Assessing and Mitigating Risk for Agentic AI: Risk Governance and Safe Adoption – Part 2

by Anna Bordioug, Chiara Alina, Elaine He, Ijeoma Ochuba, Priya Vardhini, Chhavi Dave, Femke Cornelissen, Melissa Ballesteros, Evi Van der Velden | Feb 28, 2026 | Microsoft 365 Copilot | 0 comments

Agentic AI is rapidly transforming the way that businesses function. These agents can make work faster and smoother by handling routine tasks and helping teams work more intelligently. But because they can act on their own, they also bring significant new risks. For instance, they might take an action you didn’t intend, use access they shouldn’t, or go against company policy. Therefore, agent governance is a critical part of enabling secure adoption and transformation.

As we wrote about in part 1 of the blog series, effective governance for agentic AI needs to be risk-based, with clear boundaries on what agents can do, strong access controls, monitoring, and ‑well-defined‑ human approval points for sensitive actions.

In part 2 we will:

Review the risk-based agent governance framework,
Break down agentic AI risk assessment, and
Review practical risk mitigation capabilities for safe adoption.

Risk-Based Agent Governance Framework

Effective governance for agentic AI must span the entire agent lifecycle from design through deployment and into runtime operation and retirement. Organizations should classify, assess and control agents with clear risk criteria.

Agent Inventory and Identity

Before managing risk, you must know your agents: what they are, who owns them, where they run and what they can access.

A centralized agent inventory should capture:

Agent identifier and description
Owner and business context
Connected systems and tools
Permissions and access scopes
Risk tier

In short, effective enterprise governance practices position agent inventory as the foundation of proactive control.

Classifying Agents by Risk Tier

Not all agentic intelligence systems pose equal risk. Agents should be classified and controlled in proportion to the potential negative impact that a breach would have on an organization and its stakeholders.

A simple tier matrix provides clarity on the governance approach required per agent class:

High-impact agents: Maximum control
Medium-impact agents: Managed control
Low-impact agents: Essential hygiene

Refer to Part 1 of our agentic governance series for more information on a risk-based agent governance approach.

Agentic AI Risk Assessment and Mitigation

Before we can dig deeper into mitigating agentic AI risk, it is important to discuss how organizations can effectively assess agents and determine their risk level.

Agentic AI Risk Assessment

Organizations should strive to have an agent request process wherein requestors provide information on the agent that will help determine its risk level. Such information could include:

Sensitivity of data accessed (e.g., regulated data vs internal business data)
Level of action authority (e.g., read-only vs system modification)
Scope of actions (e.g., local task vs enterprise-wide)
Impact of actions (e.g., irreversible changes)

Establishing well-defined criteria for each agent risk level is critical to applying tailored security and governance controls.

Mitigating Agentic AI Risk

Effectively mitigating risk of agentic AI is not a one-off process. Risk mitigation for agentic AI happens at multiple stages, including pre-deployment, during the agent’s lifetime, and at end of life.

Pre-Deployment Controls and Simulation

Prior to deploying agents, it is important to implement baseline security controls in your environment. For example, publishing and applying sensitivity labels alongside data loss prevention policies to restrict agents from accessing sensitive data.

It is also important from a governance perspective to establish an owner and sponsor for the agent prior to deploying it in the environment.

For particularly risk agents, it may be helpful to simulate agent behaviour in controlled environments before production release. Things to test for include:

Privilege escalation
Unauthorized tool access
Adversarial or contradictory prompts
Unexpected workflow paths

Adaptive Guardrails

Static rules are insufficient. The environment should have adaptive controls for agents such as:

Conditional policy gates based on agent risk
Context-aware thresholds that consider the agent’s assigned risk level
Dynamic tool access restrictions

This enables flexibility for safe autonomy while preventing risky operations from being executed.

Human-in-the-Loop (HITL) Checks

For sensitive actions performed by high-impact agents (e.g., financial transactions, identity changes), ensure:

Agent actions are subject to review
Approval workflows exist before execution
Clear escalation paths are defined

Runtime Monitoring and Telemetry

Agents can behave unexpectedly after deployment. Real-time telemetry helps detect:

Drift from intended behaviour
Anomalous tool invocations
Policy violations
Unusual access patterns

Microsoft supports numerous ways to get these insights including in Azure, Purview, Defender, Entra, and Sentinel.

Enforcing Decision Boundaries for Sensitive Agents

Decision boundaries keep agents operating within defined scopes and prevent actions that exceed intended authority.

Boundary Types

There are different types of boundaries that can be enforced on agents:

Data Boundaries: restrict datasets the agent can read or modify
Action Boundaries: restrict probable operations (e.g., deletions)
Context Boundaries: restrict when and where an agent can act

These should be encoded as policy enforcement rules and validated at runtime.

Logging, Explainability and Audit Trail

For compliance and accountability:

Log every agent action with context and timestamps
Capture tool calls and policy evaluations
Maintain an explainable trail for audit

Detailed logs help with retrospective analysis and regulatory reviews.

Closing Thoughts

Agentic AI’s potential hinges on effective governance and risk management. A risk-based framework that combines inventory, classification, preventive controls, HITL checkpoints, continuous monitoring and boundary enforcement enables safe adoption, even for sensitive environments.

With rigorous governance, organizations can unlock agentic AI’s benefits while maintaining security, compliance and operational resilience.