AI governance framework for enterprise teams
An AI governance framework defines who can approve, operate and stop AI systems. It connects policy to everyday decisions: which tools people may use, what data they may share, how systems are tested and who accepts remaining risk. Paul Okhrem's framework below is a practical starting point, not certification or a legal compliance guarantee.
What belongs in an AI governance framework?
Start with the decisions your organization must make. A policy without an owner, evidence or a review date is difficult to use. The following nine components connect a board-level mandate to delivery and operations.
| Component | Decision it supports | Working evidence |
|---|---|---|
| System inventory | What AI is in use and who owns it? | Register of systems, models, vendors, data and use cases |
| Risk tiers | How much review does this use case need? | Risk assessment with scope, affected people and possible harm |
| Decision rights | Who can approve, reject or stop a release? | Named business, technical, security and risk owners |
| Controls | What actions and data are permitted? | Access rules, tool limits and human approval gates |
| Evaluation | What must the system demonstrate before release? | Tests for quality, harmful outcomes, security and failure handling |
| Monitoring | What changes require intervention? | Quality, cost and incident thresholds with response owners |
| Incident response | How do we contain a problem? | Stop, rollback, investigation and communication procedures |
| Vendor evidence | Can we rely on this supplier for this purpose? | Data terms, test evidence, change notices and exit arrangements |
| Board reporting | Is the AI portfolio worth its cost and risk? | Benefits, spending, open risks and decisions needed |
NIST AI RMF, ISO/IEC 42001 and the EU AI Act
These are not interchangeable labels. Use them for different purposes and confirm which legal duties apply to your organization.
| Approach | Purpose | Boundary |
|---|---|---|
| NIST AI RMF 1.0 | Organize risk work through Govern, Map, Measure and Manage | A voluntary risk framework, not a certificate or ordered checklist |
| ISO/IEC 42001 | Establish and improve an AI management system | Management-system requirements, not approval of every model |
| EU AI Act | Apply legal rules based on role and use case | A risk assessment does not replace legal classification |
The EU rules have changed. Use the current EU AI Act checklist and official timeline rather than treating an old deadline table as legal advice.
How to put the framework into use
- Choose a bounded scope. Start with one business unit or a defined set of AI systems. Record what is excluded.
- Name the accountable executive. Give the role authority over priorities, spending and unresolved risk decisions.
- Build the inventory. Include purchased tools, internal models, embedded AI and agent workflows.
- Classify and test. Set proportionate reviews for each use case. Record actual test evidence, not just a supplier's promise.
- Operate the review cycle. Review material changes, incidents and exceptions. Escalate overdue decisions.
Use the AI risk assessment method for individual systems and the AI policy template for day-to-day staff rules. For systems that take actions, add agent-specific controls.
Example: an invoice-processing assistant
A finance team wants AI to read invoices, match records and prepare payment recommendations. The business owner defines the acceptable error rate. IT limits access to the required records. Finance keeps the payment approval. Security tests misleading document content and permission boundaries.
The release record stores test results, exceptions and the rollback plan. A wrong bank detail triggers escalation, not a silent retry. This example shows the framework in use; it is not a claim about a named client project.
Download the governance framework template
Download the AI governance framework CSV. Add a named owner, evidence link, review date and status for each component. The download is free and does not require an email address.
Keep sensitive system details in your controlled workspace. Do not upload confidential records into an unapproved AI tool to complete the template.
When to involve Paul Okhrem
Paul Okhrem's AI governance consulting fits teams that need a working approval process, system inventory, risk reviews and board reporting. A fractional CAIO engagement fits an ongoing leadership mandate.
Paul provides governance design and implementation oversight. Qualified legal advisers determine legal obligations; an independent certification body handles any certification audit. Read the engagement terms before requesting a scope.
Questions about ai governance framework for enterprise teams
Is an AI policy the same as a governance framework?
No. A policy gives rules. A governance framework also defines owners, approval decisions, controls, monitoring, incident handling and evidence.
Does this framework certify EU AI Act compliance?
No. It is an operational starting point. Legal duties depend on the system, use case, role and applicable rules, and need qualified legal review.
How often should the framework be reviewed?
Review it on a defined schedule and after material changes, serious incidents or new legal requirements. The right interval depends on the system's risk and rate of change.
Change log
- : Reviewed copy, source boundaries and supporting resources.
Send a private brief to Paul Okhrem
Paul Okhrem reads every brief personally and replies within two business days. Start with the decision, its owner and the deadline. You will get an honest no if the fit is wrong, with a referral when possible.
Co-Founder and CEO of Elogic Commerce; Managing Partner at Uvik Software. Company experience is not a guarantee of a consulting result. Read the background and published terms.