AI governance: permissions, evidence and accountable decisions

Source page: https://www.o10.io/governance

Plain-text reference for reading, copying, and citation. Examples are illustrative; use your own credentials for API requests.

Model data: https://www.o10.io/api/models.json
Content index: https://www.o10.io/llms.txt

# AI governance: permissions, evidence and accountable decisions

AI governance defines who may approve, operate, change, and review an AI system. For inference and agents, it requires explicit permissions, evaluation evidence, operating limits, and accountable owners.

## Key takeaways

### What is governance?

AI governance defines who may approve, operate, change, and review an AI system. For inference and agents, it requires explicit permissions, evaluation evidence, operating limits, and accountable owners.

## Define controls at the point of action
Write the operations a system can perform and the conditions under which human review is required. Enforce authorization in application code and connected services. A prompt describing a policy is not the same as an access control.
For agents, distinguish read operations from actions that alter external state. Record which action was approved and whether it actually completed, so recovery does not repeat it accidentally.

## Keep evidence behind each release decision
Retain the evaluation configuration, relevant failure examples, model or endpoint version, and decision owner. Protect sensitive records and document retention and access rules.
An audit log is a record of events, not automatic evidence that the events were permitted or the result was correct. Review the contents against the intended policy and task outcome.

## Describe product boundaries accurately
o10's KYI framework can structure review of performance, economics, integration, strategy, and risk. Its routing and receipt capabilities support inference operations; they do not independently establish compliance with a law or certification.
Data-residency and jurisdictional routing controls must be verified against the current product capability and applicable agreements. Do not infer that a feature is enforced merely because a glossary page explains it.

## Methodology

Cost examples are illustrative. Measure quality and total costs on your own tasks, and check current endpoint terms before implementation.

## Related links

- [AI inference: from model output to production system](https://www.o10.io/ai-inference)
- [AI agents: useful actions, measurable outcomes](https://www.o10.io/ai-agents)
- [LLM routing: choose a model per task with explicit constraints](https://www.o10.io/routing)
- [Inference spend: calculate cost per accepted outcome](https://www.o10.io/inference-spend)

## Source URL

https://www.o10.io/governance