AI governance is not only for large enterprises. Any company using AI with customer data, employee information, business decisions, or public content needs clear ownership and boundaries.
Build a simple AI inventory
List each AI use case, owner, vendor or model, data used, integrations, output audience, and business impact. You cannot manage systems that nobody knows exist.
Classify by risk
- Low: brainstorming or formatting non-sensitive internal content.
- Medium: drafting customer communication or summarizing internal documents.
- High: employment, finance, healthcare, legal, security, access, or automated customer-impacting decisions.
Define non-negotiable controls
Specify prohibited data, approved tools, retention expectations, review requirements, and who can authorize a new integration. High-risk outputs should be explainable and reviewed by a qualified person.
Evaluate vendors
Ask how data is stored, whether it is used for training, where processing occurs, how access is controlled, how incidents are reported, and what logs are available. Review subcontractors and deletion processes.
Train people, not just systems
Employees need practical examples of acceptable and unacceptable use. Teach them to verify output, protect confidential information, identify manipulated content, and report failures without fear.
Review on a schedule
Models, policies, and business processes change. Review high-risk systems frequently and the full inventory at least quarterly. Track incidents, overrides, user complaints, and evidence of bias or declining quality.
Good governance creates confidence to move faster. It gives teams a safe path for experimentation and gives leadership visibility into real risk.