---
title: "AI governance"
description: "One view of usage, access, policy, and spend across every model, agent, and cloud, with controls sized to the risks you carry."
canonical: "https://bespinus.github.io/bgus-ai-landingpage/governance/"
pillar: "governance"
updated: "2026-10-01"
---

# Govern the AI you already run.

One view of usage, access, policy, and spend across every model, agent, and cloud, with controls sized to the risks you carry.

## Adoption has outrun control.

- There are a dozen AI assistants in use, and we cannot say what data has gone into them.
- Every new agent and connector is one more access path, and we have no fast way to assess it.
- The framework covers everything. We need the part that fits our risk.
- AI spend used to be a line item. Now it touches the whole IT budget.

## One record for every model call

Every source flows into one governance plane, which answers security, finance, and engineering
from the same record.

### See it

Usage and spend from every gateway, cloud, and device in one place instead of six consoles: model gateways, Amazon Bedrock, Azure AI Foundry, Google Vertex AI, SaaS assistants, and cloud billing.

### Tag it

Every call attributed to an application, team, user, and workload, and it stays attributed as new tools arrive.

### Control it

Budgets, anomaly alerts, and a router that sends each call to the model policy allows.

### Reduce it

Right-sized models, caching, leaner context, and commitments priced against real consumption.

Point the same four steps at an agent and they answer the outcome question: what it costs
per resolved case, and whether it still earns its place. That is outcome management,
layer 7 of the stack.

## Intake that keeps pace with adoption

Every new agent, assistant, and connector passes the same four checks, inside your
existing change process: who owns it, what it can reach, whether it can write, and how to
roll it back. Low-risk requests are approved fast, and high-risk requests get full review
before they ship. Frameworks like the NIST AI RMF get cut down to the risks you actually
carry.

## Tokenomics: cost as a design input

Tokenomics is our method for the cost side of AI, through every layer of the stack. Every
source lands in a Token Lake, reconciled against what you were actually billed, so the
number you manage moves from monthly spend to cost per resolved case.

Gateways count tokens at list rates. Your invoice reflects your commitments and discounts.
Most dashboards report the first number. Tokenomics reports the one you pay.

## Two buyers, one system

Security asks what data went where. Finance asks what it cost and what it bought. They
arrive from opposite directions and want the same system: usage, access, and cost in one
record. Whichever calls first, the other follows.

Bespin is SOC 2 Type II attested, so our own controls are independently audited too.

Runs on governance and security and outcome management, with Tokenomics through every
layer. Managed AI operates it after setup.

## Where this meets the rest

The router that enforces cost and latency budgets is the same router that enforces policy
for [generative and agentic AI](https://bespinus.github.io/bgus-ai-landingpage/agentic-ai.md), [Orbit Vision AI](https://bespinus.github.io/bgus-ai-landingpage/vision-ai.md) sites show up
here as consumption, and [Managed AI](https://bespinus.github.io/bgus-ai-landingpage/managed-ai.md) runs the governance layer after
setup.

## Frequently asked questions

### What does AI governance cover?

Usage discovery, agent and connector intake, access and approval controls, audit trails, framework alignment, and cost through Tokenomics, across models, agents, assistants, and clouds.

### Where does Tokenomics fit?

Tokenomics is the cost method inside AI governance. It reconciles usage to actual billing and measures cost per outcome at every layer of the stack.

### Is this a software product or a managed engagement?

Bespin builds the governance layer in your environment and can run it for you under Managed AI.

## Talk to an AI engineer.

[Talk to an AI engineer](https://bespinglobal.us/contact) at Bespin Global.

_Machine-readable summary for agents: provider = Bespin Global; service = AI governance; focus = One view of usage, access, policy, and spend across every model, agent, and cloud, with controls sized to the risks you carry; delivery model = forward-deployed engineering, build and operate; engagement = scoped against an outcome, no self-serve product; contact = <https://bespinglobal.us/contact>._