Who is accountable when an AI agent takes a harmful action in healthcare?

Why This Matters for Security Teams

In healthcare, a harmful agent action is rarely just a model error. It is usually a governance failure involving access scope, approval flow, and missing telemetry. The practical question is not whether the AI can be blamed, but which human owner signed off on the workflow and which control failed to stop the action. Current guidance from the NIST AI Risk Management Framework treats accountability as an organisational duty, which fits healthcare where clinical, IT, and security decisions overlap.

This matters because agents behave autonomously and can chain actions faster than a human reviewer can intervene. NHIMG research on OWASP NHI Top 10 and the AI Agents: The New Attack Surface report shows how often agents exceed intended scope, including sensitive-data access and credential exposure. In a regulated setting, that means accountability must be provable through named ownership, scoped permissions, and action logs that survive audit and incident review. In practice, many security teams encounter the accountability gap only after a patient-facing workflow has already caused exposure or an unsafe downstream action.

How It Works in Practice

Accountability should be designed into the agent operating model before deployment, not assigned after an incident. Start by naming a business owner, a technical owner, and a clinical governance reviewer for every agentic workflow. Then tie each agent to a workload identity, not a shared human credential, so actions can be attributed to a specific autonomous entity. That identity should sit behind CSA MAESTRO agentic AI threat modeling framework style controls and be evaluated using OWASP Agentic AI Top 10 guidance.

For healthcare, the control pattern should look like this:

• Use role-based access only for coarse placement, not for final approval, because static RBAC does not fit goal-driven agent behaviour.
• Issue just-in-time credentials and ephemeral secrets per task, with automatic revocation when the task ends.
• Evaluate intent-based authorisation at request time, so the policy engine can check what the agent is trying to do, with which patient context, and under which escalation conditions.
• Log every tool call, data access, and policy decision to a tamper-evident system that links the action to the agent identity and the human approver.

That means the organisation remains accountable even if the agent is the immediate actor, because the deployment team chose the scope, the patient-data boundaries, and the emergency override path. Where this guidance breaks down is in loosely governed integration environments that still rely on shared service accounts, because attribution becomes ambiguous once multiple agents and legacy systems reuse the same credentials.

Common Variations and Edge Cases

Tighter controls often increase operational overhead, requiring organisations to balance clinical speed against assurance. That tradeoff is real in emergency care, ambient documentation, and triage assistants where delays can affect workflow. Best practice is evolving, but there is no universal standard for this yet, especially when agents can trigger follow-on actions across EHR, messaging, and scheduling systems.

One common edge case is delegated action. If an agent sends a prescription request, the prescriber still owns the decision, but the platform team owns whether the agent was allowed to draft, recommend, or submit. Another edge case is multi-agent orchestration, where one agent plans and another executes. In those environments, accountability must be traced across the chain, not assumed from the final step alone. The same logic applies to credential compromise: NHIMG’s AI LLM hijack breach and DeepSeek breach references show why long-lived secrets are especially risky when autonomous systems can reuse them at machine speed.

Healthcare teams should treat accountability as a control stack, not a statement of blame. The right answer is named ownership, runtime authorisation, short-lived credentials, and auditable identity binding, aligned to NIST AI Risk Management Framework and agentic security guidance. For incident response, that makes it possible to answer who authorised the agent, what it was allowed to do, and which control failed when it acted outside scope.

Related resources from NHI Mgmt Group

• Who is accountable when an AI agent performs an unauthorized action after injection?
• Who is accountable when an AI agent performs an unauthorized action in a SaaS product?
• Who is accountable when an autonomous agent takes an unsafe action?
• Who is accountable when an AI agent takes an unsafe action?