The Microsoft AI and Agent Platform — Securing and Governing Intelligent Agents
In Part 1, we explored the intelligence side of the Microsoft AI and Agent Platform: how agents understand work, reason over trusted context, use models and tools, and operate across experiences such as Microsoft 365 Copilot, Copilot Studio, Microsoft Foundry, Dynamics 365, GitHub, and custom applications. But intelligence is only half the equation. As agents move from answering questions to taking action, the security model changes. Agents can retrieve sensitive data, call APIs, invoke tools, trigger workflows, collaborate with other agents, remember context, and act with delegated or assigned authority. That creates a new enterprise requirement: agents must be not only useful, but also governable, observable, contained, policy-driven, auditable, and aligned with organizational risk boundaries. The core question for enterprise AI is no longer only, “Can the agent reason?” It is also, “Can we trust what it can access, what it can do, how it is governed, and whether we can prove what happened?” That is the focus of Part 2: Trust—showing where security, governance, assurance, isolation, posture management, and security operations apply across the agent lifecycle. The mental model: The left side presents the seven platform layers, while the foundation establishes security across the architecture. On the right, scoped control rails apply targeted governance through Microsoft commercial controls and open-source specifications, with each control operating within its defined scope. Trust is not a feature. It is a platform property. Many early AI security conversations focused narrowly on prompts: prompt injection, jailbreaks, harmful content, and model outputs. Those risks remain important, but agentic systems broaden the attack surface. An enterprise agent is not just a model endpoint. It is a system composed of: User and application interaction channels. Foundation and custom models. Agent orchestration and reasoning. MCP servers, APIs, tools, skills, and connectors. RAG pipelines and enterprise knowledge sources. Short-term and long-term memory. Identity and access controls. Runtime environments, containers, cloud PCs, and networks. Monitoring, audit, compliance, and security operations. Trust must therefore be applied across every layer. A secured agent platform needs controls before the model call, during reasoning, before and after tool execution, at data retrieval time, during output generation, and throughout runtime operation. That is why the Microsoft Security for AI Platform is best understood as an end-to-end security architecture for the AI application and agent lifecycle. The diagram follows the path from interaction channels, through the agent and grounding layers, into build and runtime environments, and finally into security operations. The new security boundary: from applications to agents Traditional application security assumes relatively deterministic behavior: users click buttons, applications call APIs, authorization checks enforce access, and logs record the result. Agentic systems introduce more dynamic behavior. An agent may interpret a goal, decompose it into tasks, choose tools, retrieve context, call APIs, invoke another agent, generate code, operate a browser, or take action through a workflow. That flexibility is where business value comes from, but it also means controls must cover more than static application paths. A secure AI platform must answer several questions: Trust question Why it matters Who is the user and who is the agent? Agents need identity, permissions, ownership, lifecycle, and accountability. What data can the agent retrieve? RAG and grounding can expose overshared or sensitive enterprise data. What tools can the agent use? APIs, MCP servers, connectors, and skills become action surfaces. When does a human need to approve? High-impact actions require explicit approval and evidence. Where does the agent run? Runtime isolation matters when agents execute code, use browsers, or automate legacy apps. What happened? Security teams need traces, logs, detections, investigations, and audit records. How do we govern at scale? Enterprises need policy, posture management, compliance, and lifecycle controls across thousands of agents. Microsoft’s approach is to apply a consistent trust foundation across the same layers that deliver intelligence: interaction channels, AI applications and agents, data and context, build platforms, runtime infrastructure, and security operations. 1. Secure the AI interaction channels Agents enter the enterprise through many channels: Microsoft 365 Copilot, Teams, SharePoint, Office, Dynamics 365, custom portals, APIs, MCP endpoints, web experiences, and developer tools. Each channel has a different risk profile. Microsoft 365 Copilot and business application experiences inherit enterprise identity, compliance, and data protection controls from Microsoft 365, Microsoft Entra, Microsoft Purview, and Microsoft Defender. Custom apps, web apps, and API-driven experiences require the same rigor: authentication, authorization, network controls, input validation, logging, monitoring, and response. For developer tools, GitHub Advanced Security, Microsoft Defender for DevOps, Microsoft Defender for Cloud, and Microsoft Security Copilot help protect the software supply chain behind AI applications. Agent systems often depend on code, prompts, infrastructure-as-code, container images, packages, actions, connectors, and deployment pipelines. Those assets need the same scanning, secret detection, dependency governance, and posture management expected of any production system. For APIs and MCP servers, Azure API Management, Microsoft Defender for APIs, Microsoft Entra, Conditional Access, Private Link, and network controls become especially important. Agent tool use should be explicit, least privileged, monitored, and policy governed. Microsoft Defender for Cloud Apps also plays an important role across interaction channels by helping organizations discover and govern SaaS usage, manage OAuth app risk, apply session controls, and understand shadow AI exposure across the enterprise. 2. Secure the AI application and agent layer The AI application and agent layer is where models, agents, MCP, skills, hooks, RAG, memory, and orchestration come together. This layer sets agent security apart because agents do more than process requests, they reason, make decisions, and take action. Agent harness and the agent loop An agent harness is the runtime scaffolding that turns model reasoning into a stateful agent workflow. It assembles instructions and context, coordinates model and tool calls through the agent loop, maintains session state, and applies configured approvals and observability. Because the harness mediates these interactions, it is a natural integration point for policy controls: Agent Hooks defines standardized interception points, while the Agent Control Specification (ACS) evaluates policy at those points and returns decisions for the host to enforce. The harness is not an execution-isolation boundary; tools and code may run in separate containers, sandboxes, Cloud PCs, or hosted environments that require their own security controls. Models Foundation and custom models should be governed through approved model catalogs, deployment policies, evaluation pipelines, content safety controls, and monitoring. Microsoft Foundry provides a governed environment for model selection, evaluation, deployment, and operations. Model choice should be treated as a policy decision, not an uncontrolled developer preference. Different workloads may require different tradeoffs across cost, latency, quality, data handling, region, safety, and compliance. Enterprises need visibility into which models are used, for which workloads, and under which controls. Azure AI Content Safety, available through Microsoft Foundry capabilities, helps protect AI applications from harmful content, prompt attacks, jailbreak attempts, unsafe outputs, and other content risks. Content safety should be treated as part of the AI control plane, not a bolt-on filter at the edge. Agents Agents need their own lifecycle: discovery, ownership, identity, permissions, classification, monitoring, and retirement. Agent 365 helps organizations discover, inventory, govern, and enforce policies across their agent estate, preventing it from becoming an unmanaged shadow application layer. This matters because agent sprawl can become the next application sprawl. Without inventory and governance, organizations may not know which agents exist, who owns them, what data they access, what tools they can invoke, or whether they are still needed. Microsoft Purview complements this by helping protect and govern the data agents use, including sensitivity labels, data loss prevention, audit, compliance, and data security posture management for AI scenarios. Microsoft Entra provides the identity fabric for users, apps, agents, and workloads, helping ensure that agent actions are tied to explicit identities and least-privilege access. MCP, APIs, tools, and skills The Model Context Protocol and tool-based agent architectures are powerful because they let agents connect to systems of record, business