an anomaly worth enforcing policy against, regardless of whether the underlying payload is known-malicious. Behavioral chains with low false-positive rates: Individual API calls and instructions are ambiguous in isolation. Context-aware analysis identifies chains of behaviors, sequences where data staging feeds into exfiltration, or where privilege escalation is followed by persistence mechanisms, that reveal intent with high confidence. Cross-file and cross-instance correlation: Behaviors observed in one file are evaluated against patterns seen across millions of other files and scan instances. Shared behavioral fingerprints reveal family relationships, evolutionary lineage, and coordinated campaigns that single-file analysis cannot surface. Historical behavioral deltas: What changed between version N and version N+1? New behaviors in an update, especially behaviors that don't correspond to documented changes, are flagged not because they match a signature, but because they deviate from the established behavioral profile. The result: dramatically higher detection confidence, lower false-positive rates, and the ability to enforce behavioral policy at the ecosystem level. Case Study: Packer_Dictator, Behavioral Detection Under Adversary Adaptation Adversaries must change their Tactics, Techniques, and Procedures (TTPs) over time. When a detection capability catches them, they adapt to evade it. This is expected behavior and it is precisely why general detections at the behavior level are more durable than signature-based approaches. Behavioral patterns are fundamentally harder for adversaries to change without breaking their own tooling. The packer family tracked as packer_dictator illustrates this dynamic clearly. Initial Detection: Obvious Indicators Early variants of packer_dictator used conspicuous binary section names: authoritarian and politically-themed strings that made identification straightforward for anyone examining the PE headers. These were low-hanging indicators, but Karambit.AI's detection wasn't built on them. The system flagged these samples based on their behavioral profile: the entropy characteristics of their packed sections, the structure of their unpacker initialization routines, and the other patterns used to unpack and execute hidden payloads. Adversary Adaptation: Surface Changes, Persistent Behavior As detections rolled out, the users of this packer had to adapted. The obvious section names disappeared, replaced by more benign alternatives: .upx0, standard "unpacked" section names, and other strings designed to blend in with legitimate software. But the underlying behavior didn't change because it couldn't, not without fundamentally rearchitecting the packer itself. Entropy Analysis: Seeing Through Surface Changes Sliding-window entropy analysis reveals why surface-level changes are insufficient to evade behavioral detection. The entropy profiles of packer_dictator samples, even after the section name changes, maintain a characteristic signature: Both profiles exhibit the same structural pattern: a low-entropy region corresponding to the unpacker stub, followed by a sharp transition to a high-entropy plateau spanning the packed payload. This entropy profile is indicative of hidden behaviors, content that has been deliberately obscured, though not necessarily malicious content on its own. The profile shape, transition points, and entropy floor/ceiling ratios form a behavioral fingerprint that persists across variants regardless of metadata changes. Unpacker Initialization: Common Structure Enables Generalized Detection At the disassembly level, packer_dictator variants share a common unpacker initialization sequence that enables generalized analysis across the family. Examining the entry-point code of two samples reveals the structural similarity: Both samples exhibit a characteristic pattern: Register preservation: PUSH R9/PUSH R11 followed by PUSHFQ to save register state and flags before the unpack routine modifies them. Immediate constant loading: Large immediate values loaded into registers (MOV R9, 0x689f8c87eebd998c / MOV R11, 0x6592b8afc22b0736) that serve as decryption keys or XOR masks for the unpacking routine. Arithmetic flag manipulation: Sequences of TEST, NEG, OR, CMP, NOT, and SETNS instructions that compute control flow decisions based on the loaded constants — a form of opaque predicate that obscures the true branch target. Stack-based payload resolution: MOV instructions referencing [RSP + local_120] / [RSP + 0x8] with additional immediate constants written to the stack, setting up parameters for the decompression/decryption loop. The structural template is consistent even as the specific constants, register assignments, and opaque predicate formulations change between variants. This is what makes behavioral detection durable: the adversary can rotate constants and rename sections, but the computational structure required to unpack the payload is constrained by the packer's architecture. By generalizing detection to this structural level, Karambit.AI's engine identifies new packer_dictator variants, and structurally related packer families, without requiring signature updates for each iteration. And this is only one example of the resilience of Karambit.AI’s resilience in the face of constantly advancing adversaries. From Karambyte to Karambiner: Engineering for Billions Karambyte: Building the Context Karambyte was Karambit.AI's original analysis engine, purpose-built for deep behavioral extraction from compiled binaries. Its core function was to extract behavioral context, disassemble control flow, API call chains, entropy profiles, packer identification, behavioral intent classification, and store it for comparison and reference. Karambyte proved the model. It demonstrated that context-aware behavioral analysis could identify threats that traditional static analysis missed, by building rich behavioral profiles and comparing them across software versions and file populations. The system extracted context and maintained it internally, enabling the cross-file and cross-version correlation that drove detections like packer_dictator. But Karambyte's architecture, extracting and storing context within the same system, created a scaling constraint. As adoption grew and the target moved from hundreds of thousands to billions of files per month, the tight coupling between analysis and context storage became the bottleneck. Karambiner: Externalizing Context for Scale Karambiner re-architected the relationship between analysis and context. Rather than each analysis instance maintaining its own behavioral context store, Karambiner externalized the context layer into a dedicated reference that can then be customized for the specific organizational context. This separation enabled three critical capabilities at scale: Horizontal analysis throughput: Analysis scales independently of the context store. Adding processing capacity doesn't require replicating the full behavioral knowledge base. Context enrichment: Behavioral context extracted from collective scans can be used in the massively scalable analysis engine. Ecosystem-wide policy enforcement: With externalized behavioral context, the system can enforce policies across a large-scale ecosystem, such as blocking all obfuscated or packed content. The move from Karambyte to Karambiner was the architectural shift that made scanning of 14 billion files per month possible: a configurable depth of behavioral analysis, with context that scales to the size of the ecosystem rather than the capacity of individual analysis nodes. The Result: Software Behavior Analysis in Microsoft's Pipeline Today, Karambiner is integrated into Microsoft's operational pipeline for build/release and plays a critical role in performing context-aware behavioral analysis across billions of files monthly. The operational impact: Ecosystem-level behavioral policy enforcement: Obfuscated and packed content that has no legitimate reason to exist in the ecosystem is blocked by policy, informed by the scaled behavioral analysis. Durable detection under adversary adaptation: The packer_dictator lineage demonstrates that behavioral detection survives TTP changes that defeat signature-based approaches. Adversaries can change section names, rotate constants, and vary metadata, but the structural behaviors required to execute their payloads remain detectable. Low false-positive rates at scale: Because detection decisions are driven by behavioral understanding and optimizing for scale, the system maintains precision even at 14 billion files per month. Understanding AI capabilities: Behavior analysis can include understanding of where and how AI is used in an ecosystem. Deep understanding of the software going to production: Developers don't always know what components and behaviors make it to the production software, behavior analysis has allowed us to catch unexpected components developers didn’t realize were going to deployment. What's Next The partnership between Karambit.AI and Microsoft demonstrates that context-aware behavior analysis operates on a massive scale in production. As software supply chain attacks grow more sophisticated and adversaries continue evolving their TTPs and the use of AI agents to develop code, the ability to understand what software actually does, in context, across billions of files, is foundational infrastructure. Software authenticity isn't about checking a signature or trusting a certificate. It's about confirming that every binary does what it should, and nothing more. Karambit.AI is the software authenticity platform, ensuring software does only what the developer intended — nothing more. Learn more at karambit.ai.
