Human-in-the-loop authorization is the real control gap for agentic AI

At a glance

What this is: This is Yubico’s RSAC 2026 recap focused on agentic AI identity controls, with the key finding that human-in-the-loop approval and hardware-backed trust are needed for high-consequence actions.

Why it matters: It matters because IAM, PAM, and NHI programmes now have to govern delegated AI actions, not just human logins or service credentials.

👉 Read Yubico's analysis of human-in-the-loop authorization for agentic AI

Context

Agentic AI changes the identity problem because the system can initiate consequential actions at runtime, while current governance still assumes a human decision-maker is visibly behind each step. In practice, that assumption breaks when an AI workflow can carry a long-lived token through directory changes, vault operations, or data movement without fresh human confirmation.

For IAM and NHI teams, the core issue is not whether the actor can authenticate, but whether intent can be proven at the point of action. That pushes identity controls toward checkpoint-based authorisation, phishing-resistant verification, and lifecycle governance for delegated access paths that sit between human oversight and machine execution.

Key questions

Q: How should security teams govern AI-enabled workflows that can act on their own?

A: Treat them as identity-governed execution paths, not just software features. Assign a named owner, define least-privilege access, log every tool call, and require revocation paths for credentials and tokens. If the workflow can touch production systems or sensitive data, its permissions must be reviewed with the same discipline used for privileged machine identities.

Q: Why do long-lived user tokens create governance risk for AI agents?

A: Long-lived tokens assume access remains valid until a person revokes it, but agent behaviour changes the risk model because the actor can decide and act at runtime. That makes persistent delegation harder to justify, especially when the task is narrow and short-lived. Session-bound authorization reduces the exposure window and fits the way agents actually execute work.

Q: What breaks when AI workflows can act without a checkpoint before privileged tasks?

A: Accountability breaks first, because the organisation can no longer prove that a verified human authorised the exact sensitive action. Then control breaks, because the workflow can continue into directory, vault, or migration tasks at machine speed. That is why checkpoint design has to be tied to the operation, not just the login event.

Q: Which identity controls matter most when autonomous workflows are involved?

A: The most important controls are action-specific approval, phishing-resistant verification, token lifetime limits, and auditable delegation paths. Those controls address who approved the action, how strong that approval was, and how long the workflow can keep acting after approval. They are the baseline for governing agentic AI safely.

Technical breakdown

Why long-lived tokens create an autonomy gap

Long-lived tokens let a delegated system continue acting after the moment of initial approval, which is where identity governance becomes weak. The article’s concern is that once an AI workflow has persistent access, the security boundary shifts from login to runtime use. That is different from ordinary automation because the harmful action is not just authenticated, it is still authorised in a way that may no longer reflect current human intent. In that model, the real risk is not token possession alone but the duration and scope of authority carried forward into later actions.

Practical implication: treat token lifetime, session scope, and approval timing as linked controls rather than separate settings.

Human-in-the-loop checkpoints and high-consequence actions

A checkpoint model inserts a verified human decision before sensitive actions such as directory edits, vault changes, or privileged migrations. This is less about adding friction and more about re-establishing accountability where an agent can otherwise act too quickly for ordinary review cycles. The control is strongest when the checkpoint is tied to a specific action, not a broad session, because that narrows the authorisation to the exact operation being taken. That makes the workflow auditable and reduces the chance that a compromised orchestrator can act unobserved.

Practical implication: define which actions require fresh human authorisation and make those checkpoints mandatory in the workflow path.

Hardware-backed identity as a trust anchor for AI workflows

Hardware-backed authentication raises the assurance level by binding the approval step to a physical security key rather than a reusable secret or soft token. In the article’s model, that matters because the AI workflow may be compromised even if the human identity system itself is intact. A phishing-resistant factor does not solve AI governance by itself, but it strengthens the proof that a real operator authorised the specific action. For enterprise identity architecture, that means hardware-backed trust becomes part of delegated workflow governance, not only user login security.

Practical implication: require phishing-resistant authentication for approval points that gate privileged or irreversible AI actions.

Threat narrative

Attacker objective: The objective is to abuse delegated AI or orchestrator access to carry out privileged changes at machine speed before human oversight can intervene.

1. Entry begins when an attacker or rogue workflow gains access to a long-lived token or a hijacked session that still has delegated authority.
2. Escalation occurs when that delegated access is used to perform high-consequence actions such as directory changes or vault operations without a fresh human checkpoint.
3. Impact follows when the compromised orchestrator uses its standing authority to alter identity infrastructure or empty sensitive systems before detection.

Breaches seen in the wild

• CoPhish OAuth Token Theft via Copilot Studio — CoPhish campaign exploits Microsoft Copilot Studio agents to steal OAuth tokens via AI-assisted phishing.
• Moltbook AI agent keys breach — Moltbook breach exposed 1.5M AI agent keys.

Read our 52 NHI Breaches Analysis report for a comprehensive view of breaches impacting Non-Human Identities including AI Agents.

NHI Mgmt Group analysis

The autonomy gap is an identity governance problem, not just an AI safety problem. Once a workflow can execute high-consequence actions without a fresh human gate, the security model has shifted from identity assurance to delegated intent assurance. That means traditional login-centric controls no longer describe where authority actually lives. The practitioner takeaway is that IAM must govern action approval, not only account authentication.