applications, knowledge stores, and automation surfaces. They are also high-value control points. Every tool is effectively a capability grant. A read-only knowledge tool, a refund API, a database query tool, a ticketing connector, a browser automation tool, and a code execution tool carry very different risk. Tool authorization should be granular, contextual, and auditable. Key controls include: Tool allowlisting and approval workflows. Least-privilege API permissions. Strong authentication through Microsoft Entra. Managed identities and workload identities for secretless access. API inspection and protection with Azure API Management and Defender for APIs. Network isolation with Private Link, Azure Firewall, WAF, and DDoS Protection. Runtime isolation with containers, Microsoft eXecution Container (MXC), Windows 365 for Agents, and confidential computing where needed. Monitoring and detection through Microsoft Defender, Microsoft Sentinel, and Azure Monitor. Agent Hooks and policy gates Because the harness coordinates the agent loop, it provides a natural host-level seam for policy enforcement. Agent Hooks defines a framework-neutral interception contract across agent input, model calls, tool calls, and output. ACS can evaluate policy at those interception points, while the conforming host applies the resulting allow, deny, transform, or approval decision. Traditional framework callbacks are often designed for observability, not governance. They may observe events but not reliably block actions, transform content, bind approvals, or produce enforceable evidence. Agent Hooks introduces a framework-neutral governance contract for AI agents with defined interception points across the lifecycle, including startup, input, model calls, tool calls, output, and shutdown. Agent Hooks and the Agent Control Specification are open, design-partner-informed control contracts for agent hosts and frameworks. They define where policy decisions can be requested and what obligations a conformant host has when a verdict requires blocking, approval, transformation, or record generation. If a policy denies a tool call, the tool should not execute. If a post-tool response is denied, it should not enter agent state. If approval is required, the approval should bind to the exact action and content that was reviewed, not loosely to a session. Agent Hooks complements, but does not replace, runtime isolation. It helps govern cooperative agent host; it does not sandbox the host, tools, or untrusted code. It therefore works alongside the Agent Control Specification, Microsoft Foundry control plane, Agent 365, Microsoft Agent Framework, Microsoft eXecution Container (MXC), and Windows 365 for Agents to combine policy governance with secure execution. RAG and knowledge grounding Agents become more useful when they can ground reasoning in enterprise and external context. But grounding also expands the agent’s trust boundary by introducing additional data sources, retrieval paths, indexes, and content into the reasoning process. Securing and governing these grounding sources is addressed in the next section as part of the data and context layer. 3. Govern data and context The data and context layer is where enterprise knowledge becomes available to AI applications and agents and where existing data-security and governance boundaries must continue to hold. Agents may ground their reasoning across enterprise data, Work IQ, Fabric IQ, Foundry IQ, Web IQ, Azure AI Search, Microsoft Graph, and other organizational knowledge sources. These sources can provide valuable context, but each introduces its own authorization model, data boundary, retrieval path, and governance requirements. Trust therefore depends not simply on connecting agents to more information, but on ensuring that grounding does not create a new path around the controls protecting that information. Securing grounding starts with identity and authorization. Retrieval should preserve the permissions of the underlying source wherever supported and ensure that users, agents, applications, and workload identities can access only the information required for the task. Microsoft 365 Copilot respects Microsoft 365 user permissions and supported Microsoft Purview protections; other grounding architectures require source-specific configuration and validation. Access through Microsoft Graph, Azure AI Search, Microsoft Fabric, Microsoft Foundry, or other retrieval mechanisms should similarly be designed around explicit identities, least privilege, and the authorization boundaries of the underlying data. Data protection must extend through the grounding pipeline. Sensitive information can exist not only in source documents and databases, but also in indexes, retrieved passages, embeddings, prompts, agent memory, model responses, logs, and downstream actions. Applicable sensitivity labels, information protection, DLP, retention, audit, encryption, and compliance controls should therefore be considered across the complete flow of grounded information rather than only at the original data source. Grounding also introduces risks that traditional access control alone does not address. Overshared content can become overshared AI context; stale or low-quality information can influence agent decisions; compromised or untrusted sources can introduce poisoned content; and instructions embedded in retrieved content can attempt to manipulate agent behavior through indirect prompt injection. Organizations should therefore govern which sources can be used for grounding, validate the trustworthiness and freshness of those sources, protect retrieval and indexing pipelines, and apply appropriate evaluation, monitoring, and content-safety controls. For retrieval-based architectures, indexes and knowledge stores should be treated as governed enterprise assets. Organizations should understand what information is indexed, where it originated, how frequently it is refreshed, which identities can query it, how access controls are enforced, and whether retrieved content can cross security or data boundaries. Retrieval relevance alone is not sufficient; the retrieval path must also preserve the security intent of the source system. This is particularly important for Microsoft 365 grounding. Microsoft Purview helps protect and govern sensitive information through capabilities such as information protection, DLP, audit, retention, compliance, and data security posture management. Microsoft Entra provides identity and access controls for users, applications, agents, and workloads. SharePoint Advanced Management can help organizations address oversharing and content-governance risks in the SharePoint estate used by Microsoft 365 Copilot and agents. Other grounding environments require equivalent controls appropriate to their data sources, identities, retrieval architecture, and runtime. The same principles apply across Work IQ, Fabric IQ, Foundry IQ, Web IQ, enterprise data, Azure AI Search, and Microsoft Graph, but the controls should not be assumed to operate identically across them. Their integration models, permissions, governance capabilities, and maturity differ. Organizations should validate the security boundary of each grounding source and explicitly determine how identity, authorization, data protection, retrieval, monitoring, and audit requirements are enforced for that implementation. A trusted grounding architecture should therefore answer several questions: Who or what is requesting the information? Is that identity authorized to retrieve it? Is the source approved and trusted for grounding? Does retrieval preserve the source system's access boundaries? Is sensitive information protected throughout retrieval, indexing, reasoning, memory, and output? Can retrieved content introduce malicious or untrusted instructions into the agent? Are indexes and knowledge stores governed, current, and appropriately isolated? Can organizations observe and audit how grounded information is being accessed and used? The principle is straightforward: grounding should extend enterprise context to agents without weakening the identity, security, privacy, and governance boundaries of the underlying information. A trusted agent should retrieve not simply the most relevant information, but the right information, from trusted sources, through authorized paths, under controls the organization can govern and audit. 