Agent Observability & Governance
Bring the growing population of AI agents into view and under control with Microsoft Agent 365 — from a centralized, cross-platform inventory of every agent and who owns it, to lifecycle governance over which agents are allowed, what tools they can call, and which policies apply.
AI agents are becoming first-class actors in the enterprise: they read documents, call tools, and move data at machine speed. Before any of them can be secured, they have to be seen and governed. This topic covers that foundation in two motions. First, observability: build a single, cross-platform inventory of the full agent estate — every Entra-registered, self-registered, and shadow agent — with usage insight into who owns each agent and how it is used. Then governance: decide which agents are admitted, who is accountable for them, what tools and data they may touch, and which policies apply across their lifecycle. Identity and access, data protection, and active defense each build on the visibility and control established here.
How to address Agent Observability & Governance
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Observe the Agent Estate
Builds visibility into every AI agent and agent identity across the tenant and connected platforms — a centralized inventory, source of truth, and usage insight into who owns each agent and how it is used. -
Govern Agent Lifecycle
Decide which agents are allowed, who owns them, what tools they can call, and which governance and compliance policies apply — establishing consistent guardrails across the agent lifecycle from onboarding through retirement.
Control coverage
Governance12
Organisational and technical controls for managing the lifecycle of AI models, agents, and workloads — including inventory, policy, and accountability frameworks.
Monitoring10
Continuous observability of AI workload behaviour, usage, and anomalies through logging, metrics, and alerting — enabling detection of drift, misuse, and security events.
Discovery8
Capabilities for discovering and inventorying AI assets, workloads, shadow AI usage, connected applications, and data sources — providing visibility as the foundation for securing what you can see.
Compliance5
Controls that help organisations meet regulatory and policy obligations for AI systems, including audit trails, data residency, and policy enforcement.
Agent Security3
Controls that secure AI agents, their tools, plugins, and external connections — including MCP server trust boundaries, tool permission scoping, prompt injection defences, and runtime behaviour constraints for autonomous and assistive agents.
Identity & Access2
Controls that ensure only authenticated and authorized principals can access AI models, APIs, data pipelines, and management planes.
Data Protection1
Mechanisms that protect data confidentiality and integrity at rest, in transit, and during processing within AI workloads — including encryption, sensitivity labeling, and data loss prevention.
Incident Response1
Capabilities that support detection, investigation, containment, and recovery for security incidents involving AI workloads — including automated response playbooks, forensic telemetry, and remediation guidance.
Threat Detection1
Capabilities that identify, alert on, and help respond to threats targeting AI models, inference endpoints, training pipelines, and supporting infrastructure.