Yes, because separating them encourages blind spots. A high-cost agent chain often signals broad delegation, excessive tool reach, or poor scope boundaries. Managing spend and privilege together gives IAM and platform teams one view of how authority expands at runtime, which is exactly where agentic risk accumulates.
Why This Matters for Security Teams
Agent spend is not just a finance metric. In autonomous workflows, it is often the first operational signal that an agent has been delegated too broadly, is looping through tools, or is being used in ways no one intended. When cost and privilege are reviewed separately, IAM sees permissions without behavioural context, while platform teams see usage without authority context. That split makes it easier for excessive access to hide in plain sight.
This is especially relevant for AI agents because their actions are dynamic, not pre-scripted. A cheap agent can still be dangerous if it has access to sensitive data or privileged APIs. A costly agent may be paying for repeated failures, broad tool chaining, or prompt injection fallout. NHI Management Group’s research on the Ultimate Guide to NHIs — Lifecycle Processes for Managing NHIs shows that 97% of NHIs carry excessive privileges, which is exactly why runtime authority and runtime consumption should be treated as one risk picture, not two.
Current guidance suggests that spend spikes should be investigated as potential identity events, not only budget events. In practice, many security teams encounter privilege creep only after an agent has already chained tools, widened scope, or made repeated expensive calls that exposed the control failure.
How It Works in Practice
The practical model is to join identity telemetry with platform telemetry so that every agent has a single operational profile: who it is, what it can do, what it actually did, and what it cost to do it. That means mapping agent identities to workload identities, then correlating tool calls, token issuance, secret access, and resource consumption in near real time. The goal is not to punish spend. The goal is to detect when high-cost behaviour and high-risk authority move together.
Security teams usually get the best results when spend controls are built as policy signals rather than finance afterthoughts. For example, an agent that suddenly needs more tokens, more API calls, or more compute to complete a task may need tighter scope, shorter-lived credentials, or a step-up approval path. This aligns with the runtime decision model in the OWASP Agentic AI Top 10 and the governance approach in the NIST AI Risk Management Framework, where controls are evaluated in context rather than assumed safe because they were approved once.
- Tie each agent to a workload identity so cost can be traced back to a specific principal.
- Set budget thresholds alongside privilege thresholds, not in separate queues.
- Use short-lived credentials so expensive failures do not persist with standing access.
- Review repeated high-cost actions as possible evidence of overreach, not only inefficiency.
When a security team sees a rising spend curve, it should ask whether the agent is doing more work or merely being allowed to do too much. These controls tend to break down in multi-agent pipelines where one agent can inherit cost and authority from another without a clear handoff boundary.
Common Variations and Edge Cases
Tighter spend controls often increase operational friction, requiring organisations to balance containment against the risk of interrupting legitimate autonomous work. That tradeoff is real, especially in environments where agents handle bursty workloads, batch retrieval, or research tasks that legitimately consume more tokens than a human would expect.
There is no universal standard for exactly where the spend threshold should sit. Current guidance suggests using baselines by agent class, not a single enterprise-wide number. A customer-support agent, a code-assistance agent, and a data-analysis agent will have very different normal patterns, so flat thresholds can create noise or blind spots. The important part is that deviations are reviewed through both identity and usage lenses.
Edge cases also appear when an agent has low direct cost but high downstream impact. For example, a lightly used agent with broad secrets access can still trigger a major incident. That is why spend cannot replace privilege review, and privilege cannot replace usage review. NHIMG’s Top 10 NHI Issues and the CSA MAESTRO agentic AI threat modeling framework both reinforce the same operational lesson: autonomous systems should be governed by how authority expands at runtime, not by static approval alone.
In practice, the hardest cases are agents that delegate to sub-agents, because spend can be fragmented while privilege remains concentrated, or the reverse. That is where combined monitoring matters most.
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, CSA MAESTRO and OWASP Non-Human Identity Top 10 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 | Covers excessive autonomy and tool use that often drives spend and privilege together. |
| CSA MAESTRO | T2 | Maps agent workflows and controls so cost signals can be tied to delegated capability. |
| NIST AI RMF | Govern and monitor AI behaviour using contextual risk signals, not isolated metrics. | |
| OWASP Non-Human Identity Top 10 | NHI-01 | Addresses excessive privilege and identity sprawl across non-human workloads. |
| NIST CSF 2.0 | PR.AC-4 | Least-privilege access management aligns with controlling agent authority as it changes. |
Correlate agent telemetry with access scope to spot over-delegation before it becomes incident response.
Related resources from NHI Mgmt Group
Deepen Your Knowledge
Reviewed and updated by the NHIMG editorial team on September 2, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
