AI economics is the study of how AI systems consume compute, storage, network bandwidth, electricity, and specialist labor, and how those inputs translate into measurable business value. In practice, it asks a simple question: does the output from a model justify its operating cost?
In cybersecurity, AI economics matters because AI services are expensive to run and easy to abuse. Attackers may trigger large numbers of model calls, scrape inference endpoints, or manipulate workflows to drive up cloud bills and exhaust GPU capacity. Defenders use the same cost lens to size controls, set rate limits, monitor token usage, and decide where AI actually reduces risk or improves detection. A secure AI program measures not only model accuracy, but also cost per alert, cost per investigation, and cost per successful outcome. If those numbers rise faster than the value created, the deployment is economically fragile even if the technology works.