Strengthening Semiconductor Supply Chain Trust with Microsoft’s Artifact Signing and Transparency
As enterprises expand their use of Confidential Computing in the cloud and in AI infrastructure, the security of the underlying hardware is more critical than ever. Organizations and their customers need confidence not only in the software they deploy, but also in the silicon and firmware executing their most sensitive workloads. AMD and Microsoft are collaborating to demonstrate how signing and transparency can strengthen trust in the semiconductor supply chain. By combining AMD Device Provenance with Microsoft’s Artifact Signing and Signing Transparency, we are exploring how device provenance, signing, and transparency could work together to support independently verifiable hardware evidence. Why Hardware Provenance Needs Transparency Provenance evidence is most useful when it can be independently verified and evaluated alongside related supply chain evidence. Semiconductor manufacturers generate evidence across selected manufacturing and lifecycle stages, which may describe relevant device attributes, manufacturing events, and validation activities. A digitally signed record establishes who issued information and its integrity. Individual signatures alone do not provide a durable and independently verifiable record of the order in which they were issued, whether they were recorded consistently, or whether unexpected activity occurred in the interval. Transparency addresses this gap. Microsoft's Signing Transparency leverages confidential computing to create a verified, tamper-evident record of signing events, allowing customers and auditors to confirm that evidence was recorded and has not been silently changed or been removed. It applies the Zero Trust principle of “trust but Verify” by giving relying parties cryptographic proof they can evaluate directly. Extending Transparency from Software to Silicon Microsoft's Signing Transparency was designed to address software supply chain threats that traditional code signing alone cannot fully prevent. The same principles can be applied to firmware, hardware provenance, and other evidence generated across the physical supply chain. AMD Device Provenance is designed to provide visibility into the manufacturing and lifecycle of data center components. Through our collaboration, AMD and Microsoft are demonstrating how this evidence can be signed, made transparent, and independently validated. This approach provides several important benefits: Transparent provenance: Changes to signed hardware evidence can be detected rather than silently accepted. Independent validation: Customers can independently validate the integrity, signing history, and transparency records associated with provenance evidence while still applying their own trust requirements to the evidence issuer. Stronger accountability: Transparency creates a durable record of signing events, providing evidence that can help identify unexpected patterns or unauthorized signatures. Audit and compliance evidence: Verifiable records may help inform supplier assessments, regulatory inquiries, and internal due diligence. Standards-based portability: Evidence can be validated across organizational boundaries using interoperable IETF specifications, including RFC 9943, An Architecture for Trustworthy and Transparent Digital Supply Chains, RFC 9942, CBOR Object Signing and Encryption Receipts, and RFC 9995, CBOR Object Signing and Encryption Hash Envelope, rather than proprietary trust mechanisms. Together, these capabilities allow relying parties to verify the origin, integrity, registration, and device binding of manufacturer-issued provenance evidence. Learn More at OCP Global Summit At OCP Global Summit 2026, AMD and Microsoft will present a proof-of-concept demonstration that prototypes how these components could operate together. Visit the AMD (#C61) booth to see the demonstration and learn how AMD and Microsoft are advancing semiconductor supply chain security. To dive deeper into the demo details read AMD’s blog Advancing Semiconductor Supply Chain Assurance. Organizations interested in applying Artifact Signing and Signing Transparency to software, firmware, or hardware can also join the Artifact Signing with Signing Transparency preview community to learn more and help shape the future of the service.
From “No” to “Now”: A 7-Layer Strategy for Enterprise AI Safety
The “block” posture on Generative AI has failed. In a global enterprise, banning these tools doesn't stop usage; it simply pushes intellectual property into unmanaged channels and creates a massive visibility gap in corporate telemetry. The priority has now shifted from stopping AI to hardening the environment so that innovation can run at velocity without compromising data sovereignty. Traditional security perimeters are ineffective against the “slow bleed” of AI leakage - where data moves through prompts, clipboards, and autonomous agents rather than bulk file transfers. To secure this environment, a 7-layer defense-in-depth model is required to treat the conversation itself as the new perimeter. 1. Identity: The Only Verifiable Perimeter Identity is the primary control plane. Access to AI services must be treated with the same rigor as administrative access to core infrastructure. The strategy centers on enforcing device-bound Conditional Access, where access is strictly contingent on device health. To solve the "Account Leak" problem, the deployment of Tenant Restrictions v2 (TRv2) is essential to prevent users from signing into personal tenants using corporate-managed devices. For enhanced coverage, Universal Tenant Restrictions (UTR) via Global Secure Access (GSA) allows for consistent enforcement at the cloud edge. While TRv2 authentication-plane is GA, data-plane protection is GA for the Microsoft 365 admin center and remains in preview for other workloads such as SharePoint and Teams. 2. Eliminating the Visibility Gap (Shadow AI) You can’t secure what you can't see. Microsoft Defender for Cloud Apps (MDCA) serves to discover and govern the enterprise AI footprint, while Purview DSPM for AI (formerly AI Hub) monitors Copilot and third-party interactions. By categorizing tools using MDCA risk scores and compliance attributes, organizations can apply automated sanctioning decisions and enforce session controls for high-risk endpoints. 3. Data Hygiene: Hardening the “Work IQ” AI acts as a mirror of internal permissions. In a "flat" environment, AI acts like a search engine for your over-shared data. Hardening the foundation requires automated sensitivity labeling in Purview Information Protection. Identifying PII and proprietary code before assigning AI licenses ensures that labels travel with the data, preventing labeled content from being exfiltrated via prompts or unauthorized sharing. 4. Session Governance: Solving the “Clipboard Leak” The most common leak in 2025 is not a file upload; it’s a simple copy-paste action or a USB transfer. Deploying Conditional Access App Control (CAAC) via MDCA session policies allows sanctioned apps to function while specifically blocking cut/copy/paste. This is complemented by Endpoint DLP, which extends governance to the physical device level, preventing sensitive data from being moved to unmanaged USB storage or printers during an AI-assisted workflow. Purview Information Protection with IRM rounds this out by enforcing encryption and usage rights on the files themselves. When a user tries to print a "Do Not Print" document, Purview triggers an alert that flows into Microsoft Sentinel. This gives the SOC visibility into actual policy violations instead of them having to hunt through generic activity logs. 5. The “Agentic” Era: Agent 365 & Sharing Controls Now that we're moving from "Chat" to "Agents", Agent 365 and Entra Agent ID provide the necessary identity and control plane for autonomous entities. A quick tip: in large-scale tenants, default settings often present a governance risk. A critical first step is navigating to the Microsoft 365 admin center (Copilot > Agents) to disable the default “Anyone in organization” sharing option. Restricting agent creation and sharing to a validated security group is essential to prevent unvetted agent sprawl and ensure that only compliant agents are discoverable. 