AI agents are becoming non-human identities in healthcare security

At a glance

What this is: This analysis argues that healthcare AI adoption is creating a non-human identity problem, because agents now need governed access to clinical data, workflows, and patient-facing systems.

Why it matters: IAM and NHI teams need to treat AI agents as distinct identities, or they will expand access without the visibility, rotation, and least-privilege controls already required for NHIs.

By the numbers:

• 40 million daily users and 66% of physicians are integrating AI into their workflows.
• Two-thirds of American physicians reported adopting AI in 2024, up from 38% the previous year.
• 7 in 10 healthcare conversations in ChatGPT occur outside normal clinic hours.
• 20% of Americans live in hospital deserts where inpatient care is vanishing.

👉 Read OpenAI's report on AI as a healthcare ally and identity risk

Context

AI in healthcare is no longer confined to experiments or back-office pilots. In practice, it is starting to sit between clinicians, patients, and medical data, which means every agent that reads, summarizes, routes, or recommends now has an identity problem as well as a workflow problem.

That shift matters to IAM and NHI governance because autonomous systems are not just another application tier. They request access, act on it, and can spread privilege across clinical and operational systems, which is why AI agent governance must be designed as identity governance, not simply model oversight.

Key questions

Q: How should security teams govern AI agents that access healthcare data?

A: Security teams should govern AI agents as non-human identities with their own credentials, owners, and policy boundaries. That means separating the agent from the human user account, scoping access to one workflow, and logging every tool call and data request. Without those controls, healthcare AI can expand access faster than teams can review it.

Q: What is the difference between an AI agent and a normal application account?

A: A normal application account usually runs a fixed workflow with predictable permissions. An AI agent can change behaviour based on context, decide which tools to call, and act across multiple systems. That makes it closer to a privileged non-human identity than a static app integration, so it needs tighter policy, oversight, and revocation controls.

Q: Why do AI agents create new identity risk in zero trust environments?

A: Zero trust assumes every request must be continuously verified, but AI agents can make many requests quickly and with changing context. If their identity is broad or inherited, they can move through trusted pathways without enough scrutiny. Teams should treat every agent action as a policy decision, not a trusted extension of the app.

Q: When should organisations use just-in-time access for AI agents?

A: Use just-in-time access when an AI agent needs elevated permissions for a narrow task such as data retrieval, triage, or a workflow trigger. JIT is most effective when paired with explicit approval, short token lifetimes, and immediate revocation after the task ends. It is less useful if the agent needs persistent broad access.

Technical breakdown

Why AI agents behave like non-human identities

An AI agent is not just a model responding to prompts. Once it can retrieve records, call APIs, or trigger downstream actions, it has execution authority and therefore functions as a non-human identity. In healthcare, that identity may sit inside a scribe, triage, research, or patient-facing workflow. The core issue is that the agent often inherits access from the surrounding application rather than receiving a distinct identity, policy boundary, and audit trail. That creates weak accountability and makes access review harder than with a service account or API token.

Practical implication: Treat each agent as a distinct identity with its own entitlement set, audit logging, and lifecycle owner.

Where least privilege breaks down in clinical AI workflows

Traditional least privilege assumes stable roles and predictable actions. AI agents break that assumption because their task scope changes with context, patient input, and tool availability. A summarisation agent may need read access to clinical notes but not billing records, scheduling systems, or export functions. If the access layer is coarse, the agent will inherit more privilege than it needs, and any compromise or misroute can widen blast radius quickly. This is where policy-based authorization, task scoping, and time-bound access become more important than static role mapping.

Practical implication: Define access by task and data class, not by broad clinician or application roles.

Why shadow AI creates a governance blind spot

Shadow AI appears when clinicians or teams use unsanctioned agents, plugins, or external tools outside approved identity controls. These tools can still process PHI, internal knowledge, or patient communications, but they often bypass inventory, approval, and review processes. The result is unmanaged access with no clear owner, no offboarding path, and limited visibility during incident response. From an NHI perspective, the blind spot is structural because the organisation may know the human user, but not the agent, the credentials it used, or where its outputs flowed.

Practical implication: Build discovery and review processes that inventory sanctioned and unsanctioned AI agents together.

Threat narrative

Attacker objective: The attacker objective is to obtain governed-looking access that can be used to read sensitive healthcare data or trigger trusted actions without obvious detection.

1. Entry begins when a clinical or operational team authorises an AI agent to access patient or workflow data through delegated credentials, API keys, or embedded application permissions.
2. Escalation occurs when the agent receives broader access than its task requires, allowing it to read, aggregate, or trigger actions across multiple systems.
3. Impact follows when the agent is misused, compromised, or over-scoped, creating unauthorized exposure of PHI, workflow manipulation, or audit gaps.

Breaches seen in the wild

• Moltbook AI agent keys breach — Moltbook breach exposed 1.5M AI agent keys.
• AI LLM hijack breach — attackers used stolen AWS access keys to hijack Anthropic LLM models on Bedrock.

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

NHI Mgmt Group analysis

AI agents are now part of the non-human identity problem, not a separate AI governance problem. Once an agent can act, call tools, and move across workflows, it needs the same identity discipline applied to service accounts and API keys. Healthcare simply makes the issue easier to see because the workflows are high-trust and data rich. Practitioners should govern AI agents through identity, not treat them as a model-only concern.

