How should security teams use an identity maturity model to prioritize IAM modernization?

Security teams should use the model to map current capabilities, identify the most material gaps, and sequence work from visibility and process standardization toward continuous governance. That approach avoids random tool sprawl and helps teams focus on controls that reduce manual effort, improve enforcement consistency, and shrink standing access before pursuing more advanced automation. The model is most useful when tied to measurable milestones and ownership.

Why Identity Maturity Models Matter for IAM Modernization

An identity maturity model is useful because it turns IAM modernization from a tool-buying exercise into a sequencing problem. Teams can compare today’s state against a defined target, then prioritize the gaps that create the most risk or manual overhead. That matters because non-human identities are often far less governed than human accounts, even though they are the ones that automate deployments, call APIs, and hold durable access. The 2024 Non-Human Identity Security Report found that 88.5% of organisations say their non-human IAM practices lag behind or only match their human IAM practices, which makes maturity-based prioritisation especially relevant.

Security teams get better outcomes when they use the model to identify where visibility, ownership, lifecycle controls, and enforcement are weakest before trying to automate everything at once. This aligns with the control emphasis in NIST SP 800-53 Rev 5 Security and Privacy Controls, which rewards consistent, measurable control improvement rather than ad hoc fixes. In practice, many teams discover the biggest gaps only after a breach review or an audit forces a full inventory they never had intentionally built.

How to Turn Maturity Findings into a Modernization Roadmap

The most effective use of a maturity model is to translate each stage into specific control outcomes, not broad aspirations. Start with inventory and ownership, then move to credential hygiene, access standardization, and finally continuous governance. That sequence reduces noise: if you cannot see the identities, you cannot govern them; if you cannot govern them, automation only scales inconsistency.

For non-human identities, this usually means prioritizing service-account discovery, secrets location, rotation discipline, and revocation workflows before advanced policy orchestration. NHIMG’s Ultimate Guide to NHIs highlights how often organisations miss these basics, including cases where secrets live outside proper managers and remain valid long after compromise is suspected. A maturity model helps teams decide whether the next dollar should go to discovery, vaulting, or policy enforcement, rather than spreading effort across all three at once.

• Map each IAM domain to a maturity level: visibility, governance, privilege management, lifecycle automation, and monitoring.
• Assign owners for each gap so the roadmap is accountable, not just architectural.
• Prioritise controls that reduce standing access, shared secrets, and manual approvals.
• Use measurable milestones, such as inventory coverage, rotation compliance, and deprovisioning time.
• Reassess regularly because maturity can stall when cloud, DevOps, and application teams drift from the standard.

This approach also fits with the principle that access controls should be validated against actual operational risk, not just policy intent. A recent identity breach analysis from NHIMG, the 52 NHI Breaches Analysis, shows why weak lifecycle controls and exposed credentials keep recurring. These controls tend to break down in hybrid estates where teams cannot maintain a reliable inventory across legacy systems, cloud services, and CI/CD pipelines.

Common Maturity Model Mistakes and Where Prioritisation Breaks Down

Tighter IAM governance often increases operational overhead, so teams have to balance control strength against delivery speed and platform complexity. The most common mistake is treating maturity as a linear checklist instead of a context-driven prioritisation tool. Current guidance suggests that the right next step depends on what is most exposed: some organisations should focus first on secrets sprawl, while others should prioritise least privilege or offboarding discipline.

Another frequent failure is over-indexing on sophisticated policy engines before basic hygiene is in place. That usually produces elegant dashboards over incomplete data. Best practice is evolving toward continuous governance, but there is no universal standard for sequencing every environment yet. For example, mature cloud-native shops may jump to policy-as-code sooner, while organisations with heavy legacy integration need better inventory and manual exception control first. The maturity model should expose that difference, not erase it.

Where prioritisation really fails is in environments with many unmanaged service accounts, inconsistent application ownership, or cross-functional approval bottlenecks. In those cases, a maturity score can look stable while actual risk remains high because dormant accounts, static credentials, and weak revocation never make it into the backlog. That is why a useful model must tie each level to evidence, not aspiration, and keep the roadmap anchored to measurable reduction in standing access.

Related resources from NHI Mgmt Group

• How should security teams prioritize AI risk in environments that use copilots, RAG, and autonomous agents?
• How should security teams align HR and IAM processes when integrating Workday with an identity governance platform?
• How should security teams prioritize hidden identity risks in applications that bypass central identity controls?
• How should security teams decide whether IAM backups belong inside their own cloud account or outside the identity perimeter?