Token costs are the billable expense created when an AI system processes input and generates output in token units. In large language models, every prompt, retrieved passage, and answer consumes tokens, so the price of a request depends on how much text is sent and returned.
In cyber security, token costs matter because AI tools are increasingly used for phishing analysis, alert summarization, chatbots, and agent workflows. A long prompt, repeated retrieval, or verbose response can turn a routine security task into a costly one. Attackers can also abuse this by forcing bots or agents into long conversations, repeated retries, or expensive loops, creating a form of financial denial of service. Defenders limit risk with token budgets, context trimming, caching, model routing, and usage monitoring per user or application. Tracking token costs helps teams control spend, reduce abuse, and keep AI systems efficient enough to trust in production.