4. Secure the build continuum: no-code, low-code, and pro-code Microsoft’s platform supports multiple build paths: Agent Builder for no-code creation, Copilot Studio for low-code agents and orchestration, and Microsoft Foundry for pro-code AI systems. AI solutions can cross boundaries, and security must travel across that continuum. Build path Primary audience Trust requirements Microsoft capabilities Guided/ No-code Business users and teams Agent discovery, ownership, sharing controls, data governance, policy enforcement Microsoft Agent 365, Microsoft Purview, Microsoft Entra, Microsoft Defender Managed/ Low code Makers, process owners, business technologists Connector governance, environment strategy, DLP, approvals, lifecycle management Copilot Studio, Power Platform DLP, Managed Environments, Entra, Purview, Agent 365 Code-first/Custom Developers and platform teams Secure SDLC, evaluations, red teaming, DevSecOps, runtime isolation, observability Microsoft Foundry, GitHub Advanced Security, Defender for DevOps, Defender for Cloud, Azure Monitor No-code: Agent Builder No-code agents make AI creation accessible to more users, which increases business agility and helps organizations bring AI into the flow of work. The trust requirement is to make that creation visible and governable without removing the simplicity that makes no-code valuable. Microsoft Agent 365 helps provide agent discovery, inventory, lifecycle management, policy enforcement, and governance across the agent estate. Microsoft Purview helps ensure that the data used by these agents remains protected by sensitivity labels, DLP, audit, and compliance controls. Microsoft Entra helps enforce identity, access, and least privilege. Together, these capabilities allow organizations to empower business users while maintaining enterprise trust. Low code: Copilot Studio Copilot Studio brings orchestration, connectors, workflows, and extensibility. That makes Power Platform governance important because low-code agents often sit close to real business processes and enterprise data. Power Platform DLP policies, Managed Environments, environment strategy, connector governance, solution lifecycle management, maker and admin roles, and approval patterns for sensitive actions are the mechanisms that turn low-code agility into trusted enterprise automation. These controls help organizations decide which connectors can be used together, which environments are appropriate for development and production, who can build and publish agents, how solutions move through lifecycle stages, and when human approval is required before action is taken. Low-code agents need both business speed and enterprise control. Trust is what allows organizations to scale low-code innovation beyond isolated pilots. Pro-code: Microsoft Foundry Pro-code agent systems demand rigorous software engineering: secure architecture and coding, dependency governance, CI/CD safeguards, container security, infrastructure-as-code scanning, environment isolation, observability, evaluation, and incident response. GitHub Advanced Security and Microsoft Defender for DevOps are central to this approach. GitHub Advanced Security supports code and secret scanning, dependency review, and software supply-chain protection. Defender for DevOps brings these findings into Microsoft Defender for Cloud, giving security teams a unified view of AI application, cloud, and workload risks. AI-specific assurance should also be embedded in the development lifecycle. Open-source tools such as PyRIT, RAMPART, ASSERT, and the Agent Governance Toolkit support red teaming, behavioral evaluation, governance evidence, and regression testing. Open specifications such as Agent Hooks and the Agent Control Specification establish interoperable control patterns across agent hosts and frameworks. These open-source tools give organizations transparent, extensible ways to test and standardize AI security across heterogeneous environments. For enterprises using multiple frameworks, models, clouds, APIs, and orchestration approaches, PyRIT, RAMPART, ASSERT, and the Agent Governance Toolkit provide practical methods to red-team, evaluate, govern, and validate systems. Agent Hooks and the Agent Control Specification complement them with a common language for integrating controls at the host level. Microsoft commercial solutions operationalize these controls at enterprise scale. Microsoft Agent 365, Microsoft Foundry, Microsoft Entra, Microsoft Purview, Microsoft Defender, Microsoft Sentinel, Microsoft Security Copilot, GitHub Advanced Security, and Azure infrastructure services provide the inventory, policy enforcement, data governance, monitoring, detection, response, compliance, and operational capabilities required for production environments. The roles are complementary: open-source tools promote transparency, interoperability, and technical assurance, while commercial solutions provide enterprise control planes, integrated operations, support, and scale. Together, they help customers and partners secure AI systems from experimentation through production. 5. Secure runtime, isolation, and execution Once agents are built, they need somewhere to run. The runtime layer determines how agents are isolated, monitored, scaled, and connected. Key runtime and infrastructure components include Microsoft Foundry, Azure AI Search, Azure Confidential Computing, Azure Kubernetes Service, Azure Container Apps, Azure Functions, Azure Private Link, Storage, Azure Firewall, WAF, DDoS Protection, Azure Monitor, Azure Policy, Windows 365 for Agents, and Microsoft eXecution Container (MXC). AI applications and agents need more than access to a model. They require secure infrastructure to host orchestration, retrieve knowledge, execute tools, process files, automate workflows, connect privately to enterprise systems, maintain state, enforce policy, and capture telemetry. This foundation turns prototypes into resilient, governable production systems. Strong infrastructure controls are especially important because agents may execute code, invoke tools, operate browsers, process files, and interact with systems that were not designed for autonomous actors. Microsoft eXecution Container (MXC) and Windows 365 for Agents are especially important for secure execution. Microsoft eXecution Container (MXC) – (in early preview now) is a sandboxed code-execution capability designed to provide isolated execution environments for agent actions and code. Windows 365 for Agents provides managed Cloud PC environments for agents that need to interact with desktop applications, browsers, or legacy systems that do not expose APIs. Azure Confidential Computing can help protect sensitive workloads in use. Azure Private Link keeps traffic private. Azure Policy enforces guardrails. Azure Monitor and Application Insights provide telemetry. Azure Key Vault and Managed HSM protect secrets and keys. Managed identities and Microsoft Entra Workload ID reduce the need for long-lived secrets. Azure Container Registry and Defender for Containers help protect image-based agent workloads. As agent autonomy and tool access increase, runtime isolation, oversight, and monitoring must strengthen accordingly. 6. Manage runtime security posture Runtime security posture provides a continuous view of whether AI applications, agents, tools, and infrastructure remain within expected security boundaries. Layer 5 defines where and how agents run; Layer 6 assesses whether those environments are hardened, observable, compliant, and improving over time. For agentic systems, posture management must combine traditional cloud controls with AI-specific oversight. Security teams need visibility into AI services, agent workloads, containers, APIs, model endpoints, grounding stores, identities, secrets, and network paths. They must also identify public exposure, missing telemetry, excessive permissions, vulnerable images or packages, unprotected data stores, and access to unapproved tools or data sources. Microsoft Defender for Cloud anchors this layer. Cloud Security Posture Management identifies misconfigurations, excessive permissions, exposed resources, weak network paths, encryption gaps, and compliance issues across cloud workloads. Defender for Containers extends that visibility to images, registries, clusters, and runtime risks. Defender for AI services threat protection and AI Security Posture Management add AI-specific discovery and protection, surface risky configurations, and connect those findings to broader cloud and workload risk. Effective runtime posture also requires identity and data context. Microsoft Entra, managed identities, Entra Workload ID, Conditional Access, and Privileged Identity Management help reduce standing privilege and reliance on secrets. Microsoft Purview adds sensitivity, DLP, audit, retention, compliance, and data-security posture signals. Agent 365 and Microsoft Foundry add context about agent inventory, ownership, evaluations, guardrails, model endpoints, and lifecycle state. The goal is not to generate more recommendations, but to prioritize the risks that matter most in production: exposed agent endpoints, unmanaged MCP tools or APIs, overprivileged workload identities, vulnerable containers, missing telemetry, unprotected secrets, overshared grounding data, noncompliant model deployments, and runtime environments that do not match an agent’s autonomy. These posture signals should flow into security operations. Defender XDR, Microsoft Sentinel, Microsoft Security Exposure Management, and Security Copilot can combine posture, exposure, detection, and audit data to help defenders investigate agent behavior, trace attack paths, and coordinate response. This connects build-time assurance, runtime isolation, continuous posture management, and security operations. 