Non-human identities vs machine identities: governance gaps to close

At a glance

What this is: This is an identity governance explainer that distinguishes machine identities from broader non-human identities and shows why security controls must match the identity type.

Why it matters: It matters because IAM, PAM, and NHI teams need different control models for workloads, service accounts, API keys, and device identities if they want to reduce exposure without overgeneralising.

By the numbers:

• 44% of NHI tokens are exposed in the wild, being sent or stored over platforms like Teams, Jira tickets, Confluence pages, and code commits.
• 91% of former employee tokens remain active after offboarding, leaving organisations vulnerable to potential security breaches.

👉 Read P0 Security's guide to non-human identities vs machine identities

Context

Non-human identity governance starts with a simple point: not every non-human identity is a machine identity, and the difference changes how you inventory, protect, and retire access. Machine identities usually cover devices, workloads, and cloud services, while broader NHIs also include service accounts, API keys, tokens, bots, and legal entities.

The control problem is not taxonomy for its own sake. If teams apply the same lifecycle and credential model to every NHI, they miss where secret sprawl, overprivilege, and offboarding failures actually occur; the result is a programme that looks unified but behaves inconsistently across identity types.

Key questions

Q: How should security teams govern machine identities and other NHIs differently?

A: Security teams should separate machine identities from broader NHI classes, then assign controls to the identity type and use case rather than to a generic non-human label. Workload certificates, service accounts, API keys, and bots have different ownership, rotation, and offboarding requirements. A single policy model usually hides those differences and leaves gaps in lifecycle control.

Q: Why do exposed secrets create more risk than isolated credential storage issues?

A: Exposed secrets create more risk because duplication expands the attack surface. A token copied into code, chat, or tickets can survive one revocation action and continue to authenticate elsewhere. When a secret has multiple uncontrolled copies, the problem is no longer storage alone. It becomes a distribution and accountability issue that increases the blast radius of compromise.

Q: What breaks when organisations treat all non-human identities as the same thing?

A: Controls break because different NHIs require different lifecycle, privilege, and monitoring patterns. A service account, a workload certificate, and an API key do not fail in the same way, so a one-size policy can leave stale access, duplicated secrets, or overprivileged automation in place. The result is tidy policy language and messy execution.

Q: How do teams reduce NHI risk after a system, vendor, or workflow is retired?

A: Teams should revoke credentials as part of the retirement process, not as a separate cleanup task. That means identifying every token, key, and service account tied to the departing system, then confirming that no dependent service still uses them. Offboarding only works when ownership is explicit and revocation is verified across all copies.

Technical breakdown

Machine identities vs non-human identities: where the boundary matters

A machine identity is a subset of NHI, typically used for devices, workloads, cloud services, and other technical assets that authenticate to each other. Broader NHIs include service accounts, API keys, OAuth tokens, bots, and organisational identifiers that may not map cleanly to infrastructure. The distinction matters because the credential type, rotation method, and lifecycle owner differ by identity class. A certificate on a workload is not governed the same way as a service account token embedded in a pipeline or a bot account used by a business process.

Practical implication: classify NHIs by identity type before assigning controls, or you will apply the wrong lifecycle and rotation model.

Why secret sprawl changes NHI security outcomes

Secrets are the operational glue for many NHIs, but they also create the fastest path to misuse when they are copied into code, tickets, chat tools, or shared repositories. API keys and tokens are especially exposed because they are easy to replicate, hard to trace, and often shared across multiple applications. Once a secret is duplicated, revocation and rotation become harder, and the blast radius grows because one compromise can affect several dependent services. That is why inventory alone is not enough; ownership and distribution matter as much as discovery.

Practical implication: track where secrets live and how many systems depend on them, not just whether they exist.

Least privilege and monitoring for mixed NHI estates

Least privilege for NHIs should be based on function, dependency, and credential type, not on a generic machine-versus-not-machine label. A service account that supports a single workflow should not inherit permissions from an application cluster, and a bot should not retain broad access after its task changes. Continuous monitoring is the other half of the model because unusual usage patterns, such as unexpected logins or cross-service calls, are often the first sign that an NHI has been overexposed or repurposed. In practice, access scope and runtime behaviour must be reviewed together.

Practical implication: pair permission scoping with behavioural monitoring so you can detect NHI misuse before it spreads.

Threat narrative

Attacker objective: The attacker wants to turn one exposed non-human credential into durable access across multiple connected systems.

1. Entry begins when a stolen or exposed NHI credential such as an API key, token, or service account secret is reused against a connected service.
2. Escalation occurs when the compromised identity has broader permissions than the original task requires, allowing access to adjacent systems or data paths.
3. Impact follows when the attacker uses that standing access to move laterally, exfiltrate data, or alter cloud and application workflows at scale.

Breaches seen in the wild

• Cisco DevHub NHI breach — IntelBroker exploited exposed Cisco credentials, API tokens and keys in DevHub.
• Dropbox Sign breach — compromised Dropbox Sign service account exposed API keys and OAuth tokens.

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

NHI Mgmt Group analysis

Machine identity governance is too narrow to cover the real NHI problem. Machine identities describe one subset of the estate, but the operational risk sits across service accounts, API keys, tokens, bots, and other non-human credentials. When governance is framed only around workloads and certificates, the programme misses where most credential exposure and lifecycle failure actually happens. The implication is that identity teams need a broader NHI governance model, not a machine-only one.

