Best practices for maintaining emergency contact information
Hello everyone. Our organization is currently reviewing its emergency contact directory as part of our business continuity and operational processes. What best practices do you recommend for keeping emergency contact information updated and ensuring it remains available during incidents or emergencies? Thank you for your suggestions.
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.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 2026Collecting 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)Why 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-servicesAuthorization and Governance for AI Agents: Runtime Authorization Beyond Identity at Scale
Designing Authorization‑Aware AI Agents at Scale Enforcing Runtime RBAC + ABAC with Approval Injection (JIT) Microsoft Entra Agent Identity enables organizations to govern and manage AI agent identities in Copilot Studio, improving visibility and identity-level control. However, as enterprises deploy multiple autonomous AI agents, identity and OAuth permissions alone cannot answer a more critical question: “Should this action be executed now, by this agent, for this user, under the current business and regulatory context?” This post introduces a reusable Authorization Fabric—combining a Policy Enforcement Point (PEP) and Policy Decision Point (PDP)—implemented as a Microsoft Entra‑protected endpoint using Azure Functions/App Service authentication. Every AI agent (Copilot Studio or AI Foundry/Semantic Kernel) calls this fabric before tool execution, receiving a deterministic runtime decision: ALLOW / DENY / REQUIRE_APPROVAL / MASK Who this is for Anyone building AI agents (Copilot Studio, AI Foundry/Semantic Kernel) that call tools, workflows, or APIs Organizations scaling to multiple agents and needing consistent runtime controls Teams operating in regulated or security‑sensitive environments, where decisions must be deterministic and auditable Why a V2? Identity is necessary—runtime authorization is missing Entra Agent Identity (preview) integrates Copilot Studio agents with Microsoft Entra so that newly created agents automatically get an Entra agent identity, manageable in the Entra admin center, and identity activity is logged in Entra. That solves who the agent is and improves identity governance visibility. But multi-agent deployments introduce a new risk class: Autonomous execution sprawl — many agents, operating with delegated privileges, invoking the same backends independently. OAuth and API permissions answer “can the agent call this API?” They do not answer “should the agent execute this action under business policy, compliance constraints, data boundaries, and approval thresholds?” This is where a runtime authorization decision plane becomes essential. The pattern: Microsoft Entra‑Protected Authorization Fabric (PEP + PDP) Instead of embedding RBAC logic independently inside every agent, use a shared fabric: PEP (Policy Enforcement Point): Gatekeeper invoked before any tool/action PDP (Policy Decision Point): Evaluates RBAC + ABAC + approval policies Decision output: ALLOW / DENY / REQUIRE_APPROVAL / MASK This Authorization Fabric functions as a shared enterprise control plane, decoupling authorization logic from individual agents and enforcing policies consistently across all autonomous execution paths. Architecture (POC reference architecture) Use a single runtime decision plane that sits between agents and tools. What’s important here Every agent (Copilot Studio or AI Foundry/SK) calls the Authorization Fabric API first The fabric is a protected endpoint (Microsoft Entra‑protected endpoint required) Tools (Graph/ERP/CRM/custom APIs) are invoked only after an ALLOW decision (or approval) Trust boundaries enforced by this architecture Agents never call business tools directly without a prior authorization decision The Authorization Fabric validates caller identity via Microsoft Entra Authorization decisions are centralized, consistent, and auditable Approval workflows act as a runtime “break-glass” control for high-impact actions This ensures identity, intent, and execution are independently enforced, rather than implicitly trusted. Runtime flow (Decision → Approval → Execution) Here is the runtime sequence as a simple flow (you can keep your Mermaid diagram too). ```mermaid flowchart TD START(["START"]) --> S1["[1] User Request"] S1 --> S2["[2] Agent Extracts Intent\n(action, resource, attributes)"] S2 --> S3["[3] Call /authorize\n(Entra protected)"] S3 --> S4 subgraph S4["[4] PDP Evaluation"] ABAC["ABAC: Tenant · Region · Data Sensitivity"] RBAC["RBAC: Entitlement Check"] Threshold["Approval Threshold"] ABAC --> RBAC --> Threshold end S4 --> Decision{"[5] Decision?"} Decision -->|"ALLOW"| Exec["Execute Tool / API"] Decision -->|"MASK"| Masked["Execute with Masked Data"] Decision -->|"DENY"| Block["Block Request"] Decision -->|"REQUIRE_APPROVAL"| Approve{"[6] Approval Flow"} Approve -->|"Approved"| Exec Approve -->|"Rejected"| Block Exec --> Audit["[7] Audit & Telemetry"] Masked --> Audit Block --> Audit Audit --> ENDNODE(["END"]) style START fill:#4A90D9,stroke:#333,color:#fff style ENDNODE fill:#4A90D9,stroke:#333,color:#fff style S1 fill:#5B5FC7,stroke:#333,color:#fff style S2 fill:#5B5FC7,stroke:#333,color:#fff style S3 fill:#E8A838,stroke:#333,color:#fff style S4 fill:#FFF3E0,stroke:#E8A838,stroke-width:2px style ABAC fill:#FCE4B2,stroke:#999 style RBAC fill:#FCE4B2,stroke:#999 style Threshold fill:#FCE4B2,stroke:#999 style Decision fill:#fff,stroke:#333 style Exec fill:#2ECC71,stroke:#333,color:#fff style