6. The Human Layer: “Safe Harbors” over Bans Security fails when it creates more friction than the risk it seeks to mitigate. Instead of an outright ban, investment in AI skilling-teaching users context minimization (redacting specifics before interacting with a model) - is the better path. Providing a sanctioned, enterprise-grade "Safe Harbor" like M365 Copilot offers a superior tool that naturally cuts down the use of Shadow AI. 7. Continuous Ops: Monitoring & Regulatory Audit Security is not a “set and forget” project, particularly with the EU AI Act on the horizon. Correlating AI interactions and DLP alerts in Microsoft Sentinel using Purview Audit (specifically the CopilotInteraction logs) data allows for real-time responses. Automated SOAR playbooks can then trigger protective actions - such as revoking an Agent ID - if an entity attempts to access sensitive HR or financial data. Final Thoughts Securing AI at scale is an architectural shift. By layering Identity, Session Governance, and Agentic Identity, AI moves from being a fragmented risk to a governed tool that actually works for the modern workplace.Migrate Sentinel to Defender - Why It Is a Security Architecture Decision, Not Just a Portal Change
Microsoft will retire the Sentinel experience in Azure on March 31, 2027. Most of the conversation around this transition focuses on cost optimization and portal consolidation. That framing undersells what is actually happening. The unified Defender portal is not a new interface for the same capabilities. It is the platform foundation for a fundamentally different SOC operating model — one built on a 2-tier data architecture, graph-based investigation, and AI agents that can hunt, enrich, and respond at machine speed. Partners who understand this will help customers build security programs that match how attackers actually operate. This document covers four things: What the unified experience delivers — the security capabilities that do not exist in standalone Sentinel and why they matter against today’s threats. What the transition really involves - is not data migration, but it is a data architecture project that changes how telemetry flows, where it lives, and who queries it. Where the partner opportunity lives — a structured progression from professional services (transactional, transition execution, and advisory) to ongoing managed security services. Why does the unified experience win competitively — factual capability advantages that give partners a defensible position against third-party SIEM alternatives. The Bigger Picture: Preparing for the Agentic SOC Before getting into transition mechanics, partners need to understand where the industry is headed — because the platform decisions made during this transition will determine whether a customer’s SOC is ready for what comes next. The security industry is moving from human-driven, alert-centric workflows to an operating model built on three pillars: Intellectual Property — the detection logic, hunting hypotheses, response playbooks, and domain expertise that differentiate one security team from another. Human Orchestration — the judgment, context, and decision-making that humans bring to complex incidents. Humans set strategy, validate findings, and make containment decisions. They do not manually triage every alert. AI Agents - built agents that execute repeatable work: enriching incidents, hunting across months of telemetry, validating security posture, drafting response actions, and flagging anomalies for human review. The SOC of 2027 will not be scaled by hiring more analysts. It will be scaled by deploying agents that encode institutional knowledge into automated workflows — orchestrated by humans who focus on the decisions that require judgment. This transformation requires a platform that provides three things: Deep telemetry — agents need months of queryable data to analyze behavioral patterns, build baselines, and detect slow-moving threats. The Sentinel data lake provides this at a cost point that makes long-retention feasible. Relationship context — agents need to understand how entities connect. Which accounts share credentials? What is the blast radius of a compromised service principle? What is the attack path from a phished user to domain admin? Sentinel Graph provides this. Extensibility — partners and customers need to build and deploy their own agents without waiting for Microsoft to ship them. The MCP framework and Copilot agent architecture provide this. None of these exist in Azure experience for Sentinel. All three ship with the Defender experience. The urgency goes beyond the March 2027 deadline. Organizations are deploying AI agents, copilots, and autonomous workflows across their businesses — and every one of those creates a new attack surface. Prompt injection, data poisoning, agent hijacking, cross-plugin exploitation — these are not theoretical risks. They are in the wild today. Defending against AI-powered attacks requires a security platform that is itself AI Agent-ready. The new experience in Defender unlocks this experience. What Unified SIEM and XDR Actually Delivers The original framing — “single pane of glass for SIEM and XDR” — is accurate but insufficient. Here is what the unified platform delivers that standalone Sentinel does not. Cross-Domain Incident Correlation The Defender correlation engine does not just group alerts by time proximity. It builds multi-stage incident graphs that link identity compromise to lateral movement to data exfiltration across SIEM and XDR telemetry — automatically. Consider a token theft chain: an infostealer harvests browser session cookies (endpoint telemetry), the attacker replays the token from a foreign IP (Entra ID sign-in logs), creates a mailbox forwarding rule (Exchange audit logs), and begins exfiltrating data (DLP alerts). In standalone Sentinel, these are four separate alerts in four different tables. In the unified platform, they are one correlated incident with a visual attack timeline. 2-Tier Data Architecture The Sentinel data lake introduces a second storage tier that changes the economics and capabilities of security telemetry: Analytics Tier Data Lake Purpose Real-time detection rules, SOAR, alerting Hunting, forensics, behavioral analysis, AI agent queries Latency Sub-5-minute query and alerting Minutes to hours acceptable Cost ~$4.30/GB PAYG ingestion (~$2.96 at 100 GB/day commitment) ~$0.05/GB ingestion + $0.10/GB data processing (at least 20x cheaper) Retention 90 days default (expensive to extend) Up to 12 years at low cost Best for High-signal, low-volume sources High-volume, investigation-critical sources The architecture decision is not “which tier is cheaper.” It is “which tier gives me the right detection capability for each data source.” Analytics tier candidates: Entra ID sign-in logs, Azure activity, audit logs, EDR alerts, PAM events, Defender for Identity alerts, email threat detections. These need sub-5-minute alerting. Data lake candidates: Raw firewall session logs, full DNS query streams, proxy request logs, Sysmon process events, NSG flow logs. These drive hunting and forensic analysis over weeks or months. Dual-ingest sources: Some sources need both tiers. Entra ID sign-in logs are the canonical example — analytics tier for real-time password spray detection, Data Lake for