Microsoft Leads a New Era of Software Supply Chain Transparency
Today, Microsoft announces the general availability of Microsoft’s Signing Transparency (MST) – a first-of-its-kind capability that brings unprecedented visibility and trust to our software supply chain. With this release, Microsoft is leading the industry by recording the build of critical cloud services into a publicly readable and verifiable SCITT standard (Supply Chain Integrity, Transparency, and Trust) compliant blockchain ledger. This means every production software build for in scope services like Azure Attestation and Azure Managed HSM (Hardware Security Module), Azure confidential ledger, Microsoft Signing Transparency itself (and others over time) – is now logged in an immutable, tamper-evident record. Only builds that are in the MST ledger are deployed to production; this gives customers confidence that the supply chain for these critical services can be audited at anytime. Notably, the MST ledger is fully open source and built to align with the emerging IETF SCITT standard. By embracing SCITT’s principles and open protocols, Microsoft ensures that MST not only secures our own ecosystem but also contributes to a broader industry movement toward standardized supply chain transparency. The open-source MST ledger serves as a verifiable trust anchor that any organization or researcher can inspect, audit, or even integrate with their own tooling. MST itself meets the highest levels of transparency, backed by a tamper-proof confidential ledger, open-source, and independently verified. Specifically, we are making the foundation of our trust model transparent and accessible to everyone – reinforcing that trust must be earned through proof, not just promises. This launch marks a major milestone in our commitment to Zero Trust principles, extending “never trust, always verify” all the way into the build itself. Building on a public preview introduced late last year, MST’s general availability delivers verifiable transparency at the software level. It transforms traditional code signing with an additive trust layer that is accessible via an open verification model. Every new software update is accompanied by a publicly auditable proof of integrity, enabling security teams to proactively confirm that each update is authentic and unaltered. To help organizations get the most out of this capability, we are also introducing a free tool to explore the contents – Ledger Explorer – an offline tool that allows security teams to examine MST ledger entries, verify cryptographic proofs, and even validate the ledger’s integrity independently. This tool, combined with MST’s open design, ensures that every Microsoft customer – and the broader community – can hold us accountable in real time for the software we run on their behalf. Key Benefits of Microsoft’s Signing Transparency (MST) Verified Code Integrity – Every software release is cryptographically logged in MST’s ledgers. This makes each build tamper-evident and traceable. If an attacker attempts to inject malicious code or sign an unauthorized update, it will be evident through the well-defined validation step built into the SCITT standard. Organizations gain the assurance that code integrity can be independently confirmed at any time. Independent Verification & Zero Trust – MST enables customers and auditors to verify software authenticity on their own, without having to solely rely on vendor attestations. For each update, Microsoft provides a transparency “receipt” (proof of logging) that you can use to prove the update was officially published and unaltered. This fosters a “don’t just trust, verify” approach, empowering security teams to double-check everything running in their environment aligns with what Microsoft intended. Audit-Trail & Compliance – The transparency ledger creates a permanent, auditable timeline of code deployments. Every entry is a record of what was released and when, backed by cryptographic proofs. This simplifies compliance reporting and accelerates forensic analysis. In the event of an incident, you can quickly audit the ledger to see if any unexpected code was introduced. For highly regulated industries, MST offers concrete evidence of software integrity and policy compliance over time. Leadership & Open Standards – We are delivering real transparency now, encouraging a future where all critical software is released with verifiable integrity. MST’s open source implementation and SCITT-compliant design exemplify our commitment to openness and collaboration. We believe widespread adoption of these standards will strengthen supply chain security for everyone, making trust verification a universal practice. Next Steps Microsoft’s Signing Transparency is more than a new security feature and shapes the advances in trust technology. As threats grow more sophisticated, we must evolve the way we assure our customers about the software they depend on. With MST now generally available, we are leading by example: proving that it is possible to open up the traditionally opaque process of software deployment and turn it into a source of strength and trust, i.e., empowering each person with verifiable transparency. We invite the industry to join us on this journey and get started by reading the documentation and exploring Ledger Explorer today! Together, by embracing transparency and open standards, we can turn “trust but verify” from a slogan into an everyday reality for digital infrastructure.Microsoft Purview enables developers with strong data security across AI apps and agents
Today, developers are at the center of a new wave of innovation—building AI applications and agents that are deeply connected to enterprise data. But with this opportunity comes a new and complex set of security challenges. AI systems operate across cloud platforms, third-party services, and even local and on-premises development environments, interacting dynamically with sensitive data such as customer records, financial information, and intellectual property. Traditional security approaches weren’t designed for this level of scale, autonomy, or fluid data movement—leaving developers to navigate fragmented tools, unclear policies, and the risk of unintentionally exposing sensitive information. At the same time, expectations are rising. Organizations need to ensure that AI applications and agents are compliant, auditable, and secure by default on an enterprise-level—not retrofitted after deployment. But for developers, adding security often means additional complexity, custom integrations, and slower time to market. This tension between speed and control has become one of the biggest barriers to moving AI from experimentation into production. Microsoft Purview is designed to help with this challenge by embedding data security and compliance controls across the development cycle. Purview provides a consistent way to govern how data is accessed, used, and shared—without requiring developers to become security experts. The result is a simpler path to building AI systems that are secure, compliant, and enterprise-ready by design. Extending data security and compliance to local agents and claws Local and endpoint agents, built in platforms such as GitHub Copilot CLI and OpenClaw, introduce a new class of data security challenges as they operate outside traditional control planes and directly on user machines. Unlike cloud systems, these agents can access local files, credentials, terminals, and enterprise apps simultaneously—often moving data across tools and environments. This expands data risks, from sensitive data being unintentionally stored, copied, or shared, to API keys and tokens being exposed, and autonomous workflows triggering data movement without explicit user intent. At the same time, many existing security controls were designed for browser or cloud-based activity, leaving a growing blind spot at the endpoint where agents are increasingly running. The result is a widening gap between how developers build agents to operate locally in the users machines, and how organizations can detect, govern, and protect the data those agents interact with. Microsoft Security and Windows are integrating management and security capabilities directly into the local agents’ development workflow, enabling security as an architectural guarantee rather than an implementation choice. At Build, we are thrilled to be extending Purview visibility and protection capabilities to local agents developed on GitHub Copilot CLI, Claude Code, OpenAI Codex, and OpenClaw - in Public Preview. Unlike traditional cloud applications, these agents operate closer to the data and often create new risks for data exposure. Purview addresses this challenge across all types of agent interactions with a clear, simplified set of scenarios: ▪ Observability: Visibility on Purview Data Security Posture Management (DSPM) across agent inventory, as well as into how local agents interact with sensitive data—across prompts, responses, and actions. ▪ Runtime data protection: Purview Data Loss Prevention (DLP) controls enforced directly into the agent execution flow, inspecting prompts and tool calls in real time to prevent sensitive data exfiltration. ▪ Agentic risk detection: Risky or anomalous agent behaviors detected through Insider Risk Management (IRM) signals, helping teams detect unsafe interactions early. ▪ Audit: Comprehensive, end-to-end logging of all local agent interactions—capturing prompts, responses, data access, and actions for data context. For example, a developer is using a local coding agent to generate code and accidentally includes sensitive credentials in a prompt. AI observability in DSPM surfaces the interaction and shows what data the agent accessed. DLP detects the sensitive data in real time and blocks it from being sent or processed (or sensitive files from being accessed and exfiltrated). At the same time, agentic risk detection flags the session as high risk based on the behavior pattern. All of this activity is captured in audit logs, enabling the security team to investigate and take action quickly. Developers and security teams gain visibility into agent activity and data interactions, while policies prevent sensitive data leakage. This ensures consistent security outcomes across both cloud and endpoint environments, without disrupting developer workflows. Strengthening visibility and controls for Foundry agents Foundry gives developers a central place to build and manage AI agents, but it also creates a need for data security context directly in that workflow—especially as prompts, model interactions, and downstream actions increasingly involve sensitive enterprise data. At Build, we are excited to announce the expansion of the Foundry integration with Purview. This includes Purview DLP runtime controls for prompt processing in Foundry, in Public Preview. As agents and applications built on Foundry increasingly interact with sensitive data, Purview ensures those interactions are governed by trusted controls, identifying Sensitive Information Types (SITs) in real time to detect and protect confidential data embedded in prompts. For example, if a user includes customer PII or financial data in a prompt, Purview can automatically identify the sensitive content and block that prompt from being processed by the model. This ensures that all Foundry apps and agents, regardless of how they’re built or deployed, inherit consistent data protection – allowing organizations to reduce risk of inadvertent data exposure, centralize compliance enforcement across AI workloads, and confidently scale AI adoption knowing sensitive data is protected by design. We’re also building up on the Purview coverage for Foundry shared at the last Microsoft Ignite by announcing Purview insights embedded directly into the Foundry Control Plane, in General Availability, bringing rich data security context to the plane where developers already work. Purview surfaces crucial signals—such as SITs detected in the agentic interactions, % of agentic interactions involving sensitive data, and spread of high-risk users — so Foundry admins can know how AI apps and agents are built in their