AI Token FinOps & Governance Glossary

AI chargeback allocates AI and LLM costs back to the teams, departments, or cost centers that incurred them. With per-request attribution and BYO provider keys, Behest makes each unit of AI spend traceable to an owner for clean internal billing.

AI cost attribution assigns every model call's token spend to the user, team, project, or feature that triggered it. It turns an aggregate provider invoice into a per-unit breakdown finance can allocate and charge back.

AI FinOps brings cloud FinOps discipline — cost attribution, budgeting, and forecasting — to AI and LLM spend. It lets organizations see, allocate, and control what their AI usage actually costs.

An AI gateway sits in front of your LLM to route, log, and cache traffic. An AI backend operates the full layer — auth, CORS, memory, PII scrubbing, prompt-injection defense, rate limiting, and token budgets. A gateway observes; a backend operates.

AI governance is how an organization decides which AI models can run, what data they can access, and what audit evidence each request must produce. Behest enforces those decisions on the request path with model allowlists, PII scrubbing, and audit trails.

AI Token FinOps is the control center for your AI budget and spending: tracking, allocating, and controlling AI Token budget at the unit level. It brings the rigor companies already apply to cloud costs (attribution, budgets, forecasting) down to every model call, so AI cost becomes visible and predictable before the invoice arrives.

PII scrubbing detects and removes personally identifiable information — names, emails, and more — from prompts before they reach an LLM. Behest's PII Shield, powered by Microsoft Presidio, can mask, redact, or block sensitive data on the request path.

Prompt injection is an attack where crafted input tricks an LLM into ignoring its instructions or leaking data. Behest's Sentinel detects and blocks common jailbreak and injection patterns on the request path before they reach the model.

Shadow AI is the use of AI models, tools, or APIs inside an organization without IT or security approval or oversight. It creates ungoverned cost, data-leak, and compliance risk because no one can see or control what those models do.

A token budget is a hard cap on the tokens or dollars a user, team, or project can spend on AI over a period. Behest enforces it on the request path, blocking or throttling calls before they exceed the limit.