graph-based blast radius analysis across months of authentication history. Implementation is straightforward: a single Data Collection Rule (DCR) transformation handles the split. One collection point, two routing destinations. The right framing: “Right data in the right tier = better detections AND lower cost.” Cost savings are a side effect of good security architecture, not the goal. Sentinel Graph Sentinel graph enables SOC teams and AI agents to answer questions that flat log queries cannot: What is the blast radius of this compromised account? Which service principals share credentials with the breached identity? What is the attack path from this phished user to domain admin? Which entities are connected to this suspicious IP across all telemetry sources? Graph-based investigation turns isolated alerts into context-rich intelligence. It is the difference between knowing “this account was compromised” and understanding “this account has access to 47 service principals, 3 of which have written access to production Key Vault.” Security Copilot Integration Security Copilot embedded in the defender portal helps analysts summarize incidents, generate hunting queries, explain attacker behavior, and draft response actions. For complex multi-stage incidents, it reduces the time from “I see an alert” to “I understand the full scope” from hours to minutes. With free SCUs available with Microsoft 365 E5, teams can apply AI to the highest-effort investigation work without adding incremental cost. MCP and the Agent Framework The Model Context Protocol (MCP) and Copilot agent architecture let partners and customers build purpose-built security agents. A concrete example: an MCP-enabled agent can automatically enrich a phishing incident by querying email metadata, checking the sender against threat intelligence, pulling the user’s recent sign-in patterns, correlating with Sentinel Graph for lateral risk, and drafting a containment recommendation — in under 60 seconds. This is where partner intellectual property becomes competitive advantage. The agent framework is the mechanism for encoding proprietary detection logic, response playbooks, and domain expertise into automated workflows that run at machine speed. Security Store Security Store allows partners to evolve from one‑time transition projects into repeatable, scalable offerings—supporting professional services, managed services, and agent‑based IP that align with the customer’s unified SecOps operating model As part of the transition, the Microsoft Security Store becomes the extension layer for the Defender —allowing partners to deliver differentiated agents, SaaS, and security services natively within Defender and Sentinel, instead of building and integrating in isolation The 4 Investigation Surfaces: A Customer Maturity Ladder The Sentinel Data Lake exposes four distinct investigation surfaces, each representing a step toward the Agentic SOC — and a partner service opportunity: Surface Capability Maturity Level Partner Opportunity KQL Query Ad-hoc hunting, forensic investigation Basic — “we can query” Hunting query libraries; KQL training Graph Analytics Blast radius, attack paths, entity relationships Intermediate — “we understand relationships” Graph investigation training; attack path workshops Notebooks (PySpark) Statistical analysis, behavioral baselines, ML models Advanced — “we predict behaviors” Custom notebook development; anomaly scoring Agent/MCP Access Autonomous hunting, triage, response at machine speed Agentic SOC — “we automate” Custom agent development; MCP integration The customer who starts with “help us hunt better” ends up at “build us agents that hunt autonomously.” That is the progression from professional services to managed services. What the Transition Actually Involves It is not a data migration — customers’ underlying log data and analytics remain in their existing Log Analytics workspaces. That is important for partners to communicate clearly. But partners should not set the expectation that nothing changes except the URL. Microsoft’s official transition guide documents significant operational changes — including automation rules and playbooks, analytics rule, RBAC restructuring to the new unified model (URBAC), API schema changes that break ServiceNow and Jira integrations, analytics rule transitions where the Fusion engine is replaced by the Defender XDR correlation engine, and data policy shifts for regulated industries. Most customers cannot navigate this complexity without professional help. Important: Transitioning to the Defender portal has no extra cost - estimate the billing with the new Sentinel Cost Estimator Optimizing the unified platform means making deliberate changes: Adding dual-ingest for critical sources that need both real-time detection and long-horizon hunting. Moving high-volume telemetry to the Data Lake — enabling hunting at scale that was previously cost-prohibitive. Retiring redundant data copies where Defender XDR already provides the investigation capability. Updating RBAC, automation, and integrations for the unified portal’s consolidated schema and permission structure. Training analysts on new investigation workflows, Sentinel Graph navigation, and Copilot-assisted triage. Threat Coverage: The Detection Gap Most Organizations Do Not Know They Have This transition is an opportunity to quantify detection maturity — and most organizations will not like what they find. Based on real-world breach analysis — infostealers, business email compromise, human-operated ransomware, cloud identity abuse, vulnerability exploitation, nation-state espionage, and other prevalent threat categories — organizations running standalone Sentinel with default configurations typically have significant detection gaps. Those gaps cluster in three areas: Cross-domain correlation gaps — attacks that span identity, endpoint, email, and cloud workloads. These require the Defender correlation engine because no single log source tells the complete story. Long-retention hunting gaps — threats like command-and-control beaconing and slow data exfiltration that unfold over weeks or months. Analytics-tier retention at 90 days is too expensive to extend and too short for historical pattern analysis. Graph-based analysis gaps — lateral movement, blast radius assessment, and attack path analysis that require understanding entity relationships rather than flat log queries. The unified platform with proper log source coverage across Microsoft-native sources can materially close these gaps — but only if the transition includes a detection coverage assessment, not just a portal cutover. Partners should use MITRE ATT&CK as the common framework for measuring detection maturity. Map existing detections to ATT&CK tactics and techniques before and after transition — a measurable, defensible improvement that justifies advisory fees and ongoing managed services. Partner Opportunity: Professional Services to Managed Services This transition creates a structured progression for all partner types — from professional services that build trust and surface findings, to managed security services that deliver ongoing value. The key insight most partners miss: do not jump from “transition assessment” to “managed services pitch.” Customers are not ready for that conversation until they have experienced the value of professional services. The bridge engagement — whether transactional, transition execution, or advisory — builds trust, demonstrates the expertise, and surfaces the findings that make the managed services conversation a logical next step. Professional Services (transactional + transition execution + advisory) → Managed Security Services (MSSP) The USX transition is the ideal professional services entry point because it combines a mandatory deadline (March 2027) with genuine