TL;DR: Machine identities outnumber humans by more than 80 to 1, and the source argues that treating all non-human identities as one category obscures the access, rotation, and revocation controls needed for Zero Trust and AI agent governance, according to CyberArk. Precision, not broad labeling, is now the practical security boundary for machine identities.
At a glance
What this is: This is CyberArk's argument that machine identity is the real security boundary inside the broader NHI conversation, because vague categorisation hides the access, rotation and revocation controls practitioners actually need.
Why it matters: IAM, PAM and NHI teams need a tighter machine identity model because the controls that secure workloads, certificates, tokens and AI agents depend on precision, not umbrella terminology.
Context
Machine identity security is the problem of governing the credentials and cryptographic identities used by workloads, services, APIs, certificates and AI agents. The article argues that grouping all non-human identities together is too vague to support a defensible IAM strategy.
CyberArk's point is that modern environments need to distinguish between human identity, machine identity and emerging agentic AI identity. The governance risk is not only scale, but misclassification: when the programme cannot name the identity type precisely, it cannot apply the right access, auditing and revocation controls.
Key questions
Q: What breaks when all machine identities are treated as one NHI category?
A: The control model loses the distinctions that make access governance workable. Certificates, service accounts, tokens and AI agents have different issuance, scope, monitoring and revocation needs. When teams collapse them into one bucket, they misapply lifecycle controls, miss overprivilege and weaken response because they cannot tell which identity type needs which action.
Q: Why do machine identities increase lateral movement risk in cloud and SaaS environments?
A: Machine identities often have persistent access, broad integration reach and fewer human friction points than user accounts. If one is compromised, attackers can reuse it across connected systems, admin APIs and automation workflows. That turns a single secret into a path for broader compromise.
Q: How do security teams know whether machine identity governance is working?
A: They know it is working when every non-human credential has a named owner, a visible system scope, and a documented retirement path. If access reviews cannot answer when the identity was last used or why it still exists, governance is only partial. High confidence comes from evidence of cleanup, not just policy coverage.
Q: What is the difference between NHI and machine identity?
A: Machine identity refers specifically to identities assigned to physical or virtual machines and devices. Non-Human Identity is the broader category that encompasses machine identities but extends to software workloads, service accounts, API keys, OAuth tokens, bots, RPA agents, CI/CD pipeline identities, and AI agents.
Technical breakdown
Why broad NHI language breaks machine identity governance
A machine identity is a non-human identity that a system uses to authenticate and authorise itself, such as an API key, service account, TLS certificate or token. Broad NHI language collapses different control problems into one label, even though certificates, keys, workloads and agents have different issuance, scope and revocation requirements. That matters because governance depends on knowing what is being controlled, where it lives, and how quickly it can be changed or withdrawn. Precision is therefore not a branding preference. It is the prerequisite for assigning the right policy, owner and lifecycle.
Practical implication: Classify machine identities by type before assigning controls, so lifecycle, access scope and revocation can be governed differently.
How machine identity precision supports Zero Trust
Zero Trust only works when the verifier knows exactly what is authenticating and what level of trust it deserves. Machine identity precision enables granular policy decisions for containers, workloads, devices and services rather than treating all non-human actors the same. It also supports continuous verification, because cryptographic identity can be checked in real time and revoked when behaviour changes. Without that granularity, Zero Trust becomes a slogan instead of an operating model. The practical issue is not whether trust is reduced, but whether the organisation can identify the thing requesting access at the level needed to enforce policy.
Practical implication: Map machine identity types to Zero Trust policy boundaries so enforcement happens at the right scope, not the broadest category.
Why agentic AI raises the stakes for identity precision
Agentic AI changes the identity problem because the actor can create, call and manage other identities at runtime. That makes unique, verifiable identity essential, since a compromised or spoofed agent can move through APIs, workloads and other systems at machine speed. The article's core warning is that generic NHI treatment does not distinguish a static service account from an actor that can initiate actions independently. That distinction matters for authorisation, rollback and accountability. Machine identity precision becomes the only way to keep agent behaviour attributable and containable.
Practical implication: Treat AI agents as distinct machine identities with explicit verification, attribution and revocation paths.
Threat narrative
Attacker objective: The attacker aims to abuse trusted machine credentials to reach cloud and infrastructure assets, expand access and keep operating under legitimate identity.
- Entry occurs through compromised machine credentials such as API keys, service accounts or tokens that already exist in modern infrastructure.
- Escalation happens when those machine identities are overbroad, reused or insufficiently segmented, allowing lateral movement into connected systems.
- Impact follows when attackers use the trusted identity to access cloud services, certificates or APIs at machine speed and without immediate suspicion.
Breaches seen in the wild
- Twitter source code leak 2023: Twitter source code and internal tools were posted to GitHub by "FreeSpeechEnthusiast" and stayed public for months before a 2023 takedown.
- Sisense breach 2024: A credential in Sisense's GitLab reportedly opened S3 buckets of customer tokens, passwords and certificates; CISA urged a full reset.
Read and download The State of NHI & AI Agent Breach Report 2026, covering 200+ breaches impacting Non-Human Identities including AI Agents.
NHI Mgmt Group analysis
Machine identity precision is now the control plane, not a terminology debate. Broad NHI language masks the real governance problem, which is that certificates, keys, tokens and service accounts do not fail in the same way. When teams cannot distinguish the identity type, they cannot define the correct lifecycle, policy boundary or owner. The implication is that identity programmes must stop optimising for inclusive labels and start optimising for enforceable control boundaries.
