Look for duplicate subscriptions, unapproved tools used through personal accounts, stalled pilots waiting on risk approval, and AI activity that cannot be tied to a business owner. Those are all signs that spend, access, and accountability are drifting apart.
Why This Matters for Security Teams
AI spend becomes a governance problem when cost growth is really a symptom of uncontrolled identity, access, and ownership. Duplicate subscriptions, personal-account usage, and stalled pilots are not just finance issues. They usually indicate that procurement, security, and engineering are all seeing different versions of the same AI estate. That is exactly how blind spots form around NHI lifecycle management, which is why the Top 10 NHI Issues and the Ultimate Guide to NHIs — Regulatory and Audit Perspectives both tie governance failures to visibility gaps, weak ownership, and weak auditability. NIST’s NIST Cybersecurity Framework 2.0 also reinforces that governance is not separate from risk management. If AI tools cannot be mapped to a business owner, a policy, and a budget line, then the organisation is effectively funding shadow capability.
Practitioners often miss the early warning sign: spend starts to fragment long before a formal policy breach is reported, and by then the organisation is already paying for duplicate risk, duplicate access, and duplicate compliance work.
How It Works in Practice
In practice, the strongest signals are patterns that show AI is being adopted faster than it is being governed. A single department may buy one tool, then another team buys a similar tool because the first one is not visible, approved, or easy to onboard. Employees may also route AI work through personal accounts because business provisioning is slow, which creates unmanaged secrets, unclear data handling, and no defensible audit trail. Those are classic Non-Human Identity symptoms, especially when subscriptions, API keys, and service tokens are created outside standard lifecycle controls.
Security teams should look for three linked control failures:
- Ownership gaps, where no business sponsor can explain why the tool exists or what data it touches.
- Access gaps, where AI workloads have standing credentials but no clear approval path, rotation schedule, or revocation trigger.
- Usage gaps, where pilots continue consuming budget even though the risk review, legal review, or data classification review never finished.
This is where the connection to NHI governance becomes practical. If an AI agent or automated workflow can call tools, reach data, or make decisions, then its identity, entitlement, and secret handling must be managed like any other privileged workload. That is why lifecycle discipline matters, and why the Ultimate Guide to NHIs — Lifecycle Processes for Managing NHIs is relevant here. It also helps to compare investment patterns against vendor-reported attack behaviour, such as the rapid abuse of exposed credentials described in the DeepSeek breach discussion and the related Entro Security research on LLMjacking. Current guidance suggests that if AI spend is rising while credential control, logging, and ownership are all weak, the organisation is not scaling safely, it is scaling unmanaged exposure.
These controls tend to break down in federated environments where teams can buy cloud AI services directly, because finance, security, and identity tooling are not integrated closely enough to reconcile spend with approval, identity, and data access.
Common Variations and Edge Cases
Tighter spend control often increases friction for experimentation, so organisations have to balance speed against visibility and accountability. That tradeoff is real, especially where AI usage supports revenue teams, product prototyping, or internal productivity work. Best practice is evolving, and there is no universal standard for this yet, but the direction is clear: governance should focus less on blocking every tool and more on making every tool attributable.
One common edge case is the “approved platform, unapproved usage” problem. A sanctioned AI service may still be misgoverned if teams create duplicate workspaces, attach unmanaged data sources, or store long-lived secrets outside central controls. Another edge case is the “pilot that never ends” problem, where the business case has expired but the subscriptions, integrations, and service credentials remain active. In both cases, spend is the observable symptom, but the root cause is weak accountability across identity and lifecycle management. That is why governance teams should pair financial signals with NHI controls, especially when reviewing the warning signs covered in the Top 10 NHI Issues.
For organisations trying to formalise the response, NIST’s NIST Cybersecurity Framework 2.0 is useful for linking governance, access control, and monitoring into one operating model. The practical test is simple: if a tool, agent, or subscription cannot be traced to an owner, a purpose, and a revocation path, it is already a governance issue even if the invoice still looks small.
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 |
|---|---|---|
| OWASP Non-Human Identity Top 10 | NHI-03 | Weak rotation and secret sprawl often show up first as unmanaged AI spend. |
| NIST CSF 2.0 | GV.OC-02 | AI spend drift is a business ownership and accountability problem. |
| NIST AI RMF | AI RMF governance requires accountability for AI systems and their impacts. |
Inventory AI-linked NHI secrets, rotate them on a schedule, and retire orphaned credentials quickly.
Related resources from NHI Mgmt Group
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Reviewed and updated by the NHIMG editorial team on June 4, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
