Why do human and machine identities need to be measured together?

Because AI agents can generate the same business risk as employees, but at machine speed and with different access patterns. If you measure only human behaviour, you miss delegated tool use, hidden automation, and access abuse that occurs through non-human identities. Joint measurement makes it possible to separate user error from agent-driven risk.

Why This Matters for Security Teams

Human and machine identities now share the same enforcement plane, so security teams need a measurement model that can compare them without pretending they behave the same way. A person might authenticate once and work through a managed session, while an AI agent or script may call APIs, invoke tools, and chain actions across systems in seconds. That creates blind spots in risk reporting, access reviews, and incident triage if metrics only cover employees.

The practical issue is not just volume. Human identities are usually governed through joiner, mover, leaver processes, while non-human identities often appear through automation, service onboarding, or embedded code paths that bypass the normal review cycle. If those identities are measured separately, teams may undercount privilege, overstate control coverage, or miss where delegated authority has drifted beyond its original intent. This is why control thinking should align with NIST SP 800-53 Rev 5 Security and Privacy Controls rather than rely on ad hoc dashboards.

For NHIMG, the key point is accountability: when an AI agent acts through a human-approved workflow, the risk is shared across identity types and should be measured as one operational surface. In practice, many security teams encounter this only after a “normal” user account is used to approve machine actions that were never separately governed.

How It Works in Practice

Measuring human and machine identities together means building a common identity inventory, then tagging each identity by type, privilege, workload, owner, and purpose. The goal is not to force the same policy on both groups, but to put them into one reporting model so teams can see where trust is concentrated. That usually requires correlating IAM records, PAM sessions, cloud entitlements, API tokens, and agent tool permissions in the same control view.

In a workable model, every identity gets a measurable set of attributes: who or what owns it, what it can reach, how it authenticates, how often it is used, and whether it can delegate or chain access. Human identities are assessed for interactive behavior, dormant accounts, and privileged elevation. Machine identities are assessed for secret hygiene, rotation discipline, workload scope, and evidence of over-permissioned automation. Where agentic AI is involved, best practice is evolving toward tracking not just the agent itself, but also the human sponsor, the tool set, and the data boundaries it can reach.

• Use one inventory, but separate identity classes with clear labels for human, service, workload, and agent.
• Measure privilege exposure in both groups using the same unit of analysis, such as reachable systems or sensitive actions.
• Correlate authentication events with action logs so delegated machine use can be distinguished from direct human activity.
• Review orphaned secrets, stale accounts, and unused tokens together because they often represent the same governance failure in different forms.

Where identity telemetry is mature, this approach supports better detections, cleaner recertification, and more accurate blast-radius analysis. It also aligns with the access control intent in NIST Cybersecurity Framework 2.0 and the behavior-focused threat patterns in MITRE ATT&CK. These controls tend to break down when identity data is scattered across separate cloud tenants, shadow automation, and unmanaged agent tooling because no single system can reconstruct the full chain of authority.

Common Variations and Edge Cases

Tighter joint measurement often increases governance overhead, requiring organisations to balance better assurance against the cost of maintaining a richer identity model. That tradeoff is real, especially where business units move quickly and automation is created outside central platform teams.

There is no universal standard for this yet. Some organisations measure humans and machines on a shared risk scale, while others keep separate scores and only aggregate them at reporting time. The right approach depends on whether the main concern is auditability, threat detection, or operational resilience. For example, a call center with limited automation may only need lightweight NHI metrics, while a software engineering environment with AI-assisted deployment may need joint measurement down to tool-level permissions and signing keys.

Edge cases matter. Shared service accounts can blur ownership, federated identities can hide where authority actually sits, and AI agents can behave like privileged operators even when their direct credentials look low risk. That is why current guidance suggests measuring effective access, not just assigned access. In regulated environments, the measurement model should also reflect control ownership, evidence retention, and reviewer accountability. Where identity and machine actions are tightly coupled, separate metrics may look cleaner on paper but fail to show how access is actually exercised in production.

For teams building a governance baseline, the most useful question is not whether an identity is human or machine, but whether its actions can be explained, bounded, and reviewed. That is the standard that supports both security operations and credible assurance.

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?