When should teams prioritise AI cost controls over expanding new agentic AI use cases?

Why This Matters for Security Teams

AI cost control stops being a finance-only problem once agentic systems start chaining calls, tools, and external services at machine speed. At that point, token burn is not just an efficiency issue. It becomes a signal of sprawl, weak ownership, and poor governance. NHI Management Group has documented how compromised identities and exposed credentials can be abused to drive AI misuse, including in the LLMjacking: How Attackers Hijack AI Using Compromised NHIs research.

The practical threshold is not a fixed dollar amount. Teams should prioritise cost controls when usage is growing faster than policy, inventory, and review processes can keep up. That is especially true for systems that make external API calls, trigger downstream workflows, or spawn multiple agent steps per user request. Industry guidance from the OWASP Top 10 for Agentic Applications 2026 and the NIST AI Risk Management Framework both point to governance, measurement, and operational oversight as prerequisites for safe scale. In practice, many security teams only discover runaway spend after a single workflow has already multiplied across departments and vendors.

How It Works in Practice

The right decision point is usually based on unit economics and control maturity, not adoption enthusiasm. If a new agent use case cannot show cost per task, request volume, latency, and business value, expansion should slow until those metrics are visible and repeatable. This is where cost controls and security controls reinforce each other. When every agent action is metered, authenticated, and attributable, teams can identify whether waste comes from prompt loops, excessive tool calls, poor routing, or unnecessary human-in-the-loop escalations.

For agentic systems, cost controls should be built into the operating model. That often means per-workflow budgets, quota enforcement, caching, model tiering, and routing low-risk tasks to cheaper models while reserving higher-cost models for sensitive or ambiguous cases. It also means tracking which teams own which agent, which secrets it can use, and which external services it can call. The point is not to block innovation. The point is to prevent uncontrolled scale from masking governance failures.

• Set a cost baseline for each workflow before broad rollout.
• Require runtime attribution for agent actions, external calls, and tool use.
• Use approval gates when spend rises faster than incident review can explain it.
• Link budgets to business outcomes, not just raw token counts.

NHIMG research on agent risk shows why this matters: AI Agents: The New Attack Surface report found that 80% of organisations report agents performing actions beyond intended scope, while only 52% can track and audit the data those agents access. That is a cost problem only on the surface. In reality, it is often the first measurable symptom of ungoverned autonomy. These controls tend to break down in highly distributed environments where multiple teams can spin up agents and external API usage without central ownership.

Common Variations and Edge Cases

Tighter cost controls often increase friction for product teams, so organisations have to balance speed of experimentation against budget discipline and governance maturity. The tradeoff is real: too much restriction can suppress useful automation, but too little oversight lets low-value usage consume capacity that should be reserved for higher-impact workflows.

There is no universal standard for exactly when to freeze expansion, but current guidance suggests a pause is warranted when spend rises faster than observability, when output quality is inconsistent, or when an agent’s business value cannot be tied to a clear owner. This is especially important for customer-facing agents, code-generation pipelines, and multi-agent orchestration, where one request can fan out into many hidden model calls.

Teams should also watch for edge cases where low average cost hides high tail risk. A cheap workflow that occasionally triggers expensive retries, long context windows, or external tool chains can become costly very quickly at scale. The safer approach is to normalise the operating model first, then expand use cases with explicit guardrails. Related NHIMG analysis in the OWASP NHI Top 10 and the CoPhish OAuth Token Theft via Copilot Studio case study shows how quickly unbounded agent activity can become both expensive and unsafe.

Related resources from NHI Mgmt Group

• How do IAM teams decide whether an AI use case needs new controls or better NHI hygiene?
• When should teams prioritise request-path controls over more AI dashboards?
• How should security teams use agentic AI to validate exposures without losing human control over risk decisions?
• Why do AI systems need data security controls before enterprises scale agentic use cases?