Finance can see the bill, but it usually cannot determine which identity, application, or workflow generated the usage. That means approved use, accidental use, and unmanaged use can look the same. Governance fails when cost control is disconnected from identity control and there is no operational owner for exceptions.
Why This Matters for Security Teams
When AI usage is managed only as a spend problem, the organisation loses the signal that matters most: which non-human identity, application, or workflow actually consumed the capability. Finance can show trend lines, but it cannot distinguish approved automation from shadow AI, nor can it prove whether a secret, token, or API key was used by the intended workload. That gap turns budget oversight into an incomplete control plane.
This is where NHIs stop being a back-office inventory issue and become an operational security concern. The NHIMG Ultimate Guide to NHIs — Lifecycle Processes for Managing NHIs and Top 10 NHI Issues both emphasise lifecycle ownership because identity state, not invoice data, is what determines whether AI activity is governed. The NIST Cybersecurity Framework 2.0 reinforces the same principle: asset visibility and access governance need to be tied to accountable ownership.
In practice, many security teams only discover unmanaged AI use after spend spikes or a leaked secret reveals the workload behind it, rather than through intentional identity governance.
How It Works in Practice
Finance-led management usually starts with chargeback, showback, and policy limits on model usage. That is useful for cost containment, but it is not enough to govern autonomous or semi-autonomous AI workloads. The security problem is that usage events are often detached from workload identity, so there is no reliable way to tell whether the request came from a sanctioned agent, a developer notebook, a shared service account, or an abandoned integration.
Effective governance links spend to identity. That means every AI-producing workflow should have an owned NHI, a clear lifecycle, and a control path for exception handling. In mature environments, the workflow is more like this:
- Assign each AI app, agent, or pipeline a unique workload identity.
- Bind that identity to approved secrets, keys, or tokens with short time-to-live values.
- Log usage with identity context, not just cost centre tags.
- Route exceptions to an operational owner who can revoke access, not just approve the invoice.
- Review both cost anomalies and identity anomalies together.
This is consistent with NHI lifecycle discipline in the NHIMG NHI Lifecycle Management Guide and with the control intent behind the NIST Cybersecurity Framework 2.0, which expects organisations to know what is running, who owns it, and what it can access. Where secrets governance is weak, the risk escalates quickly; NHIMG notes in The State of Secrets in AppSec that the average estimated time to remediate a leaked secret is 27 days.
These controls tend to break down when shared AI platforms aggregate usage across many teams because cost data becomes centralised while identity ownership remains fragmented.
Common Variations and Edge Cases
Tighter cost controls often increase operational overhead, requiring organisations to balance budget discipline against security ownership. That tradeoff becomes sharper when AI is embedded in customer-facing products, internal copilots, or multi-agent workflows, because one bill may hide dozens of identities and trust relationships.
There is no universal standard for this yet, but current guidance suggests finance should be a consumer of governance data, not the owner of it. In regulated or high-risk environments, security teams usually need identity-linked telemetry, policy-based approvals, and a clear escalation path for overages that indicate misuse rather than just growth. The NHIMG Ultimate Guide to NHIs — Regulatory and Audit Perspectives is useful here because auditability depends on proving who had access, when, and under which control.
The edge case is legitimate burst usage, such as training runs, incident response automation, or seasonal customer demand. Finance may see those as anomalies even when they are approved. The fix is not stricter billing alone, but pre-approved exception profiles tied to workload identity and revocation rules. NHIMG’s DeepSeek breach analysis shows why unmanaged AI exposure is not hypothetical: once identities, secrets, and usage are decoupled, investigation becomes far harder than prevention.
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, OWASP Agentic AI Top 10 and CSA MAESTRO address the attack and risk surface, while NIST AI RMF and NIST CSF 2.0 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Non-Human Identity Top 10 | NHI-01 | Finance-only control misses lifecycle ownership of non-human identities. |
| OWASP Agentic AI Top 10 | AI-01 | Agent usage must be tied to runtime identity, not just cost tracking. |
| CSA MAESTRO | IC-1 | Agentic systems need identity and control ownership beyond finance oversight. |
| NIST AI RMF | AI risk governance requires accountability, not just spend visibility. | |
| NIST CSF 2.0 | PR.AC-1 | Access governance must identify who or what is using AI services. |
Use AI RMF governance to connect approvals, monitoring, and incident response to accountable owners.