Masked fill:#27AE60,stroke:#333,color:#fff style Block fill:#C0392B,stroke:#333,color:#fff style Approve fill:#F39C12,stroke:#333,color:#fff style Audit fill:#3498DB,stroke:#333,color:#fff ``` Design principle: No tool execution occurs until the Authorization Fabric returns ALLOW or REQUIRE_APPROVAL is satisfied via an approval workflow. Where Power Automate fits (important for readers) In most Copilot Studio implementations, Agents calls Power Automate (agent flows), is the practical integration layer that calls enterprise services and APIs. Copilot Studio supports “agent flows” as a way to extend agent capabilities with low-code workflows. For this pattern, Power Automate typically: acquires/uses the right identity context for the call (depending on your tenant setup), and calls the /authorize endpoint of the Authorization Fabric, returns the decision payload to the agent for branching. Copilot Studio also supports calling REST endpoints directly using the HTTP Request node, including passing headers such as Authorization: Bearer <token>. Protected endpoint only: Securing the Authorization Fabric with Microsoft Entra For this V2 pattern, the Authorization Fabric must be protected using Microsoft Entra‑protected endpoint on Azure Functions/App Service (built‑in auth). Microsoft Learn provides the configuration guidance for enabling Microsoft Entra as the authentication provider for Azure App Service / Azure Functions. Step 1 — Create the Authorization Fabric API (Azure Function) Expose an authorization endpoint: HTTP Step 2 — Enable Microsoft Entra‑protected endpoint on the Function App In Azure Portal: Function App → Authentication Add identity provider → Microsoft Choose Workforce configuration (enterprise tenant) Set Require authentication for all requests This ensures the Authorization Fabric is not callable without a valid Entra token. Step 3 — Optional hardening (recommended) Depending on enterprise posture, layer: IP restrictions / Private endpoints APIM in front of the Function for rate limiting, request normalization, centralized logging (For a POC, keep it minimal—add hardening incrementally.) Externalizing policy (so governance scales) To make this pattern reusable across multiple agents, policies should not be hardcoded inside each agent. Instead, store policy definitions in a central policy store such as Cosmos DB (or equivalent configuration store), and have the PDP load/evaluate policies at runtime. Why this matters: Policy changes apply across all agents instantly (no agent republish) Central governance + versioning + rollback becomes possible Audit and reporting become consistent across environments (For the POC, a single JSON document per policy pack in Cosmos DB is sufficient. For production, add versioning and staged rollout.) Store one PolicyPack JSON document per environment (dev/test/prod). Include version, effectiveFrom, priority for safe rollout/rollback. Minimal decision contract (standard request / response) To keep the fabric reusable across agents, standardize the request payload. Request payload (example) Decision response (deterministic) Example scenario (1 minute to understand) Scenario: A user asks a Finance agent to create a Purchase Order for 70,000. Even if the user has API permission and the agent can technically call the ERP API, runtime policy should return: REQUIRE_APPROVAL (threshold exceeded) trigger an approval workflow execute only after approval is granted This is the difference between API access and authorized business execution. Sample Policy Model (RBAC + ABAC + Approval) This POC policy model intentionally stays simple while demonstrating both coarse and fine-grained governance. 1) Coarse‑grained RBAC (roles → actions) FinanceAnalyst CreatePO up to 50,000 ViewVendor FinanceManager CreatePO up to 100,000 and/or approve higher spend 2) Fine‑grained ABAC (conditions at runtime) ABAC evaluates context such as region, classification, tenant boundary, and risk: 3) Approval injection (Agent‑level JIT execution) For higher-risk/high-impact actions, the fabric returns REQUIRE_APPROVAL rather than hard deny (when appropriate): How policies should be evaluated (deterministic order) To ensure predictable and auditable behavior, evaluate in a deterministic order: Tenant isolation & residency (ABAC hard deny first) Classification rules (deny or mask) RBAC entitlement validation Threshold/risk evaluation Approval injection (JIT step-up) This prevents approval workflows from bypassing foundational security boundaries such as tenant isolation or data sovereignty. Copilot Studio integration (enforcing runtime authorization) Copilot Studio can call external REST APIs using the HTTP Request node, including passing headers such as Authorization: Bearer <token> and binding response schema for branching logic. Copilot Studio also supports using flows with agents (“agent flows”) to extend capabilities and orchestrate actions. Option A (Recommended): Copilot Studio → Agent Flow (Power Automate) → Authorization Fabric Why: Flows are a practical place to handle token acquisition patterns, approval orchestration, and standardized logging. Topic flow: Extract user intent + parameters Call an agent flow that: calls /authorize returns decision payload Branch in the topic: If ALLOW → proceed to tool call If REQUIRE_APPROVAL → trigger approval flow; proceed only if approved If DENY → stop and explain policy reason Important: Tool execution must never be reachable through an alternate topic path that bypasses the authorization check. Option B: Direct HTTP Request node to Authorization Fabric Use the Send HTTP request node to call the authorization endpoint and branch using the response schema. This approach is clean, but token acquisition and secure secretless authentication are often simpler when handled via a managed integration layer (flow + connector). AI Foundry / Semantic Kernel integration (tool invocation gate) For Foundry/SK agents, the integration point is before tool execution. Semantic Kernel supports Azure AI agent patterns and tool integration, making it a natural place to enforce a pre-tool authorization check. Pseudo-pattern: Agent extracts intent + context Calls Authorization Fabric Enforces decision Executes tool only when allowed (or after approval) Telemetry & audit (what Security Architects will ask for) Even the best policy engine is incomplete without audit trails. At minimum, log: agentId, userUPN, action, resource decision + reason + policyIds approval outcome (if any) correlationId for downstream tool execution Why it matters: you now have a defensible answer to: “Why did an autonomous agent execute this action?” Security signal bonus: Denials, unusual approval rates, and repeated policy mismatches can also indicate prompt injection attempts, mis-scoped agents, or governance drift. What this enables (and why it scales) With a shared Authorization Fabric: Avoid duplicating authorization logic across agents Standardize decisions across Copilot Studio + Foundry agents Update governance once (policy change) and apply everywhere Make autonomy safer without blocking productivity Closing: Identity gets you who. Runtime authorization gets you whether/when/how. Copilot Studio can automatically create Entra agent identities (preview), improving identity governance and visibility for agents. But safe autonomy requires a runtime decision plane. Securing that plane as an Entra-protected endpoint is foundational for enterprise deployments. In enterprise environments, autonomous execution without runtime authorization is equivalent to privileged access without PIM—powerful, fast, and operationally risky.Microsoft Purview Data Quality Thresholds: More Control, More Trust
What Are Data Quality Thresholds? A data quality threshold defines the minimum acceptable score for a rule to pass. Instead of applying a single fixed standard across all data, organizations can now set expectations that align with business context and criticality. For example: An email column may require 99% completeness A product description column may only require 85% completeness Financial or regulatory data may require 100% accuracy With customizable thresholds, quality expectations become more meaningful and business-aligned. Why Does This Matter? Previously, using a single hardcoded threshold could lead to misleading quality scores. Critical data might appear “healthy” even when it didn’t meet business standards. With Data Quality Thresholds, you can: Define rule-level expectations Align quality scores with business risk Increase trust in DQ reporting Improve governance decision-making Data Asset-Level Quality Threshold Users can define data quality thresholds at the data asset level to measure how suitable a dataset is for specific business use cases. This allows organizations to quantify the overall health and fitness of a data asset before it is used in analytics, reporting, or data products. If the measured data quality score falls below the predefined threshold, the system can trigger notifications to the data asset owner or steward, prompting them to take corrective actions. It is important to note that not all data assets are equally critical. Therefore, thresholds should be context-driven and use-case specific. Example Scenario A marketing dataset used for campaign analysis may tolerate a lower quality threshold (e.g., 80%), since minor inconsistencies may not significantly impact insights. However, a financial reporting dataset used for regulatory filings may require a very high threshold (e.g., 98–100%), as even small errors can lead to compliance risks. Data Quality Rule-Level Threshold Thresholds can also be defined at the individual rule level, particularly for rules applied to specific columns. This provides more granular control and ensures that critical data elements are held to higher standards. Not all attributes have the same importance, so thresholds should reflect business criticality. Example Scenarios Email vs. Gender (Customer Contact Data) A completeness rule for a customer’s email address should have a higher threshold (e.g., 95–100%), since missing or invalid email addresses directly impact communication and engagement. In contrast, a gender attribute may have a lower threshold (e.g., 70–80%), as it is often less critical for most use cases. Billing Address vs. CRM Address A billing address is highly critical because it directly impacts: Invoice generation Tax calculations Timely delivery of invoices Therefore, the threshold for billing address quality should be very high (e.g., 98–100%). On the other hand, a CRM address used for general customer profiling may have a lower threshold, as occasional inaccuracies may not significantly affect business operations. The Impact By enabling flexible, context-aware scoring, Data Quality Thresholds help organizations move beyond generic quality checks and toward business-driven data quality management. Summary Data Quality Thresholds define the minimum acceptable score for data quality rules, allowing organizations to move beyond a one-size-fits-all approach and align quality expectations with business context and criticality. Instead of using fixed thresholds, organizations can set custom thresholds based on how important