technical complexity (analytics rule, automation behavioral changes, RBAC restructuring, API schema shifts) that most customers cannot navigate alone. Every engagement produces findings — detection gaps, automation fragility, staffing shortfalls — that are the most credible possible evidence for managed services. Professional Services Transactional Partners Offer Customer Value Key Deliverables Transition Readiness Assessment Risk-mitigated transition with clear scope Sentinel deployment inventory; Defender portal compatibility check; transition roadmap with timeline; MITRE ATT&CK detection coverage baseline Transition Execution and Enablement Accelerated time-to-value, minimal disruption Workspace onboarding; RBAC and automation updates; Dual-portal testing and validation; SOC team training on unified workflows Security Posture and Detection Optimization Better detections and lower cost Data ingestion and tiering strategy; Dual-ingest implementation for critical sources; Detection coverage gap analysis; Automation and Copilot/MCP recommendations Advisory Partners Offer Customer Value Key Deliverables Executive and Strategy Advisory Leadership alignment on why this transition matters Unified SecOps vision and business case; Zero Trust and SOC modernization alignment; Stakeholder alignment across security, IT, and leadership Architecture and Design Advisory Future-ready architecture optimized for the Agentic SOC Target-state 2-tier data architecture; Dual-ingest routing decisions mapped to MITRE tactics; RBAC, retention, and access model design Detection Coverage and Gap Analysis Measurable detection maturity improvement Current-state MITRE ATT&CK coverage mapping; Gap analysis against 24 threat patterns; Detection improvement roadmap with priority recommendations SOC Operating Model Advisory Smooth analyst adoption with clear ownership Redesigned SOC workflows for unified portal; Incident triage and investigation playbooks; RACI for detection engineering, hunting, and platform ops Agentic SOC Readiness Preparation for AI-driven security operations MCP and agent architecture assessment; Custom agent development roadmap; IP + Human Orchestration + Agent operating model design Cost, Licensing and Value Advisory Transparent cost impact with strong business case Current vs. future cost analysis; Data tiering optimization recommendations; TCO and ROI modeling for leadership The conversion to managed services is evidence-based. Every professional services engagement produces findings — detection gaps, automation fragility, staffing shortfalls. Those findings are the most credible possible case for ongoing managed services. Managed Security Services The unified platform changes the managed security conversation. Partners are no longer selling “we watch your alerts 24/7.” They are selling an operating model where proprietary AI agents handle the repeatable work — enrichment, hunting, posture validation, response drafting — and human experts focus on the decisions that require judgment. This is where the competitive moat forms. The formula: IP + Human Orchestration + AI Agents = differentiated managed security. The unified platform enables this through: Multi-tenancy — the built-in multitenant portal eliminates the need for third-party management layers. Sentinel Data Lake — agents can query months of customer telemetry for behavioral analysis without cost constraints. Sentinel Graph — agents can traverse entity relationships to assess blast radius and map attack paths. MCP extensibility — partners can build agents that integrate with proprietary tools and customer-specific systems. Partners who build proprietary agents encoding their detection logic into the MCP framework will differentiate from partners who rely on out-of-box capabilities. The Securing AI Opportunity Organizations are deploying AI agents, copilots, and autonomous workflows across their businesses at an accelerating pace. Every AI deployment creates a new attack surface — prompt injection, data poisoning, agent hijacking, cross-plugin exploitation, unauthorized data access through agentic workflows. These are not theoretical risks. They are in the wild today. Partners who can help customers secure their AI deployments while also using AI to strengthen their SOC will command premium positioning. This requires a security platform that is itself AI Agent-ready — one that can deploy defensive agents at the same pace organizations deploy business AI. The unified Defender portal is that platform. Partners who position USX as “preparing your SOC for AI-driven security operations” will differentiate from partners who position it as “moving to a new portal.” Cost and Operational Benefits Better security architecture also costs less. This is not a contradiction — it is the natural result of putting the right data in the right tier. Benefit How It Works Eliminate low-value ingestion Identify and remove log sources that are never used for detections, investigations, or hunting. Immediately lowers analytics-tier costs without impacting security outcomes. Right-size analytics rules Disable unused rules, consolidate overlapping detections, and remove automation that does not reduce SOC effort. Pay only for processing that delivers measurable security value. Avoid SIEM/XDR duplication Many threats can be investigated directly in Defender XDR without duplicating telemetry into Sentinel. Stop re-ingesting data that Defender already provides. Tier data by detection need Store high-volume, hunt-oriented telemetry in the Data Lake at at least 20x lower cost. Promote only high-signal sources to the analytics tier. Full data fidelity preserved in both tiers. Reduce operational overhead Unified SIEM+XDR workflows in a single portal reduce tool switching, accelerate investigations, simplify analyst onboarding, and enable SOC teams to scale without proportional headcount increases. Improve detection quality The Defender correlation engine produces higher-fidelity incidents with fewer false positives. SOC teams spend less time triaging noise and more time on real threats. Competitive Positioning Partners need defensible talking points when customers evaluate third-party SIEM alternatives. The following advantages are factual, sourced from Microsoft’s transition documentation and platform capabilities — not marketing claims. No extra cost for transitioning — even for non-E5 customers. Third-party SIEM migrations involve licensing, data migration, detection rewrite, and integration rebuild costs. Native cross-domain correlation across Sentinel + Defender products into multi-stage incident graphs. Third-party SIEMs receive Microsoft logs as flat events — they lack the internal signal context, entity resolution, and product-specific intelligence that powers cross-domain correlation. Custom detections across SIEM + XDR — query both Sentinel and Defender XDR tables without ingesting Defender data into Sentinel. Eliminates redundant ingestion cost. Alert tuning extends to Sentinel — previously Defender-only capability, now applicable to Sentinel analytics rules. Net-new noise reduction. Unified entity pages — consolidated user, device, and IP address pages with data from both Sentinel and Defender XDR, plus global search across SIEM and XDR. Third-party SIEMs provide entity views from ingested data only. Built-in multi-tenancy for MSSPs — multitenant portal manages incidents, alerts, and hunting across tenants without third-party management layers. Try out the new GDAP capabilities in Defender portal. Industry validation: Microsoft’s SIEM+XDR platform has been recognized as a Leader by both Forrester (Security Analytics Platforms, 2025) and Gartner (SIEM Magic Quadrant, 2025). Summary: What Partners Should Take Away Topic Key Message Framing USX is a security architecture transformation, not a portal