We define governance as the operating rules around AI use. A responsible AI policy for an SMB can fit on a few pages if it names use cases, data rules, approval tiers, vendor checks, human review, logs, and incident steps.
Create a use-case inventory.
Start by listing where AI is already used. Include writing, research, meeting notes, support replies, code, reporting, hiring, sales outreach, finance, operations, and customer work. Shadow AI use matters too, because employees may already be using public tools without a shared rule set.
For each use case, name the owner, users, tool, data involved, output, reviewer, and business purpose. This inventory becomes the basic map for AI risk management. Leadership can then see which uses are low risk, which need approval, and which should stop until rules are clearer.
Set data rules and approval tiers.
Data rules should be specific. State what can go into approved tools, what requires permission, and what is restricted. Customer records, employee records, financial data, contracts, trade secrets, credentials, and regulated information should have clear handling rules.
Approval tiers keep governance light. Low-risk uses may only need manager awareness. Medium-risk uses may need an owner, review process, and approved tool. High-risk uses, such as customer-facing decisions, financial commitments, legal content, or employment decisions, need leadership approval and human review.
Review vendors before the work depends on them.
Vendor review should happen before a tool becomes part of daily operations. Check data retention, training use, access controls, admin controls, export options, audit logs, security documentation, pricing risk, and whether the vendor supports the workflow you are trusting it with.
The output of vendor review should be a simple decision: approved, approved with limits, or rejected. Record the owner, renewal date, allowed data, blocked data, and what happens if the tool is unavailable.
Define human review, logs, and incident response.
Every material AI workflow needs a human review rule. The reviewer checks accuracy, policy fit, tone, customer impact, and exceptions. Some outputs can be sampled. Others require review before anything reaches a customer, vendor, regulator, or employee file.
Keep logs simple. Track the use case, tool, owner, date, source data category, output category, review status, and issue reports. For incidents, name who receives the report, who pauses the workflow, who checks affected records, who contacts impacted parties, and who approves restart.
Governance works when every AI use case has an owner, allowed data, approval level, review rule, log, and incident path.