Yes. Human Zero Trust, authentication UX, and session controls do not automatically govern service accounts, tokens, or API keys that operate continuously behind the scenes. Machine access needs its own policy, monitoring, and lifecycle controls because its failure mode is different.
Why human controls and machine controls are not the same control problem
Healthcare teams often try to extend workforce controls to service accounts, API keys, and automation because the user-facing policy looks familiar. That works only up to a point. Human access is episodic, interactive, and easier to challenge with MFA, session timeouts, and user-driven approvals. Machine access is continuous, non-interactive, and usually tied to application behaviour rather than a person’s login.
The operational difference matters because the control objective changes. For humans, you want strong authentication, good session hygiene, and clear accountability. For machines, the priority is bounded privilege, secret handling, rotation, and reliable service-to-service authentication. A control set that is excellent for clinicians and administrators can still leave integrations overexposed if it assumes every actor presents through a browser or a login prompt.
That is why “one identity policy” is usually too coarse for mixed environments. A Human vs Non-Human Identity model helps teams separate user-facing access from service and automation access without pretending they are governed the same way. The same point applies to access design: Authorisation Models Guide is useful when you need to decide whether role, attribute, relationship, or policy-based control is the right fit for a workload rather than a person.
What breaks when machine access is treated like human access
The most common failure is assuming a strong human sign-in process protects everything downstream. It does not. A service account with a long-lived token can keep operating long after a user session has expired, and an API key embedded in an integration will not benefit from a prompt for step-up authentication. When teams rely on the human login layer alone, they often miss the real asset that needs governance: the credential or trust relationship that lets the workload act.
Another failure mode is over-relying on shared operational patterns. Healthcare integration teams often reuse the same secret across environments, systems, or vendors because it is simple to deploy. That convenience creates a larger blast radius when one key is exposed, and it makes revocation harder because nobody can tell which automation actually depends on it. The same class of problem appears when machine permissions are never reviewed with the same discipline as human entitlements.
Current guidance for identity governance supports treating people and machines as related but distinct populations. The practical takeaway is not to duplicate every workforce rule, but to make sure machine identities have ownership, expiry, rotation, monitoring, and scoped authorization that fit their runtime behaviour. An overview such as IAM and IGA Basics is useful because it frames provisioning, entitlement review, and lifecycle governance as a discipline that applies beyond employee accounts.
How to design separate controls without creating two disconnected programs
The right pattern is usually shared governance with separate control implementations. The policy layer should define who owns the identity, how privilege is approved, what “normal” access looks like, and when a secret or token must be rotated or retired. The implementation layer then differs by actor type. For humans, that means MFA, device and session controls, and access review. For machine access, it means workload authentication, secret inventory, narrow scopes, certificate or token lifecycle management, and telemetry that can spot abnormal non-interactive use.
In healthcare, this separation matters most where integrations touch clinical systems, billing, patient portals, and partner APIs. A credential that only needs to call one downstream service should not inherit broad network, directory, or database reach just because the owning application was approved by a person. The cleanest design is to grant the machine only the minimum permission required for the exact service path, then monitor that path as a distinct control surface.
If your environment spans cloud, APIs, and third-party services, use the same governance model across all of them and let the control mechanics differ by actor. Identity Convergence Guide is helpful when you need one ownership model that still preserves distinct treatment for workforce, privileged, customer, NHI, and agent identities. For machine-to-machine access patterns, RFC 6749: The OAuth 2.0 Authorization Framework is a useful reference because it formalises client-based authorisation rather than pretending every caller is a browser user.
Risk and Threat Considerations
When machine access is governed like human access, the main risk is silent over-permission and weak revocation. A leaked key, token, or shared secret can continue to operate without obvious user behaviour, which makes detection slower and containment harder. In healthcare, that can expose patient data, disrupt integrations, or allow an attacker to move through trusted service paths that look legitimate on paper.
Failure mechanism: Interactive controls such as MFA, session timeouts, and user prompts do not stop non-interactive credentials from authenticating, so exposed machine secrets can stay valid and overprivileged until they are explicitly discovered and revoked.
Impact: Compromise can persist through automation, create broad data access, and amplify operational damage because downstream systems may continue trusting the compromised workload.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Non-Human Identity Top 10 addresses the attack and risk surface, while NIST SP 800-53 Rev 5 and CIS Controls v8 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST SP 800-53 Rev 5 | IA-9 — Identification and Authentication (Non-Organizational Users) | Machine-to-machine access needs distinct authentication controls. |
| IA-5 — Authenticator Management | Service tokens and API keys need lifecycle control, not just login policy. | |
| AC-6 — Least Privilege | Separate access models must still limit machine permissions to what each service needs. | |
| Recommendation — Apply IA-9 to authenticate workloads and service accounts separately from human users. Manage machine secrets with rotation, expiry, and revocation controls. Constrain each machine identity to the minimum permissions required for its function. | ||
| OWASP Non-Human Identity Top 10 | NHI-07 — Long-Lived Secrets | Continuous machine access is often sustained by secrets that outlive their safe window. |
| NHI-05 — Overprivileged NHI | Machine access is risky when service identities inherit broad, human-like access. | |
| NHI-02 — Secret Leakage | Service credentials can leak through code, configs, or integrations and remain usable. | |
| Recommendation — Replace long-lived machine secrets with shorter-lived credentials and enforced rotation. Review and reduce machine privileges to the minimum needed for each integration. Scan for exposed machine secrets and revoke any credential found outside approved vaulting. | ||
| CIS Controls v8 | CIS-6 — Access Control Management | Separate human and machine access needs distinct account and entitlement governance. |
| CIS-5 — Account Management | Machine identities need ownership, provisioning, and deprovisioning discipline. | |
| Recommendation — Inventory accounts and enforce role-appropriate access rules for both users and services. Track machine accounts through join, change, and offboarding events with clear ownership. | ||
Practitioner Guidance
What to verify: Confirm that every non-human credential has a named owner, an expiry or rotation rule, and a documented service dependency. If you cannot identify the consumer, the secret is already too hard to govern.
Decision rule: If a credential can authenticate without a human present, treat it as machine access and require separate lifecycle, scope, and monitoring controls rather than letting it inherit workforce policy by default.
What good looks like: Human sign-in controls protect people, while machine controls prove that service accounts, API keys, and tokens are short-lived, purpose-bound, and observable in logs and reviews.
Practitioner takeaway: The safest model is one governance standard with two control patterns, because the accountability can be shared while the authentication and failure modes are not.
Related resources from NHI Mgmt Group
- How should security teams run access reviews for non-human identities?
- How should security teams govern non-human identities that have persistent access?
- How should security teams extend identity and access controls across human users, infrastructure, cloud workloads, and AI agents without creating four separate operating models?
- How should security teams govern non-human identities at scale?