FinOps for AI is the practice of tracking, attributing, and optimizing the cost of AI workloads. It applies standard cloud cost-management ideas to model calls, token usage, GPU time, vector databases, and other services that can make AI spending hard to predict. The goal is to give teams clear visibility into what each model, application, or business unit consumes.
In cyber security, FinOps for AI matters because opaque usage often signals weak control. If an attacker abuses an API key, automates prompt flooding, or triggers expensive inference jobs, costs can spike before the misuse is obvious. Good FinOps controls help defenders detect anomalies, set budgets and usage limits, and tie spend back to identities, workloads, and data access paths. It also supports safer rollouts by using telemetry, guardrails, and approvals to keep AI adoption measurable and contained.