Identity blast radius is the right concept for healthcare AI governance. The real risk is not simply that an agent may fail, but that its permissions determine how far that failure can spread across clinical, billing, and patient-facing systems. The article’s scenario shows why broad inherited access is dangerous when agents parse notes or interface with patients. Teams should measure how much damage one agent credential can do.

Ephemeral access is necessary, but ephemeral trust is the harder problem. Task-scoped credentials reduce standing exposure, yet they do not solve the question of who approved the task, what data the agent can infer, or whether outputs can trigger unsafe action. In healthcare, governance must extend beyond token lifetime into policy, oversight, and workflow constraints. Practitioners should design control points before the agent reaches production.

Healthcare will force NHI governance to become operational, not theoretical. Clinical teams will not wait for perfect taxonomies before using agents to reduce load and speed decisions. That means security leaders must build controls that fit live care settings, where time, access, and accountability all matter. The organisations that adapt now will be better positioned to govern the wider wave of agentic AI identities.

Shadow AI is the category pressure test for IAM programmes. If teams cannot inventory where unsanctioned agents are already handling PHI or internal knowledge, then policy and review processes are incomplete. The practical answer is discovery, attribution, and offboarding for agents, not just for humans. Practitioners should expect AI inventory to become a standing IAM control.

From our research:

• 97% of NHIs carry excessive privileges, increasing unauthorised access and broadening the attack surface, according to Ultimate Guide to NHIs.
• Only 20% have formal processes for offboarding and revoking API keys, and even fewer have procedures for rotating them.
• For lifecycle control patterns, see Ultimate Guide to NHIs for governance, visibility, rotation, and offboarding practices.

What this signals

Healthcare AI adoption will force identity programmes to shift from account administration to continuous agent governance. With only 5.7% of organisations having full visibility into their service accounts, the visibility problem that already exists for NHIs becomes more severe when clinicians start using autonomous tools. Practitioners should expect AI agent inventory, approval, and revocation to become core operating controls, not side projects.

Identity blast radius: the practical measure of how much damage one agent credential can cause across clinical data, scheduling, and patient-facing systems. In healthcare, this will become a programme-level metric because agent scope can expand faster than traditional access reviews. Teams that can reduce blast radius early will be better positioned to adopt AI without creating unmanaged exposure.

The next governance gap is not whether AI is allowed, but whether the organisation can prove which agent accessed which data and why. That question aligns with broader NHI controls and zero trust principles, and it will increasingly shape audit readiness, incident response, and clinical trust. Security leaders should align agent controls with NIST AI Risk Management Framework and identity governance workflows.

For practitioners

• Inventory all sanctioned and unsanctioned agents Map every AI system that can read, summarize, route, or act on healthcare data, then assign an owner, purpose, and data boundary. Include embedded copilots, vendor tools, and any workflow automation that can touch PHI.
• Assign unique identities to each agent Do not let agents inherit human user accounts or shared service credentials. Issue separate identities, separate credentials, and separate audit trails so access reviews can distinguish user activity from agent activity.
• Enforce task-scoped access policies Restrict each agent to the minimum data class and system action required for one workflow. Use time-bound approvals for high-risk access and review whether the agent needs read, write, or execute permissions at all.
• Build offboarding for agent credentials Create a revocation process for AI agent tokens, API keys, certificates, and embedded permissions so decommissioned or replaced agents lose access immediately.
• Monitor for abnormal agent behaviour Alert on large data pulls, unusual tool calls, repeated retries, or requests that exceed the normal scope of a clinical workflow. Pair detection with incident playbooks that can disable the agent quickly.

Key takeaways

• Healthcare AI is becoming an NHI governance issue because agents now act with execution authority, not just analytical output.
• The scale problem is already visible, with clinician adoption rising quickly and unmanaged access expanding the attack surface around PHI and workflows.
• Security teams should respond by inventorying agents, assigning distinct identities, and enforcing task-scoped access before AI becomes embedded in routine care.

Key terms

• Non-Human Identity: A non-human identity is any credentialed software actor that can authenticate, request access, or perform actions in a system. In practice, this includes service accounts, API keys, tokens, certificates, bots, and AI agents. These identities need ownership, lifecycle control, and review just like human users do.
• AI Agent: An AI agent is software that can make decisions, call tools, and execute actions with some degree of autonomy. Unlike a simple application feature, it can move across systems and contexts, which makes its access profile dynamic and its governance needs closer to privileged identity management.
• Identity Blast Radius: Identity blast radius is the amount of damage one compromised or over-privileged identity can cause across systems and data. For NHIs, it is shaped by access scope, token lifetime, downstream permissions, and the number of services the identity can reach before detection or revocation.
• Shadow AI: Shadow AI is the use of AI tools or agents that the organisation has not inventoried, approved, or governed. These systems can still process sensitive data or interact with internal workflows, which makes them a visibility and offboarding problem as much as a model risk issue.

Deepen your knowledge

AI agent identity governance is a core topic in our NHI Foundation Level course, the industry's only accredited NHI security programme. If you are building controls for clinical AI or patient-facing automation, the course gives you a structured starting point.

This post draws on content published by OpenAI: AI as a Healthcare Ally. Read the original.