How can organisations reduce AI cost without slowing adoption?

Why This Matters for Security Teams

Reducing AI cost without slowing adoption is mostly an identity and control problem, not just a model-selection problem. The hidden spend usually comes from uncontrolled model calls, duplicated tooling, over-permissioned integrations, and preventable rework when AI actions create messy downstream incidents. The same weak controls that inflate operational cost also expand exposure, which is why NHI governance and AI cost discipline need to be designed together. Current guidance increasingly aligns around visibility, policy-based routing, and runtime controls rather than blanket restriction. NIST frames this as a governance and measurement problem in the NIST Cybersecurity Framework 2.0, while NHIMG research shows how quickly compromised credentials can be abused in practice in the DeepSeek breach.

The practical issue is that AI adoption often spreads faster than access governance. Teams approve a model, a connector, or an agent workflow, and then lose sight of which users, workloads, and secrets are driving consumption. That makes spend attribution noisy and prevention reactive. Cost control works best when organisations treat AI as an identity-bearing workload with policy attached, not as an ungoverned feature bolted onto existing apps. In practice, many security teams encounter the budget blowout only after noisy model usage and overbroad tool access have already multiplied operational work.

How It Works in Practice

The strongest pattern is to make AI usage visible first, then steer requests based on risk and business value. Start with continuous discovery across apps, copilots, APIs, and agent workflows so the organisation can see which models are called, by whom, and for what purpose. From there, policy-based routing can send low-risk tasks to cheaper approved models, reserve premium models for higher-value cases, and block unsupported prompts or actions before they create cleanup work. This is consistent with the control logic in the NIST Cybersecurity Framework 2.0, which emphasises governance, asset visibility, and protective controls as operational disciplines rather than one-time projects.

For agentic or workflow-driven AI, cost reduction depends on runtime authorisation. Static RBAC alone is usually too coarse because an agent’s tool use changes with context, task stage, and data exposure. Better practice is to combine intent-based authorisation, JIT credential issuance, and short-lived secrets so the agent receives only the access needed for the current task. That limits both blast radius and waste. It also reduces the chance that a compromised integration will generate large-scale follow-on work, which is one reason NHIMG continues to highlight credential abuse patterns in the DeepSeek breach coverage.

• Use workload identity for each agent or service, not shared API keys.
• Issue ephemeral credentials per task and revoke them on completion.
• Route simple requests to cheaper models and reserve premium models for high-impact actions.
• Block tool calls that exceed policy, budget, or data-handling thresholds at runtime.

These controls tend to break down when legacy applications reuse shared secrets across environments because attribution, revocation, and policy enforcement become too weak to steer usage safely.

Common Variations and Edge Cases

Tighter control often increases engineering overhead, requiring organisations to balance immediate savings against integration effort and developer friction. That tradeoff is real, especially in fast-moving product teams where model choice changes weekly. Best practice is evolving, but there is no universal standard for whether routing should live in the app layer, gateway layer, or an AI orchestration service. The right answer usually depends on where telemetry is strongest and where policy can be enforced consistently.

Some environments also need to preserve experimentation. If every low-value prompt is aggressively blocked, adoption can stall because teams lose the ability to test use cases cheaply. The better pattern is tiered governance: allow sandbox access, then tighten controls as the use case moves toward production. Where agents are involved, the bar should be higher because autonomous behaviour can chain tools, create hidden usage spikes, and amplify cost through retries or unintended loops. That is why the combination of policy, identity, and runtime limits matters more than any single safeguard. The NIST Cybersecurity Framework 2.0 supports this layered approach, while NHIMG’s DeepSeek breach analysis is a useful reminder that exposed secrets and uncontrolled access quickly turn into both security and cost problems.

In environments with many secrets managers, multi-cloud sprawl, or unmanaged agent sprawl, savings from model optimisation are often erased by the cost of poor control. In those cases, organisations should prioritise governance hygiene first, then optimise model spend second.

Related resources from NHI Mgmt Group

• How can organisations reduce risk from AI clients without blocking adoption?
• How can organisations reduce shadow AI risk without slowing adoption?
• How can organisations reduce AI agent blast radius without blocking adoption?
• How can organisations reduce shadow AI risk without blocking adoption?