Topic starter
23/10/2026 12:45 am
TL;DR: Physical AI pushes AI agent risk into machine behaviour, VicOne says, so access control alone no longer covers the full chain from decision to action. Runtime authority, inputs, and tool use now need continuous checks before robots or vehicles move outside safe bounds.
Editorial analysis by NHI Mgmt Group, based on content published by VicOne: “AI Agents Can Shape the Physical World. We Can Secure Their Actions.”.
Key questions
Q: Why can an AI agent be authorised and still be unsafe in physical AI?
A: An agent can have valid permissions yet still become unsafe if its inputs are poisoned, its tools are compromised, or the operating context changes faster than the policy can adapt.
Q: What are the signs that AI agent governance is too broad for robots or vehicles?
A: Warning signs include agents that can act across multiple operational states, tool permissions that exceed the immediate task, weak audit trails for decision-to-action chains, and safety teams relying on static approval rather than runtime checks.
Q: How should security teams govern AI agents that can take runtime response actions?
A: Treat them as privileged NHI workloads with explicit scope, short-lived authority, and full action logging.
Practitioner guidance
- Define action-scoped agent permissions Map each AI agent to the smallest set of actions it needs, and separate read, planning, and actuation privileges so a compromised decision path cannot trigger broad machine behaviour.
- Verify identity before high-impact actions Require strong identity binding for any agent that can change vehicle, robot, or safety-state behaviour, including authentication of the agent identity and approval of the action context.
- Test manipulated-input scenarios Simulate poisoned data, compromised tool responses, and misleading instructions to see whether the agent changes machine behaviour in ways that violate safety boundaries.
Bottom line: Physical AI expands AI agent risk from software misuse into real-world machine behaviour, which means access control alone is not sufficient.
Glossary terms: True AI, Agentic AI Identity, Runtime Boundary Monitoring, Action-Scoped Authorization
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This topic was modified 6 days ago by NHI Mgmt Group
23/10/2026 9:49 am
Physical AI turns agent authority into a safety control, not just an access problem. When an AI agent can influence a robot or vehicle, the consequence of bad permission design is no longer limited to data exposure or misuse of a tool. The machine itself may move, stop, or act in a way that creates physical risk. Practitioners should therefore treat authority boundaries as part of operational safety design, not an IAM afterthought.
A few things that frame the scale:
- 19% of organisations give AI systems dramatically more access than human employees, nearly one in five granting unrestricted privilege, according to the 2026 Infrastructure Identity Survey.
- 70% of organisations grant AI systems more access than they would give a human employee performing the exact same job, according to the 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: What is the difference between securing an AI model and securing an AI agent?
A: Securing a model focuses on inputs, outputs, and misuse of the model itself. Securing an agent requires identity governance, privilege control, tool authorization, and auditability because the agent can act, not just generate text. The agent is therefore an access problem as much as an AI problem.
👉 Read our full editorial: Securing AI agent actions in physical systems raises the stakes
This post was modified 6 days ago by NHI Mgmt Group