environment. This shift enables developers to make faster, better decisions in the moment, reducing rework and closing security gaps early on. For customers, the value is clear: stronger security by design and at enterprise scale, accelerated development cycles, and reduced risk of data leaks or compliance issues—without slowing down innovation. Innovating for developers everywhere, at the pace of AI growth Microsoft is also expanding Purview’s reach across the broader developer ecosystem. New integrations help organizations apply consistent oversight to AI tools and platforms developers already use, without adding separate compliance workflows. GitHub Copilot is a critical productivity layer for developers, accelerating how code is written and shipped—making it equally important that developer interactions with GitHub Copilot are governed and secured with the same rigor as enterprise data. Microsoft Purview now extends data governance and compliance capabilities to GitHub Copilot interactions, in Public Preview, enabling GitHub Enterprise customers with Entra SSO to stream audit logs directly into Purview. This brings centralized visibility for AI activity, allowing security and compliance teams to analyze GitHub Copilot agent session activity alongside other AI workloads. With this native integration into GitHub workflows, Purview audits Copilot activity across repositories, pull requests, and developer sessions—ensuring AI-generated code aligns with enterprise data policies, compliance requirements, and secure development standards. By integrating Purview into existing workflows, organizations can govern GitHub AI usage without building parallel pipelines—reducing complexity while ensuring consistent compliance coverage across their entire data estate. Today’s AI agents aren’t built in just one ecosystem—they span custom apps, third-party platforms, and open-source frameworks. Without consistent controls, this creates blind spots where sensitive data can be exposed outside enterprise guardrails. That’s why extending Purview protection beyond Microsoft environments is critical: it ensures developers can apply the same data security, DLP policies, and compliance controls to any agent, anywhere—so innovation can scale without increasing risk. Developers already use Microsoft Purview APIs to embed data protection into enterprise workflows. Today, we’re introducing the Microsoft Purview SDK for .NET — a simple, drop-in toolkit that brings Purview capabilities directly into any application, in Public Preview. Instead of weeks spent wiring APIs, authentication, and error handling, developers can add content scanning, DLP checks, and sensitivity labeling in just a few lines of code. The SDK handles the heavy lifting — including auth, retries, caching, and telemetry — so teams can focus on building experiences. For AI apps and agents built outside of the Microsoft AI platforms, SDK adds built-in support and can evaluate prompts and responses in real time against DLP and content policies — helping prevent data exposure at runtime without custom logic. Designed for both real-time and asynchronous patterns, and for authenticated or anonymous flows, the SDK also feeds activity back into Purview to give security teams centralized visibility and control. The bottom line is- the Microsoft Purview SDK enables developers to build AI apps and agents that are secure and compliant by default — cutting integration time from weeks to days while ensuring data protection scales with AI. The SDK will be available in public preview within the next month. Together, these announcements represent a significant step forward in how developers build secure AI systems. Microsoft Purview is no longer just a data security and compliance solution—it is a first-class layer of the development process by protecting data across AI applications and agents, and enables a bridge between developers and security teams. As AI becomes more agentic, distributed, and deeply connected to enterprise data, the need for built-in security will only grow. With Purview, developers no longer must choose between speed and security—they can build both into every application from the start Getting connected with Microsoft Purview and learn more Learn more about Microsoft Purview on our website and Microsoft Learn. Explore Agent 365. Try Microsoft Purview data security. Learn more about Microsoft Purview SDK.Security Dashboard for AI: 3 Ways CISOs Drive Impact Today
AI is reshaping the enterprise and, with it, the threat landscape. Today's organizations face new threats with AI agents that modify configurations, execute workflows, and access data without direct human oversight. As a result, the gap between AI adoption and AI governance is widening, and CISOs face growing challenges to maintain visibility, control, and compliance across an increasingly complex ecosystem. As AI becomes embedded across the enterprise, CISOs face four key challenges: Scale without visibility: Over 75% of enterprises surveyed by PWC report they are already adopting AI agents. ¹ At the same time, over 80% of security teams surveyed by Nokod report visibility gaps into the applications and AI agents created within their organization. ² Rapid AI proliferation and evolving regulations make unified visibility across AI platforms, apps, and agents critical for CISOs. Fragmentation: Organizations rely on multiple siloed tools for AI asset visibility, making oversight fragmented and inefficient. According to Gartner’s 2024 survey of 162 enterprises, organizations use 45 cybersecurity tools on average. Expanding AI risk: AI proliferation is rapidly increasing the attack and risk surface, with the surge of AI-generated identities. By 2027, 4 out of 5 organizations will face phishing attacks powered by AI-generated synthetic identities, according to IDC. ³ This makes it harder for CISOs to track emerging threats, unmanaged assets, and shifting risk patterns. Overload: Alert fatigue is now a top challenge, with organizations now receiving an average of 2,992 security alerts daily, yet 63% go unaddressed. ⁴ Increasing AI risk without a way to prioritize what matters most compounds pressure on CISOs. In conversations between Microsoft and CISOs, one common need emerged: a single place to view integrated AI risk across the enterprise. To address these growing challenges, we are excited to provide CISOs with the Security Dashboard for AI, which recently became generally available. This unified dashboard aggregates posture and real-time risk signals from Microsoft Defender, Entra, and Purview into one unified, executive-level view of AI posture, risk, and inventory across agents, apps, and platforms. The Security Dashboard for AI helps CISOs: Gain unified AI risk visibility: Discover AI agents and applications and continuously monitor posture across the environment Prioritize critical risks: Correlate signals across identity, data, and threat protection to surface the most urgent issues Drive risk mitigations: Investigate activity and take action to help reduce exposure across the AI ecosystem The dashboard is capable of aggregating and surfacing AI risks from across Microsoft Defender, Entra, Purview - including Microsoft 365 Copilot, Microsoft Copilot Studio agents, and Microsoft Foundry applications and agents as well as cross-platform AI risks with Microsoft network-based or SDK-enabled integrations, and MCP servers. This supports comprehensive visibility and control, regardless of where applications and agents are built. As you activate Microsoft Security for AI capabilities, you can gain richer visibility into different aspects of your AI risk posture. Figure 1: Security Dashboard for AI in browser Getting Started with the Security Dashboard for AI The Security Dashboard for AI is provided at no additional cost to customers already using Defender, Entra, and/or Purview to protect their AI innovation. Based on how early adopter CISOs are using the dashboard, here are three ways you can start leveraging the dashboard today. 1. Manage Daily AI Risk Beyond reporting, you must stay hands-on with AI risks, scanning for emerging issues, verifying asset governance, and delegating remediations. The Security Dashboard for AI consolidates daily operations into a single pane of glass, surfacing critical alerts, unmanaged assets, and emerging risks. Use the dashboard as a daily AI risk radar, enabling rapid triage and ensuring you focus on the most urgent threats. Scan and triage daily AI risk: Start each day by identifying and prioritizing the highest-risk AI exposures. Risks are prioritized on severity reported by underlying security tools, helping you focus on the most critical exposures. Track AI asset inventory and monitor agent sprawl: Use the Inventory page to gain comprehensive visibility into all AI assets. Identify newly registered assets to mitigate the risk of shadow or unmanaged IT and surface inactive agents to proactively monitor and control agent sprawl. Delegate tasks for remediation: Move from insight to action by delegating tasks to your security team with easy click delegation. Delegation routes ownership via email or Microsoft Teams with notifications, due date, and ownership tracking. Delegate actions to specific roles such as global admin and AI administrator, without granting full access to underlying tools. Figure 2: Security Dashboard for AI risk page 2. Guide Briefings with Security Teams You require up-to-date intelligence to guide conversations with Security Teams about what is happening across the AI estate. The Security Dashboard for AI helps you anchor discussions in specific risks, trends, and ownership gaps surfaced in the data. The dashboard becomes a conversation driver, helping you ask the right questions about risk and security posture, to help ensure you and your team are triaging the right priorities. Because the dashboard consolidates signals from Defender, Entra, and Purview, both CISO and security teams operate from the same facts, enabling more outcome-driven discussions and faster prioritization, so you can shift the conversations from status updates to targeted action planning. Prioritize top AI Risk: Use the dashboard to help you prioritize the AI risk that matters the most. In preparation for team meetings, use Microsoft Security Copilot to explore AI risks, agent activity, and security recommendations via prompts to strengthen your AI security posture. With your team, take a closer look at risk vectors like data leakage, oversharing and unethical behavior, and discuss what actions need to be taken. Review Security Recommendations: Create a routine with your security team to review the recommended Microsoft security actions and track your progress over time. Across regular team check‑ins, review what has been addressed, what remains open, and which actions require follow‑up so you are prepared to respond to regulatory, audit, or executive questions with up‑to‑date metrics. Figure 3: Security Dashboard for AI inventory page Figure 4: Security Dashboard for AI delegation 3. Executive Reporting Reporting to the board on AI security posture has historically meant weeks of manual data gathering across multiple tools. The Security Dashboard for AI streamlines the data collection process with a single source of truth for AI risk, enabling confident, data-backed insights for your board presentations and conversations. Early adopters confirm the value and are using it for quarterly executive briefings. Prepare for Board Discussions: Use the dashboard to help get the right insights at the right altitude to help you prepare for discussions with your board. The Overview page aggregates identity, data security, and threat protection signals from Defender, Entra, and Purview into an AI risk scorecard with risk factors. The embedded Security Copilot AI-powered insights provide suggested prompts with risk assessments, summaries, and recommendations to help you prioritize what matters most. Extend Observability to Executive Stakeholders: Authorize AI risk follow‑ups to the appropriate security, identity, or governance owners using Microsoft Teams or email. Distribute visibility across GRC lead, AI governance, and IT leaders, while maintaining executive‑level oversight. Figure 5: Security Dashboard for AI Copilot prompt gallery Next Steps The Security Dashboard for AI helps CISOs manage AI risk faster, more confidently and more collaboratively with their team. Defender, Entra, and Purview signals are surfaced in a single pane of glass, providing observability across your AI estate. Drive faster triage, use data to support board-level discussions about AI risk, and enable coordinated action with integrated insights, recommendations, and delegation to help accelerate remediation across existing security workflows. The Security Dashboard for AI is generally available now. If your organization uses Microsoft Defender, Entra, and/or Purview, you already have access, no additional licensing is required. Visit ai.security.microsoft.com to access the dashboard directly, or navigate to it from the Defender, Entra, or Purview portals. Learn more about the Security Dashboard for AI on the MS Learn page and the Security Dashboard for AI Security Blog. Discover new features in the Security Dashboard for AI such as the Security Reader role, new delegation flow, and new identity risk section here. ¹AI agent survey. PwC, May 2025 ²Security Teams Taking on Expanded AI Data Responsibilities. Bedrock Data, March 2025 ³IDC FutureScape: Worldwide Security and Trust 2026 Predictions, November 2025 ⁴2026 State of Threat Detection and Response Report. Vectra AI, February 2026The Advantages of Premium Cases in Purview eDiscovery
