Two Different Layers of AI Risk Management
When governance leaders compare GRC-based AI risk modules to runtime AI governance platforms, they are often comparing tools that were built to solve different problems at different points in time. GRC AI risk modules exist to help an organization assess risk before a system goes live and to document how that risk maps to controls. Runtime AI governance exists to apply and enforce those controls while an AI agent is actually operating, taking actions, and making decisions in production. Neither layer replaces the other; each addresses a distinct stage of the AI risk lifecycle.
What GRC-Based AI Risk Modules Typically Cover
AI risk modules inside platforms such as MetricStream and LogicGate are extensions of established GRC workflows. Their core function is to help teams identify, score, and document risk prior to deployment, and to keep that documentation aligned with internal policy and external frameworks.
- Structured risk assessments completed before an AI system or agent is approved for use
- A risk register that records identified risks, owners, and mitigation status
- Mapping of documented risks to internal controls and to frameworks such as NIST AI RMF or ISO/IEC 42001
- Workflow and sign-off trails that support audit and compliance reporting
What Runtime AI Governance Adds
Runtime AI governance, the layer Trussed operates in, picks up where pre-deployment assessment ends. It applies policy enforcement and least-privilege controls to AI agents while they are actively running, rather than only describing intended controls on paper.
- Monitoring of AI agent behavior during live operation, not only at the point of initial review
- Enforcement of policy and permission boundaries on the specific actions an agent attempts to take
- Least-privilege and tool-call level controls applied to agents in production
- Audit logging generated from actual agent activity, rather than from planned or documented activity
Lifecycle Stage Coverage by Category
The two categories align to distinct stages of the AI risk lifecycle. The table below maps each stage to the layer that primarily addresses it.
| Lifecycle stage | What happens at this stage | Primary layer |
|---|---|---|
| Assessment | Identifying and scoring AI risk before deployment | GRC AI risk module |
| Documentation | Mapping risks to controls and compliance frameworks | GRC AI risk module |
| Monitoring | Observing AI agent behavior during live operation | Runtime AI governance |
| Enforcement | Applying policy controls to active agent actions | Runtime AI governance |
Where the Procurement Confusion Comes From
Procurement teams sometimes assume that because a GRC platform documents an AI risk (for example, recording that an agent should operate under least-privilege access), that documentation is equivalent to the access actually being enforced. It is not. A risk register entry describes an intended control; runtime enforcement is what determines whether that control holds when the agent is executing a task. This distinction matters most when the two systems disagree: the risk register may show a control as mitigated while the runtime environment has no mechanism enforcing it, which is why organizations need clarity on which system is the system of record for AI risk decisions, and who owns each layer.