Your team is already using AI. Maybe it’s a handful of licensed tools. Maybe it’s a growing patchwork of copilots and assistants that nobody fully inventoried. Either way, “we’ll figure it out later” stopped being a strategy a while ago.

Here’s the thing: you don’t need a massive governance apparatus to get this right. You need a clear owner, a good framework, and enough structure to make informed decisions about risk.

First Problem: Nobody Owns This

AI doesn’t fit cleanly under any one team. Engineering builds and integrates it. Security evaluates risk. Legal worries about data and liability. Privacy cares about what’s being fed into models. Procurement is fielding vendor pitches weekly.

When ownership is ambiguous, one of two things happens: nobody governs AI, or everybody tries to. Neither works.

Fix it: Stand up a small cross-functional steering committee. At an SMB this might be 3–4 people — security, engineering, legal or compliance, and a business stakeholder. Their job is straightforward:

  • Decide which AI tools and use cases are approved
  • Set boundaries for how AI interacts with company data
  • Review higher-risk implementations before they go live
  • Own the policy that says “this is how we do AI here”

Give them a charter, a monthly meeting, and the authority to say yes or no. Without clear ownership, governance is just a suggestion.

Pick a Framework — Don’t Build One

The NIST AI Risk Management Framework (AI RMF) is the strongest starting point for most organizations. It organizes AI risk management into four functions:

  1. Govern — Establish policies, roles, and culture for responsible AI use
  2. Map — Understand the context, capabilities, and risks of each AI system
  3. Measure — Assess and track AI risks with defined metrics
  4. Manage — Prioritize and act on what you find

This isn’t a compliance checkbox. It’s a thinking model. It helps you ask better questions: Who’s accountable? What data is involved? How do we know if something goes wrong?

For SMBs, don’t try to implement every sub-category on day one. Start with Govern and Map. Get ownership right and build an inventory of what tools are in use, what data they touch, and who’s responsible.

Governance and Use Case Review Are Different Work

This trips up a lot of organizations. Governance is the standing structure — policies, roles, and oversight that persist over time. Use case review is the operational process of evaluating specific AI tools as they come in the door.

Both matter. They’re not the same thing.

Use case review is where privacy, security, and engineering collaborate on specific decisions:

  • Privacy evaluates what data flows into the model and what the vendor does with it
  • Security assesses integration risk, access controls, and attack surface
  • Engineering determines architectural fit and monitoring needs

Your steering committee sets the policy. Your review process applies it. Keep them connected but don’t collapse them into one activity.

Build a Risk Model That Fits AI

Generic risk assessments don’t work well here because AI tools vary dramatically in what they do and how they do it. A code completion tool has a fundamentally different risk profile than a customer-facing chatbot with access to your CRM.

Break it down by:

  • Function — What is it actually doing? Generating content, summarizing data, writing code, talking to customers?
  • Mode — Autonomous or human-reviewed? Internal or customer-facing? Does it learn from your data?
  • Data exposure — What does it ingest, process, or store? Anything sensitive, regulated, or proprietary?
  • Integration depth — Standalone tool or embedded in critical systems? Does it have write access?

Once you’ve mapped these dimensions, you can define guardrails: how different categories of AI tools should be configured, monitored, and tested. Low-risk internal summarization gets a lighter touch. High-risk tools with customer data access and autonomous decision-making get a full review with ongoing monitoring.

The AI Security & Governance Workbook from Jason Robbins is worth grabbing here. It’s a free, practical Excel-based framework that includes a tool registry, intake form, function-to-risk mapping, guardrail checklist, compliance mapping, and program metrics. It’s one of the most operationally useful resources I’ve seen for teams that need to move from “we should probably govern AI” to actually doing it. Released under Creative Commons.

Frameworks Worth Knowing

You don’t need to implement all of these, but you should know they exist:

NIST AI RMF — Your foundation. Voluntary, flexible, works at any scale.

ISO/IEC 42001 — The international standard for AI management systems. Think ISO 27001 but for AI. More relevant if you need to demonstrate governance maturity to customers or partners.

CSA AI Controls — Control frameworks for AI security in cloud environments. Useful since most of your AI tools are probably SaaS.

OWASP Top 10 for LLM Applications — If you’re building with or deploying large language models, this is required reading. Prompt injection, data leakage, insecure output handling, supply chain risks. Share it with your engineering team.

Five Things You Can Do This Month

  1. Inventory your AI tools. Ask every team what they’re using — licensed, free tier, browser extensions, all of it. You can’t govern what you can’t see.

  2. Name an owner. Designate someone as accountable for AI governance. Give them a mandate and air cover.

  3. Grab the workbook. Download the AI Security & Governance Workbook and start with the tool registry and intake form.

  4. Write a one-page AI use policy. It doesn’t need to be perfect. What’s allowed, what requires review, what’s off-limits. Communicate it.

  5. Read the NIST AI RMF. Even a skim gives you a vocabulary and mental model for the conversations ahead.

AI governance isn’t about locking things down. It’s about making deliberate choices — knowing what’s in play, what risks it carries, and who’s watching. At an SMB you actually have an advantage here: less bureaucracy, faster decisions, and the chance to build this right instead of retrofitting it later.

Start small. Build deliberately. Govern what you use.