What should organisations prioritise first in IAM hygiene work?

Why This Matters for Security Teams

IAM hygiene is not a paperwork exercise. When inactive access, orphaned service accounts, and stale entitlements remain in place, attackers inherit old trust decisions that no longer match operational reality. That is why identity drift becomes a direct control failure, not just an audit issue. NHI Management Group’s research shows that only 5.7% of organisations have full visibility into their service accounts, and that lack of visibility makes prioritisation guesswork rather than governance.

For security teams, the first priority is to remove access that no longer has a clear owner, then correct the authorization model so it reflects current systems and workflows. Without that baseline, access reviews become noisy, AI-assisted analysis becomes misleading, and exceptions pile up faster than they can be resolved. Control families like NIST SP 800-53 Rev 5 Security and Privacy Controls assume access governance is based on current, enforceable policy. In practice, many security teams discover stale access only after a secrets leak or account misuse has already exposed production systems, as seen in cases like TruffleNet BEC Attack — Stolen AWS Credentials.

This is also where NHI hygiene matters most, because service accounts and API keys often outlive the people who created them and the systems they were meant to support.

How It Works in Practice

The practical sequence is simple, but it must be disciplined. First, identify all access that lacks a current business owner or technical owner. That includes dormant human accounts, service accounts tied to retired applications, stale API keys, and secrets stored outside approved managers. Second, verify whether the current authorization model still matches how the system actually operates. If the model says a workload needs broad write access but the workload now only performs read operations, the model is already behind reality.

After that cleanup, teams can use AI to accelerate review and prioritisation, but AI should classify and surface drift, not decide governance from stale inputs. Current guidance suggests pairing review automation with explicit policy checks and human accountability. A strong baseline usually includes:

• Inventory every identity and secret, including those outside central vaults.
• Map each item to a current owner, system, and purpose.
• Remove or quarantine access with no owner or no recent legitimate use.
• Rebuild authorization around least privilege and current application behavior.
• Use AI to rank anomalies, not to excuse missing ownership or policy.

That sequence aligns with broader control expectations in NIST SP 800-53 Rev 5 Security and Privacy Controls and with NHI-specific lessons from Ultimate Guide to NHIs, especially around visibility, rotation, and offboarding. It also matches the operational reality that stale access and exposed secrets frequently travel together, as shown in Azure Key Vault privilege escalation exposure. These controls tend to break down when ownership records are fragmented across cloud teams, CI/CD tooling, and ticketing systems because no single source of truth exists.

Common Variations and Edge Cases

Tighter access cleanup often increases short-term workload, requiring organisations to balance rapid risk reduction against service continuity and change-management overhead. That tradeoff is real, especially in environments with shared service accounts, legacy applications, or cross-functional platform teams. In those cases, the right first move is still to remove obviously ownerless access, but some entitlements may need temporary containment rather than immediate deletion.

Best practice is evolving for environments that rely heavily on automation. There is no universal standard for this yet, but many teams now treat workload identities, ephemeral tokens, and CI/CD secrets as higher-priority hygiene targets than rarely used human accounts because they can be abused at machine speed. The important nuance is that AI does not fix weak authorization models. If the underlying role design is wrong, AI will only help teams review the wrong thing faster.

NHIMG research suggests organisations are already feeling the gap: 88.5% say non-human IAM lags behind human IAM, and 59.8% see value in dynamic ephemeral credentials. That is a strong signal that prioritisation should start with drift removal, then move to identity modernisation. For teams aligning to control frameworks, that means using NIST SP 800-53 Rev 5 Security and Privacy Controls for governance structure and the NHIMG guidance in Ultimate Guide to NHIs for the identity-specific lifecycle problems that generic IAM programs often miss.

Related resources from NHI Mgmt Group

• Should organisations prioritise external exposure or internal credential governance first?
• When should organisations prioritise OAuth 2.1 over other IAM work?
• When should organisations prioritise workload identity controls over more user-focused IAM work?
• When should organisations prioritise SAP IDM replacement over other IAM work?