the data is. For example, financial data may require near-perfect accuracy, while less critical fields can tolerate lower thresholds. Thresholds can be applied at two levels: Data Asset Level: Measures the overall fitness of a dataset for a specific use case. Critical datasets (e.g., financial reporting) require higher thresholds than less critical ones (e.g., marketing analytics). Rule Level: Applies to individual columns or rules, ensuring that critical attributes (e.g., email, billing address) have stricter quality requirements than less important ones. This approach improves: Alignment with business risk and priorities Trust in data quality reporting Governance decision-making Focus on high-impact data issues Overall, data quality thresholds enable more meaningful, context-aware, and business-driven data quality management, helping organizations prioritize what matters most and build confidence in their data.
Building Secure, Enterprise Ready AI Agents with Purview SDK and Agent Framework
At Microsoft Ignite, we announced the public preview of Purview integration with the Agent Framework SDK—making it easier to build AI agents that are secure, compliant, and enterprise‑ready from day one. AI agents are quickly moving from demos to production. They reason over enterprise data, collaborate with other agents, and take real actions. As that happens, one thing becomes non‑negotiable: Governance has to be built in. That’s where Purview SDK comes in. Agentic AI Changes the Security Model Traditional apps expose risks at the UI or API layer. AI agents are different. Agents can: Process sensitive enterprise data in prompts and responses Collaborate with other agents across workflows Act autonomously on behalf of users Without built‑in controls, even a well‑designed agent can create compliance gaps. Purview SDK brings Microsoft’s enterprise data security and compliance directly into the agent runtime, so governance travels with the agent—not after it. What You Get with Purview SDK + Agent Framework This integration delivers a few key things developers and enterprises care about most: Inline Data Protection Evaluate prompts and responses against Data Loss Prevention (DLP) policies in real time. Content can be allowed or blocked automatically. Built‑In Governance Send AI interactions to Purview for audit, eDiscovery, communication compliance, and lifecycle management—without custom plumbing. Enterprise‑Ready by Design Ship agents that meet enterprise security expectations from the start, not as a follow‑up project. All of this is done natively through Agent Framework middleware, so governance feels like part of the platform—not an add‑on. How Enforcement Works (Quickly) When an agent runs: Prompts and responses flow through the Agent Framework pipeline Purview SDK evaluates content against configured policies A decision is returned: allow, redact, or block Governance signals are logged for audit and compliance This same model works for: User‑to‑agent interactions Agent‑to‑agent communication Multi‑agent workflows Try It: Add Purview SDK in Minutes Here’s a minimal Python example using Agent Framework: That’s it! From that point on: Prompts and responses are evaluated against Purview policies setup within the enterprise tenant Sensitive data can be automatically blocked Interactions are logged for governance and audit Designed for Real Agent Systems Most production AI apps aren’t single‑agent systems. Purview SDK supports: Agent‑level enforcement for fine‑grained control Workflow‑level enforcement across orchestration steps Agent‑to‑agent governance to protect data as agents collaborate This makes it a natural fit for enterprise‑scale, multi‑agent architectures. Get Started Today You can start experimenting right away: Try the Purview SDK with Agent Framework Follow the Microsoft Learn docs to configure Purview SDK with Agent Framework. Explore the GitHub samples See examples of policy‑enforced agents in Python and .NET. Secure AI, Without Slowing It Down AI agents are quickly becoming production systems—not experiments. By integrating Purview SDK directly into the Agent Framework, Microsoft is making governance a default capability, not a deployment blocker. Build intelligent agents. Protect sensitive data. Scale with confidence.Empowering organizations with integrated data security: What’s new in Microsoft Purview
Today, data moves across clouds, apps, and devices at an unprecedented speed, often outside the visibility of siloed legacy tools. The rise of autonomous agents, generative AI, and distributed data ecosystems means that traditional perimeter-based security models are no longer sufficient. Even though companies are spending more than $213 billion globally, they still face several persistent security challenges: Fragmented tools don’t integrate together well and leave customers lacking full visibility of their data security risks The growing use of AI in the workplace is creating new data risks for companies to manage The shortage of skilled cybersecurity professionals is making it difficult to accomplish data security objectives Microsoft is a global leader in cloud, productivity, and security solutions. Microsoft Purview benefits from this breadth of offerings, integrating seamlessly across Microsoft 365, Azure, Microsoft Fabric, and other Microsoft platforms — while also working in harmony with complementary security tools. Unlike fragmented point solutions, Purview delivers an end-to-end data security platform built into the productivity and collaboration tools organizations already rely on. This deep understanding of data within Microsoft