They lose visibility into the full cost structure and create blind spots in both pricing and governance. Backend APIs, data services, and integration endpoints can consume substantial budget and shape the economics of an AI product. If those layers are not metered alongside the model, teams cannot price accurately or prevent overages at runtime.
Why This Matters for Security Teams
Tracking only LLM spend gives a false sense of control because the model is rarely the only metered component in an AI system. Retrieval layers, orchestration services, vector databases, data enrichment APIs, and downstream tool calls can dominate both cost and risk. When those dependencies are invisible, product teams misprice usage, finance cannot forecast accurately, and security teams miss the places where abuse actually happens.
This is not just a billing issue. External APIs often carry the credentials, rate limits, and data exposure that define an AI workflow’s real blast radius. An attacker or buggy agent does not need to exhaust the model budget first; it can trigger expensive tool chains, drain quota, or pivot through integrations that were never included in the spend model. NHI Management Group research on the AI Agents: The New Attack Surface report shows how quickly agent behaviour can exceed intended scope, which is exactly why cost telemetry and governance telemetry need to cover the full pipeline. In practice, many security teams discover the exposure only after an integration bill spikes or an agent has already reached services nobody expected it to touch.
How It Works in Practice
The practical fix is to treat the AI application as a chain of metered dependencies, not a single model line item. Current guidance suggests instrumenting the model, the API gateway, retrieval services, enrichment endpoints, and any tool-using agent separately so that cost, latency, and authorisation can be correlated at runtime. That means attaching request IDs, tenant context, and workload identity to every hop, then enforcing policy based on the full transaction rather than on the prompt alone.
For security and finance teams, the useful question is not “How much did the model cost?” but “What did this request invoke, what data did it touch, and what service did it consume?” That is where workload identity, short-lived tokens, and request-scoped quotas become operationally important. Patterns described in the OWASP NHI Top 10 and the external OWASP Top 10 for Agentic Applications 2026 both point to the same operational need: govern what an agent can invoke, not just what model it can access. A mature control stack usually includes:
- Per-request metering for model calls and upstream API calls.
- Separate budgets and alerts for retrieval, data enrichment, and external tool usage.
- Ephemeral credentials tied to the specific workload and task.
- Policy checks at runtime before any paid or sensitive API is invoked.
When these controls are in place, finance can attribute cost correctly and security can see which service was used, by whom, and under what policy decision. These controls tend to break down in highly dynamic multi-agent workflows because one agent can trigger another through opaque tool chains and the resulting usage is difficult to attribute cleanly.
Common Variations and Edge Cases
Tighter metering often increases engineering overhead, requiring organisations to balance pricing precision against implementation complexity. That tradeoff is especially sharp when teams rely on third-party APIs with bundled pricing, shared service accounts, or opaque downstream usage. In those cases, “full visibility” may not mean perfect attribution at every hop; current guidance suggests establishing the highest-value boundaries first, then extending coverage as the architecture matures.
Edge cases usually appear in agentic systems, not simple chat interfaces. A single user request can fan out into search, summarisation, scoring, and external tool execution, and each step may be billed differently or controlled by a different owner. The AI Agents: The New Attack Surface report and the external NIST AI Risk Management Framework both reinforce that governance must include traceability, accountability, and runtime monitoring. NHI Management Group’s McKinsey AI platform breach illustrates how quickly an AI stack can become a data governance problem when the surrounding services are not controlled as tightly as the model itself.
Best practice is evolving, but one point is already clear: organisations that only watch LLM usage will miss the APIs where cost overruns, credential abuse, and sensitive data exposure usually begin.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10, OWASP Non-Human Identity 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 Agentic AI Top 10 | A2 | Agent tool chaining drives hidden API spend and control gaps. |
| OWASP Non-Human Identity Top 10 | NHI-03 | API keys and tokens feeding AI systems are the real cost and abuse surface. |
| CSA MAESTRO | TM-2 | MAESTRO covers threat modeling for agent workflows and downstream services. |
| NIST AI RMF | AI RMF emphasizes traceability and governance across the AI lifecycle. | |
| NIST CSF 2.0 | PR.AC-4 | Least privilege must extend to APIs and service credentials in the AI stack. |
Model the full AI execution path, including retrieval and external service dependencies.
Related resources from NHI Mgmt Group
- What breaks when organisations only track approved SaaS apps and ignore shadow AI usage?
- What breaks when organisations do not track what AI tools can access across email and data systems?
- What breaks when organisations do not inventory all AI and LLM systems?
- What breaks when organisations rely on periodic access reviews for AI systems?