transition. Lead with detection capability, not cost savings. Platform foundation Sentinel Data Lake + Sentinel Graph + MCP/Agent Framework = the platform for the Agentic SOC. 4 investigation surfaces KQL → Graph → Notebooks → Agent/MCP. A maturity ladder from “we can query” to “we automate at machine speed.” Architecture 2-tier data model (analytics + Data Lake) with dual-ingest for critical sources. Cost savings are a side effect of good architecture. Transition complexity Analytics rules and automation rules. API schema changes. RBAC restructuring. Most customers need professional help. Partner engagement model Professional Services (transactional + transition execution + advisory) → Managed Services (MSSP). Competitive positioning No extra cost. Native correlation. Cross-domain detections. Built-in multi-tenancy. Capabilities third-party SIEMs cannot replicate. Partner differentiation IP + Human Orchestration + AI Agents. Partners who build proprietary agents on MCP have competitive advantage. Timeline March 31, 2027. Start now — phased transition with one telemetry domain first, then scale.Authorization 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.MCP safety & evaluation with the Agent 365 CLI & Agent Governance Toolkit
Co Author: JiteshThakur AI agents are useful because they can act. They call tools, query databases, send messages, and hand work to other agents. That same freedom creates a problem: access control can tell you which service an agent may reach, but it does not always tell you whether a particular action is sensible, safe, or permitted. MCP is how most agents now act. Two Control Points: This post examines two control points that address different parts of the MCP lifecycle. Agent 365 CLI evaluates the MCP server before an agent uses it. Agent Governance Toolkit (AGT) governs sensitive tool calls while the agent runs. One improves what the agent sees. The other governs what the agent does. The Agent 365 CLI is a cross-platform command-line tool for Agent 365 applications on Azure. Its evaluation command examines MCP tool definitions and scores their quality. AGT evaluates actions against policy and records each decision. Together, these tools support a practical model: evaluate the server first, then provide proper scaffolding for the developer to test this in a dry run. Agent 365 CLI: Score an MCP server from the command line: The Agent 365 CLI can evaluate an MCP server against research-based practices for production readiness. The result is more useful than a simple pass or fail. The evaluation gives you: A score for each tool name, description, and parameter schema; A prioritized list of improvements; An overall maturity score for the server; and Local output that you can use early in development. This report turns a vague question, "Is this MCP server ready?", into a concrete list of work. The evaluate command a365 develop-mcp evaluate --server-url <server-url> [--auth-token <auth-token>] [options] The command reads the tool schemas from the server. It then produces guidance for names, descriptions, parameters, and schema structure. A local coding-agent CLI scores the semantic checks. You can use GitHub Copilot CLI or Claude Code under your account and AI subscription. The command does not send tool-schema data to Microsoft. Prerequisites: Install the following software: Agent 365 CLI; Node.js 18 or later for GitHub Copilot CLI; and A supported coding-agent CLI for semantic scoring. For example, install GitHub Copilot CLI with this command: powershell npm install -g @github/copilot This bring-your-own-LLM model keeps the scoring step in your local development environment. It is useful when model calls must remain inside an approved deployment. How the evaluation works The command runs a five-step pipeline and logs progress as it goes. Fig 1: MCP Evaluation using Agent 365 Cli Connect to the MCP server and collect its tool schemas. Generate an evaluation checklist in the output directory. Score the semantic checks with the selected coding agent. Calculate the maturity level and action priorities. Write the JSON and HTML reports. The evaluation contains two types of checks: Deterministic checks use exact rules in the CLI. For example, a tool name cannot be empty. Semantic checks use a coding agent to score clarity and meaning. Each result includes a reason for the score. Examples Set the authentication token in an environment variable. Then evaluate an authenticated server and write the artifacts to a subfolder. powershell $env:A365_MCP_AUTH_TOKEN = "<bearer-token>" a365 develop-mcp evaluate --server-url "https://my-mcp-server.contoso.com/mcp" --output-dir "./eval" Use a specific scoring engine with the `--eval-engine` option: powershell a365 develop-mcp evaluate --server-url "http://localhost:5000/mcp" --eval-engine claude-code Scenario: Evaluate a malicious MCP server For this demonstration, we hosted a deliberately malicious MCP server at `http://127.0.0.1:8124/`. It exposes tools that demonstrate tool poisoning, credential leakage, prompt injection, schema mismatch, sandbox escape, and other attacks. The server is intentionally unsafe and is for demonstration only. Fig 2: Setting up a test MCP server for evaluation We ran the evaluation in two steps. First, we generated the checklist without automatic semantic scoring: a365 develop-mcp evaluate --server-url "http://127.0.0.1:8124/" --eval-engine none Fig 3: Agent365CLI MCP Evaluation The command wrote the checklist and a semantic-evaluation prompt to the output directory. It also displayed the next steps. Second, we gave the prompt and checklist to a coding agent. The agent completed each unscored semantic check with a Boolean score and a short reason. After we saved the completed checklist, we ran the command again to generate the report: a365 develop-mcp evaluate --server-url "http://127.0.0.1:8124/" --output-dir "C:\temp\MaliciousMCP" Fig 4: Creating the report with Agent365 CLI MCP Evaluate command Understanding the evaluation report Open `<server-name>_eval_report.html` from the output directory. The report contains: The overall score from 0 to 100; The maturity level from 0 to 4; Scores for each tool and quality category; and A prioritized action list for the next maturity level. Fig 5: MCP Evaluation Report In our demonstration, the server scored 86.0 and reached Level 3: Optimized for AI. That strong overall score did not mean that every tool was safe or clear. The report found 58 action items, including one critical item and 33 high-priority items. That contrast matters. A server can have valid schemas and consistent names while still exposing misleading or dangerous tools. Fig 6: MCP Evaluation Report - Tool-By-Tool Detail What to look for Read the per-tool results before the overall score. A single weak tool can create more risk than the server average suggests. Focus on these report sections: Tool names: Can an agent select the correct tool from its name? Tool descriptions: Does each description explain the purpose and correct use? Parameter names: Do the names identify the data that the tool requires? Parameter descriptions: Do they explain the format, type, and constraints? Schema structure: Are the schemas valid and processable? Action items: Which changes have the highest effect on tool selection and use? The command processes static tool schemas from `tools/list`. It does not process runtime payloads, end-user data, or personal data. The command keeps the `--auth-token` value in memory. It sends the value only in the HTTP `Authorization` header. It does not write the token to disk or give it to the coding agent. AGT: Put governance in the execution path: Microsoft's open-source Agent Governance Toolkit (AGT) evaluates an action before execution. It adds identity and policy context, records the decision, and can send risky work