Capacity & Scale Feature Description Advantage over E3 Enhanced Limits Supports significantly higher limits, including eDiscovery case count and export volume. For example, up to 50,000 cases and 5 TB per search in E5 (versus 10,000 cases and 2 TB in E3). Handles large investigations without splitting into multiple cases or searches. E3’s lower limits would force breaking up big jobs, adding overhead and risk of errors. E5’s higher capacity means fewer workarounds and seamless handling of large-scale litigation. Tenant-Wide eDiscovery Process and Holds Reports (Preview) Provides a central dashboard of all eDiscovery activities and eDiscovery holds across the tenant. Compliance and IT teams get at-a-glance status of ongoing jobs and active holds. Improves oversight and management efficiency for eDiscovery. E3 lacks centralized reporting, making it harder to track many cases. E5’s reporting gives better visibility into operations, which is crucial for heavy workloads and tight deadlines. Expanded Hold Capacity Each legal hold in E5 can encompass up to 2,000 mailboxes and 2,000 sites in one policy. E3 holds are limited to 1,000 mailboxes or 100 sites per policy. Enables placing very large custodian sets on hold with a single action. In E3, exceeding hold limits means juggling multiple policies for one case, increasing complexity. E5 simplifies hold management by consolidating more custodians per hold, reducing admin burden. Search & Collection Feature Description Advantage over E3 Advanced Search Filters Offers richer search criteria beyond keywords. You can filter by sensitive info types (credit cards, SSNs), specific message IDs, or sensitivity labels on documents. This helps pinpoint relevant sensitive content directly. Enables more precise and speedy discovery of critical data. In E3, finding the same info might require complex keyword strings or separate tools (with a higher chance of missing items). E5’s advanced filters mean faster, targeted searches for things like confidential data or GDPR content. Data Source Sync Allows you to refresh custodians’ data sources in a search or hold to catch updates to locations. For example, if a custodian adds a new OneDrive, E5 will detect and prompt you to include it. Ensures no content location is overlooked as the case evolves. E3 has no easy way to know if data moved or new sites were created, potentially leaving gaps. E5’s sync provides complete and defensible collection by keeping holds/searches up-to-date. Cloud Attachment Collection (Hyper-linked Documents) Automatically collects the content of files shared via cloud links (OneDrive/SharePoint) in emails or chats. E5 can retrieve the actual document (and its versions) that was linked, even pulling the specific version that was shared at the time if the version shared feature is enabled. Preserves evidence that E3 would miss. E3 eDiscovery does not fetch linked file content. It would only show a hyperlink, making it difficult to return the associated file. E5 ensures linked documents (with version history) are collected, so the full context of communications is retained. Conversation Threading (Chats & Email) Reconstructs conversations in a threaded view for Microsoft Teams chats and email chains. Reviewers can see messages in context (like a chat transcript or email thread) rather than as isolated items. Greatly improves contextual understanding. E3 exports chats as separate messages with no threading, making it hard to follow the story. E5’s threaded view lets reviewers grasp the full conversation at a glance, reducing confusion and ensuring nothing is interpreted out of context. Custodian & Hold Management Feature Description Advantage over E3 Case-Level Custodian Management Provides a dedicated tab to manage custodians (people) within each case. You add custodians once and can easily apply holds or searches to all their data without re-entering their information each time. Streamlines hold setup and ensures clarity on who is in the case. E3 has no concept of custodians. You must manually input email or site addresses for each search/hold. E5’s approach saves time, reduces errors, and gives a clear view of all people involved in the matter. Bulk Custodian Import Supports importing up to 1,000 custodians at once from a list into a case. Useful for large investigations (e.g., adding an entire department as custodians in one go). Dramatically faster setup for big cases. In E3, adding hundreds of people means typing or pasting each individually, which is time-consuming and error prone. E5’s bulk import means quick, one-time setup for large custodian lists, ensuring no one is missed. “Explore & Add” Custodian Sources Provides an intelligent way to discover related data sources for a given custodian. For example, it can list Teams, SharePoint sites, or groups the person is part of, and let you add those to the case. Helps capture all relevant locations for each person. In E3, you might overlook a Teams channel or group mailbox a custodian was involved in. E5’s explore feature surfaces those connections, improving completeness of your holds and searches by including collaboration spaces that might otherwise be missed. In-Place Review & Analytics Feature Description Advantage over E3 Advanced Indexing and OCR Automatically re-indexes content that was partially indexed or had errors and performs OCR (Optical Character Recognition) on images to extract text. This means files with images or previously unsearchable formats become searchable in E5. Ensures “no stone is left unturned.” E3 would flag such content as “unindexed” (meaning you know a file exists but not what’s inside it). With E5, far more data is searchable, even text inside images or scanned PDFs, reducing the amount of partially indexed content and the chance of missing critical evidence due to format issues. In-Place Review Sets Lets you create a review set of collected data in the cloud. Review sets offer contextual review of conversations, powerful query and filtering capabilities, and query reports for additional insights. Pre-review culling is possible in E5. E3 has no in-product review capability. You must export everything to an outside tool for examination. E5’s review sets allow the team to filter out irrelevant data and focus on what matters before exporting. This reduces the volume (and cost) of data sent for attorney review and keeps data in a secure, auditable environment during analysis. Tagging and Metadata Filters Enables applying tags (labels like “Responsive,” “Privileged,” “Personal Data”) to documents and emails in a review set, and filtering by these tags or other metadata fields. Improves organization and review workflow. E3 cannot tag items in-place, so keeping track of important documents is harder. In E5, tagging allows systematic categorization for quick retrieval (e.g., find all items tagged Highly Relevant instantly). These tags also carry over on export, so any work done during review isn’t lost when handing off to external counsel. Email Threading and Analytics Automatically identifies and stitches together email threads, showing only the last inclusive email that contains the entire conversation. Earlier duplicate emails in the chain are noted and can be skipped. Cuts down review volume and improves context. E3 reviewers would see every single email (even if content repeats across replies). This saves review time and ensures attorneys see the full discussion in one place rather than piecemeal. Conversation View Displays collected Teams (and other chat) messages in a conversation format in a review set, similar to how one would view a chat in the app, instead of individual out-of-context messages. Makes reviewing chat evidence much easier. In E3, chat messages are isolated, forcing reviewers to manually piece together who said what when. E5’s conversational view provides full context at a glance, so nothing is misunderstood or missed in chat-based communications. Near-Duplicate Detection Finds and groups nearly identical documents (e.g. multiple versions of a file or emails with only slight differences). Reviewers are informed which items are alike. Saves time and ensures consistency. E3 requires manually spotting similar files. E5 can let a reviewer examine one version and then quickly tag all its close duplicates the same way. This speeds up review and ensures similar content is handled uniformly (no conflicting judgments on essentially the same document). Themes (Topic Analytics) Uses analytics to cluster documents by themes/topics. For example, it might reveal a group of emails all discussing “Project X” or detect an unusual theme (like frequent mentions of “resignation”). Uncovers hidden patterns that simple keyword searches in E3 might miss. This insight helps investigators spot important threads of discussion or issues they weren’t explicitly searching for, leading to a more thorough understanding of the data set. It adds a layer of proactive insight absent in E3. Global Deduplication Automatically de-duplicates exact copies of emails or files across all custodians using review sets. Each unique item is retained once for review, with duplicates noted. Prevents redundant review work. In E3, the same email stored in five mailboxes would appear five times and could be reviewed and tagged inconsistently by different people. E5’s deduplication means reviewers spend time only on unique content improving efficiency and ensuring consistency in treatment of identical items. Export & Integration Feature Description Advantage over E3 Guest Reviewer Access Allows secure, read-only external access to a review set for outside experts (like outside counsel). Guest reviewers can be invited to review and tag documents in your E5 case via secure Azure AD access (with MFA), without data leaving the tenant. Enables collaboration with outside counsel without exporting data. E3 cannot extend access to external users. You’d have to export files and send them out, which is slower and riskier. E5 keeps the data in-place and governed, letting external reviewers work more efficiently while your organization retains control and visibility. Import External Data Supports ingestion of data from outside M365 into eDiscovery. You can load files like PST emails, PDFs, or documents from file shares into an E5 review set, maintaining custodians’ identity and metadata. Brings all relevant data under one roof. E3 cannot handle content beyond Exchange/SharePoint/Teams, so any non-M365 data would be reviewed separately. E5’s ingestion means even file server or third-party data can be included in the case, making your eDiscovery truly comprehensive and eliminating blind spots between different