What governance controls should be documented?

Undocumented controls are not reliable controls

When firms say they have AI governance in place, the next question should be simple: which controls are actually documented?

That matters because undocumented controls tend to rely on memory, habit, or individual judgement. They may work for a while in a small team, but they usually break once usage spreads, staff change, or higher-risk work enters the workflow.

If a firm wants AI governance that survives real operational pressure, the important controls need to be written down clearly.

The controls that should be documented first

Not every document needs to be long or legalistic. But several control areas should be explicit from the start.

• Tool approval controls: which tools are approved, for which teams, and for which tasks.
• Data handling controls: what data may be entered, what is restricted, and what must never be used in a tool.
• Human review controls: which outputs require checking, who reviews them, and what sign-off is needed before external use.
• Role and accountability controls: who owns policy, who approves exceptions, and who is responsible for live workflows.
• Monitoring controls: how usage, incidents, drift, or misuse will be spotted and escalated.

Those controls give teams a usable operating baseline rather than a vague commitment to safe adoption.

Identify which AI controls matter most in your live workflows

Why documentation matters more than firms expect

Many organisations assume people already know the rules. In reality, staff usually know fragments of the rules.

One person believes a tool is approved for all drafting. Another thinks client data is acceptable if names are removed. A manager expects human review, but the reviewer assumes the first user already checked the output properly.

These gaps are not unusual. They are what happens when governance is implied instead of documented.

What good documentation looks like in practice

Useful governance documentation is specific enough to guide behaviour without becoming so heavy that no one reads it.

In practice, that often means documenting controls across a few simple layers:

• a short top-level policy explaining principles and scope
• workflow rules for higher-risk functions such as client work, analysis, or regulated outputs
• tool and task approval matrices
• exception and escalation paths
• review checklists for recurring use cases

The point is not paperwork for its own sake. The point is making the right behaviour repeatable.

Turn AI governance expectations into real operating controls

The minimum questions your documentation should answer

If the documentation is working, staff should be able to answer questions like these quickly:

• Which tools may I use for this task?
• Can I use this tool with internal, client, or regulated data?
• What review is required before this output is shared?
• Who approves exceptions?
• What should I do if the tool output looks wrong, risky, or unclear?

If the answers are buried, inconsistent, or missing, the controls are not documented well enough yet.

The main mistake firms make

The biggest mistake is documenting principles but not decisions. Many firms can say they care about transparency, accountability, and safe use. Fewer can show the actual operational controls that translate those principles into daily behaviour.

That is where governance becomes real or stays theoretical.

A practical documentation standard

Document the controls that change user behaviour, reduce ambiguity, and make oversight possible. If a control affects what tool someone may use, what data they may enter, what review they must complete, or who carries responsibility, it should usually be written down.

Otherwise the control is too fragile to rely on.

Build a governance model your teams can actually follow

Conclusion

Firms should document governance controls around tool approval, data handling, review, accountability, and monitoring because these are the controls that shape real behaviour. Principles matter, but documented operating controls are what make governance durable.

If people cannot point to the rule, the control is probably not strong enough yet.

Need a safer AI route for your firm?

If this article reflects a live decision in your practice, the useful next step is to map the workflows, confidentiality risks, supplier controls and governance gaps before tools spread informally.

• Map AI risk and opportunity
• Put usable AI governance in place
• Roll out AI with privacy and supplier controls
• See AI support for legal services