How should retailers govern AI agents that handle refunds and returns?

Retailers should govern refund and return agents as decisioning identities with bounded authority. The agent needs only the data required for the specific action, plus real-time risk context before it approves anything. Final authorisation should remain constrained by policy, with clear escalation paths for ambiguous or high-value cases.

Why This Matters for Security Teams

Refund and return agents sit on a direct path to money movement, customer data, and dispute resolution, which makes them high-value decisioning identities rather than simple workflow automations. The risk is not just fraud; it is also accidental over-approval, policy drift, and misuse of sensitive account context. Current guidance suggests retailers should treat these agents as bounded actors with explicit authority, not as trusted assistants. The NIST AI Risk Management Framework emphasizes governance and measurement for AI systems, while NHIMG research shows how quickly agent scope can outrun oversight when controls are weak.

That matters because a refund agent often has enough context to resolve a case, but not enough judgment to override policy. If it can query order history, payment data, loyalty status, and support transcripts in one flow, it can also expose unnecessary secrets or approve a refund outside intended thresholds. The same pattern appears in broader agentic risk research from AI Agents: The New Attack Surface report and in the OWASP Agentic AI Top 10, both of which highlight scope creep, tool abuse, and weak authorization boundaries. In practice, many security teams encounter refund abuse only after chargebacks, complaint spikes, or audit gaps have already exposed the control failure.

How It Works in Practice

Retailers should govern refund agents with runtime authorization, short-lived credentials, and policy-backed escalation. The central idea is that the agent receives only the minimum identity proof and data needed for the current case, then is re-evaluated before each sensitive action. That is closer to workload identity than to human IAM. A common pattern is to issue an ephemeral token for the specific task, bind it to the order or customer session, and revoke it as soon as the case closes. This reduces the blast radius if the agent is prompted, rerouted, or compromised.

Practical controls usually include:

• Policy-as-code checks before any refund is issued, using current order value, fraud signals, case age, and customer history.
• JIT access to payment, CRM, and logistics systems instead of standing access across all support tools.
• Step-up approval for high-value, duplicate, or policy-exception refunds.
• Full audit logging of tool calls, prompts, retrieved records, and decision rationale.
• Separate permissions for read, recommend, and execute actions so the agent cannot silently promote itself.

For implementation, NIST AI Risk Management Framework provides the governance lens, while CSA MAESTRO agentic AI threat modeling framework helps teams map tool use, escalation paths, and trust boundaries. NHIMG’s OWASP NHI Top 10 is also useful because refund agents fail when secrets, sessions, and tool permissions are treated as static assets instead of task-scoped controls. These controls tend to break down in legacy commerce stacks where refund decisions are distributed across ERP, payment gateways, and helpdesk plugins with no central policy layer.

Common Variations and Edge Cases

Tighter refund controls often increase friction for customer support teams, requiring organisations to balance fraud prevention against checkout-to-resolution speed. That tradeoff is real, especially during peak retail periods when exceptions rise and manual review can become a bottleneck. Best practice is evolving, but current guidance suggests that retailers should classify refund scenarios by risk tier rather than use one blanket policy for every return.

Edge cases matter. Low-value self-service returns may allow automated approval with limited context, while prepaid card refunds, gift-card balances, and cross-border orders usually need stricter review because reversal risk is higher. High-trust customers are not a reason to remove controls; they are a reason to route decisions through more precise policy. Retailers also need to decide whether the agent can only recommend an outcome or can execute it. In many environments, recommendation-only is safer until monitoring proves that the model remains stable under prompt injection, customer manipulation, and operational stress.

NHIMG’s reporting on agent overreach shows why this distinction matters: once an agent can access the wrong system or reveal the wrong record, the incident becomes a governance problem, not just a customer service issue. The same logic appears in the The State of Secrets in AppSec research, where leakage and delayed remediation show how quickly seemingly small access issues become persistent exposure. Retailers should expect these controls to be hardest to sustain where support teams rely on manual overrides, shared admin accounts, or disconnected refund tools.

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