for approval. This can be used by developers during the build time for dynamic evaluation of the MCP server. AGT lets developers put part of that intent into the execution path. Remote tools still need secure implementations, sandboxes need hard boundaries, and audit records need appropriate storage and access controls. You do not need to replace your agent framework to use it. What sits in the decision path? AGT wraps the tools that an agent already uses. You can start to govern a tool with two lines of Python: python from agentmesh.governance import govern safe_tool = govern(my_tool, policy="policy.yaml") On each call, `safe_tool` evaluates the configured policy. An allowed action reaches the original tool. A denied action raises `GovernanceDenied` and creates a decision record. This wrapper model reduces the cost of adoption. Teams can add governance to an existing agent stack without rebuilding it. AGT supports Python, TypeScript, .NET, Rust, and Go. Its documented integrations include popular agent frameworks, MCP, and A2A. Teams can also adopt AGT in stages. A team can begin with policy checks and audit records. It can add identity, approvals, sandboxing, and operational controls as risk increases. Each control answers a different question: Policy: Is this action allowed? Identity and trust: Which agent made the request? Runtime controls: What limits apply to execution? Audit evidence: Why did AGT allow or deny the action? A low-risk assistant can need only a deny rule and basic logging. An agent that moves money or changes production systems needs stronger controls. Fig 7: AGT Architecture Scenario: Govern the same malicious MCP server For this scenario demonstration, we used AGT Python packages as an MCP gateway. The gateway sat between an agent and the same malicious server from the earlier evaluation. This setup let us examine both control points against one target. The Agent 365 CLI examined the server's static tool definitions. The AGT gateway examined real requests and responses for the developer during its testing. Fig 8: AGT findings at runtime In the policy interface, a developer can edit runtime limits and detection rules. The developer can also validate the policy against sample tool metadata, save a revision, and activate it with a recorded reason. Fig 9: AGT control coverage The control-coverage view shows which AGT capabilities are active in the gateway. It also links each capability to package checks and end-to-end evidence. In our demonstration, we included the following controls: Tool metadata poisoning detection; Tool change and rug-pull detection; Dangerous argument blocking; Tool-response content scanning; Per-client tool-call budgets; and A redacted decision audit trail. You can build your detection & input security by reading more about it here. The gateway detected malicious content. For one blocked `tools/list` request, it recorded the findings. The important result was not only that AGT blocked the request. It also preserved the matched evidence, affected tool locations, policy modes, and request context. Fig 10: Example detection via AGT The dashboard then summarized block-mode findings, leading risk drivers, and tools that required review. This evidence can help a team prioritize policy changes and investigate repeated attacks. Fig 11: Sample AGT metrics AGT does not require this UI, gateway, or architecture. Its structured decisions can feed an admin console, SIEM, incident workflow, or approval queue. AGT also includes an Agent Compliance package with mappings for OWASP and other controls. These mappings give developers and governance teams a common record of applied controls. Teams do not need to reconstruct the agent's behavior after an incident. Check Compliance - Agent Governance Toolkit for more information. Conclusion: MCP safety needs controls before and during execution. The Agent 365 CLI improves the MCP interface before deployment. It exposes unclear tool definitions, scores server maturity, and turns quality gaps into prioritized work. While AGT is implemented at the build phase, it provides developers the ability to test policy, identity, execution context & preserve evidence for allowed or denied decisions. Neither tool replaces secure server code, strong sandbox boundaries, or protected audit storage. Instead, they make those controls easier to evaluate and explain. Start with one MCP server and one consequential tool call. Evaluate the server with the Agent 365 CLI. Then put an AGT policy around the action that carries the most risk. While these controls help secure the build phase of an agent, once agents move into production, runtime controls become essential. Agent365 provides those controls at runtime. With thanks to Ashik Kuppil for his inputs and collaboration on this post.Microsoft Defender for Cloud Customer Newsletter
*This will be the last monthly MDC newsletter. To keep up with the latest, please visit: What's new in MDC What's new in Defender for Cloud? Multiple container security features are now Generally Available: Container-level misconfiguration recommendations for Kubernetes, Upgrade AKS version recommendations, VA for runtime-discovered container images on EKS and GKE, Kubernetes notes VA for EKS and GKE, scanning support for Docker hardened container images. For more information, see this page here. Database-level recommendations for SQL VA now GA The SQL vulnerability assessment recommendations created as part of the transition from grouped to individual recommendations are now generally available. Each SQL vulnerability assessment rule is surfaced as its own recommendation, reported directly on the affected SQL database resource. For more details, please refer to this documentation . Blogs of the month In July, our team published the following blog posts we would like to share: 1. Built to Protect: The Architecture Behind Codename MDASH Customer journey Discover how other organizations successfully use Microsoft Defender for Cloud to protect their cloud workloads. This month we are featuring NTT Data. NTT Data, a top global IT services provider, leverages Azure, OpenAI and Microsoft Defender to launch their AI agents, adopting secure by design principles, to help enhance, competitiveness organization change and data usage. Defender for AI, and Defender CSPM, as part of the Defender family, address the emerging risks and threats like prompt injection and data poisoning that come with generative AI. Join our community! We offer several customer connection programs within our private communities. By signing up, you can help us shape our products through activities such as reviewing product roadmaps, participating in co-design, previewing features, and staying up-to-date with announcements. Sign up at aka.ms/JoinCCP. We greatly value your input on the types of content that enhance your understanding of our security products. Your insights are crucial in guiding the development of our future public content. We aim to deliver material that not only educates but also resonates with your daily security challenges. Whether it’s through in-depth live webinars, real-world case studies, comprehensive best practice guides through blogs, or the latest product updates, we want to ensure our content meets your needs. Please submit your feedback on which of these formats do you find most beneficial and are there any specific topics you’re interested in https://aka.ms/PublicContentFeedback. Note: If you want to stay current with Defender for Cloud and receive updates in your inbox, please consider subscribing to our monthly newsletter: https://aka.ms/MDCNewsSubscribeSecuring AI Agents at Runtime: Real-Time Protection and Threat Detection for Microsoft Agent 365
