Start with the identities that can reach the organisation’s crown jewels, then layer in privilege level and data sensitivity. Source code, customer data, and production systems should be treated as separate risk tiers. That lets teams focus monitoring where abuse would matter most to the business.
Why This Matters for Security Teams
IAM and SOC teams cannot watch every identity with equal intensity, so the real task is to identify where misuse would create the greatest business and operational impact. That usually means prioritising accounts with access to sensitive data, production infrastructure, admin consoles, CI/CD systems, and security tooling. A risk-based approach also reduces alert fatigue by focusing monitoring on identities that are both powerful and exposed. NIST’s NIST SP 800-53 Rev 5 Security and Privacy Controls is a useful reference point for tying monitoring effort to access control, auditability, and accountability.
Practitioners often get this wrong by starting with identity volume rather than identity impact. A large organisation may have thousands of low-risk human users, but a much smaller set of privileged administrators, service accounts, API tokens, and third-party integrations can create the most severe blast radius if compromised. That is why scrutiny should be driven by reach, privilege, and the sensitivity of the assets behind the account. The same logic applies to non-human identities when they can deploy code, move data, or modify controls.
In practice, many security teams encounter their biggest identity blind spots only after a privileged account or service credential has already been used to move laterally or exfiltrate data, rather than through intentional tiering and review.
How It Works in Practice
The practical approach is to build an identity risk model that combines entitlement scope, asset criticality, authentication strength, and behavioural exposure. IAM usually owns the inventory and access graph, while SOC adds detection logic and threat context. Together, they can rank identities into tiers such as high, medium, and low scrutiny, then apply different control expectations for each tier.
A useful starting point is to map identities to the systems they can reach and then ask what abuse would look like if the account were compromised. Identities that can alter production, read regulated data, approve payments, change security settings, or access source repositories deserve the most scrutiny. Non-human identities should be included in the same model when they have persistent secrets, broad API scope, or automated execution authority. Threat patterns documented in the ENISA Threat Landscape reinforce why credential theft, privilege abuse, and lateral movement remain central concerns.
- Prioritise identities with standing access to crown-jewel systems.
- Flag privileged roles, break-glass accounts, and inactive accounts with high-value entitlements.
- Include service accounts, workload identities, and API keys in the same review cycle as human admins.
- Use behavioural baselines to detect unusual login locations, command patterns, or access times.
- Increase monitoring on identities that can disable logging, change policies, or create new credentials.
Operationally, this is strongest when IAM and SOC share a common identity taxonomy and access graph. If one team labels accounts by department while the other labels by alert source or log stream, the risk view becomes fragmented. Control owners should also define review cadence by tier, not by a one-size-fits-all calendar. These controls tend to break down when identity data is spread across many cloud tenants and SaaS platforms because entitlement discovery and ownership mapping become inconsistent.
Common Variations and Edge Cases
Tighter scrutiny often increases review overhead and alert volume, requiring organisations to balance detection depth against operational capacity. That tradeoff becomes especially visible when there are many short-lived workloads, outsourced operations, or rapid DevOps changes. Best practice is evolving for these environments, but there is no universal standard for identity tiering that fits every business model.
Some identities are high-risk for different reasons. A developer may not be a traditional privileged user, but access to source code, build pipelines, or secrets stores can make that identity more sensitive than a helpdesk admin in a tightly segmented environment. Likewise, a machine identity with limited permissions can still merit scrutiny if it can create tokens, call customer-facing APIs, or trigger downstream automation. Current guidance suggests treating these cases as risk exceptions rather than forcing them into a simplistic privileged versus non-privileged model.
Another edge case is shared or delegated access. Where shared accounts still exist, the identity itself becomes a weak signal, so teams need compensating controls such as session logging, device binding, and stronger approval workflows. The same applies to external partners and managed service providers, where trust is partly contractual but must still be verified through telemetry and access reviews. In practice, scrutiny should follow what the identity can do, not just who owns it.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
MITRE ATT&CK address the attack and risk surface, while NIST CSF 2.0 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST CSF 2.0 | PR.AC-4 | Least-privilege access is central to deciding which identities need the most scrutiny. |
| MITRE ATT&CK | T1078 | Valid accounts abuse is a common path for compromising high-value identities. |
Rank identities by effective access and tighten review on those with the broadest reach.
Related resources from NHI Mgmt Group
- How should security teams govern non-human identities for SOC 2 compliance?
- How should security teams decide whether JIT access is safe for non-human identities?
- Why do non-human identities change the way IAM teams should think about risk?
- How can teams decide which identities need just-in-time access?
