The AI Policy Pack

Part 4 of the AI Governance Series

Let’s be honest: most AI policies are garbage.

They’re downloaded templates with company names swapped in. Vague language that sounds important but commits to nothing. PDFs that get signed once and forgotten.

That’s not governance. That’s theater.

Real policies drive behavior, create accountability, and generate evidence. Here’s how to build them.

Why Auditors Hate Your AI Policy

An auditor picks up your AI Acceptable Use Policy. Within 30 seconds, they know if it’s real or fake.

Here’s what gives you away:

Problem 1: Vague Language

What does “responsibly” mean? What values? This language commits to nothing and can’t be enforced.

Specific. Measurable. Enforceable. Creates evidence when followed.

Problem 2: No Ownership

Who is “the organization”? Who’s accountable? Who makes decisions? Policies without owners are wishes.

Problem 3: No Enforcement Mechanism

Should? What happens when they are? How would you know?

Problem 4: No Evidence Trail

If your policy can’t generate proof of compliance, auditors assume non-compliance.

Every policy requirement should produce artifacts: logs, approvals, attestations, reviews. No artifacts = no evidence = audit findings.

Policies Should Trigger Work

A policy that just sits on a share drive isn’t governance. It’s filing.

Effective policies are executable. They trigger workflows:

• New employee onboarding triggers AI acceptable use training
• New AI tool request triggers risk assessment workflow
• High-risk AI use case triggers committee review
• Policy violation detected triggers incident response
• Quarterly review date triggers policy attestations

If your policy doesn’t connect to workflows, it won’t generate evidence. If it doesn’t generate evidence, you can’t prove compliance.

The Essential AI Policy Set

What policies do you actually need? Here’s a framework:

These aren’t separate documents for the sake of it. Each addresses a distinct governance need with specific controls and evidence requirements.

The Policy-to-Evidence Chain

Every policy statement should connect to evidence. Here’s how:

Now when an auditor asks “How do you ensure AI systems don’t access customer data without approval?”—you have a complete answer with proof.

Templates vs. Systems

Here’s the temptation: download a policy template, customize it, call it done.

Templates are starting points, not solutions. They fail because:

• They don’t reflect your risk. A healthcare company and a marketing agency have different AI risks. Same template won’t work.
• They don’t connect to your systems. Your policy mentions “approved AI tools” but doesn’t list what those are or how they’re managed.
• They use generic language. “Appropriate use” means nothing without your context.
• They don’t generate evidence. Following a template policy produces no proof of compliance.

A policy system, on the other hand, connects documentation to workflows, monitoring, and evidence collection. It’s alive, not static.

Making Policies Executable

How do you turn policy documents into working governance?

1. Map each policy requirement to a control. If the policy says “approval required,” define the approval workflow.
2. Connect controls to evidence. Every control should produce artifacts: logs, records, attestations.
3. Automate where possible. Manual compliance doesn’t scale. Integrate with your tools.
4. Review regularly. Policies need updates as AI capabilities and risks evolve.
5. Test enforcement. Can you actually detect violations? What happens when you do?

The Ownership Problem

Policies without owners are policies that fail.

Every AI policy needs:

• An executive sponsor who’s accountable to the board
• A policy owner who maintains and updates the document
• Process owners who implement specific requirements
• Exception owners who approve deviations

When auditors ask “who’s responsible for this policy?” and you can’t name someone—that’s a finding.

What Good Looks Like

A mature AI policy framework includes:

What Comes Next

Policies are the foundation. But implementation matters more. In Part 5, we’ll walk through the AI 90 Playbook—a 90-day path from zero governance to operational control.