systems. Rich Export with Metadata Exports include a detailed load file with extensive metadata from the review (custodian info, email thread indices, attachment names, message IDs, tags applied, etc.). This is in addition to the actual content files. Simplifies downstream processing and preserves review decisions. E3’s export is basic (limited metadata), often requiring additional data processing in third-party tools. E5’s comprehensive load file means that all important context (including tags like “Privileged” that your team applied) travels with the exported data, so external reviewers immediately see those cues. This saves time and prevents rework. MIP Search and Decryption Integration Can automatically decrypt protected content (encrypted by Microsoft Information Protection, e.g. with sensitivity labels/Azure RMS) during eDiscovery. Encrypted emails and documents are made readable and searchable when added to a review set. Ensures encrypted files aren’t “invisible” in your investigation. E3 often cannot search or preview MIP-protected emails/docs until they’re manually decrypted after export (if at all). E5 seamlessly includes these encrypted items in search results and review, so you don’t miss evidence that was simply locked behind encryption. Insider Risk Management Escalation Integrates with Microsoft Insider Risk Management (IRM) alerts. With E5, if an insider risk policy flags a user (e.g., for a potential data theft), you can one-click escalate to create an eDiscovery case that automatically targets that user’s content around the incident. Enables a fast, seamless response to insider threats. E3 has no IRM at all, so there’s no such trigger. In E5, the moment a high-risk activity is detected, the legal team can immediately jump into collecting and reviewing the related data. This tight integration means quicker investigations and potentially mitigating issues before they escalate. Communication Compliance Escalation Ties into Communication Compliance (E5’s internal communications monitoring for policy violations). If a serious policy violation is found (e.g., harassment in Teams chats or inappropriate sharing of sensitive info), it can be escalated directly into an eDiscovery case for further investigation. Offers proactive discovery of misconduct. E3 lacks built-in communication monitoring, so issues may go unnoticed until too late. With E5, compliance officers can swiftly pivot from detecting a problem to launching a full eDiscovery inquiry, ensuring faster and more thorough handling of incidents like HR violations or data leaks. Graph API & Automation Fully supports the Microsoft Graph API for eDiscovery. This means eDiscovery tasks (case creation, adding custodians, running searches, exporting data) can be automated or integrated into other applications via scripting/programming without additional cost. While API support is supported for E3, the E3 export API is a metered solution. E5 allows organizations to streamline eDiscovery workflows – for example, auto-create a case and hold when HR flags an employee exit, or integrate with third-party legal management tools without additional cost. Teams and Copilot Interactions Purge Provides an incident response capability to search and purge Teams chats or Microsoft 365 Copilot interactions if sensitive information was shared. Authorized investigators can directly delete up to 100 Teams chat messages (across participant mailboxes) in one go via the eDiscovery interface (leveraging Graph API) when necessary to contain a data leak. Allows quick containment of spills that E3 cannot do. E3’s content search can purge emails but cannot delete Teams messages or Copilot content. With E5, if confidential data pops up in a Teams chat, compliance can not only find it but also bulk-delete those messages from user mailboxes to mitigate further exposure. This capability is crucial for responding to internal data mishandling in real time.Collecting Microsoft 365 Copilot Data with Microsoft Purview eDiscovery
Copilot Data Collection Reference Table Data Type Storage Location Item Class Collection Strategy Copilot Prompts (user questions sent to M365 Copilot) Exchange Online: Hidden folder in the user's mailbox. Compliance copies stored similar to Teams chats, but with unique item classes. IPM.SkypeTeams.Message.Copilot.<AppName> (e.g., .Word, .Excel, .Outlook, .BizChat). Additional AI-related classes may also apply: IPM.SkypeTeams.Message.ConnectedAIApp*, IPM.SkypeTeams.Message.CloudAIApp*, IPM.SkypeTeams.Message.TeamCopilot*, IPM.SkypeTeams.TeamCopilot* 1. Add the user's Exchange mailbox as a data source to the search. 2. In the condition builder you can optionally filter the search to only return Copilot prompts by adding a condition of "Item class contains any of Copilot activity". This automatically applies all relevant M365 Copilot item classes as a condition of the search. 3. Add any further additional conditions such as date range or keywords to narrow results as required. You can also use the Item Class condition to exclude M365 Copilot interactions from your collections when targeting a user’s mailbox. Notes: · Additional item classes may be added. The item class condition will be updated accordingly. Copilot Responses (AI-generated answers) Exchange Online: The same hidden folder in the user's mailbox as prompts. The same IPM.SkypeTeams.Message.Copilot.<AppName> pattern as prompts The same collection strategy used for prompts. Copilot Memories (personalized saved information Copilot "remembers") Exchange Online: Hidden CopilotMemory subfolder within the user's mailbox contacts. Stored as contact entries separate from prompts and responses. IPM.Contact Each memory item appears as a contact card within Exchange, which is distinct from the message-based item classes used for prompts/responses. 1. Add the user's Exchange mailbox as a data source to the search. 2. In the condition builder you can optionally filter the search to only return Contacts by adding a condition of "Item class contains any of Contacts". Notes: · Copilot memories will not be preserved under a legal hold or retention policy. · This will return both Copilot memories stored in contacts as well as traditional contacts from the user’s Exchange mailbox. Copilot Pages (AI-generated, user-editable documents) SharePoint Online: Stored in a user-owned SharePoint embedded container (shared with Loop workspace content and Copilot Notebooks). File format is .page. Not stored in the user's mailbox. N/A These are SharePoint files (not Exchange items), so no item class applies. Identify them in search results by the .page file extension. 1. Add the custodian’s SharePoint embedded site URL as a data source to the search. Alternatively, tenant-wide searches of all SPO sites will include all SharePoint Embedded containers 2. Optionally use the condition builder with conditions such as date range, keywords or file type to further filter results returned Facilitator agent interactions in a Team meeting chat Exchange Online: Hidden folder in all meeting attendees’ mailboxes. Compliance copies stored as Teams chats IPM.SkypeTeams.Message 1. Add the user's Exchange mailbox as a data source to the search. 2. In the condition builder you can optionally filter the search to only return Copilot prompts by adding a condition of "Item class contains any of Instant messages". 3. Add any further additional conditions such as date range or keywords to narrow results as required. Facilitator agent meeting notes (loop) SharePoint Online: Facilitator meeting notes are stored as a .loop file in a OneDrive folder titled Meetings of the user who initiated Facilitator in Teams N/A These are SharePoint files (not Exchange items), so no item class applies. Identify them in search results by the .loop file extension. 1. Add the user's OneDrive URL as a data source to the search. 2. In the condition builder you can optionally filter the search to only return loop files by adding a condition of "File type equals any of loop". 3. Add any further additional conditions such as date range or keywords to narrow results as required. Notes: · With eDiscovery premium enabled cases you can follow the standard workflow for collecting Team meeting messages and select to include cloud attachments in your collection. This will automatically pull into the export or review set any Facilitator agent meeting notes. Facilitator created word/loop documents SharePoint Online: When the facilitator agent is asked to create a word or loop document during a meeting they are stored in the requesters OneDrive in a folder called N/A These are SharePoint files (not Exchange items), so no item class applies. Identify them in search results by the .loop file extension. 1. Add the user's OneDrive URL as a data source to the search. 2. In the condition builder you can optionally filter the search to only return loop and doc files by adding a condition of "File type equals any of loop, docx". 3. Add any further additional conditions such as date range or keywords to narrow results as required. Notes: · With eDiscovery premium enabled cases you can follow the standard workflow for collecting Team meeting messages and select to include cloud attachments in your collection. This will automatically pull into the export or review set any Facilitator generated loop or word documents. Facilitator generated and assigned tasks Exchange Online: When the facilitator agent creates and assigns a task to an individual, it is created as a to-do item in the assigned individual's Exchange Mailbox IPM.Task 1. Add the user's Exchange mailbox as a data source to the search. 2. In the condition builder you can optionally filter the search to only return Tasks by adding a condition of "Item class contains any of Tasks". 3. Add any further additional conditions such as date range or keywords to narrow results as required. Application-Specific Item Classes for Prompts & Responses For more granular filtering by Copilot application, the following item class values can be used in KQL queries: Application Context Item Class Value Microsoft Copilot Chat (BizChat / Teams) IPM.SkypeTeams.Message.Copilot.BizChat Copilot in Excel IPM.SkypeTeams.Message.Copilot.Excel Copilot in Loop IPM.SkypeTeams.Message.Copilot.Loop Copilot in Outlook IPM.SkypeTeams.Message.Copilot.Outlook Copilot in PowerPoint IPM.SkypeTeams.Message.Copilot.PowerPoint Copilot in Teams IPM.SkypeTeams.Message.Copilot.Teams Copilot in Whiteboard IPM.SkypeTeams.Message.Copilot.Whiteboard Copilot in Word IPM.SkypeTeams.Message.Copilot.Word To target all Copilot applications at once, use the wildcard query ItemClass:IPM.SkypeTeams.Message.Copilot.*. For a wider list of AI data sources, see the following link: https://learn.microsoft.com/en-us/purview/edisc-search-copilot-data#data-sources-for-ai-data Important Notes for eDiscovery Practitioners Excluding Copilot Data from Broader Searches Because Copilot prompts and responses reside in the same Exchange mailbox as emails and Teams chats, they will appear in broad mailbox searches unless explicitly filtered out. To exclude Copilot items, use the condition "Item Class Contains none of Copilot activity" in the condition builder, or add (-ItemClass:IPM.SkypeTeams.Message.Copilot.