Organizations are rapidly adopting AI agents to automate workflows, access enterprise data, invoke tools, and take actions on behalf of users. This autonomy creates a fundamentally new security challenge. Unlike traditional AI applications, agents operate across dynamic execution flows, interacting with external content, calling tools, and accessing sensitive resources. These interactions create new attack paths that traditional security controls were not designed to address. Today, we're announcing two major milestones for Security for AI in Microsoft Defender for Microsoft Agent 365: Threat detection for Microsoft Agent 365 agents — now in public preview. Real-time protection for Microsoft Agent 365 tooling servers — now generally available. Together, these capabilities help security teams detect, investigate, and block attacks targeting AI agents, extending Microsoft Defender's threat protection capabilities into the agent runtime. Threat detection for Microsoft Agent 365 Agents (Public Preview) Threat detection provides SOC teams with detailed visibility into attacks and suspicious activity targeting AI agents. By analyzing runtime signals across agent interactions, tool usage, and execution patterns, Microsoft Defender identifies suspicious and malicious behavior throughout the agent execution lifecycle and surfaces actionable security alerts for SOC teams. Threat detection supports cloud agent types that emit observability logs to Microsoft Agent 365, including: Microsoft Copilot Studio Microsoft Foundry Microsoft 365 Copilot Agent Builder Agents integrated through the Microsoft Agent 365 SDK This provides consistent threat visibility across supported Microsoft Agent 365 agent experiences, regardless of how the agent was built. Fig. 1. Microsoft Security for AI alerts in Microsoft Defender XDR (Preview) Microsoft Defender identifies a broad range of AI-specific threats, including: Indirect prompt injection (XPIA) — malicious instructions embedded in external content designed to manipulate agent behavior. Evasion techniques — attempts to bypass agent instructions or security controls. Malicious content propagation — attempts to use agents to generate or distribute malicious content. Secret leakage — exposure of credentials, API keys, or other sensitive information through agent interactions. LLM reconnaissance — attempts to probe agent capabilities, instructions, or security boundaries. Suspicious IP access — agent access originating from anonymized or suspicious IP addresses. Alerts are surfaced directly in Microsoft Defender, enabling SOC analysts to investigate and respond using familiar workflows, Advanced Hunting queries, and the Defender XDR investigation experience. Real-time protection for WorkIQ and Custom MCP servers (General Availability) Real-time protection moves beyond detection by blocking threats inline when AI agents interact with WorkIQ and custom MCP servers (see Microsoft Agent 365 tooling servers). When an agent invokes a registered tool or receives a tool response, Defender evaluates the interaction against configured security policies and determines whether to allow or block it directly within the agent's execution flow. This helps prevent malicious actions and data leakage in real time, without requiring agent developers to implement custom security logic. Fig. 2. Microsoft Security for AI Real-Time Protection policy in Defender Real-time protection currently guards against high-impact threats, including: Evasion techniques — attempts to bypass agent guardrails or security controls. Malicious content propagation — preventing agents from spreading malicious content through tool actions. Secret leakage — blocking agents from inadvertently exposing credentials or sensitive data through tool calls. Communication with untrusted domains — preventing agents from sending email or data to high-risk or untrusted email domains. Better Together: Detection and Protection Threat detection and real-time protection address complementary parts of the agent security lifecycle. Real-time protection provides inline enforcement to block malicious interactions during execution, while threat detection gives SOC teams the visibility and investigation context needed to identify attack patterns, assess impact, and respond to suspicious activity. Together, they provide a defense-in-depth approach that combines runtime enforcement with SOC-driven detection and investigation, purpose-built for AI agents. Getting Started Both capabilities are available through Microsoft Defender, using a dedicated Security for AI workload experience that brings together AI threat detections, investigations, and runtime protection policies. To learn more: Enable security for AI agents using Microsoft Defender Detect and investigate threats to AI agents using Microsoft Defender (Preview) Protect AI agents in real time using Microsoft Defender As AI agents become more autonomous and gain access to enterprise data and tools, securing their runtime behavior becomes critical. With Threat Detection and Real-Time Protection, Microsoft Defender helps organizations adopt AI agents with security controls designed for how agents actually operate—detecting attacks, enabling SOC investigation, and blocking malicious interactions at runtime.Registration 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 SharingMicrosoft Defender for Cloud Customer Newsletter
What's new in Defender for Cloud? Microsoft Defender for Open-Source Relational Databases is now generally available for Amazon Web Services Relational Database Service (AWS RDS) instances. Receive database threat protection and sensitive data discovery insights for supported open-source relational databases, including Aurora PostgreSQL, Aurora MySQL, PostgreSQL, MySQL, and MariaDB on AWS RDS. For more information, see our public documentation. Expanded multicloud security coverage now GA Microsoft Defender for Cloud's expanded multicloud security coverage is now generally available. This release significantly broadens posture assessment for AWS and GCP environments, adding support for about 90 new resource types and over 200 new security recommendations across data, identity and access, networking, compute, and container categories. For more details, please refer to this documentation. Check out other updates from last month here! Check out monthly news for the rest of the MTP suite here! Blogs of the month In June, our team published the following blog posts we would like to share: Now Generally Available: Microsoft Defender for open source relational databases on AWS RDS Start Secure, Stay Secure: How Microsoft is Closing the Gap from Code to Runtime The end of patching era for containers: Microsoft Defender for Cloud expands hardened image support Closing the loop on container security: From code to runtime in the AI era Microsoft Defender for Cloud expands multicloud coverage across AWS and Google Cloud Defender for Cloud in the field Watch the latest Defender for Cloud in the Field YouTube episode here: Stay Secure: AI-powered Faster fixes Visit our YouTube page GitHub Community Check out Defender for Cloud GitHub lab module 25. It walks through the integration between Defender for Cloud and XDR to provide a comprehensive CDR solution. Module 25 - MDC and Defender portal integration Visit our GitHub page Customer journey Discover how other organizations successfully use Microsoft Defender for Cloud to protect their cloud workloads. This month we are featuring Hassan Allam Holding. Hassan Allam Holding is an Egyptian private-sector company with operations spanning engineering, construction, and infrastructure investment and was facing operational complexity. To overcome, they leveraged an end-to-end Microsoft Security ecosystem with Defender XDR, which integrates with Defender for Cloud and other products to centralize detection and signals across endpoint, identity, email and cloud workloads. As a result, alert