Identity governance maturity now depends on non-human identities

At a glance

What this is: This is an identity governance maturity model article that argues maturity now depends on consistent governance across human and non-human identities.

Why it matters: It matters because IAM, IGA, and PAM teams cannot treat access reviews, lifecycle automation, and non-human identity governance as separate programmes if they want measurable control.

By the numbers:

• Only 44% of organisations have implemented any policies to manage their AI agents, despite 92% agreeing that governing AI agents is critical to enterprise security.

👉 Read SecurEnds' identity governance maturity model for IGA and NHI

Context

Identity governance maturity is the measure of whether access controls actually work consistently across the enterprise, not whether a tool has been deployed. In practice, many programmes still rely on manual access reviews, fragmented lifecycle workflows, and incomplete visibility into service accounts, workloads, and other non-human identities.

That gap becomes more visible as environments spread across SaaS, cloud, remote work, and automated systems. The right question is no longer whether an IGA platform exists, but whether governance decisions remain reliable when applied to people, applications, and machine identities at scale.

Key questions

Q: How should organisations extend identity governance maturity models to non-human identities?

A: They should score non-human identities in the same maturity framework as human accounts, but with machine-specific controls for ownership, lifecycle, privilege scope, and review coverage. That means including service accounts, APIs, workloads, and certificates in access governance, then measuring whether review outcomes actually change entitlements rather than only documenting them.

Q: Why do access reviews often fail to reflect real identity governance maturity?

A: Because completion does not prove correction. A programme can finish certifications on time while stale entitlements remain, especially when reviews are manual, spreadsheet-driven, or disconnected from deprovisioning workflows. Maturity rises when review decisions are tied to remediation and verified entitlement removal.

Q: What breaks when non-human identities are excluded from governance scoring?

A: The maturity model understates risk because the identities with the most persistent or high-privilege access are left out of the control picture. Service accounts, workloads, and automation identities can accumulate access outside normal workforce processes, which means the programme can appear compliant while critical access remains unmanaged.

Q: Which frameworks help assess identity governance maturity across people and machines?

A: The NIST Cybersecurity Framework 2.0 helps anchor governance, protect, detect, respond, and recover activities, while the OWASP Non-Human Identity Top 10 helps focus on machine identity risks such as overprivilege, visibility gaps, and credential management. Together they support a maturity view that includes both enterprise process and identity-specific control coverage.

Technical breakdown

Identity governance maturity is a control-consistency problem

A maturity model is useful only when it measures whether governance behaves the same way across systems, not whether policies exist on paper. In IGA, the real test is consistency across access reviews, provisioning, remediation, and evidence collection. Ad hoc and repeatable programmes often fail because approvals, certifications, and deprovisioning happen differently by application, team, or identity type. That creates hidden variance, which is why audit success can coexist with weak operational control.

Practical implication: measure governance consistency by workflow outcome, not by policy existence.

Lifecycle automation and access reviews are the main maturity inflection points

Lifecycle automation and access review quality usually separate low maturity from managed governance. When onboarding, transfers, and offboarding remain manual, access lingers and role drift accumulates. When certifications are spreadsheet-driven, reviews often validate noise instead of removing risk. Mature programmes connect authoritative identity data to lifecycle triggers, then close the loop with remediation so access changes are actioned rather than merely recorded. That is what makes maturity operational, not rhetorical.

Practical implication: tie lifecycle events to automated entitlement changes and verified remediation.

Non-human identity governance belongs in the maturity model

Modern IGA programmes fail when they stop at human users and ignore service accounts, APIs, workloads, certificates, and automation identities. These identities often have standing access, weak ownership, and limited review coverage, which makes them difficult to govern with human-centric processes. The maturity model therefore has to extend beyond joiner-mover-leaver mechanics into machine identity visibility, privilege scope, and ownership. Without that, the programme can look mature on paper while the highest-risk identities remain outside governance.

Practical implication: add machine identities to certification, ownership, and lifecycle governance scope.

NHI Mgmt Group analysis

Identity governance maturity is no longer defined by human access management alone. The article is right to place non-human identities inside the maturity model, because service accounts, APIs, workloads, and certificates now carry business-critical access. A programme that measures only employee lifecycle control is measuring a shrinking part of the risk surface. The practitioner conclusion is simple: maturity scoring must reflect the full identity estate, not just workforce governance.

