They fail because they capture a snapshot of control activity rather than the control itself. Modern regulations expect continuous proof, especially where data, reports, and AI use cases change frequently. Manual tracking cannot keep pace with moving assets, changing policies, and time-sensitive remediation, so the evidence arrives too late to be credible.
Why This Matters for Security Teams
Spreadsheet-based compliance checks fail because they describe yesterday’s control state, not today’s operational reality. Modern programmes now expect evidence that is continuous, traceable, and timely, especially when access paths, data flows, and AI-enabled workflows change weekly. That makes static trackers a weak substitute for control telemetry, policy enforcement, and remediation proof. The gap is especially visible in regulated environments that are already being pushed toward more dynamic assurance models, such as the NIST Cybersecurity Framework 2.0 and the EU AI Act regulatory framework.
NHI Management Group’s Ultimate Guide to NHIs — Regulatory and Audit Perspectives frames the core issue well: auditability depends on lifecycle evidence, not manual status reporting. Spreadsheets often miss the fact that secrets rotate, service accounts drift, and approvals expire long before the next quarterly review. In practice, many security teams encounter noncompliance only after an audit request or incident has already exposed the control gap, rather than through intentional continuous monitoring.
How It Works in Practice
Effective compliance programmes treat evidence as a live output of the control environment. That means pulling signals from identity systems, cloud logs, ticketing, configuration baselines, and secret stores, then mapping them to specific obligations in near real time. A spreadsheet can still be used as a summary layer, but it should not be the system of record for control performance. For NHI-heavy environments, the relevant evidence usually includes who or what has access, whether that access is approved, how long credentials remain valid, and whether revocation occurred after the task or incident.
Practitioners increasingly align this approach with lifecycle governance, as described in Ultimate Guide to NHIs — Lifecycle Processes for Managing NHIs, because compliance failures often begin with unmanaged identity sprawl. The right operating model usually includes:
- continuous control checks instead of monthly evidence collection;
- automated timestamps for approval, deployment, rotation, and revocation events;
- policy-as-code for consistent rule evaluation across environments;
- exception handling that records compensating controls and expiry dates;
- attestation only after evidence is machine-verified.
This is especially important where secrets are exposed or reused. NHI Management Group’s The State of Secrets in AppSec notes that the average estimated time to remediate a leaked secret is 27 days, which is far too slow for a compliance process that claims current assurance. These controls tend to break down when evidence sources remain siloed across SaaS, cloud, and development pipelines because manual reconciliation cannot keep pace with control drift.
Common Variations and Edge Cases
Tighter compliance automation often increases operational overhead, requiring organisations to balance assurance quality against integration complexity and reporting fatigue. That tradeoff becomes real in hybrid estates, legacy applications, and highly decentralised business units where not every control can be instrumented immediately.
Best practice is evolving, but there is no universal standard for how much spreadsheet usage is acceptable as a secondary record. In some programmes, spreadsheets remain useful for exception tracking or executive rollups; in others, they become a liability the moment they are treated as authoritative evidence. The safest approach is to use them only as a presentation layer above authoritative systems such as IAM, ticketing, SIEM, GRC platforms, and secret management tooling.
Edge cases also appear in AI governance. If a programme covers model outputs, agent activity, or automated decision-making, spreadsheet checks become even less reliable because the control state can change between runs. That is why control evidence should be tied to runtime logs, policy evaluations, and lifecycle events, not manually entered dates. The Top 10 NHI Issues reflects this broader pattern: once identities, secrets, and approvals move faster than review cycles, static tracking stops being a credible source of assurance.
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.RM-03 | Risk oversight requires timely, reliable evidence instead of manual snapshots. |
| OWASP Non-Human Identity Top 10 | NHI-03 | Stale secret and identity tracking is a common NHI control failure mode. |
| NIST AI RMF | AI governance needs traceable, continuous evidence for changing model use cases. |
Use AI RMF governance practices to maintain live accountability for AI-related controls.
