AI spend is the money used to build, train, run, or serve AI systems and models. It includes GPU time, storage, data pipelines, model hosting, API calls, and the engineering overhead needed to keep those services available. Unlike a fixed software license, AI spend often changes with usage, making it harder to predict and easier to abuse.
In cyber security, AI spend matters because unusual cost growth can be a signal of compromise or misuse. Stolen credentials may be used to launch expensive training jobs, drain inference quotas, or call paid model APIs at scale. Attackers can also trigger resource exhaustion by sending large volumes of requests to public AI endpoints. Defenders watch AI spend with usage baselines, identity controls, rate limits, and per-workload attribution so they can spot anomalies quickly and stop waste before it becomes a security or budget problem.