*) in KQL. Some eDiscovery managers run separate searches, one for Copilot data and one for other communications, to keep collections distinct. Copilot Memories: Retention & Hold Limitations Purview retention policies and eDiscovery holds do not currently apply to Copilot memory items. Memory items remain until a user deletes them or an admin explicitly removes them via eDiscovery or Graph API. Additionally, deleting a Copilot prompt and response does not delete any memory derived from that conversation. Memories must be removed separately if required. Copilot Pages: Do Not Treat Like Prompts/Responses Copilot Pages are not stored in Exchange mailboxes. Searching only a custodian’s mailbox will not return Copilot Pages. Treat Copilot Pages the same way as you do for SharePoint content in your existing eDiscovery workflow. For collections, keyword searches will generate hits on text content within the .page file if either the SharePoint Embedded URL is included in the search or the search is a tenant-wide search of all SharePoint sites Be aware that full-text search within .page files in Purview eDiscovery review sets is not currently available. Instead you can use filters such as Subject/Title or Native File Type to locate Copilot Pages in your review set and review the content. When an eDiscovery hold is placed on a custodian’s mailbox, it does not automatically extend to the SharePoint Embedded site where the Copilot Pages are stored. Instead, ensure the hold policy includes the URL for the user-owned SharePoint Embedded site that contains the Copilot Page(s) that must be preserved. Audit Logs vs. eDiscovery for Copilot Content Audit logs record that a Copilot interaction occurred (time, user, workload context) but do not include the actual prompt or response text. To retrieve the substance of Copilot interactions, use Purview eDiscovery searches against the mailbox. Copilot Prompts and Responses: HTML Transcription Copilot prompts and responses are stored as individual messages within the user’s mailbox. When collecting Copilot interactions, enabling the “Organize conversations into HTML transcripts” premium option will convert these individual messages into HTML transcripts making for easier review and linkage between the user’s original prompt and the Copilot responses. Copilot Prompts and Responses: Contextual prompts and responses When using the Keywords condition as part of your collection in eDiscovery, it will only return items that match the keywords included in the query. This means that you may only return a part of the Copilot interaction. If using keywords in your collection query you can enable the “Include full conversation for Copilot, Teams and Viva Engage messages” premium option. This will include in the export or review set any prompts or responses from the Copilot interaction within a 12-hour window before and after each responsive item. This means that you are able to see the full context of the prompt or response that was responsive to search. Collecting Referenced Documents (Cloud Attachments) Copilot responses may reference or summarize SharePoint/OneDrive files. When collecting Copilot interactions, enabling the "Access links (cloud attachments) in messages" premium option will additionally collect the files referenced in the prompt or response and include them in the export package. This provides full evidentiary context but can significantly increase export size and processing time so consider if collecting these artifacts are relevant to the investigation. If so, look to use additional conditions such as date to effectively manage volumes or reduce the number of custodians in the collection. Facilitator agent in Microsoft Teams Meetings The Facilitator agent in Microsoft Teams is an AI-powered assistant (included with Microsoft 365 Copilot) that enhances meeting productivity by generating real-time notes, summarizing key decisions, and managing action items. It acts as an active participant, allowing for collaborative editing of notes and answering chat questions during calls. As the Facilitator works within the context of Microsoft Teams meetings (scheduled private meetings only) your existing workflows for collecting Microsoft Teams meetings chat should be used. In addition, enabling the "Access links (cloud attachments) in messages" premium setting will automatically collect any meeting note (loop) or loop or word documents created by the Facilitator agent. Copilot Retention Reference Table Data Type Microsoft Purview Retention Policy Location/Scope Copilot prompts and responses Microsoft Copilot experiences Copilot Memories (personalized saved information Copilot "remembers") Not supported Copilot Pages (AI-generated, user-editable documents) SharePoint classic and communications sites (Static Scopes only) Facilitator interactions in a Team meeting Teams chats Facilitator meeting notes (loop) OneDrive Accounts Facilitator created word/loop documents OneDrive Accounts Facilitator generated and assigned tasks Exchange mailboxes (Tasks with end dates only)Security Community Spotlight: Fabrício Assumpção
Meet Fabrício Assumpção, a Technical Specialist Architect for a Microsoft Security and Compliance Certified Partner, based in Brazil. Fabrício considers his involvement with the Microsoft Security Community defined by a dual approach: architectural innovation and technical enablement. As a Microsoft Certified Trainer (MCT) since 2021, he has been dedicated to bridging the gap between theory and real-world implementation for security professionals globally. What do you find most rewarding about being a member of the Microsoft Security Community? The most rewarding part of being a member of the Microsoft Security Community is the direct access to the pulse of cybersecurity innovation. As a Microsoft Certified Trainer (MCT) and a developer/engineer/architect focused on Cloud Security/M365 Security and SIEM, being in this ecosystem allows me to bridge the gap between complex architectural challenges and AI-driven solutions. Developing security agents for Microsoft Security Copilot is particularly fulfilling because I can see how the community’s collective knowledge shapes the future of automated defense. For me, it’s not just about the tools, but about being part of a global movement that empowers defenders to stay ahead of sophisticated threats through intelligence and automation. How would you describe your Microsoft Community involvement? In my role as a Security Architect and Engineer at adaQuest, I advocate for Microsoft’s vision by designing and deploying complex security infrastructures. My work spans the entire Microsoft Security stack, from high-level XDR (Microsoft Defender) strategies and SIEM (Microsoft Sentinel) deployments to the cutting edge of AI-driven defense. Currently, alongside my other activities, I'm focused on developing custom security agents for Microsoft Security Copilot, a task that allows me to push the boundaries of how automation and AI can empower modern SOCs. While my primary involvement has been focused on technical architecture and developing security Copilot agents, my ideal community experience would be centered on deep-tier technical co-creation. I envision a community space that facilitates direct architectural dialogues between Microsoft product teams and the engineers who are building on top of those platforms. For me, the most valuable community experience is one that prioritizes 'early-access' feedback loops and specialized hackathons where we can stress-test new features—like advanced XDR integrations or AI agent capabilities—before they hit the mainstream. My ideal is a community that functions as a high-octane R&D hub, where the collective expertise of architects and developers directly influences the roadmap of the security tools we use every day Editor’s note: The scenario Fabrício describes above is much like the Security Advisors program, which gives you early access to products, features, and private previews. Your feedback to engineering has the power to directly influence Microsoft Security products. If this interests you, consider joining! How long have you been working with Microsoft Security products? My Microsoft security journey is a story of evolution—from a cloud support engineer resolving complex L3/L4 infrastructure issues to a Security Architect leading global SOC operations. I have spent the last decade mastering the transition to the cloud, starting with identity and endpoint management (Entra ID and Intune) and progressing to end-to-end administration of the Microsoft 365 and Azure security stack. A turning point was joining adaQuest, where I took the lead on SOCaaS and began bridging the gap between governance and hands-on engineering and Sentinel. Today, my journey has reached its most exciting phase: pioneering the use of Generative AI in security to build scalable, automated solutions that protect clients worldwide. What features or products have provided the most impact? Please describe how it has helped you or your customers. The most impactful solution has been the integration of Microsoft Sentinel with Security Copilot through custom-developed security agents. This combination has revolutionized how our customers manage their security posture, allowing them to orchestrate and query the entire Defender XDR, Entra ID, and Purview stack through natural language automation. The most direct benefit for our clients has been a drastic reduction in Mean Time to Respond (MTTR) and a significant increase in operational efficiency, transforming complex security data into proactive defense. This unified approach ensures that our customers maximize their investment in the Microsoft ecosystem while maintaining high-speed resilience against sophisticated threats. You’ve indeed been instrumental in building with Microsoft Security. What can you share with us, and can you tell us about your journey? I am incredibly proud of being a pioneer in the Microsoft Security Copilot ecosystem. In early 2025, before official documentation was fully available or the feature had reached General Availability (GA), I conceptualized and developed six custom security agents designed to enhance automated defense and incident response. These agents were the result of a deep dive into the underlying architecture of AI-driven security, where I had to materialize complex ideas into functional, real-world tools without a predefined roadmap. My work was officially showcased and published during the historic announcement of the Microsoft Security Store in 2025, marking the debut of third-party security agents. Seeing these agents evolve from initial concepts to essential tools for the SOC of the future—enabling faster, more intelligent decision-making—is my most rewarding professional achievement. It represents my commitment to pushing the boundaries. Fabricio’s agents are available in the Microsoft Security Store. Here’s what he’s built (so far…) Admin Guard Insight An agent focused on privileged identity and access analysis. It reviews administrative roles, sensitive changes, and risk signals to identify exposure, misuse of privileges, and opportunities to strengthen security posture. Login Investigator An agent designed to investigate suspicious sign-in activity. It correlates authentication details, IPs, locations, devices, user risk, and related incidents to determine whether a login is legitimate or potentially malicious. Entity Guard An entity-centric investigation agent for users, devices, applications, or service principals. It consolidates signals from multiple sources to enrich entity context and identify abnormal behavior, exposure, and associated risks. Data Leak Agent An agent specialized in investigating potential data leakage and sensitive information exposure. It validates and correlates incidents across Microsoft Defender XDR and Microsoft Sentinel to produce a more reliable and contextualized investigation. L1 SOC Triage An agent built to support first-level SOC alert and incident triage. It helps classify events, enrich context, prioritize severity, and recommend next steps or escalation paths for analysts. Ransomware Kill Chain Investigator An agent focused on ransomware investigations. It correlates evidence and maps observed activity to the ransomware kill chain to help teams understand the attack, impacted assets, and priority response actions. EWS Sunset Readiness Assessor An agent that assesses an organization’s readiness for Exchange Web Services (EWS) deprecation. It identifies application and service principal dependencies and supports planning for migration to more modern and secure alternatives. What impact has integrating with Microsoft Security had on your business or your customers? Integrating with Microsoft Security has had a significant impact on both our business and our customers. For our business, it has enabled us to build higher-value security services and differentiated solutions, such as Security Copilot agents tailored to real operational challenges in identity protection, incident triage, data leakage investigations, ransomware analysis, and legacy dependency assessments. For