Topic starter
26/08/2026 11:51 am
TL;DR: AI agents are moving from advisory tools to digital workers that can execute workflows, but traditional identity models still assume a human-authenticated request path and cannot prove who authorized high-risk actions, according to Yubico. The real control gap is not automation itself, but the lack of cryptographically verified human approval for consequential agent activity.
Editorial analysis by NHI Mgmt Group, based on content published by Yubico: “Securing agentic AI: Why automation still needs human oversight”.
Key questions
Q: What breaks when AI agents can act without a verified human behind them?
A: Fraud and IAM controls lose attribution.
A: Those workflows combine real-world impact with compressed decision time.
Q: How can security teams tell whether AI agent approval controls are actually working?
A: Look for evidence that sensitive actions trigger a fresh approval event, that approvals are tied to a specific human principal, and that the agent cannot proceed when the checkpoint fails.
Practitioner guidance
- Define high-risk action thresholds Classify the agent actions that must require human approval, such as transfers above a threshold, new counterparties, sensitive documents, production code deployment and availability-impacting changes.
- Separate routine execution from consequential approval Allow agents to proceed autonomously for low-risk work, but route material actions into an explicit approval path so governance is triggered by the action type, not by the tool used.
- Require cryptographic proof of approval Use hardware-backed authentication for the human decision maker so the approval is tied to a verified identity and cannot be replayed or casually attributed after the fact.
Bottom line: Agentic AI creates a control gap when valid credentials let systems act faster than human oversight can prove approval.
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26/08/2026 11:51 am
Human-in-the-loop approval is the missing control layer for consequential agent actions: Agentic AI changes the unit of governance from login to decision. Traditional IAM can establish who authenticated, but it cannot by itself prove who authorised a high-risk act once the agent is in motion. The implication is that approval must become a first-class identity control for selected actions, not an audit afterthought.
A few things that frame the scale:
- 53% of security leaders expect AI to run major portions of their infrastructure autonomously within the next three years, according to the 2026 Infrastructure Identity Survey.
- 69% of security leaders agree identity management must fundamentally shift to address agentic AI systems, according to the 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: Who is accountable when an AI agent makes a risky decision?
A: Accountability should rest with the organisation that authorised the agent, the human owner of the workflow, and the control process that allowed the behaviour. If an agent can act independently, the programme must preserve attribution, action logs, and policy decisions so audit and remediation are possible after the event.
👉 Read our full editorial: Human-in-the-loop authorization is the control gap in agentic AI