Secret distribution is the control surface, not just secret storage. A secret that exists in a vault but also appears in tickets, chats, and code is governed by its weakest copy. That makes duplication and uncontrolled propagation a first-order risk, because revocation is only effective when all copies are known. Practitioners should treat secret sprawl as an ownership problem, not just a storage problem.

Least privilege for NHIs fails when permissions are assigned to identity type instead of task context. Service accounts, bots, and workload identities do not behave like humans, so broad role assignment quickly turns into standing access that no one revisits. The right governance question is not whether the identity is machine or non-machine, but whether its current permission set still matches the function it performs. Teams should align review cycles to task ownership and runtime dependency.

NHI lifecycle failures are usually offboarding failures in disguise. The most common breakdown is not creation, but persistence after use, especially when keys, tokens, and service accounts are left active after the original purpose ends. That turns temporary access into unnecessary exposure and complicates accountability across application owners and platform teams. The implication is that identity governance must include explicit deprovisioning ownership for every non-human credential.

Identity programmes need a named boundary between machine identities and broader NHI classes. Without that boundary, controls become inconsistent: some teams manage certificates well but ignore API keys, while others rotate secrets but never map service-account ownership. Identity type drift: when organisations let different NHI classes inherit the same governance model, they create blind spots that look tidy in policy but fail in execution. Practitioners should standardise classification before standardising controls.

From our research:

• 85% of organisations lack full visibility into third-party vendors connected via OAuth apps, according to The State of Non-Human Identity Security.
• Only 1.5 out of 10 organisations are highly confident in their ability to secure NHIs, compared to nearly 1 in 4 for securing human identities.
• For lifecycle controls and offboarding, see NHI Lifecycle Management Guide, which connects ownership, retirement, and governance into one operational model.

What this signals

Identity type drift: many programmes already manage workloads, service accounts, API keys, and bot accounts through different owners and different tooling, but they describe the estate as if it were one control domain. The practical signal is that classification comes first. If teams cannot distinguish machine identities from broader NHI classes, they cannot measure exposure, assign lifecycle ownership, or prove offboarding.

The next governance shift is toward secret distribution tracking rather than secret storage alone. Once a credential is copied into code, tickets, or collaboration systems, the organisation is managing a propagation problem, not a vault problem. Teams that can map those paths will find it easier to reduce blast radius and explain residual risk to audit and IAM stakeholders. For a broader baseline, the Top 10 NHI Issues is a useful starting point.

As NHI estates grow, the boundary between machine identity and broader non-human identity will become a programme design issue, not a taxonomy debate. Identity leaders should expect more pressure to define ownership, review cadence, and offboarding by identity class, especially where service accounts and API keys support multiple applications. That is where the governance model becomes operational rather than theoretical.

For practitioners

• Separate machine identity from broader NHI classes Create distinct inventory fields for workloads, devices, service accounts, API keys, tokens, and bots so each can carry its own owner, rotation method, and retirement path.
• Map secret distribution paths Track every place a secret is copied, including code repositories, tickets, collaboration tools, and deployment pipelines, so revocation can remove all live copies.
• Tie least privilege to task context Review each NHI against the specific workflow it supports, then remove inherited permissions that are not required for that function or dependency chain.
• Assign offboarding ownership for non-human credentials Make application, platform, and security owners explicitly accountable for revoking tokens, keys, and service accounts when a system, vendor, or process is retired.
• Monitor runtime behaviour for identity drift Alert on unusual call patterns, unexpected service-to-service access, and credentials that are used outside their normal scope or operating window.

Key takeaways

• Machine identities are only one subset of the broader NHI problem, so governance that stops at workloads and certificates will miss service accounts, tokens, bots, and API keys.
• Secret duplication and unmanaged distribution turn a single credential into a wider compromise path, which is why ownership and revocation matter as much as storage.
• Teams that classify NHI types correctly can assign better lifecycle controls, privilege boundaries, and monitoring, reducing the gap between policy and execution.

Key terms

• Machine Identity: A machine identity is a non-human identity used by a device, workload, or service to authenticate to another system. In practice it is often certificate-based and tied to infrastructure components that exchange data automatically, so its lifecycle, ownership, and revocation must be managed as operational controls, not as user access.
• Non-Human Identity: A non-human identity is any digital identity that is not tied to a person. It includes service accounts, API keys, tokens, certificates, bots, and other automated entities. The security challenge is that these identities often exist at scale, can be overused, and may persist without the same lifecycle scrutiny as human accounts.
• Secret Sprawl: Secret sprawl is the uncontrolled spread of credentials across systems, tools, and teams. A secret may exist in a vault, but if it also appears in code, tickets, chat tools, or build pipelines, the organisation has multiple copies to govern, revoke, and audit, which increases exposure and weakens accountability.
• Identity Lifecycle Management: Identity lifecycle management is the process of creating, maintaining, reviewing, and retiring access over time. For NHIs, that means provisioning credentials only when needed, tracking who owns them, monitoring usage, and revoking them when the system, process, or vendor relationship ends.

What's in the full article

P0 Security's full article covers the operational detail this post intentionally leaves for the source:

• The article breaks down the practical examples used to distinguish devices, applications, automated processes, and legal entities.
• It outlines the recommended steps for discovery, central management, least privilege, credential security, and monitoring.
• It gives the FAQ-style explanations that teams can use internally when standardising terminology across IAM and security stakeholders.

👉 The full P0 Security article covers the examples, controls, and FAQs in more implementation detail.

Deepen your knowledge

NHI governance, agentic AI identity, and machine identity security are core topics in our NHI Foundation Level course, the industry's only accredited NHI security programme. If you are building or maturing an identity security programme, it is worth exploring.