Long-lived approval paths are the weakest point in agentic AI governance. The article correctly identifies that persistent tokens let execution continue well after the original approval moment. That is a lifecycle issue as much as an access issue, because authority outlives the operator’s immediate intent. The practical conclusion is that teams should re-map who can approve what, and for how long, across every delegated workflow.

Hardware-backed checkpoints create a higher-assurance intent model than software-only consent. A physical security key is not just stronger MFA, it is evidence that a verified person was present at the point of action. For agentic systems, that matters because the risk is not only compromise but ambiguity over who authorised the specific runtime decision. Practitioners should treat this as a control for accountability in autonomous workflows.

Human-in-the-loop authorization is becoming the new boundary between automation and autonomy. The industry is moving toward systems that can act on their own, but governance still has to decide where independence ends. That boundary cannot be inferred from the AI model alone, because the identity risk sits in the delegation chain. The implication is that access governance, PAM, and workflow design now need to be evaluated together.

From our research:

• 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems (39%), inappropriately sharing sensitive data (31%), and revealing access credentials (23%), according to the AI Agents: The New Attack Surface report.
• Only 52% of companies can track and audit the data their AI agents access, leaving 48% with a complete blind spot for compliance and breach investigation, according to the AI Agents: The New Attack Surface report.
• For a broader control lens, see OWASP Agentic AI Top 10 for the agentic risks that checkpoint governance needs to cover.

What this signals

Checkpoint governance will become a core IAM and PAM design pattern for agentic systems. As more workflows gain runtime decision authority, the question is no longer whether they authenticate, but where the organisation can still force a verified human decision before irreversible action. That makes approval timing, token lifetime, and auditability part of one control set. For practitioners, the next step is to map which workflows need human-in-the-loop gates before they scale further.

Verified intent is emerging as a distinct identity control plane for autonomous workflows. The problem is not simply access control, because access alone does not prove the human meant to authorise that specific action. Teams that already anchor login with phishing-resistant authentication should extend that assurance to delegated actions and privilege changes. For a standards lens, the NIST AI Risk Management Framework is a useful anchor for governance design.

Autonomy changes the scope of lifecycle governance because approval can expire faster than review cycles can see it. That creates a named runtime governance gap, where access review exists but the actual decision has already happened and moved on. The practical signal is to separate human login assurance from action authorisation assurance in the programme roadmap.

For practitioners

• Define checkpoint actions for delegated AI workflows Identify which AI-driven actions require fresh human approval before execution, especially directory modifications, vault access, and sensitive data movement. Make the checkpoint specific to the action, not the entire session, so approval reflects the actual risk.
• Shorten delegated token exposure windows Review long-lived tokens used by orchestrators and agentic workflows, then reduce the time they remain valid when they are tied to privileged or irreversible operations. Pair the shorter lifetime with revocation paths that can be triggered immediately when the approval context changes.
• Require phishing-resistant approval factors Use hardware-backed authentication for any human-in-the-loop step that authorises privileged AI activity. The goal is to bind the approval event to a verified operator and reduce the chance that consent can be replayed, phished, or silently delegated.

Key takeaways

• Agentic AI creates an autonomy gap when delegated access outlives the human intent that originally authorised it.
• Hardware-backed checkpoints and phishing-resistant approval are central controls when AI workflows can perform privileged actions.
• Identity teams should govern specific runtime actions, not just accounts and sessions, if they want accountability to survive automation.

Key terms

• Human-in-the-loop authorization: A control pattern where a verified person must approve a specific action before an AI workflow can proceed. It shifts governance from account login to action-level intent, which is critical when autonomous or delegated systems can perform high-consequence tasks quickly.
• Autonomy gap: The gap between initial human approval and later machine execution when delegated access persists beyond the original decision moment. In identity terms, it is the point where an AI workflow keeps acting under authority that no longer maps cleanly to current human intent.
• Hardware-backed Trust: Hardware-backed trust means storing or using cryptographic material in a chip or module designed to resist extraction, such as a TPM or secure element. It reduces the chance that a stolen file system, image, or configuration export exposes the device's identity credentials.
• High-consequence action: An operation that can materially change access, data integrity, or system state if executed incorrectly or maliciously. Examples include directory modifications, privileged vault actions, and large-scale data migration, all of which need stronger authorisation than ordinary routine tasks.

What's in the full article

Yubico's full post covers the operational detail this analysis intentionally leaves for the source:

• Specific product integrations and partner listings in the Works with YubiKey catalog for directory, network, and privileged vault controls.
• Detailed rollout examples for FIDO pre-registration and passwordless onboarding with Ping Identity.
• Operational guidance on checkpoint-based authorization flows for AI-driven directory and vault actions.
• Partner-specific implementation paths for hardware-backed authentication across enterprise environments.

👉 Yubico's full post covers the partnership details, integration catalog, and workflow checkpoints in more operational depth.

Deepen your knowledge

NHI governance, agentic AI identity, and machine identity security are core topics in our NHI Foundation Level course, the industry's only accredited NHI security programme. If you are building or maturing an identity security programme, it is worth exploring.