TL;DR: Unosecur finds that AI governance platforms document approval, policy, and oversight, but AI agent security platforms control runtime permissions, tool access, and session revocation. The boundary matters because compliance can exist on paper while an agent still holds live authority that governance tooling cannot contain.
Editorial analysis by NHI Mgmt Group, based on content published by Unosecur: “AI agent security VS AI governance platforms: what does each control?”.
Key questions
Q: What breaks when AI agents are not governed at runtime?
A: Without runtime governance, an agent can shift behaviour after provisioning and still execute actions that were never reviewed in context.
Q: Why do AI agents create compliance risk even when policies exist on paper?
A: Policies do not satisfy auditors if the organisation cannot prove enforcement.
Q: How do security teams know whether an AI agent platform is actually enforcing policy?
A: A platform is enforcing policy if a prohibited action is blocked at the moment the agent attempts it, not if the violation is recorded after the fact.
Practitioner guidance
- Map approval state against live authority Inventory which agents have been approved and compare that list to their current effective permissions, reachable tools, and downstream systems.
- Test runtime policy enforcement Write a prohibited action into policy, then verify whether the control plane blocks the agent at the moment it attempts the action.
- Separate committee oversight from containment Assign review boards to approval and policy decisions, but require a runtime control layer that can revoke tokens, suspend sessions, and remove access without waiting on a meeting cycle.
Bottom line: AI governance and AI agent security are separate control layers, and only the latter is designed to manage live permissions and session authority.
What's in the full article
Unosecur's full article covers the operational detail this post intentionally leaves for the source:
- Step-by-step evaluation tests for comparing approval records with live agent permissions
- Detailed distinctions across policy documentation, model-level risk, and identity-level exposure
- Examples of runtime containment actions such as token revocation and session termination
- How the vendor positions its identity fabric and MCP gateway in the control stack
👉 Read Unosecur's analysis of AI agent security versus AI governance platforms →
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AI governance and AI agent security solve different identity problems. Governance establishes whether an agent should exist, who approved it, and whether the use case meets policy. Security determines what that agent can currently reach, what it can do with those permissions, and whether live authority can be revoked before impact. Organisations that blur the two end up with process compliance and control gaps at the same time. The practitioner takeaway is that approval does not equal containment.
A few things that frame the scale:
- 52% of respondents see AI security decision-making power shifting toward platform and infrastructure teams rather than the executive suite, according to the 2026 Infrastructure Identity Survey.
- Only 44% of organisations have implemented any policies to manage their AI agents, despite 92% agreeing that governing AI agents is critical to enterprise security, according to the 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: Should organisations separate AI governance workflows from AI runtime containment?
A: Yes. Governance workflows are suited to approval, documentation, and policy sign-off, while runtime containment must handle live permissions, token revocation, and session termination. Combining them loosely often leaves neither function strong enough. Mature programmes separate oversight from enforcement and make the enforcement layer measurable.
👉 Read our full editorial: AI agent security vs AI governance platforms: what each controls