7. Operationalize AI security across the Microsoft Platform AI security must extend beyond design and deployment into continuous operations. As agents move into production, organizations need to connect agent and application governance, identity, data security, cloud and workload protection, exposure management, detection, investigation, and response so agent activity can be understood and governed in the context of the broader enterprise environment. Microsoft provides complementary control planes across this operating model. Microsoft Agent 365 and Microsoft Foundry Control Plane help govern the agent estate and AI platform; Copilot Studio and Power Platform governance provide environment, connector, and DLP controls for low-code agents and applications; Microsoft Entra governs identity and access; Microsoft Purview protects and governs data; and Microsoft Defender, Microsoft Security Exposure Management, Microsoft Sentinel, and Microsoft Security Copilot extend protection, exposure management, detection, investigation, and response across the environment. Microsoft Security provides an integrated operating model across these domains: Security capability Microsoft solutions Agent and application governance Microsoft Agent 365, Microsoft Foundry Control Plane, Copilot Studio and Power Platform governance, Managed Environments, Power Platform DLP Identity and access security Microsoft Entra, Conditional Access, Identity Protection, Privileged Identity Management, Workload ID, Agent ID Data security and compliance Microsoft Purview, Data Security Posture Management, Information Protection, DLP, Audit, eDiscovery Cloud, workload, and AI protection Microsoft Defender for Cloud, Defender CSPM and AI security posture management, Defender for AI Services, Defender for Containers Endpoint and user protection Microsoft Defender XDR, Defender for Endpoint, Microsoft Intune API and application protection Azure API Management, Defender for APIs, Defender for Cloud Apps Exposure management Microsoft Security Exposure Management, Defender External Attack Surface Management Security operations Microsoft Defender XDR, Microsoft Sentinel DevSecOps and software assurance GitHub Code Security, GitHub Secret Protection, Defender for DevOps, MDASH Secrets and network protection Azure Key Vault, Managed HSM, Azure Firewall, WAF, DDoS Protection, Private Link, Global Secure Access For agentic systems, this connected view is especially important. An agent may authenticate with one identity, retrieve sensitive data from another system, invoke APIs or MCP tools, execute code in a runtime, and take action in a business application. Security operations therefore need to correlate relevant identity, data, application, API, cloud, endpoint, workload, and agent signals rather than assess each component in isolation. Posture, exposure, telemetry, detections, and audit signals can then help defenders understand suspicious agent activity in context—for example, an over-permissioned identity, exposed API, vulnerable runtime, unusual data access, or anomalous agent behavior. The goal is not to create a separate security operations model for AI, but to extend existing enterprise security operations to AI applications and agents. Defend with AI Trust also means moving from defending AI to defending with AI. Microsoft is applying AI across security operations, software assurance, and proactive threat discovery to help defenders identify risk, investigate threats, and accelerate response. Microsoft Security Copilot augments security teams with AI-assisted investigation, incident summarization, signal correlation, query generation, and response workflows. Codename MDASH, currently in preview, is Microsoft’s multi-model agentic scanning harness, an agentic code scanner in Microsoft Defender that coordinates specialized agents and models to discover, validate, and help remediate source-code vulnerabilities. Project Perception, currently in limited preview, coordinates red, blue, and green team agents to uncover weaknesses, investigate threats, and recommend or perform approved remediation. Together, these capabilities demonstrate the next evolution of security operations: using security to protect AI while using AI to strengthen security combining human expertise with AI-assisted and agentic defense across prevention, exposure management, detection, investigation, and response. Apply security foundations across every layer Security foundations provide consistent principles that underpin trust across the AI and agent lifecycle. Zero Trust, encryption, observability, continuous monitoring, Responsible AI, and governance become actionable when they are applied as concrete controls across identity, data, applications, agents, tools, grounding, and runtime environments. Zero Trust: Verify explicitly, enforce least privilege, and assume breach across user, agent, workload, tool, data, and runtime access. Encryption: Protect prompts, responses, grounding data, memory, logs, and tool payloads at rest, in transit, and where supported, in use. Observability and logging: Capture relevant agent activity including tool calls, data access, policy decisions, approvals, errors, and outcomes with appropriate privacy controls. Responsible AI and governance: Evaluate agents for safety, reliability, privacy, security, transparency, and accountability before and after deployment. Continuous monitoring: Monitor for drift, misuse, anomalous behavior, unexpected data access, and emerging threats as models, data, and tools change. Audit and accountability: Maintain evidence of who initiated an action, which agent acted, what resources were accessed, which controls were applied, and what outcome occurred. Privacy and data protection: Apply appropriate information protection, DLP, retention, audit, and compliance controls across prompts, grounding, memory, outputs, logs, and downstream actions. These controls must operate continuously across the agent lifecycle. As agents move from design and build through grounding, deployment, monitoring, and response, their models, data, tools, identities, permissions, and runtime environments will continue to change. Trust therefore cannot be established once at deployment; it must be continuously evaluated, governed, monitored, and improved as the agent and its operating environment evolve. The outcome: trusted autonomy Trust is not a constraint on AI transformation; it is what enables AI to scale responsibly across the enterprise. As organizations move from assistive copilots to increasingly autonomous agents and multi-agent systems, they need confidence that those systems operate within clear boundaries for identity, data access, tool use, execution, governance, and accountability. The goal is not to eliminate autonomy, but to make autonomy intentional, bounded, observable, and governable. Microsoft brings together AI platforms with identity, data security, application security, cloud and runtime protection, security operations, and open security tooling to help organizations establish these boundaries across the agent lifecycle. Rather than creating a separate security model for every agent, framework, runtime, or data source, organizations can apply a consistent trust architecture while adapting controls to the risk and autonomy of each scenario. This is trusted autonomy: enabling agents to reason and act with increasing independence while keeping their access, actions, and outcomes within enterprise-defined security and governance boundaries. Combining the intelligence that makes agents useful with trust that allows organizations to deploy them confidently at enterprise scale. Intelligence + Trust = Frontier Transformation.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.Introducing Multi-Account Support for Connectors in Microsoft Sentinel
We're excited to announce that Microsoft Sentinel's data connectors for Auth0, CrowdStrike Falcon, and Salesforce Service Cloud now support multi-account ingestion — enabling you to connect and monitor multiple accounts or tenants from a single, unified connector configuration. The Challenge with Multi-Account Environments Modern enterprises don't run on a single account. Whether it's multiple Salesforce orgs across business units, several CrowdStrike tenants spanning subsidiaries, or Auth0 environments segmented by product line — security teams have long struggled to get unified visibility across all of them in a single SIEM. Until now, connecting multiple accounts from the same platform required painful workarounds: duplicate configurations, custom scripts, or dangerous blind spots in security coverage. Introducing Multi-Account Support for Auth0, CrowdStrike, and Salesforce in Microsoft Sentinel We're excited to announce that Microsoft Sentinel's data connectors for Auth0, CrowdStrike Falcon, and Salesforce Service Cloud now support multi-account ingestion — powered by the Codeless Connector Framework (CCF). You can now connect and monitor multiple accounts or tenants from a single, unified connector configuration — no scripts, no hacks. What's New? 🔑 Auth0 — Multi-Tenant Identity Monitoring Security teams managing multiple Auth0 tenants can now ingest logs from all of them into a single Sentinel workspace. Get complete visibility into authentication events, anomalous login patterns, and policy violations across every tenant without switching contexts. 