AI Governance Playbook (Part3) | Making Governance Real – Infrastructure & Operations

Infrastructure and Operational Scaffolding.

(The AI Governance Playbook (Part1) and AI Governance Playbook (Part 2) of this series explored the risks AI is already creating in the enterprise as well as the organization and policies companies can create to start getting their arms around the problem.  In this final part 3 we'll explore the infrastructure and operations needed to complete the picture of a full-fledged governance program.)

Technical Infrastructure

This is where AI governance gets real. Policies and councils are critical, but if you can’t track, enforce, and adapt in real time, you're just publishing PDFs.

The infrastructure side of governance connects your ideas to operations. It’s what allows you to see what’s happening and do something about it when things go sideways.

A well-structured governance system will usually include:

• Registries - Systems of record for what exists GPTs, agents, models, tools, users, projects, domains. If it’s in production, it should be in a registry with at least an accountable owner, a link to the tool/project/agent itself, and some kind of usage telemetry that shows usage trends to surface fast adoption for extra attention.

• Access controls - RBAC, team scoping, and identity integrations (like Okta or Azure AD). Who can do what, and with which tools?

• Sensitive data controls - Detection and filtering for PII, PHI, and proprietary data applied at input, output, and storage layers.

• Governance data store - Centralized metadata repository for logging, auditability, and policy enforcement.  Highly regulated environments might require all prompt, result, and explainability data retention for a period of time.

• Control plane - The brains of the system. A rules engine + workflow engine that takes in signals (risk, usage, violations) and drives actions (alerts, escalations, restrictions).

• Dashboards and analytics - For security, risk, compliance, and leadership to see what’s going on and where.

If you can’t see it, you can’t govern it. If you can’t act on it, you’re just watching TV.

Operational Enablement

To make AI governance real, it has to actually show up... in day-to-day operations, onboarding, decision-making, and accountability loops. Here's what that usually includes -

• Policy rollout and communication -  If no one knows the policy exists it's not worth much. This means internal enablement, FAQ docs, and in-tool reminders—not just a PDF in a SharePoint folder.  And it all must be repeated ad nauseum because regulation doesn't stop at having the meeting, it stops at actual compliance.

• Playbooks -  For GPT registration, incident escalation, change management, and lifecycle review.  If you make people guess how to comply they'll let you down.

• Training and literacy -  Everyone from legal to dev teams to line-of-business analysts needs to know what responsible AI looks like in practice. Think contextual, targeted, branded, not generic training.

• AI Champion networks -  Distributed subject-matter advocates who help others adopt responsibly and serve as eyes and ears across departments.

• Change management -  Governance is a cultural shift. That means stakeholder alignment, feedback loops and a WIIFM (What's In It For Me?) made plain for everyone in the company.

• KPI identification -  How will you know governance is working? Look at policy adherence, prompt/response safety metrics, ownership coverage, incident counts, registration velocity, and more.

Governance isn't a checklist. It’s not an activity.  It’s an organizational capability.  Full stop.  

Now What?

We've covered all the pieces for a truly comprehensive governance framework that can scale up or down, works across industries and is shaped and tied by the decades of IT governance on which its foundation was built.  Putting it all together sounds like a lot, because it is.  Like I said before all of this is relevant for every company but not everything is practicable.  Don't let the perfect be the enemy of the good.  AI governance isn’t trivial but it is achievable. And you don’t have to figure it out alone.

We help companies of all sizes design and operationalize AI governance systems that are scaled to their tools, their teams, and their risk.

Need help getting started? Want to pressure test your approach? Curious where you stand on the maturity curve?

Get in touch. We’re here to help.