environments, combined with continually improving external data risk detections, allows customers to simplify their security stack, increase visibility, and act on data risks more quickly. At Ignite, we’re introducing the next generation of data security — delivering advanced protection and operational efficiency, so security teams can move at business speed while maintaining control of their data. Go beyond visibility into action, across your data estate Many customers today lack a comprehensive view of how to holistically address data security risks and properly manage their data security posture. To help customers strengthen data security across their data estate, we are excited to announce the new, enhanced Microsoft Purview Data Security Posture Management (DSPM). This new AI-powered DSPM experience unifies current Purview DSPM and DSPM for AI capabilities to create a central entry point for data security insights and controls, from which organizations can take action to continually improve their data security posture and prioritize risks. The new capabilities in the enhanced DSPM experience are: Outcome-Based workflows: Choose a data security objective and see related metrics, risk patterns, a recommended action plan and its impact - going from insight to action. Expanded coverage and remediation on Data Risk Assessments: Conduct item-level analysis with new remediation actions like bulk disabling of overshared SharePoint links. Out-of-box posture reports: Uncover data protection gaps and track security posture improvements with out-of-box reports that provide rich context on label usage, auto-labeling effectiveness, posture drift through label transitions, and DLP policy activities. AI Observability: Surface an organization’s agent inventory with assigned agent risk level and agent posture metrics based on agentic interactions with the organization’s data. New Security Copilot Agent: Accelerate the discovery and analysis of sensitive data to uncover hidden risks across files, emails, and messages. Gain visibility of non-Microsoft data within your data estate: Enable a unified view of data risks by gaining visibility into Salesforce, Snowflake, Google Cloud Platform, and Databricks – available through integrations with external partners via Microsoft Sentinel. These DSPM enhancements will be available in Public Preview within the upcoming weeks. Learn more in our blog dedicated to the announcement of the new Microsoft Purview DSPM. Together, these innovations reflect a larger shift: data security is no longer about silos—it’s about unified visibility and control everywhere data lives and having a comprehensive understanding of the data estate to detect and prevent data risks. Organizations trust Microsoft for their productivity and security platforms, but their footprint spans across third-party data environments too. That’s why Purview continues to expand protection beyond Microsoft environments. In addition to bringing in 3rd party data into DSPM, we are also expanding auto-labeling to three new Data Map sources, adding to the data sources we previously announced. Currently in public preview, the new sources include Snowflake, SQL Server, and Amazon S3. Once connected to Purview, admins gain an “at-a-glance” view of all data sources and can automatically apply sensitivity labels, enforcing consistent security policies without manual effort. This helps organizations discover sensitive information at scale, reduce the risk of data exposure, and ensure safer AI adoption all while simplifying governance through centralized policy management and visibility across their entire data estate. Enable AI adoption and prevent data oversharing As organizations adopt more autonomous agents, new risks emerge, such as unsupervised data access and creation, cascading agent interactions, and unclear data activity accountability. Besides AI Observability in DSPM providing details on the inventory and risk level of the agents, Purview is expanding its industry-leading data security and compliance capabilities to secure and govern agents that inherit users’ policies and controls, as well as agents that have their own unique IDs, policies, and controls. This includes agent types across Microsoft 365 Copilot, Copilot Studio, Microsoft Foundry, and third-party platforms. Key enhancements include: Extension of Purview Information Protection and Data Loss Prevention policies to autonomous agents: Scope autonomous agents with an Agent ID into Purview policies that work for users across Microsoft 365 apps, including Exchange, SharePoint, and Teams. Microsoft Purview Insider Risk Management for Agents: With dedicated indicators and behavioral analytics to flag specific risky agent activities, enable proactive investigation by assigning risk levels to each agent. Extension of Purview data compliance capabilities to agent interactions: Microsoft Purview Communication Compliance, Data Lifecycle Management, Audit, and eDiscovery extend to agent interactions, supporting responsible use, secure retention, and agentic accountability. Purview SDK embedded in Agent Framework SDK: Purview SDK embedded in Agent Framework SDK enables developers to integrate enterprise-grade security, compliance, and governance into AI agents. It delivers automatic data classification, prevents sensitive data leaks and oversharing, and provides visibility and control for regulatory compliance, empowering secure adoption of AI agents in complex environments. Purview integration with Foundry: Purview is now enabled within Foundry, allowing Foundry admins to activate Microsoft Purview on their