🦅 CrowdStrike Falcon — Consolidated Endpoint Telemetry Organizations running multiple CrowdStrike tenants (e.g., across M&A entities or regional subsidiaries) can now stream detection alerts, threat intelligence, and endpoint telemetry from all tenants into Sentinel. One workspace. Full coverage. ☁️ Salesforce — Cross-Org Security Insights Enterprises with multiple Salesforce orgs can now centralize audit logs, login history, and API activity across all orgs. Detect insider threats, unauthorized access, and compliance gaps without stitching data together manually. 📖Find relevant connectors at Discover connectors Why It Matters Before After One connector = one account One connector = multiple accounts Manual workarounds for multi-tenant coverage Native, built-in multi-account support Fragmented detection across environments Unified analytics and incident correlation Higher operational overhead Streamlined configuration and management Getting Started Connecting multiple accounts is straightforward: 1. Navigate to Microsoft Sentinel → Data Connectors 2. Search for Auth0, CrowdStrike Falcon, or Salesforce 3. Open the connector and select "Add Account" 4. Authenticate and authorize each additional account 5. Start ingesting — your analytics rules, workbooks, and playbooks apply automatically across all accounts Built for Scale, Built for SOC Teams This update is part of our continued investment in making Microsoft Sentinel the most comprehensive and operationally efficient SIEM for enterprise environments. Multi-account support reduces configuration overhead, closes coverage gaps, and empowers SOC analysts to detect and respond to threats wherever they originate. What's Next? We're actively expanding multi-account support to more connectors. Stay tuned to the Microsoft Sentinel Blog and share your feedback!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.The Microsoft AI and Agent Platform — The Platform Behind Intelligent Agents
Why the platform around the model is the real enterprise differentiator Enterprise AI has reached a turning point. Beyond answering questions, it can now reason over business context, retrieve knowledge, use tools, coordinate workflows, and act across enterprise systems. This shift raises a critical question: How can organizations build agents intelligent enough to transform work while ensuring they remain trusted, governed, and ready to operate at enterprise scale? The answer is not a single model, chatbot, or orchestration framework. Foundation models are advancing quickly and increasingly becoming a commodity input — Azure AI Foundry alone provides access to more than 11,000 models. What determines enterprise value is not the model alone, but the platform around the model: the data that grounds it, the tools it can use, the experiences where people engage it, the runtime where it operates, and the enterprise foundation that gives it identity, context, governance, and operational control. The Microsoft AI and Agent platform enables organizations to build, ground, govern, and operate AI apps and agents at scale, bringing together the full agent lifecycle with open development, built-in intelligence, and consistent security, compliance, and policy controls. One ecosystem, multiple experiences, shared intelligence, flexible build paths, multiple runtime choices, and an enterprise foundation that carries security, governance, compliance, and Responsible AI across the stack. The reference mental model below expresses this as a layered platform — Users → Experiences → Agents → Intelligence → Runtime → Foundation with security, governance, compliance, and Responsible AI applied across every layer. An agent that is brilliant but ungoverned never leaves the pilot stage. An agent that is locked down but context-blind never delivers real value. Impact compounds only when both dimensions advance together, on the same platform, so that intelligence and control share one identity model, one data plane, and one control plane. Part 1 — Intelligence (this post): dives into how Microsoft's platform helps organizations build agents that understand work, reason over trusted context, and act through business systems to deliver real business value. Part 2 — Trust: will go deeper on how those agents are secured, governed, monitored, and managed across their lifecycle. Intelligence + Trust = Frontier Transformation Part 1: Intelligence Most enterprise AI programs begin with model experimentation - prompts, model comparisons, prototypes, accuracy evaluations. That is necessary but not sufficient. A model alone does not know your organization, your processes, your permissions, your systems of record, your compliance obligations, or your operating model. Experience layer: meet users where work already happens Agents deliver value only when they reach people in the flow of work. Enterprise AI adoption rarely happens through a single interface or experience. A sales leader, financial analyst, security operator, developer, field technician, and HR specialist do not need the same interface they need agents surfaced in the tools and workflows they already use. Microsoft's approach is not to force every agent into one portal. The platform supports multiple experiences over a shared foundation: Microsoft 365 Copilot for productivity and business users. Security Copilot for security operations. Azure Copilot for IT operations, cloud, and infrastructure. GitHub Copilot for developers. Dynamics 365 experiences for sales, service, finance, and supply chain workflows. Power Platform and Copilot Studio experiences for business applications and low-code extensions. Custom experiences for line-of-business apps, portals, websites, and industry-specific workflows. Regardless of where users engage, the underlying intelligence, governance, and runtime capabilities remain consistent across experiences. Agent layer: specialize by domain, tools, and autonomy Specialization with a shared substrate Generic agents often fail because enterprise work is domain specific. A security agent must understand incidents, alerts, identities, and threat intelligence. A finance agent must understand reconciliations, receivables, approvals, and controls. A developer agent must understand repositories, branches, pull requests, tests, and pipelines. Microsoft's platform supports both prebuilt domain agents and custom agents. Organizations should leverage the domain specific agents where possible and focus custom development on capabilities that create unique business value. Whether an agent is out of the box or custom, it inherits the same governance, so built-in and custom are never two different compliance islands. Agent systems form an autonomy spectrum, allowing organizations to progressively increase capability while maintaining appropriate levels of human oversight. Assistive: The agent recommends; a human decides. Example - A finance agent drafts a reconciliation for review. Supervised autonomy: the agent acts within bounded authority and escalates exceptions. Example - An SRE agent auto-remediates known alert classes and escalates novel incidents. Multi-agent orchestration: A coordinating agent decomposes a goal and delegates to specialist agents. Example - One agent retrieves data, another analyzes it, another drafts a response, and another executes an approved action. Intelligence layer: grounding as a first-class platform tier An agent is only as good as the context it can reason over. The hardest part of building a useful enterprise agent is not calling a model. It is giving the agent the right context. Without trusted context, agents produce generic answers. The IQ Platform is the intelligence fabric that separates enterprise-grade agents from generic AI assistants. A generic model can answer questions based on its training data or a narrow retrieval source. A Microsoft agent, by contrast, can be grounded in multiple dimensions of your organizational intelligence: how people work, what business data means, which knowledge is authoritative, and what external signals matter. With the right intelligence fabric, agents become role-aware, process-aware, data-aware, and policy-aware. Microsoft's IQ model treats grounding as a reusable platform capability rather than per-project plumbing. IQ layer What it gives agents Why it matters Work IQ Collaboration context: people, skills, meetings, documents, decisions, workflows, and organizational relationships. Helps agents understand how work actually happens, not just what content exists. Fabric IQ Governed business data, metrics, semantic models, and analytical context. Helps agents reason over trusted enterprise data with consistent business definitions. Foundry IQ Models, curated knowledge, retrieval assets, memory, guardrails, and AI development capabilities delivered from Microsoft Foundry with plug-and-play memory, knowledge, and tool integrations. Helps teams build reliable, purpose-built agents with governed model and knowledge choices. Web IQ Public web, current external signals, research, news, and external context. Helps agents augment internal context with timely external intelligence. In a conventional application, data access is deterministic queries against known schemas. In an agentic system, the equivalent tier must serve retrieval for reasoning, semantically matching an ambiguous natural-language intent to the right passages, records, and metrics across unstructured collaboration content, structured business data, curated knowledge, and the live web. The four IQ sources correspond to those four retrieval modalities, and the IQ Platform gives agents a composable intelligence model. Each IQ layer adds a distinct signal, and together they allow agents to move from simple assistance to informed action. Intelligence is more than model capability. It emerges from the combination of grounding, memory, model selection, orchestration, and guardrails working together as a coordinated system. Grounding, fine-tuning, and adaptation Microsoft gives teams multiple adaptation levers within a governed environment rather than forcing every use case into one technique. Grounding is not a sidecar retrieval capability; it is an enterprise intelligence layer. Because the model layer is a platform tier rather than a single endpoint, adaptation techniques fine-tuning, distillation into smaller task models, and retrieval-augmented grounding are first-class options selected per workload. The common pattern: prefer grounding (RAG) for freshness and provenance, reserve fine-tuning for durable behavior, format, or domain-tone requirements, and distill to smaller models where latency and cost dominate. Memory In addition to retrieval and reasoning, enterprise agents increasingly rely on memory to preserve context across conversations, tasks, and workflows. Memory enables agents to maintain continuity, learn from prior interactions, and provide more personalized, adaptive, and goal-oriented experiences over time. Multi-model choice Agent workloads are not uniform. Some steps require simple classification. Others require complex reasoning, synthesis, code generation, or tool orchestration. Model choice is becoming a strategic architecture decision, balancing quality, latency, cost, sovereignty, and specialization requirements. Microsoft Foundry supports model choice as part of the platform rather than forcing all workloads through one endpoint with a curated catalog of leading foundation, open-source, and partner models spanning capabilities, performance trade-offs, and use cases so teams can move from experimentation to production confidently. Model routing Microsoft Foundry's Model Router selects the optimal LLM for each agent request per turn, not per session — a simple greeting can route to a fast, inexpensive model, while a complex tool-calling chain can route to a frontier model, all through one endpoint with zero routing logic. Model selection becomes a runtime policy, not hard-coded application logic providing automatic failover when an upstream provider is unavailable, prompt caching across models for identical inputs, and consistent tool-use semantics regardless of which underlying model handles a call. Key routing capabilities include per-request optimization, complexity-aware model selection, tool-aware routing, multi-agent support, resiliency, and cost optimization. Orchestration Orchestration transforms individual model interactions into coordinated agentic and multi-agent workflows. An LLM-driven planning layer that interprets user intent, breaks down complex requests, selects the right tools and knowledge, and executes multi-step plans and multi-agent workflows with guardrails for safety and compliance. Guardrails A guardrail is a named collection of controls; each control defines a risk to be detected, intervention points to scan the risk, and the response action to take when the risk is detected. Guardrails help ensure that agent behavior remains aligned with organizational policies, safety requirements, and business objectives. How agents are built: one continuum from no-code to pro-code Different builders. Different depth. One platform. The progression from no-code to low-code to pro-code is more than a tooling choice; it reflects increasing levels of customization, control, and organizational maturity. Different teams need different levels of control. A business user may need a simple knowledge agent. A process owner may need a workflow agent with connectors and approvals. An engineering team may need a custom multi-agent system with model routing, evaluation, tool use, and deployment automation. Organizations can start with simple productivity agents, evolve into governed workflow agents, and eventually build deeply integrated agentic systems. No-code - M365 Agent Builder: create simple agents from natural language and your organizational data. This is useful for lightweight departmental workflows, knowledge assistants, and task-specific copilots. Low-code - Copilot Studio: design, extend, and orchestrate agents with connectors, workflows, and enterprise governance. This is where business technologists and app makers can build more sophisticated agents that integrate with systems, automate processes, and enforce organizational rules. Pro-code - Microsoft Foundry: enables developers to build custom AI systems with full control over models, orchestration, infrastructure, and code. This is where organizations can build highly specialized agents with advanced reasoning patterns, custom retrieval, tool use, evaluation pipelines, and deployment strategies. The key principle is continuity; moving from no-code to low-code to pro-code should not require rethinking the architecture. Identity, grounding, governance, policy, and operational controls should carry forward including centralized identity and policy enforcement. Regardless of the development approach, the same intelligence, runtime, governance, and operational capabilities can be reused across the platform. Where agents run: one platform, multiple runtime choices Match the runtime to the requirement A mature enterprise platform must support more than one runtime pattern. Some agents need elastic cloud scale. Others need local execution because of latency, data sensitivity, offline operation, or regulated environments. Some need to interact with legacy applications that do not expose APIs. Runtime should be selected based on business, operational, and regulatory requirements rather than tooling limitations. Build path and runtime path should vary independently over a shared foundation. The ability to deploy the same agent architecture across multiple runtime environments helps organizations balance performance, compliance, and operational flexibility. Local / edge (Foundry Local, Windows AI): Local or edge execution supports scenarios where data sensitivity, latency, offline access, regulatory requirements, disconnected operation or device-specific context matter. Examples include on-device models, Windows AI capabilities, and local execution for regulated or disconnected environments. Cloud runtime (Azure / Copilot stack): supports scalable, API-driven agents with multi-agent orchestration running in Azure and Copilot with the default for enterprise workflows, multi-agent orchestration, connected systems, and data-connected scenarios that need elasticity. Cloud PC (Windows 365 agents): enables agents to operate in managed desktop environments. agents run on a Windows 365 Cloud PC using a check-out/check-in model, driving UI automation, browsers, and legacy apps as a human operator would in a managed and governed environment. This is the bridge to systems that expose no API, the agent operates the actual application UI in a governed, isolated desktop. Foundation layer: shared trust fabric The enterprise foundation for intelligence and trust The same enterprise services that secure, govern, and operate modern organizations now extend to agents, creating a shared foundation for both intelligence and trust. This inheritance model allows organizations to extend existing investments in identity, governance, security, compliance, and operations directly to agent systems rather than introducing a separate control model for AI. Key foundation services include: Microsoft Graph – Provides agents the context across users, groups, files, meetings, messages, relationships, and activity signals. It gives agents a permission-aware understanding of work, not just isolated documents. Microsoft Entra – Agents are governed using the same identity fabric that governs users, devices, apps, and resources enabling role-based and attribute-based access control plus risk-based Conditional Access policies. Microsoft Fabric - Governed data, analytics, semantic models, and business metrics. Foundry includes SharePoint and Microsoft Fabric among its built-in tools. Agents reason over trusted business definitions instead of disconnected raw tables. Microsoft Purview - Data protection, sensitivity labeling, DLP, compliance, and governance. Agent 365 uses Microsoft Purview for data protection and compliance controls on agent activity and data, complementing Microsoft Defender for threat detection and behavior monitoring. Agent interactions inherit enterprise compliance expectations. Azure - Provides enterprise-grade cloud infrastructure and operational maturity. Foundry emphasizes centralized observability, traces, evaluated runs, and production performance monitoring with full traceability for enterprise-scale security, audit, and compliance requirements. Microsoft 365 - Brings agents into the tools where employees already work. Agents can be surfaced in the productivity tools users already leverage. Dynamics 365 - Business application context for sales, service, finance, supply chain, and operations. Grounds