Machine identity governance: this is the point where maturity stops being an IGA score and becomes a control model for the whole enterprise. The model's value comes from showing whether governance is operational across humans and machines, not whether a review cycle exists in one application. That broader view aligns with the NIST Cybersecurity Framework 2.0 and OWASP Non-Human Identity Top 10, where identity scope and control consistency matter more than isolated process presence. Practitioners should treat NHI coverage as a maturity requirement, not an optional extension.

Low-maturity programmes fail because access is easier to grant than to prove is still justified. Manual reviews, incomplete lifecycle automation, and inconsistent SoD enforcement all allow entitlement drift to accumulate faster than governance can remove it. The article correctly shows that compliance evidence is not the same as control effectiveness. The practitioner conclusion is that maturity has to be measured by the speed and reliability of entitlement correction, not by the existence of a policy.

Optimised governance depends on linking identity lifecycle, risk analytics, and machine ownership. That combination is what turns maturity from static certification into continuous governance. Without ownership coverage and remediation telemetry, service accounts and automation identities remain structurally under-governed even in otherwise advanced programmes. The practitioner conclusion is to benchmark governance around operational closure, not dashboard completeness.

From our research:

• 69% of security leaders agree identity management must fundamentally shift to address agentic AI systems, according to The 2026 Infrastructure Identity Survey.
• Only 13% of organisations feel extremely prepared for the reality of agentic AI despite the majority racing toward autonomous adoption.
• For adjacent context, see Top 10 NHI Issues for the control gaps that keep maturity scores from reflecting real-world machine identity risk.

What this signals

Machine identity governance will increasingly become the maturity benchmark that separates governance theatre from operational control. As infrastructure becomes more automated, teams will need a single programme view that covers human users, service accounts, and autonomous systems without treating each as a separate exception domain.

A useful signal for practitioners is whether lifecycle events actually reduce standing access in a measurable way. If access reviews, offboarding, and role changes do not consistently change entitlements, the maturity model is recording activity rather than governing risk.

The next planning question is how to connect governance scoring to ownership and remediation workflows. Programmes that can prove who owns each non-human identity and how quickly violations are closed will have a far stronger maturity story than those relying on manual certification volume alone.

For practitioners

• Expand maturity assessments to include machine identities Add service accounts, APIs, workloads, certificates, and automation identities to the same governance scorecard used for human access reviews and lifecycle controls.
• Measure entitlement correction, not just review completion Track how quickly access is removed after a termination, role change, or policy violation, because completion metrics can hide unresolved exposure.
• Connect authoritative identity data to lifecycle triggers Use a trusted source of identity truth so onboarding, transfer, and offboarding events automatically drive provisioning and deprovisioning decisions.
• Baseline ownership coverage for non-human identities Inventory which service accounts and automation identities have named owners, then prioritize remediation where ownership is unknown or stale.

Key takeaways

• Identity governance maturity is only credible when it measures how consistently controls operate across both human and non-human identities.
• Manual reviews and partial lifecycle automation can make a programme look mature while leaving high-risk access unmanaged.
• The next maturity step is operational closure: visible ownership, automated lifecycle action, and verified entitlement removal.

Key terms

• Identity Governance Maturity Model: A framework for assessing how consistently an organisation controls access, enforces policy, and proves compliance across its identity estate. In practice, maturity is measured by operational reliability, remediation speed, and the ability to scale governance across human and non-human identities.
• Non-Human Identity Governance: The governance of service accounts, APIs, workloads, certificates, tokens, and automation identities. It focuses on ownership, lifecycle handling, privilege scope, and review coverage, because machine identities often outlive human approval processes and create persistent access risk if they are not continuously managed.
• Segregation of Duties: A control that prevents a single identity from holding incompatible privileges that could enable fraud, abuse, or unauthorised change. In mature identity governance, SoD is enforced continuously, not just during audits, so toxic combinations are detected and remediated before they create business exposure.

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 responsible for identity security strategy or NHI governance in your organisation, it is worth exploring.

This post draws on content published by SecurEnds: an identity governance maturity model for evaluating access, policy enforcement, and compliance. Read the original.