Start with discovery, not enforcement. Build authoritative inventories for users, service accounts, devices, applications, workloads, and data paths, then map ownership and access boundaries. Without that baseline, least privilege and continuous verification become assumptions rather than controls, and policy decisions will be too brittle to trust.
Why This Matters for Security Teams
A zero trust programme cannot be credible if the organisation does not know what identities exist, who owns them, or what they can reach. That is especially true when service accounts, API keys, and workloads outnumber humans and are often the first path attackers use to move laterally. NHI Management Group’s Ultimate Guide to NHIs notes that only 5.7% of organisations have full visibility into their service accounts, which explains why many Zero Trust efforts stall at policy design instead of operational control.
The practical issue is not just missing inventory. It is missing confidence. If identity data is incomplete, teams tend to overgrant access, exempt unknown systems, or delay enforcement indefinitely. NIST’s NIST SP 800-207 Zero Trust Architecture emphasizes continuous verification and explicit policy decisions, but those controls depend on accurate subject, device, and workload context. In practice, many security teams discover the gaps only after a secrets leak, a cloud compromise, or an audit exception forces the inventory work they should have started earlier.
How It Works in Practice
The safest way to start is to treat discovery as the first control. Build an authoritative map of users, service accounts, machines, applications, data stores, and the paths between them, then assign clear ownership for each identity class. Use current-state evidence from directories, cloud IAM, CI/CD pipelines, endpoint tools, and vaults to reconcile duplicates, orphaned accounts, and unknown integrations. NHI Management Group’s 52 NHI Breaches Analysis shows why this matters: identity abuse frequently begins where governance is weakest, not where policy is strongest.
From there, define policy in layers rather than trying to enforce full Zero Trust everywhere at once:
- Start with the highest-risk identities, such as admin service accounts, CI/CD tokens, and third-party integrations.
- Classify each identity by owner, environment, and allowed data paths.
- Replace standing access with just-in-time approval or short-lived credentials where possible.
- Apply continuous verification to new requests before expanding to legacy systems.
- Track exceptions explicitly so unknown access does not become permanent access.
For workload identity, use cryptographic proof of identity rather than informal naming conventions. The Guide to SPIFFE and SPIRE is a useful reference for moving from brittle shared secrets to verifiable workload identities that can support Zero Trust decisions. That approach aligns with the intent of Zero Trust while reducing dependence on manually maintained inventories. These controls tend to break down when identities are deeply embedded in legacy batch jobs or unmanaged third-party tools because ownership, rotation, and runtime context are too inconsistent for reliable policy enforcement.
Common Variations and Edge Cases
Tighter identity control often increases operational overhead, so organisations have to balance faster enforcement against the cost of correcting incomplete data. Best practice is evolving, and there is no universal standard for how complete the inventory must be before limited enforcement begins. In lower-risk environments, teams can start with alerting and segmentation. In higher-risk environments, especially where privileged service accounts or external contractors are involved, delay carries more downside than progressive rollout.
Some identities are hard to classify on day one. Shared admin accounts, embedded API keys, and ephemeral cloud workloads may not fit cleanly into the initial model, but they still need explicit handling. Use temporary compensating controls such as network restrictions, tighter token TTLs, and manual approval gates while the inventory matures. Where the organisation relies heavily on automation, the better question is not whether identity data is perfect, but whether the most dangerous access paths are already constrained enough to survive discovery gaps. That distinction is central to the research in the Ultimate Guide to NHIs and the broader Top 10 NHI Issues.
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, NIST Zero Trust (SP 800-207), NIST SP 800-63 and NIST AI RMF set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST CSF 2.0 | ID.AM | Asset and identity inventory is the starting point when identity data is incomplete. |
| NIST Zero Trust (SP 800-207) | Zero Trust depends on continuous verification grounded in trustworthy identity context. | |
| OWASP Non-Human Identity Top 10 | NHI-01 | Discovery and visibility are core when service accounts and secrets are unknown. |
| NIST SP 800-63 | IAL2 | Identity proofing concepts help distinguish known identities from unverified records. |
| NIST AI RMF | Governance requires risk-based decisions when identity evidence is incomplete. |
Establish explicit verification and policy decisions only after identity context is reliable.
Related resources from NHI Mgmt Group
- What should organisations do first when building OT zero trust controls?
- Why is it important to integrate identity and data governance?
- How can security teams tell whether their identity programme is ready for zero trust?
- How should organisations start planning for quantum-safe identity and trust systems?