our customers, the impact has been: improved speed, consistency, and depth in security operations. By leveraging Microsoft Security signals and platforms such as Microsoft Defender, Microsoft Sentinel, and Entra, we help teams investigate incidents faster, reduce manual effort, improve decision-making, and strengthen overall security posture. In practice, this means customers gain more actionable insights, better prioritization, and more efficient use of their security resources. What advice do you have for others who would like to get involved in the Microsoft Community? My advice is to bridge the gap between learning and building. Don’t just consume content; start creating solutions for real-world challenges, such as AI-driven automation in Security Copilot or Microsoft Sentinel. Use your practical experience to help others, and remember that teaching is one of the most powerful ways to contribute. In an era of rapid AI evolution, being a proactive 'early adopter' who shares insights is the best way to grow within the Microsoft Community and help protect the global digital landscape. Fabrício beyond Microsoft Security Beyond my technical career, I am a lifelong learner with a deep passion for understanding how the world works, from the complexities of Quantum Computing—which I studied at the University of Coimbra—to the fundamental principles of Physics, Astronomy, and Philosophy. I am currently pursuing two Master’s degrees, as I believe that diverse knowledge fuels creativity. I am also a polyglot at heart, teaching myself Italian, Spanish, Russian, and Chinese using open-source materials. My creative side is expressed through music, as I play both the violin and the piano. In my spare time, I enjoy the discipline of sports; I have a history as both a player and coach of Rugby, and I am a fan of Ice Hockey. My future plans include completing my Doctorate and embracing a nomadic lifestyle to experience different cultures and perspectives. For me, life is about the continuous pursuit of wisdom and the belief that we can always expand the boundaries of our own understanding. Connect with Fabrício on LinkedIn. ____________________________________________________________________________________________ Learn and Engage with the Microsoft Security Community Log in and follow this Microsoft Security Community Blog. Follow = Click the heart in the upper right when you're logged in 🤍. Join the Microsoft Security Community and be notified of upcoming events, product feedback surveys, and more. Get early access to Microsoft Security products and provide feedback to engineers by joining the Microsoft Security Advisors. Join the Microsoft Security Community LinkedIn Group and follow the Microsoft Entra Community on LinkedInWhy UK Enterprise Cybersecurity Is Failing in 2026 (And What Leaders Must Change)
Enterprise cybersecurity in large organisations has always been an asymmetric game. But with the rise of AI‑enabled cyber attacks, that imbalance has widened dramatically - particularly for UK and EMEA enterprises operating complex cloud, SaaS, and identity‑driven environments. Microsoft Threat Intelligence and Microsoft Defender Security Research have publicly reported a clear shift in how attackers operate: AI is now embedded across the entire attack lifecycle. Threat actors use AI to accelerate reconnaissance, generate highly targeted phishing at scale, automate infrastructure, and adapt tactics in real time - dramatically reducing the time required to move from initial access to business 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 attacks rely on automation, dynamic code generation, and highly personalised lures - not on exploiting traditional vulnerabilities or stealing passwords. The Reality Gap: Adaptive Attackers vs. Static Enterprise Defences Meanwhile, many UK enterprises still rely on legacy cybersecurity controls designed for a very different threat model - one rooted in a far more predictable world. This creates a dangerous "Resilience Gap." Here is why your current stack is failing- and the C-Suite strategy required to fix it. 1. The Failure of Traditional Antivirus in the AI Era Traditional antivirus (AV) relies on static signatures and hashes. It assumes malicious code remains identical across different targets. AI has rendered this assumption obsolete. Modern malware now uses automated mutation to generate unique code variants at execution time, and adapts behaviour based on its environment. Microsoft Threat Intelligence has observed threat actors using AI‑assisted tooling to rapidly rewrite payload components, ensuring that every deployment looks subtly different. In this model, there is no reliable signature to detect. By the time a pattern exists, the attacker has already moved on. Signature‑based detection is not just slow - it is structurally misaligned with AI‑driven attacks. The Risk: If your security relies on "recognising" a threat, you are already breached. By the time a signature exists, the attacker has evolved. The C-Suite Pivot: Shift investment from artifact detection to EDR/XDR (Extended Detection and Response). We must prioritise behavioural analytics and machine learning models that identify intent rather than file names. 2. Why Perimeter Firewalls Fail in a Cloud-First World Many UK enterprise still rely on firewalls enforcing static allow/deny rules based on IP addresses and ports. This model worked when applications were predictable and networks clearly segmented. Today, enterprise traffic is encrypted, cloud‑hosted, API‑driven, and deeply integrated with SaaS and identity services. AI‑assisted phishing campaigns abusing OAuth and device‑code flows demonstrate this clearly. From a network perspective, everything looks legitimate: HTTPS traffic to trusted identity providers. No suspicious port. No malicious domain. Yet the attacker successfully compromises identity. The Risk: Traditional firewalls are "blind" to identity-based breaches in cloud environments. The C-Suite Pivot: Move to Identity-First Security. Treat Identity as the new Control Plane, integrating signals like user risk, device health, and geolocation into every access decision. 3. The Critical Weakness of Single-Factor Authentication Despite clear NCSC guidance, single-factor passwords remain a common vulnerability in legacy applications and VPNs. AI-driven credential abuse has changed the economics of these attacks. Threat actors now deploy adaptive phishing campaigns that evolve in real-time. Microsoft has observed attackers using AI to hyper-target high-value UK identities- specifically CEOs, Finance Directors, and Procurement leads. The Risk: Static passwords are now the primary weak link in UK supply chain security. The C-Suite Pivot: Mandate Phishing‑resistant MFA (Passkeys or hardware security keys). Implement Conditional Access policies that evaluate risk dynamically at the moment of access, not just at login. Legacy Security vs. AI‑Era Reality 4. The Inherent Risk of VPN-Centric Security VPNs were built on a flawed assumption: that anyone "inside" the network is trustworthy. In 2026, this logic is a liability. AI-assisted attackers now use automation to map internal networks and identify escalation paths the moment they gain VPN access. Furthermore, Microsoft has tracked nation-state actors using AI to create synthetic employee identities- complete with fake resumes and deepfake communication. In these scenarios, VPN access isn't "hacked"; it is legally granted to a fraudster. The Risk: A compromised VPN gives an attacker the "keys to the kingdom." The C-Suite Pivot: Transition to Zero Trust Architecture (ZTA). Access must be explicit, scoped to the specific application, and continuously re‑evaluated using behavioural signals. 5. Data: The High-Velocity Target Sensitive data sitting unencrypted in legacy databases or backups is a ticking time bomb. In the AI era, data discovery is no longer a slow, manual process for a hacker. Attackers now use AI to instantly analyse your directory structures, classify your files, and prioritise high-value data for theft. Unencrypted data significantly increases your "blast radius," turning a containable incident into a catastrophic board-level crisis. The Risk: Beyond the technical breach, unencrypted data leads to massive UK GDPR fines and irreparable brand damage. The C-Suite Pivot: Adopt Data-Centric Security. Implement encryption by default, classify data while adding sensitivity labels and start board-level discussions regarding post‑quantum cryptography (PQC) to future-proof your most sensitive assets. 6. The Failure of Static IDS Traditional Intrusion Detection Systems (IDS) rely on known indicators of compromise - assuming attackers reuse the same tools and techniques. AI‑driven attacks deliberately avoid that assumption. Threat actors are now using Large Language Models (LLMs) to weaponize newly disclosed vulnerabilities within hours. While your team waits for a "known pattern" to be updated in your system, the attacker is already using a custom, AI-generated exploit. The Risk: Your team is defending against yesterday's news while the attacker is moving at machine speed. The C-Suite Pivot: Invest in Adaptive Threat Detection. Move toward Graph‑based XDR platforms that correlate signals across email, endpoint, and cloud to automate investigation and response before the damage spreads. From Static Security to Continuous Security Closing Thought: Security Is a Journey, Not a Destination For UK enterprises, the shift toward adaptive cybersecurity is no longer optional - it is increasingly driven by regulatory expectation, board oversight, and accountability for operational resilience. Recent UK cyber resilience reforms and evolving regulatory frameworks signal a clear direction of travel: cybersecurity is now a board‑level responsibility, not a back‑office technical concern. Directors and executive leaders are expected to demonstrate effective governance, risk ownership, and preparedness for cyber disruption - particularly as AI reshapes the threat landscape. AI is not a future cybersecurity problem. It is a current force multiplier for attackers, exposing the limits of legacy enterprise security architectures faster than many organisations are willing to admit. The uncomfortable truth for boards in 2026 is that no enterprise is 100% secure. Intrusions are inevitable. 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 risk is managed when they occur. In mature organisations, this means assuming breach and designing for containment: Access controls that limit blast radius Least privilege and conditional access restricting attackers to the smallest possible scope if an identity is compromised Data‑centric security using automated classification and encryption, ensuring that even when access is misused, sensitive data cannot be freely exfiltrated As a Senior Enterprise 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. We now have a rare chance to embed security from day one - designing identity controls, data boundaries, automated monitoring, and governance before AI systems become business‑critical. When security is built in upfront, enterprises don’t just reduce risk - they gain the confidence to move faster and unlock AI’s value safely. Security is no longer a “department”. In the age of AI, it is a continuous business function - essential to preserving trust and maintaining operational continuity as attackers move at machine speed. 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 | Microsoft https://www.ncsc.gov.uk/news/government-adopt-passkey-technology-digital-services
Why External Users Can’t Open Encrypted Attachments in Certain Conditions & How to Fix It Securely
