Organisations should measure the number of separate control points, the frequency of manual handoffs, the time required to revoke access, and whether audit teams can trace each decision back to one accountable system. Those signals show whether consolidation will reduce complexity or merely hide it.
Why This Matters for Security Teams
Consolidating identity and device administration changes more than tooling. It changes how risk is measured, how approvals are enforced, and how quickly a bad decision can spread across users, endpoints, and service accounts. If teams measure the wrong baseline, consolidation can look efficient while actually increasing blast radius and weakening accountability. NIST’s Cybersecurity Framework 2.0 emphasises governance and measurable outcomes, which is the right lens here.
For NHI-heavy environments, the question is not whether one team can click fewer consoles. It is whether one control plane can prove stronger traceability, faster revocation, and fewer unresolved exceptions than the current split model. That matters because identity and device control often intersect with secrets, service accounts, and admin privileges that already appear in incident data. NHI Mgmt Group notes in the Ultimate Guide to NHIs that only 5.7% of organisations have full visibility into their service accounts, which makes consolidation risky if visibility is not established first. In practice, many security teams discover weak accountability only after a revocation delay, audit gap, or lateral movement event has already exposed the problem.
How It Works in Practice
The best pre-consolidation baseline is operational, not theoretical. Start by measuring how many distinct systems currently participate in identity and device decisions, then map where those systems hand off responsibility. A useful baseline also tracks how long it takes to disable a user, revoke an admin token, quarantine an endpoint, and close the audit trail afterward. If those events require different teams or ticket queues, consolidation may reduce friction later, but only if the new platform can preserve decision quality.
Practitioners should also measure control coherence. For example, compare whether RBAC, device posture, and privileged session controls all resolve to the same source of truth, or whether policy exceptions are buried across tools. NIST’s AI 600-1 GenAI Profile and IR 8596 Cyber AI Profile are not identity consolidation standards, but their emphasis on governance, traceability, and resilience mirrors what mature consolidation planning requires. The same logic appears in NHIMG research such as the Top 10 NHI Issues, where weak visibility and poor rotation repeatedly show up as root causes.
- Measure control points per lifecycle stage: join, move, admin elevation, revoke, and offboard.
- Measure manual handoffs: ticket transfers, approvals, and exception handling across teams.
- Measure revocation time separately for humans, devices, API keys, and service accounts.
- Measure audit traceability: whether one decision can be reconstructed end to end without spreadsheet work.
- Measure exception volume: the number of temporary overrides needed to keep operations running.
These controls tend to break down in hybrid environments where device posture data is stale and identity events are asynchronous, because the consolidation layer cannot reliably make one timely decision from conflicting signals.
Common Variations and Edge Cases
Tighter consolidation often improves consistency, but it can also increase dependency on one platform, one policy engine, and one failure domain, so organisations must balance operational simplicity against resilience. There is no universal standard for exactly which metrics should drive consolidation, but current guidance suggests prioritising measurability over architectural preference.
Some environments need separate thresholds for regulated workloads, shared kiosks, third-party contractors, and non-human identities. A single metric like “number of tools reduced” can hide important differences in assurance. For example, if service account revocation still depends on manual review, consolidating human identity and device management will not fix the underlying NHI control gap. Similarly, if device administration is centralised but secrets remain spread across code and CI/CD systems, the organisation has only moved the problem. NHIMG’s definition of non-human identities is useful here because it keeps the focus on what must be governed, not just what must be catalogued.
In practice, consolidation is safest when the baseline shows fewer handoffs, faster revocation, and better traceability before the migration starts, not after.
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 address the attack and risk surface, while NIST CSF 2.0 and NIST AI RMF set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST CSF 2.0 | GV.OC-01 | Consolidation should improve governance outcomes and measurable accountability. |
| OWASP Non-Human Identity Top 10 | NHI-03 | Revocation speed and control-point reduction directly affect NHI lifecycle risk. |
| NIST AI RMF | Risk measurement and traceability align with AI governance-style control evaluation. |
Define consolidation success metrics first, then verify the new model improves governance and traceability.
Related resources from NHI Mgmt Group
Deepen Your Knowledge
Reviewed and updated by the NHIMG editorial team on June 9, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
