Skills-Depot People + Processes + Technology + Control

Governance

Stay in control.

AI governance is not about slowing innovation down. It is about knowing what AI is being used, what it can access, what it can do, who is responsible and what evidence remains.

From intelligence to action

AI → Decision → Action

PoliciesContextPermissions
AIDecisionAction
Human oversightEvidenceAccountability

Five questions

Before AI acts, know the answers.

1

Who authorized it?

Know who approved the use case, model, data access and scope of action.

2

What data can it use?

Define which sources are permitted, how sensitive information is handled and what context is appropriate.

3

What can it do?

Separate what the system may recommend from what it may execute, modify or approve.

4

When must a human intervene?

Design review, approval and escalation for sensitive decisions, exceptions and uncertainty.

5

What evidence remains?

Retain the records needed to reconstruct what happened, why it happened and who was involved.

6

How do we improve it?

Monitor outcomes, incidents, model changes and new requirements so governance evolves with the system.

Operating model

Discover. Assess. Control. Monitor. Evidence. Improve.

Governance starts with visibility. An organization needs to know where AI is being used — including third-party tools, embedded capabilities and employee-created workflows.

From there, controls should reflect the risk and consequence of the use case. A summarization assistant does not need the same oversight as an agent that can approve a payment or change a customer record.

Governance is not only about controlling AI. It is about being able to demonstrate how AI is controlled.

What governance connects

Controls should follow the real system.

Inventory

Know which models, agents, automations and embedded AI capabilities are actually in use.

Risk & compliance

Connect AI use to privacy, security, sector requirements, internal policy and management systems.

Permissions

Limit tools, credentials, data sources and actions to what the use case genuinely requires.

Human oversight

Place people where judgment, accountability, uncertainty or material consequences require them.

Monitoring

Watch performance, exceptions, incidents and model or provider changes after deployment.

Evidence

Keep logs, approvals, versions, sources and decisions so the process can be explained and audited.

Related insight

Your AI made a decision. Can you explain why?

A useful test for governance is simple: after the AI acts, can your organization reconstruct what information it received, what rules applied, whether human approval was required, what action occurred and what evidence remains?

Read the insight