subscription. Once enabled, interaction data from all apps and agents flows into Purview for centralized compliance, governance, and posture management of AI data. Azure AI Search honors Purview labels and policies: Azure AI Search now ingests Microsoft Purview sensitivity labels and enforces corresponding protection policies through built-in indexers (SharePoint, OneLake, Azure Blob, ADLS Gen2). This ensures secure, policy-aligned search over enterprise data, enabling agentic RAG scenarios where only authorized documents are returned or sent to LLMs, preventing oversharing and aligning with enterprise data protection standards. Extension of Purview Data Loss Prevention policies to Copilot Mode in Edge for Business: This week, Microsoft Edge for Business introduced Copilot Mode, transforming the browser into a proactive, agentic partner. This is AI-assisted browsing will honor the user’s existing DLP protections, such as endpoint DLP policies that prevent pasting to sensitive service domains, or summarizing sensitive page content. Learn more in our blog dedicated to the announcements of Microsoft Purview for Agents. New capabilities in Microsoft Purview, now in public preview, to help prevent data oversharing and leakage through AI include: Expansion of Microsoft Purview Data Loss Prevention (DLP) for Microsoft 365 Copilot: Previously, we introduced DLP for Microsoft 365 Copilot to prevent labeled files & emails from being used as grounding data for responses, therefore reducing the risk of oversharing. Today, we are expanding DLP for Microsoft 365 Copilot to safeguard prompts containing sensitive data. This real-time control helps organizations mitigate data leakage and oversharing risks by preventing Microsoft 365 Copilot, Copilot Chat, and Microsoft 365 Copilot agents from returning a response when prompts contain sensitive data or using that sensitive data for grounding in Microsoft 365 or the web. For example, if a user searches, “Can you tell me more about my customer based on their address: 1234 Main Street,” Copilot will both inform the user that organizational policies prevent it from responding to their prompt, as well as block any web queries to Bing for “1234 Main Street.” Enhancements to inline data protection in Edge for Business: Earlier this year, we introduced inline data protection in Edge for Business to prevent sensitive data from being leaked to unmanaged consumer AI apps, starting with ChatGPT, Google Gemini, and DeepSeek. We are not only making this capability generally available for the initial set of AI apps, but also expanding the capability to 30+ new apps in public preview and supporting file upload activity in addition to text. This addresses potential data leakage that can occur when employees send organizational files or data to consumer AI apps for help with work-related tasks, such as document creation or code reviews. Inline data protection for the network: For user activity outside of the browser, we are also enabling inline data protection at the network layer. Earlier this year, we introduced integrations with supported secure service edge (SSE) providers to detect when sensitive data is shared to unmanaged cloud locations, such as consumer AI apps or personal cloud storage, even if sharing occurs outside of the Edge browser. In addition to the discovery of sensitive data, these integrations now support protection controls that block sensitive data from leaving a user device and reaching an unmanaged cloud service or application. These capabilities are now generally available through the Netskope and iboss integrations, and inline data discovery is available in public preview through the Palo Alto Networks integration. Extension of Purview protection to on-device AI: Purview DLP policies now extend to the Recall experience in Copilot+ PC devices to prevent sensitive organizational data from being undesirably captured and retained. Admins can now block Recall snapshots based on sensitivity label or the presence of Purview sensitive information types (SITs) in a document open on the device, or simply honor and display the sensitivity labels of content captured in the Recall snapshot library. For example, a DLP policy can be configured to prevent recall from taking snapshots of any documents labeled “Highly Confidential,” or a product design file that contains intellectual property. Learn more in the Windows IT Pro blog. Best-in-class data security for Microsoft environments Microsoft Purview sets the standard for data security within its own ecosystem. Organizations benefit from unified security policies and seamless compliance controls that are purpose-built for Microsoft environments, ensuring sensitive data remains secure without compromising productivity. We also are constantly investing in expanding protections and controls to Microsoft collaboration tools including SharePoint, Teams, Fabric, Azure and across Microsoft 365. On-demand classification adds meeting transcript coverage and new enhancements: To help organizations protect sensitive data sitting in data-at-rest, on-demand classification now extends to meeting transcripts, enabling the discovery and classification of sensitive information shared in existing recorded meeting transcripts. Once classified, admins can set up DLP or Data Lifecycle Management (DLM) policies to properly protect and retain this data according to organizational policies. This is now generally available, empowering organizations to strengthen data security, streamline compliance, and ensure even sensitive information in data-at-rest is discovered, protected, and governed more