agents in business processes and systems of record. Power Platform - Low-code apps, automation, connectors, and business process integration — reachable via Foundry through Azure Logic Apps integration with more than 1,400 connectors. Business technologists can extend agent workflows without building everything in code. GitHub - Developer workflows, repositories, pull requests, code context, and DevOps integration. Extends agentic assistance into software development lifecycle. Windows & Windows 365 - Endpoint and Cloud PC environments for local, desktop, and legacy app scenarios. Extends agent reach beyond APIs into managed desktop execution patterns. Alongside these services, Agent 365 and the Foundry Control Plane provide the trust layer for enterprise agents, combining security, governance, compliance, and Responsible AI with centralized visibility, policy enforcement, lifecycle management, and secure AI operations from development through production. End-to-end request journey: how the layers work together The true value of the platform emerges when all the layers work together as a coordinated system. Intelligence emerges from the combined effect of experience, domain specialization, grounding, memory, models, orchestration, runtime, and foundation. An example request, from a user - “Reconcile last month's receivables and flag anomalies for my region." Experience - The user asks from Microsoft 365 Copilot or a finance workflow surface, the agent is reached through the same stable endpoint used across Microsoft 365 and Teams. Identity context - The platform attaches user identity, and, for the agent, its Microsoft Entra Agent ID assigned in Foundry. Agent selection - A finance agent interprets the goal. If the request spans domains, Copilot Studio generative orchestration decomposes it into a plan, choosing tools, topics, knowledge sources, or connected agents. Grounding - Fabric IQ provides receivables data and metric definitions; Work IQ provides relevant approvals and prior decisions; Foundry IQ provides reconciliation rules and policy knowledge; Web IQ can add external signals when needed. Model routing - The Foundry Model Router selects the model per turn. A simple classification step goes to a nano-tier model; anomaly reasoning routes to a mid-tier model; multi-document synthesis routes to a frontier model, all through one endpoint with zero routing logic. Guardrails - Foundry guardrails scan user input, tool calls, tool responses, and final output for defined risks and take the configured action (annotate or annotate-and-block). Tool use - The agent queries systems, invokes reconciliation logic, runs anomaly detection, or calls another specialist agent via Copilot Studio connected agents or Foundry's MCP integration. Runtime execution - The workflow runs in cloud, local, or Windows 365 Cloud PC environments depending on system access, data sensitivity, latency, and legacy application constraints. Response - The agent returns a reconciled view, flagged anomalies, rationale, and recommended next steps — with citations pulled from the knowledge layer for transparency. Bridge to Trust - Every action generated by the agent remains observable, governable, and auditable through the platform's trust capabilities, which are explored further in Part 2. Conclusion The hard problem in enterprise AI was never obtaining a capable model; it was grounding that model in governed enterprise context, enabling it to act through governed tools, and doing so within the security, compliance, and operational controls organizations already rely on. Microsoft's answer is a platform approach: a dedicated grounding tier through the IQ Platform, a flexible intelligence layer spanning models, memory, routing, orchestration, and guardrails, specialized agent families aligned to business domains, a build-to-run continuum spanning no-code to pro-code, and a shared trust foundation that every agent inherits. Integrate once with this fabric, and the payoff compounds: one identity model, one grounding tier, and one governance spine become reusable across every persona surface, every agent family, every build-and-run target. Coming next — Part 2: Trust Intelligence is only half the equation. In Part 2 we turn to the other axis: how Microsoft secures and governs every component of an agent - models, tools, MCP connectors, memory, and orchestration across the full lifecycle.Securing 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.Extend data security to the network with Microsoft Purview and Microsoft Entra
Protection that keeps up with how data moves in the AI era Enterprise data used to be easier to contain. It lived in files, in apps you managed, within boundaries you controlled. Security teams could focus on endpoints and known systems, and that was often enough. That’s no longer the case. Today, data travels constantly between trusted endpoints and unmanaged web apps, SaaS apps, and most critically, generative AI tools over the network. Employees type and paste sensitive information into prompts, upload work-related files to external services or personal cloud storage, and interact with systems that sit entirely outside the traditional enterprise perimeter. AI has expanded the risk surface for potential enterprise data loss. That’s why Microsoft Purview and Microsoft Entra now integrate to extend data security to the network layer (available in public preview). Traditional data loss prevention (DLP) approaches lack real-time visibility and enforcement, flagging incidents after data has already left the organization. In other cases, vendors rely heavily on physical network appliances that are complex and expensive to deploy, or compute resources that can add significant latency. In the era of AI, that model breaks down quickly. Real-time data protection for how work happens today To adapt to how enterprise data moves in the AI era, we’re announcing the extension of data security to the network layer, powered by Microsoft Purview and Microsoft Entra, now in public preview. This integration brings together data context and identity-aware enforcement to help protect sensitive data in transit, in real time: Detect how sensitive data is shared to shadow AI tools, unmanaged SaaS apps, and personal cloud repositories Help block the sharing of sensitive data in real time based on identity, user activity, and data context, before data leakage occurs Unify investigation workflows by correlating identity, data, and insider risk signals across Purview, Entra, and Defender Prevent employees from sharing proprietary or sensitive organizational data to potentially risky locations such as consumer AI apps. By combining Purview data classification, DLP policies, and insider risk detection with identity-aware enforcement at the network layer through Entra, organizations can dynamically apply protections based on: The sensitivity of the data Who the user is How that user has interacted with sensitive data over time Together, Purview and Entra enable a modern approach to data protection that follows the data to prevent leakage instead of relying on at-rest controls alone. Not only that, but the same Purview classification and policies that you already leverage for the rest of your enterprise data can now be applied consistently across data in motion, at rest, and in use. Learn more in the detailed blog. See the capabilities in action here. Start your free trial of Purview Suite here.Microsoft Purview enables developers with strong data security across AI apps and agents
Today, developers are at the center of a new wave of innovation—building AI applications and agents that are deeply connected to enterprise data. But with this opportunity comes a new and complex set of security challenges. AI systems operate across cloud platforms, third-party services, and even local and on-premises development environments, interacting dynamically with sensitive data such as customer records, financial information, and intellectual property. Traditional security approaches weren’t designed for this level of scale, autonomy, or fluid data movement—leaving developers to navigate fragmented tools, unclear policies, and the risk of unintentionally exposing sensitive information. At the same time, expectations are rising. Organizations need to ensure that AI applications and agents are compliant, auditable, and secure by default on an enterprise-level—not retrofitted after deployment. But for developers, adding security often means additional complexity, custom integrations, and slower time to market. This tension between speed and control has become one of the biggest barriers to moving AI from experimentation into production. Microsoft Purview is designed to help with this challenge by embedding data security and compliance controls across the development cycle. Purview provides a consistent way to govern how data is accessed, used, and shared—without requiring developers to become security experts. The result is a simpler path to building AI systems that are secure, compliant, and enterprise-ready by design. Extending data security and compliance to local agents and claws Local and endpoint agents, built in platforms such as GitHub Copilot CLI and OpenClaw, introduce a new class of data security challenges as