Human and machine identities are the two classes of identities an organisation must govern: people and the software, services, and devices acting on their behalf. Human identities cover employees, contractors, and partners. Machine identities cover service accounts, APIs, workloads, certificates, tokens, and AI agents that authenticate, authorise, and interact autonomously.
How human and machine identities fit together
Human identities and machine identities are not separate problems, they are two halves of the same governance model. People initiate, approve, and own access, while software and devices carry out most of the authenticated interaction that modern systems depend on.
The practical boundary matters because the same organisation often manages employees, contractors, partners, service accounts, APIs, workloads, certificates, tokens, and AI agents in one operating environment. When those identity classes are treated differently, control gaps appear at the handoff points: delegated access, shared credentials, automation, and service-to-service trust.
Why the distinction matters for security
Human identities usually require strong proofing, user lifecycle management, and sign-in governance. Machine identities, by contrast, are often created at scale, used non-interactively, and embedded in applications, pipelines, infrastructure, and integrations. That makes them harder to inventory and easier to leave overprivileged or long-lived.
The security impact is not just about authentication. Human accounts are the usual source of approval and oversight, but machine identities often hold the keys to production systems, cloud resources, and backend data paths. If a machine identity is compromised, the attacker may bypass normal user-facing controls entirely and operate through trusted automation.
Where the control boundaries usually break
Breakdowns most often happen when organisations assume a service account, token, or certificate is “just technical plumbing.” In reality, those objects represent authority, and their scope, ownership, rotation, and offboarding need the same discipline as user access. Human vs Non-Human Identity is a useful reference point for understanding where people-driven governance ends and machine-driven access begins.
Another common failure mode is reuse. Shared credentials, copied secrets, and duplicated service identities blur accountability and make blast radius much larger than it appears. The more an organisation reuses identity material across systems, the harder it becomes to answer basic questions about ownership, privilege, and revocation.
How to govern the full identity estate
Effective governance starts with a single inventory of who, or what, can authenticate and what each identity is allowed to do. That means tracking human and machine identities together, but applying different lifecycle rules where their risk profiles differ. Ultimate Guide to NHIs gives the broader machine-identity context, while NHI Ownership and Accountability Guide shows why clear ownership is the control that makes lifecycle management work.
For machine identities in particular, strong authentication and short-lived trust are preferable to static secrets that persist indefinitely. NHI Authentication Guide and Guide to NHI Rotation Challenges together frame the core operational problem: how to authenticate non-human actors safely, and how to rotate their credentials without breaking production services.
Risk and Threat Considerations
Human and machine identities create compound risk because attackers rarely need to choose only one path. A compromised user account can expose approval workflows, while a compromised machine identity can expose backend systems, tokens, and secrets that were never meant for interactive use. That combination makes identity boundaries a prime target for privilege escalation and lateral movement.
Failure mechanism: Weak ownership, shared secrets, excessive privilege, and long-lived credentials let an attacker move from one identity class into the other and inherit trusted access paths.
Impact: The result can be unauthorized system access, data exposure, persistence inside production services, and difficult-to-detect abuse of trusted automation.
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 CSA Cloud Controls Matrix set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST SP 800-53 Rev 5 | IA-5 — Authenticator Management | Covers lifecycle control of credentials and tokens used by human and machine identities |
| IA-9 — Service Identification and Authentication | Directly addresses authentication between services, workloads, and other non-human actors | |
| AC-6 — Least Privilege | Applies to limiting human and machine identities to only the access they need | |
| Recommendation — Manage credential issuance, rotation, storage, and revocation for all identity classes. Use mutual authentication and strong service identity for machine-to-machine trust. Constrain entitlements for users, service accounts, and automation to minimum necessary privilege. | ||
| CSA Cloud Controls Matrix | IAM — Identity and Access Management | Defines cloud identity governance across users, services, and non-human actors |
| Recommendation — Centralize identity governance for both workforce and machine identities in cloud environments. | ||
| OWASP Non-Human Identity Top 10 | NHI-05 — Overprivileged NHI | Addresses excessive permissions in machine identities and service accounts |
| Recommendation — Review machine identity privileges and remove access that is not operationally required. | ||
Practitioner Guidance
Governance implication: Treat human and machine identities as one identity estate with different control patterns, not as separate silos. The useful question is not whether an identity is human or non-human, but whether its owner, purpose, privilege, and lifecycle are actually controlled.
What to watch for: orphaned service accounts, shared access material, stale certificates, unexplained automation accounts, and machine identities whose permissions grew faster than their documented purpose. Those are usually the early signs that the identity model has drifted away from the actual operating model.
Related resources from NHI Mgmt Group
- Why do machine identities create more risk than human identities in some environments?
- Why do machine identities complicate identity governance more than human accounts?
- How should security teams govern machine identities differently from human users?
- Should organisations prioritise machine identities before human access reviews?