effectively. In addition, on-demand classification for endpoints is also generally available, giving organizations even broader coverage across their data estate. New usage posture and consumption reports: We’re introducing new usage posture and consumption reports, now in public preview. Admins can quickly identify compliance gaps, optimize Purview seat assignments, and understand how consumptive features are driving spend. With granular insights by feature, policy, and user type, admins can analyze usage trends, forecast costs, and toggle consumptive features on and off directly, all from a unified dashboard. The result: stronger compliance, easier cost management, and better alignment of Purview investments to your organization’s needs. Enable DLP and Copilot protection with extended SharePoint permissions: Extended SharePoint permissions, now generally available, make it simple to protect and manage files in SharePoint by allowing library owners to apply a default sensitivity label to an entire document library. When this is enabled, the label is dynamically enforced across all unprotected files in the library, both new and existing, within the library. Downloaded files are automatically encrypted, and access is managed based on SharePoint site membership, giving organizations powerful, scalable access control. With extended SharePoint permissions, teams can consistently apply labels at scale, automate DLP policy enforcement, and confidently deploy Copilot, all without the need for manually labeling files. Whether for internal teams, external partners, or any group where permissions need to be tightly controlled, extended SharePoint permissions streamline protection and compliance in SharePoint. Network file filtering via Entra GSA integration: We are integrating Purview with Microsoft Entra to enable file filtering at the network layer. These filtering controls help prevent sensitive content from being shared to unauthorized services based on properties such as sensitivity labels or presence of Purview sensitive information types (SITs) within the file. For example, Entra admins can now create a file policy to block files containing credit card numbers from passing through the network. Learn more here. Expanded protection scenarios enabled by Purview endpoint DLP: We are introducing several noteworthy enhancements to Purview endpoint DLP to protect an even broader range of exfiltration or leakage scenarios from organizational devices, without hindering user productivity. These enhancements, initially available on Windows devices, include: Extending protection to unsaved files: Files no longer need to be saved to disk to be protected under a DLP policy. With this improvement, unsaved files will undergo a point-in-time evaluation to detect the presence of sensitive data and apply the appropriate protections. Expanded support for removable media: Admins can now prevent data exfiltration to broader list of removable media devices, including iPhones, Android devices, and CD-ROMs. Protection for Outlook attachments downloaded to removable media or network shares: Admins can now prevent exfiltration of email attachments when users attempt to drag and drop them into USB devices, network shares, and other removable media. Expanded capability support for macOS: In addition to the new endpoint DLP protections introduced above, we are also expanding the following capabilities, already available for Windows devices, to devices running on macOS: Expanded file type coverage to 110+ file types, blanket protections for non-Office or PDF file types, addition of “allow” and “off” policy actions, device-based policy scoping to scope policies to specific devices or device groups (or apply exclusions), and integration with Power Automate. Manageability and alert investigation improvements in Purview DLP: Lastly, we are also introducing device manageability and alert investigation improvements in Purview DLP to simplify the day-to-day experience for admins. These improvements include: Reporting and troubleshooting improvements for devices onboarded to endpoint DLP: We are introducing additional tools for admins to build confidence in their Purview DLP protections for endpoint devices. These enhancements, designed to maximize reliability and enable better troubleshooting of potential issues, include near real-time reporting of policy syncs initiated on devices and policy health insights into devices’ compliance status and readiness to receive policies. Enhancements to always-on diagnostics: Earlier this year, we introduced always-on diagnostics to automatically collect logs from Windows endpoint devices, eliminating the need to reproduce issues when submitting an investigation request or raising a support ticket. This capability is expanding so that admins now have on-demand access to diagnostic logs from users’ devices without intervening in their operations. This further streamlines the issue resolution process for DLP admins while minimizing end user disruption. Simplified DLP alert investigation, including easier navigation to crucial alert details in just 1 click, and the ability to aggregate alerts originating from a single user for more streamlined investigation and response. For organizations who manage Purview DLP alerts within their broader incident management process in Microsoft Defender, we are pleased to share that alert severities will now be synced between the Purview portal and the Defender portal. Expanding enterprise-grade data security to small and medium businesses (SMBs): Purview is extending its reach beyond large enterprises by introducing