Companies that run into trouble with Claude usually skipped the governance setup. It’s not complicated work, but it does require someone to make decisions before usage spreads to the point where making them gets harder.

What Governance Actually Means

Governance covers four things: who has access to Claude and for what, what data’s appropriate to bring into a conversation, what visibility you have into how Claude’s being used across your team, and how you respond when something goes wrong.

That’s it. There’s no fifth thing.

Treat Claude like any other business-critical system. You wouldn’t give every employee admin access to your CRM with no training and no usage policy. Same thinking applies here.

The Shadow AI Problem

If you haven’t rolled out a central AI tool for your team, they’re almost certainly using something already. Personal Claude accounts, ChatGPT, Gemini — often with a company email address, no oversight, and no one tracking what data went where.

These are useful tools and they’re freely available. But company data’s moving through systems you haven’t vetted, under terms you haven’t read, with no record of what happened. A central platform like Claude Enterprise gives your team a sanctioned option with controls you can actually see.

What Is Shadow AI and Why It’s Already Happening in Your Business

What Claude Enterprise Actually Gives You

Data Handling

By default, your data isn’t used to train Anthropic’s models. Conversations don’t persist after the session ends. At the enterprise tier, that’s a contractual commitment — not a setting buried in a terms page. For businesses in regulated industries, a Data Processing Agreement is available, and HIPAA-eligible workloads can be accommodated with the right agreements in place.

It doesn’t hand governance off to Anthropic. It gives you a documented starting point for making your own decisions.

Access and Administration

Administrators control who has access to Claude across the organization. Single sign-on with SAML or SCIM handles provisioning and deprovisioning cleanly. Spending limits can be set at the organization level and the individual level. Role-based permissions let you give different groups different capabilities — not everyone needs the same access, and now you can reflect that in how the tool’s configured.

Connector and Tool Controls

Claude can connect to Salesforce, Google Drive, HubSpot, and other systems. At the enterprise level, you control which connectors are available to which groups, and you can limit what those connectors can do. Read-only access to Salesforce is a different situation than read-write. Starting read-only, seeing what Claude surfaces and how it uses the data, and expanding from there is how the businesses we work with typically approach it.

How to Set Up Claude Enterprise for Your Team

Accountability Doesn’t Transfer

When someone on your team uses Claude to produce a document, a proposal, or a client-facing report, they own it. Claude helped. They’re responsible. That’s true whether Claude wrote 10% of it or 90%.

Your usage policy needs to say this plainly. When people know AI assistance doesn’t move responsibility off their plate, they treat the output differently. They check it. They apply judgment before it goes anywhere.

How to Think About Risk

Not moving is also a risk. Companies that don’t build a governance framework for AI aren’t avoiding exposure — they’re leaving the decision unmade while usage spreads anyway.

Start with your data. What does your business actually handle day to day? Client contact information carries a different risk profile than protected health information or proprietary financial records. Not all of it requires the same decisions.

Then think about use cases. Drafting internal documentation is a different situation than processing client records. Start with lower-sensitivity workflows, build confidence, and expand from there.

How to Evaluate the Risks of Deploying Claude in Your Business

Industry context matters too. Healthcare and financial services businesses have compliance obligations that shape what governance looks like. Professional services and manufacturing businesses face a different set of questions. There’s no universal framework — yours should reflect your specific data landscape.

Building the Framework

Data Classification

Understand what categories of data your organization holds and which are appropriate for AI workflows. A simple tiered approach works fine to start: what’s clearly acceptable, what’s clearly off-limits, what requires a judgment call. Some platforms like Salesforce let you classify data at the field level, which feeds directly into these decisions.

How to Classify Your Business Data Before Rolling Out AI

Usage Policy

A usage policy puts your data classification decisions into language your team can act on. What’s in scope for Claude, what’s out of scope, and what to do when they’re not sure. At Fast Slow Motion, we use a traffic-light model internally: green means proceed, red means never, yellow means stop and ask someone. It doesn’t need to be longer than one page to work.

How to Write an AI Usage Policy Your Team Will Actually Follow

Access and Admin Controls

Set up Claude Enterprise so the right people have access and you have visibility into usage. Access reviews should happen on a regular schedule — not once at setup and then ignored for a year. Roles change. People leave. Access needs to reflect that.

Team Training

Your governance framework’s only as useful as your team’s understanding of it. A 30-minute conversation covering what good Claude usage looks like in your specific context is enough to start. What data’s off-limits, what the usage policy means in practice, who to ask when something isn’t clear.

When Something Goes Wrong

Something will. A document goes somewhere it shouldn’t. A team member uses Claude in a way that wasn’t intended. A connected system surfaces data it shouldn’t have accessed.

The companies that handle these situations cleanly had a clear record of what data was in scope, documented policies defining what was permitted, and audit logs showing what happened. With that in place, you can diagnose fast, fix the actual problem, and communicate clearly. Without it, you’re mapping your own system under pressure with no reference point.

What to Do When Something Goes Wrong With AI at Your Business

Where to Start

If your team’s already using Claude and you haven’t worked through these decisions, that’s where to start. If you’re evaluating Claude and want to get governance right before rollout, that’s the better position to be in. Either way, the framework doesn’t have to be complete on day one. It has to be intentional.

Listen to the full podcast episode here.

Related Resources

• What Is Shadow AI and Why It’s Already Happening in Your Business
• How to Write an AI Usage Policy Your Team Will Actually Follow
• How to Classify Your Business Data Before Rolling Out AI
• How to Evaluate the Risks of Deploying Claude in Your Business
• What Is Claude Enterprise and How Is It Different From Regular Claude