noise reduce by up to 90%, and Hassan Allam Holding is able to respond faster with far less complexity. Join our community! We offer several customer connection programs within our private communities. By signing up, you can help us shape our products through activities such as reviewing product roadmaps, participating in co-design, previewing features, and staying up-to-date with announcements. Sign up at aka.ms/JoinCCP. We greatly value your input on the types of content that enhance your understanding of our security products. Your insights are crucial in guiding the development of our future public content. We aim to deliver material that not only educates but also resonates with your daily security challenges. Whether it’s through in-depth live webinars, real-world case studies, comprehensive best practice guides through blogs, or the latest product updates, we want to ensure our content meets your needs. Please submit your feedback on which of these formats do you find most beneficial and are there any specific topics you’re interested in https://aka.ms/PublicContentFeedback. Note: If you want to stay current with Defender for Cloud and receive updates in your inbox, please consider subscribing to our monthly newsletter: https://aka.ms/MDCNewsSubscribe
Now generally available: Serverless posture coverage in Microsoft Defender CSPM
Serverless workloads are a foundation of modern application development, powering everything from low-code and no-code solutions to AI applications and agentic workflows. Development teams use functions, app services, containers, APIs, and event-driven infrastructure to move quickly, scale on demand, and reduce operational overhead. As AI applications and autonomous workflows increasingly rely on distributed services, background tasks, retrieval pipelines, and event-driven components, serverless architectures have become a natural fit. At the same time, they introduce new visibility and posture management challenges. While cloud providers manage the underlying infrastructure, organizations remain responsible for securing their applications, including code, container images, dependencies, configurations, identities, permissions, and access paths. Today, we're announcing the general availability of Serverless Container posture in Microsoft Defender Cloud Security Posture Management (Defender CSPM). Building on the recent general availability of Serverless Compute posture in Defender CSPM, these capabilities extend agentless posture coverage across supported serverless containers, applications, and functions in Azure and AWS. With this expanded serverless posture coverage, security teams can: Discover serverless workloads as first-class assets in a unified cloud inventory Assess workload-specific vulnerabilities, insecure dependencies, and risky configurations Surface exposure, identity, permission, and configuration context that helps prioritize contextual attack paths to broader applications Prioritize risk using security graph context and attack path analysis Act on severity-ranked recommendations in Defender for Cloud Serverless posture coverage across containers, apps, and functions Defender CSPM extends posture management across supported serverless workloads in Azure and AWS. Category Covered workloads Serverless applications and functions Azure Functions, Azure Web Apps, AWS Lambda Serverless containers Azure Container Apps, Azure Container Instances, AWS ECS on Fargate These resources are automatically discovered and surfaced in Defender cloud inventory, helping security teams maintain visibility across dynamic, event-driven application environments. Across supported serverless workloads, multiple layers of posture assessment are provided: Inventory: Serverless compute and container workloads are automatically discovered and mapped with key properties, helping teams understand what is running across their environment. Misconfiguration assessment: identify risky settings such as internet exposure, missing HTTPS, and other configuration issues that can increase exposure. Vulnerability management: Vulnerabilities are detected across supported serverless workloads and tracked over time, helping teams understand where exposed workloads may also contain known CVEs. Attack path analysis: Serverless resources are connected into the Security Graph, so teams can understand how a compromised function or container could reach sensitive assets. Actionable recommendations: Findings are surfaced as resource-level recommendations, making it easier to assign ownership, remediate, and track progress. Secure Score impact: Serverless findings contribute to the broader Secure Score, so risk reduction is reflected in the organization’s overall posture. These findings are incorporated into Security Graph and attack path analysis, helping security teams understand risk in context and prioritize remediation based on how serverless workloads connect to other resources across the cloud environment. In practice Exposed function with access to sensitive data: A serverless function may look like a simple HTTP endpoint, but if it is internet-facing, running with broad identity permissions, and connected to sensitive storage, it can become part of a real attack path. Defender CSPM helps connect these signals across exposure, vulnerabilities, identity, and data access so teams can prioritize the workload based on actual risk, not just isolated findings. Serverless container running a vulnerable image: A serverless container running on Azure Container Apps, Azure Container Instances, or Amazon ECS on AWS Fargate may be fully managed at the infrastructure layer, but the customer still owns the image, application code, identity, configuration, and exposure. If that workload is internet-facing, built from an image with a critical CVE, and connected to Key Vault, storage, or other sensitive services, Defender CSPM helps teams discover it, assess the risk, and prioritize remediation in context. One consistent Defender experience Defender CSPM now natively includes serverless posture coverage. Discovered functions, web apps, and serverless containers appear in the unified cloud inventory, are evaluated through security recommendations, integrate into attack path analysis, and are queryable through Cloud Security Explorer. This consistency matters because serverless workloads rarely operate in isolation. A function might call an API, read from a queue, authenticate with a managed identity, and write to storage. A serverless container might expose an endpoint, pull an image from a registry, process events, and connect to secrets or databases. Defender CSPM helps security teams evaluate these workloads with surrounding context, including exposure, vulnerabilities, identity permissions, configuration risk, and relationships to other resources. That context helps teams move from isolated findings to prioritized remediation. Built for modern and AI-ready applications As organizations build AI-powered applications, agentic workflows, and distributed cloud services, serverless infrastructure continues to play a growing role in delivering scalability and operational efficiency. Defender CSPM helps security teams gain visibility into supported serverless containers, applications, and functions, assess vulnerabilities and misconfigurations, and prioritize remediation using Security Graph context and attack path analysis. By bringing serverless workloads into inventory, recommendations, Cloud Security Explorer, and attack path analysis experiences, Defender CSPM helps organizations better understand and reduce risk across their cloud environments. Explore the documentation for serverless protection and posture for serverless container workloads, and discover the latest innovations in Microsoft Defender for Cloud in the release notes.