a new add-on for Microsoft 365 Business Premium, bringing advanced data security and compliance capabilities to SMBs. The Microsoft Purview suite for Business Premium brings the same enterprise-grade protection, such as sensitivity labeling, data loss prevention, and compliance management, to organizations with up to 300 users. This enables SMBs to operate with the same level of compliance and data security as large enterprises, all within a simplified, cost-effective experience built for smaller teams. Stepping into the new era of technology with AI-powered data security Globally, there is a shortage of skilled cybersecurity professionals. Simultaneously, the volume of alerts and incidents is ever growing. By infusing AI into data security solutions, admins can scale their impact. By reducing manual workloads, they enhance operational effectiveness and strengthen overall security posture – allowing defenders to stay ahead. In 2025, 82% of organizations have developed plans to use GenAI to fortify their data security programs. With its cutting-edge generative AI-powered investigative capabilities, Microsoft Purview Data Security Investigations (DSI) is transforming and scaling how data security admins analyze incident-related data. Since being released into public preview in April, the product has made a big impact with customers like Toyota Motors North America. "Data Security Investigations eliminates manual work, automating investigations in minutes. It’s designed to handle the scale and complexity of large data sets by correlating user activity with data movement, giving analysts a faster, more efficient path to meaningful insights,” said solution architect Dan Garawecki. This Ignite, we are introducing several new capabilities in DSI, including: DSI integration with DSPM: View proactive, summary insights and launch a Data Security Investigation directly from DSPM. This integration brings the full power of DSI analysis to your fingertips, enabling admins to drill into data risks surfaced in DSPM with speed and precision. Enhancements in DSI AI-powered deep content analysis capabilities: Admins can now add context before AI analysis for higher-quality, more efficient investigations. A new AI-powered natural language search function lets admins locate specific files using keywords, metadata, and embeddings. Vector search and content categorization enhancements allow admins to better identify risky assets. Together, these enhancements equip admins with sharper, faster tools for identifying buried data risks – both proactively and reactively. DSI cost transparency report and in-product estimator: To help customers manage pay-as-you-go billing, DSI is adding a new lightweight in-product cost estimator and transparency report. We are also expanding Security Copilot in Microsoft Purview with AI-powered capabilities that strengthen both the protection and investigation of sensitive data by introducing the Data Security Posture Agent and Data Security Triage Agent. Data Security Posture Agent: Available in preview, the new Data Security Posture Agent uses LLMs to help admins answer “Is this happening?” across thousands of files—delivering fast, intent-driven discovery and risk profiling, even when explicit keywords are absent. Integrated with Purview DSPM, it surfaces actionable insights and improves compliance, helping teams reduce risk and respond to threats before they escalate. Data Security Triage Agent: Alongside this, the Data Security Triage Agent, now generally available, enables analysts to efficiently triage and remediate the most critical alerts, automating incident response and surfacing the threats that matter most. Together, these agentic capabilities convert high-volume signals into consistent, closed-loop action, accelerate investigations and remediation, reduce policy-violation dwell time, and improve audit readiness, all natively integrated within Microsoft 365 and Purview so security teams can scale outcomes without scaling headcount. To make the agents easily accessible and help teams get started more quickly, we are excited to announce that Security Copilot will be available to all Microsoft 365 E5 customers. Rollout starts today for existing Security Copilot customers with Microsoft 365 E5 and will continue in the upcoming months for all Microsoft 365 E5 customers. Customers will receive advanced notice before activation. Learn more: https://aka.ms/SCP-Ignite25 Data security that keeps innovating alongside you As we look ahead, Microsoft Purview remains focused on empowering organizations with scalable solutions that address the evolving challenges of data security. While we deliver best-in-class security for Microsoft, we recognize that today’s organizations rarely operate in a single cloud, many businesses rely on a diverse mix of platforms to power their operations and innovation. That’s why we have been extending Purview’s capabilities beyond Microsoft environments, helping customers protect data across their entire digital estate. In a world where data is the lifeblood of innovation, securing it must be more than a checkbox—it must be a catalyst for progress. As organizations embrace AI, autonomous agents, and increasingly complex digital ecosystems, Microsoft Purview empowers them to move forward with confidence. By unifying visibility, governance, and protection across the entire data estate, Purview transforms security from a fragmented challenge into a strategic advantage. The future of data security isn’t just about defense—it’s about enabling bold, responsible innovation at scale. Let’s build that future together.