they operate outside traditional control planes and directly on user machines. Unlike cloud systems, these agents can access local files, credentials, terminals, and enterprise apps simultaneously—often moving data across tools and environments. This expands data risks, from sensitive data being unintentionally stored, copied, or shared, to API keys and tokens being exposed, and autonomous workflows triggering data movement without explicit user intent. At the same time, many existing security controls were designed for browser or cloud-based activity, leaving a growing blind spot at the endpoint where agents are increasingly running. The result is a widening gap between how developers build agents to operate locally in the users machines, and how organizations can detect, govern, and protect the data those agents interact with. Microsoft Security and Windows are integrating management and security capabilities directly into the local agents’ development workflow, enabling security as an architectural guarantee rather than an implementation choice. At Build, we are thrilled to be extending Purview visibility and protection capabilities to local agents developed on GitHub Copilot CLI, Claude Code, OpenAI Codex, and OpenClaw - in Public Preview. Unlike traditional cloud applications, these agents operate closer to the data and often create new risks for data exposure. Purview addresses this challenge across all types of agent interactions with a clear, simplified set of scenarios: ▪ Observability: Visibility on Purview Data Security Posture Management (DSPM) across agent inventory, as well as into how local agents interact with sensitive data—across prompts, responses, and actions. ▪ Runtime data protection: Purview Data Loss Prevention (DLP) controls enforced directly into the agent execution flow, inspecting prompts and tool calls in real time to prevent sensitive data exfiltration. ▪ Agentic risk detection: Risky or anomalous agent behaviors detected through Insider Risk Management (IRM) signals, helping teams detect unsafe interactions early. ▪ Audit: Comprehensive, end-to-end logging of all local agent interactions—capturing prompts, responses, data access, and actions for data context. For example, a developer is using a local coding agent to generate code and accidentally includes sensitive credentials in a prompt. AI observability in DSPM surfaces the interaction and shows what data the agent accessed. DLP detects the sensitive data in real time and blocks it from being sent or processed (or sensitive files from being accessed and exfiltrated). At the same time, agentic risk detection flags the session as high risk based on the behavior pattern. All of this activity is captured in audit logs, enabling the security team to investigate and take action quickly. Developers and security teams gain visibility into agent activity and data interactions, while policies prevent sensitive data leakage. This ensures consistent security outcomes across both cloud and endpoint environments, without disrupting developer workflows. Strengthening visibility and controls for Foundry agents Foundry gives developers a central place to build and manage AI agents, but it also creates a need for data security context directly in that workflow—especially as prompts, model interactions, and downstream actions increasingly involve sensitive enterprise data. At Build, we are excited to announce the expansion of the Foundry integration with Purview. This includes Purview DLP runtime controls for prompt processing in Foundry, in Public Preview. As agents and applications built on Foundry increasingly interact with sensitive data, Purview ensures those interactions are governed by trusted controls, identifying Sensitive Information Types (SITs) in real time to detect and protect confidential data embedded in prompts. For example, if a user includes customer PII or financial data in a prompt, Purview can automatically identify the sensitive content and block that prompt from being processed by the model. This ensures that all Foundry apps and agents, regardless of how they’re built or deployed, inherit consistent data protection – allowing organizations to reduce risk of inadvertent data exposure, centralize compliance enforcement across AI workloads, and confidently scale AI adoption knowing sensitive data is protected by design. We’re also building up on the Purview coverage for Foundry shared at the last Microsoft Ignite by announcing Purview insights embedded directly into the Foundry Control Plane, in General Availability, bringing rich data security context to the plane where developers already work. Purview surfaces crucial signals—such as SITs detected in the agentic interactions, % of agentic interactions involving sensitive data, and spread of high-risk users — so Foundry admins can know how AI apps and agents are built in their environment. This shift enables developers to make faster, better decisions in the moment, reducing rework and closing security gaps early on. For customers, the value is clear: stronger security by design and at enterprise scale, accelerated development cycles, and reduced risk of data leaks or compliance issues—without slowing down innovation. Innovating for developers everywhere, at the pace of AI growth Microsoft is also expanding Purview’s reach across the broader developer ecosystem. New integrations help organizations apply consistent oversight to AI tools and platforms developers already use, without adding separate compliance workflows. GitHub Copilot is a critical productivity layer for developers, accelerating how code is written and shipped—making it equally important that developer interactions with GitHub Copilot are governed and secured with the same rigor as enterprise data. Microsoft Purview now extends data governance and compliance capabilities to GitHub Copilot interactions, in Public Preview, enabling GitHub Enterprise customers with Entra SSO to stream audit logs directly into Purview. This brings centralized visibility for AI activity, allowing security and compliance teams to analyze GitHub Copilot agent session activity alongside other AI workloads. With this native integration into GitHub workflows, Purview audits Copilot activity across repositories, pull requests, and developer sessions—ensuring AI-generated code aligns with enterprise data policies, compliance requirements, and secure development standards. By integrating Purview into existing workflows, organizations can govern GitHub AI usage without building parallel pipelines—reducing complexity while ensuring consistent compliance coverage across their entire data estate. Today’s AI agents aren’t built in just one ecosystem—they span custom apps, third-party platforms, and open-source frameworks. Without consistent controls, this creates blind spots where sensitive data can be exposed outside enterprise guardrails. That’s why extending Purview protection beyond Microsoft environments is critical: it ensures developers can apply the same data security, DLP policies, and compliance controls to any agent, anywhere—so innovation can scale without increasing risk. Developers already use Microsoft Purview APIs to embed data protection into enterprise workflows. Today, we’re introducing the Microsoft Purview SDK for .NET — a simple, drop-in toolkit that brings Purview capabilities directly into any application, in Public Preview. Instead of weeks spent wiring APIs, authentication, and error handling, developers can add content scanning, DLP checks, and sensitivity labeling in just a few lines of code. The SDK handles the heavy lifting — including auth, retries, caching, and telemetry — so teams can focus on building experiences. For AI apps and agents built outside of the Microsoft AI platforms, SDK adds built-in support and can evaluate prompts and responses in real time against DLP and content policies — helping prevent data exposure at runtime without custom logic. Designed for both real-time and asynchronous patterns, and for authenticated or anonymous flows, the SDK also feeds activity back into Purview to give security teams centralized visibility and control. The bottom line is- the Microsoft Purview SDK enables developers to build AI apps and agents that are secure and compliant by default — cutting integration time from weeks to days while ensuring data protection scales with AI. The SDK will be available in public preview within the next month. Together, these announcements represent a significant step forward in how developers build secure AI systems. Microsoft Purview is no longer just a data security and compliance solution—it is a first-class layer of the development process by protecting data across AI applications and agents, and enables a bridge between developers and security teams. As AI becomes more agentic, distributed, and deeply connected to enterprise data, the need for built-in security will only grow. With Purview, developers no longer must choose between speed and security—they can build both into every application from the start Getting connected with Microsoft Purview and learn more Learn more about Microsoft Purview on our website and Microsoft Learn. Explore Agent 365. Try Microsoft Purview data security. Learn more about Microsoft Purview SDK.
