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02/06/2026 6:59 pm
TL;DR: AI agents are expanding enterprise identity attack surfaces because they authenticate, call tools, and act across cloud and SaaS systems with privileges that teams often cannot fully inventory or govern, according to Silverfort. Runtime identity controls at the gateway layer shift enforcement earlier, but they also expose how incomplete most agent governance models still are.
NHIMG editorial — what this means for NHI practitioners
Questions worth separating out
Q: How should security teams implement runtime controls for AI agents in enterprise environments?
A: Start by enforcing policy at the point where the agent requests access, not only where the data lives.
Q: Why do AI agents complicate non-human identity governance?
A: AI agents complicate governance because they combine autonomy, tool use, and delegated identity into one workflow.
Q: What breaks when AI agent access is reviewed only after the fact?
A: After-the-fact review leaves a gap between action and containment.
Practitioner guidance
- Inventory every production AI agent and its owner Build a current register of agents, the human accountable for each one, the systems it can reach, and the service accounts or tokens it uses.
- Enforce runtime policy at the request path Place access decisions where the agent request is made, not only in post-event review.
- Bound agent privilege to task scope Remove standing access where possible and constrain agents to narrowly defined actions, datasets, and destinations.
It is constraining how far the agent can move, which resources it can reach, and how quickly an unsafe action can be interrupted?
👉 Read Silverfort's analysis of runtime identity security for AI agents in Gemini Enterprise →
Explore further
02/06/2026 7:14 pm
Runtime enforcement is becoming the deciding control for AI agent governance. Discovery alone is not enough when agents can act in real time and change behaviour mid-workflow. The practical question is whether access can be evaluated at the point of action, not after the fact. Organisations that keep relying on inventory-first models will keep finding blind spots after privilege has already been exercised.
A few things that frame the scale:
- 96% of technology professionals identify AI agents as a growing security threat, and 66% believe this risk is immediate, according to AI Agents: The New Attack Surface report.
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems, inappropriately sharing sensitive data, and revealing access credentials.
A question worth separating out:
Q: How do organisations know if AI agent governance is actually working?
A: Look for three signals: every production agent has a named owner, access decisions are enforced during runtime, and audit trails show when requests were allowed, denied, or escalated. If teams can only describe agent behaviour in hindsight, governance is still incomplete.
👉 Read our full editorial: Runtime identity security for AI agents in Gemini Enterprise
