AI Governance, Risk and Controls for Enterprise Use

Define approved use and decision authority

State the use case, users, decision supported, prohibited actions, accountable owner, approval authority, and conditions that require re-review.

The AI consulting and implementation route provides an internal path when controls must become part of delivery.

Control data, access, and external actions

Record data sources, permitted purpose, access roles, sensitive fields, retention responsibility, tool permissions, and external actions.

Evaluate outputs and human oversight

Define the evaluation question, reviewer, acceptance condition, escalation path, and disablement decision. Human oversight must identify what the reviewer can change, reject, or stop.

Record exceptions and evidence

Retain approvals, evaluation results, material changes, incidents, challenged outputs, exceptions, and decisions.

Review controls as the use case changes

Revisit controls when data, model, prompt, tool, workflow, user group, integration, or consequence changes.

Questions teams ask about AI controls

Human review is not enough by itself. The reviewer needs authority, context, time, evidence, and a clear action when the output is wrong or uncertain.