Zero Trust depends on knowing the exact non-human actor, not just that it is non-human. The article is right that never-trust-always-verify only works when the verifier can name the subject precisely enough to apply different policies to workloads, devices and agents. If every non-human identity is treated as one class, the architecture loses fidelity at the exact point where trust decisions are made. Practitioners should treat identity granularity as a Zero Trust requirement, not an implementation detail.
Agentic AI turns machine identity from static entitlement management into runtime accountability. A service account that authenticates a workload is not the same governance problem as an AI agent that can create work, invoke tools and delegate further actions. That is where machine identity precision becomes indispensable: the control model must reflect who or what can act, when it can act and how the action is attributed. The implication is that agentic governance cannot inherit generic NHI controls unchanged.
Ephemeral action, durable accountability: machine identity governance has to separate short-lived execution from long-lived responsibility. The article points toward a future where hybrid environments combine human, machine and agentic actors, but the current gap is that many programmes still track identity categories instead of control-bearing entities. When the identity type is misnamed, offboarding, revocation and traceability all degrade. Practitioners should align governance to the specific machine identity lifecycle they can actually enforce.
Precision beats umbrella governance because attackers exploit the seams between identity types. The risk is not that organisations lack a category called NHI. The risk is that they apply one policy model to identities with different exposure windows, different rotation needs and different blast radii. That creates the seam where compromise becomes lateral movement. Security teams should interpret machine identity precision as a way to close those seams before attackers do.
What this signals
Machine identity precision is the governance upgrade that Zero Trust actually needs. When policy is written against broad non-human categories, teams lose the ability to enforce different controls for workloads, certificates and agentic systems. The better operating model is to govern the identity type that is actually authenticating, because that is where access decisions become enforceable.
AI agents make identity classification operational, not academic. A machine that can create workloads, call APIs and manage other identities changes the meaning of access control. Security teams should assume that generic NHI inventories will understate the governance problem unless agent identities are named, tracked and isolated on their own terms.
For practitioners
- Define machine identity classes explicitly Separate service accounts, API keys, tokens, certificates and agent identities into distinct inventory groups with different owners, policies and revocation paths.
- Map access to identity type Apply different access rules to workloads, APIs, containers and AI agents so entitlement decisions reflect the thing authenticating, not a generic non-human label.
- Build revocation into machine identity lifecycle Ensure every machine identity can be traced, rotated and revoked quickly when misuse, compromise or scope drift is detected.
- Treat AI agents as separate governance objects Give agentic systems unique identity, explicit policy boundaries and rollback paths so their actions can be isolated from other machine identities.
Key takeaways
- Machine identity precision matters because the same controls do not fit every non-human actor, and broad NHI language hides that mismatch.
- The article ties credential theft, secrets sprawl and AI-driven runtime action to the same governance gap: unclear identity type boundaries.
- Practitioners should inventory, classify and revoke machine identities by form factor, then align Zero Trust and AI governance to that classification.
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 and MITRE ATT&CK address the attack and risk surface, while NIST Zero Trust (SP 800-207) sets the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Non-Human Identity Top 10 | NHI-05 — Overprivileged NHI | The article centres on machine identities whose broad treatment obscures overbroad access. |
| NHI-07 — Long-Lived Secrets | Machine identity risk here includes credentials and certificates that persist beyond safe operational windows. | |
| NHI-10 — Human Use of NHI | The article warns against treating machine identities as a vague catch-all instead of governed technical identities. | |
| Recommendation — Inventory machine identities separately and remove broad access that cannot be justified by identity type. Shorten credential lifetimes and rotate machine secrets according to identity criticality. Prevent manual, ad hoc handling of machine identities by enforcing standard ownership and lifecycle rules. | ||
| NIST Zero Trust (SP 800-207) | Zero Trust principles — Zero Trust principles | The piece argues that Zero Trust depends on precise identity verification and scoped trust decisions. |
| Recommendation — Apply Zero Trust policy at the exact machine identity boundary rather than at a generic non-human category. | ||
| MITRE ATT&CK | TA0006;TA0008 — Credential Access; Lateral Movement | The article ties stolen machine credentials to lateral movement across cloud and enterprise systems. |
| Recommendation — Map exposed machine credentials to credential access and lateral movement paths in threat hunting. | ||
Key terms
- Machine Identity: The digital identity of a machine, device, or workload, such as a server, container, or VM, used to authenticate it within a network. Sometimes used interchangeably with NHI, though NHI is the broader category.
- NHI Precision: NHI precision is the practice of distinguishing between different non-human identity types instead of treating them as one bucket. It matters because access policy, monitoring, offboarding and revocation work only when the identity subject is specific enough to govern in practice.
- Agentic AI Identity: The complete set of credentials, permissions, and governance controls applied to an autonomous AI agent, covering authentication, authorisation, action logging, and access revocation. Distinct from traditional NHI because agent identities are often ephemeral, delegated, and multi-hop.
- Zero Trust for Machine Identities: Zero Trust for machine identities means every access request is verified at the point of use rather than trusted because the workload is internal or previously approved. It requires continuous validation, narrow scope, and telemetry that can prove the identity is still operating within policy.
Deepen your knowledge
NHI governance, agentic AI identity, and machine identity lifecycle are core topics in our NHI Foundation Level course, the industry's only accredited NHI security programme. If you are building or maturing an IAM programme, it is worth exploring.
Published by the NHIMG editorial team on May 29, 2026.
Updated on October 8, 2026.
NHI Mgmt Group, the independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org