When Conditional Access policies enforce MFA across all cloud apps and include external users, encrypted attachments may require additional considerations. This post explains why. This behavior applies only in environments where all of the following are true: Microsoft Purview encryption is used for emails and attachments A Conditional Access (CA) policy is configured to: Require MFA Apply to all cloud applications Include guest or external users The Situation: Email Opens, Attachment Doesn’t When an email is encrypted using: Microsoft Purview Sensitivity Labels, or Information Rights Management (IRM) Any attached Office document automatically inherits encryption. This inheritance is intentional and enforced by the service, Ensures consistent protection of sensitive content. That inheritance is mandatory and cannot be disabled. So far, so good. But here’s where things break for external recipients. The Hidden Dependency: Identity & Conditional Access Reading an encrypted email and opening an encrypted attachment are two different flows. External users can usually read encrypted emails by authenticating through: One-Time Passcode (OTP) Microsoft personal accounts Their own organization’s identity However, encrypted attachments use Microsoft Rights Management Services (RMS) — and RMS expects an identity the sender’s tenant can evaluate. If your organization has: A global Conditional Access policy Enforcing MFA for all users Applied to all cloud apps external users can get blocked even after successful email decryption. This commonly results in errors like: “This account does not exist in the sender’s tenant…” AADSTS90072: The external user account does not exist in our tenant and cannot access the Microsoft Office application. The account needs to be added as an external user in the tenant or use an alternative authentication method. When It Works (and Why It Often Doesn’t) External access to encrypted attachments works only when one of these conditions is met: The sender trusts the recipient’s tenant MFA via Cross‑Tenant Access (MFA trust) The recipient already exists as a guest account in the sender’s tenant In real-world scenarios, these conditions often fail: External recipients use consumer or non‑Entra identities Recipient domains are not predictable Guest onboarding does not scale Cross‑tenant trust is intentionally restricted In such cases, Conditional Access policies designed for internal users can affect RMS evaluation for external users. So what’s the alternative? The Practical, Secure Alternative When the two standard access conditions (cross‑tenant trust or guest presence) cannot be met , you can refine Conditional Access evaluation without weakening encryption. The goal is not to remove MFA, but to ensure it is applied appropriately based on identity type and access path. In this scenario: MFA remains enforced for all internal users, including access to Microsoft Rights Management Services (RMS) MFA remains enforced for external users across cloud applications other than RMS The Key Idea Let encryption stay strong, but stop blocking external RMS authentication. This is achieved by: Keeping the existing Conditional Access policy that enforces MFA for all internal users across all cloud applications, including RMS Excluding guest and external users from that internal‑only policy Deploying a separate Conditional Access policy scoped to guest and external users to: Continue enforcing MFA for external users where supported Explicitly exclude Microsoft Rights Management Services (RMS) from evaluation RMS can be excluded from the external‑user policy by specifying the following application (client) ID: RMS App ID: 00000012-0000-0000-c000-000000000000 Why This Is Still Secure This approach: ✅ Keeps email and attachment encryption fully intact ✅ Internal security posture is unchanged ✅ External users remain protected by MFA where applicable ✅ Allows external users to authenticate using supported methods ✅ Avoids over-trusting external tenants ✅ Scales for large, unpredictable recipient sets Final Takeaway Encrypted attachment access is governed by identity recognition and policy design, not by email encryption alone. By aligning Conditional Access with how encrypted content is evaluated, organizations can enable secure external collaboration while maintaining strong protection standardsRegistration 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. ❗UPDATE❗This event is expected to last around 2 hours and 15 minutes, due to the incredible number of community sessions that were submitted! 💖 Please see the timing table below broken out into sections of four talks each, and plan to arrive 10 minutes before the section that interests you, OR stay for the whole time! Speakers will be available in the chat to answer your questions; please ask your questions during their session. Spillover Q&A forum links will also be shared. The full session recording will be indexed and posted to Microsoft Security Community YouTube within 24 hours after the event. Bookmark this page or follow this blog post for updates! Agenda Legend ↩️ Data Lifecycle Management 🔐 Information Protection 🚫 Data Loss Prevention (DLP) 🦾 Data Security Posture Management (DSPM) for AI 🤖 Purview for AI 👁️ Insider Risk Management (IRM) 🔍 eDiscovery 📊 Governance 🗒️ Compliance Manager 🛡️ Data Security All times are listed in US Pacific/Redmond Time. Session lengths are rounded to the nearest minute. AGENDA Section 1 - approximately 8:00 am - 8:43 am ↩️ The Day Offboarding Exposed Infinite Retention — Nikki Chapple Length: 10 minutes | Topic: Data Lifecycle Management A routine Purview request led to an unexpected discovery: more than 9,000 orphaned OneDrives and thousands of inactive mailboxes still storing content long after employees had left. This talk explains how a retain-only policy created hidden retention debt and how Adaptive Scopes can help organisations separate active users from leavers to avoid similar pitfalls. 🔐 The Purview Label Engine: Automated Classification, Translation, and co-Documentation for Enterprise Tenants — Michael Kirst-Neshva Length: 12 minutes | Topic: Information Protection Global enterprises face the challenge of implementing uniform data protection standards across borders and languages. In this talk, I’ll present a framework that makes Microsoft Purview labels truly scalable. Discover how to roll out parent and child label logics automatically, manage priorities with a single click, and generate instant compliance documentation for every business unit. 🗒️ What's In My Compliance Manager Toolbox: A Cloud Security Architect's Perspective — Jerrad Dahlager Length: 8 minutes | Topic: Compliance Manager A practical walkthrough of how I use Compliance Manager across real client engagements to map controls, track improvement actions, and simplify multi-framework compliance. No theory, just what works in the field. 🛡️ Stop, Think, Protect: Data Security in Real Life with Purview — Oliver Sahlmann Length: 8 minutes | Topic: Data Security With simple labels and matching DLP policies, Purview offers a practical and accessible way to approach data security. This lightning talk uses a real-life traffic light concept to show how a low barrier to adoption can still drive meaningful protection and awareness. Section 2 - approximately 8:44 am - 9:15 am 🔐 Using Purview to prevent oversharing with AI services — Viktor Hedberg Length: 10 minutes | Topic: Information Protection In this day and age, AI is the big thing. However, Copilot has access to everything you can access, including potentially sensitive data. In this session we will look at how to prevent Copilot to access highly sensitive data, using Information Protection. 🦾 How I Helped My Customers Understand their AI Usage (and protect their sensitive data) — Bram de Jager Length: 5 minutes | Topic: Data Security Posture Management (DSPM) for AI As AI tools explode across the web, many organizations still have no idea what’s actually happening in the browser—where employees type prompts, paste sensitive data, or visit public AI sites outside corporate governance. In this lightning talk, I’ll share how I helped customers shine a light on this issue. We’ll explore how Purview Data Security Posture Management (DSPM) can reveal which AI tools employees use, what types of data they input, and where sensitive information may leak through prompts. I’ll walk through real customer scenario where we detected risky AI usage patterns—such as employees pasting confidential documents into public chatbots. 🔐 Four Labels Max for Daily Use: Which Ones & Why? — Romain Dalle Length: 8 minutes | Topic: Information Protection Sensitivity labels are one of the most critical parts of a Purview Risk and compliance deployment, if not the most critical, because it directly impacts how end-users and business units should allow or restrict themselves to share their business data, internally and externally, on a daily basis. Labels have not other options than being precise, meaningful, and balanced in terms of embedded data security. Setting the right taxonomy is core to success, and is everything but a one-time project. 🚫 Data-driven Endpoint DLP Solution with Advanced Hunting — Tatu Seppälä Length: 8 minutes | Topic: Data Loss Prevention (DLP) This lightning talk shows you how to use KQL queries in advanced hunting to easily build initial sensitive service domain groups for authorized and unauthorized domains based on your organization's usage patterns. The same approach can be used for numerous other similar solution refinement and design purposes. Section 3 - approximately 9:16 am - 9:46 am 🔐 The Purview Hack No One Talks About: Container Sensitivity Labels That Fix Oversharing Fast — Nikki Chapple Length: 10 minutes | Topic: Information Protection Most organizations tackle oversharing with manual fixes, but the fastest solution is often overlooked. In this lightning talk, I show how container sensitivity labels automatically apply the right sharing and collaboration controls, ensuring every new Group, Team or SharePoint site starts secure by default. 🔍 Does M365 Support eDiscovery? — Julian Kusenberg Length: 11 minutes | Topic: eDiscovery A myth-busting session that separates perception from reality when it comes to Microsoft 365 eDiscovery capabilities. 📊 Improving Discovery, Trust, and Reuse of Analytics with Purview Data Products — Craig Wyndowe Length: 5 minutes | Topic: Governance This talk shows how bringing Power BI and Fabric assets into Microsoft Purview Governance Domains and Data Products creates a single, trusted view of enterprise analytics. By connecting reports, semantic models, and underlying data with shared metadata, ownership, and business context, organizations can make existing assets easy to discover and safe to reuse. 🔐 Why You Should Create Your Own Sensitive Information Types (SITs) — Niels Jakobsen Length: 5 minutes | Topic: Information Protection An in depth analysis of why Microsoft SITs are not one-size-fits-all, and how to create your own using what Microsoft has already built for you. Section 4 - approximately 9:47 am-10:30 am 👁️ From Zero to First Signal: Insider Risk Management Prerequisites That Actually Matter — Sathish Veerapandian Length: 8 minutes | Topic: Insider Risk Management (IRM) A focused live demo showing the real world prerequisites required for Microsoft Purview Insider Risk Management to work effectively. This session highlights the critical Entra ID, Intune, Microsoft Defender for Endpoint, and Purview DLP configurations that must be in place before creating IRM policies. 🤖 Securing data in the age of AI — Júlio César Gonçalves Vasconcelos Length: 11 minutes | Topic: Purview for AI AI will transform business as we know it; but without proper governance, it can introduce serious risks. We’ll show you how Microsoft Purview enables organizations to accelerate AI adoption while maintaining security, compliance, and transparency. 🔍 Beyond eDiscovery - Purview DSI for Security Investigation — Susantha Silva Length: 11 minutes | Topic: eDiscovery Most people hear “Microsoft Purview” and immediately think compliance, eDiscovery, or legal holds. But this session highlights Data Security Investigations, showing how DSI lets you take a DLP alert or insider risk signal and turn it into a structured investigation. 🚫 Elevating Purview DLP with a real world use case — Victor Wingsing Length: 14 minutes | Topic: Data Loss Prevention (DLP) Learn how I hardened Microsoft Purview DLP beyond out of the box defaults—closing real world data loss gaps, tuning policies to actual user behavior, and turning noisy alerts into protection that really blocks exfiltration. - Quick Closing/ Resource Sharing
