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Checklist | 12 min read

AI Governance Blind Spots Checklist

Review privacy, accountability, bias, vendor, security, and shadow-AI questions before an AI use becomes routine.

By Dr. Gbemisola Adetayo

For: Leadership teams, managers, and small organizations adopting AI

Last reviewed: August 11, 2026

Short answer

Short answer

The most common governance blind spots appear when an organization cannot name the use, owner, information, affected people, review standard, vendor conditions, escalation path, or evidence. Use this checklist to make those decisions visible before scaling.

1. Inventory and shadow AI

Governance starts with knowing where AI is already being used.

  • Can we list the AI-supported workflows used by employees, contractors, and vendors?
  • Do people know which uses are approved, restricted, or prohibited?
  • Is there a safe way to disclose experimentation without creating incentives to hide it?

2. Purpose, people, and accountability

A use without a named owner is not ready to become routine.

  • Is the intended purpose and boundary documented?
  • Who owns the workflow, reviews output, makes the final decision, and handles escalation?
  • Who may be affected, and how can they challenge or correct a material error?

3. Information, privacy, and intellectual property

The convenience of a tool does not determine whether information may be used.

  • What personal, confidential, proprietary, privileged, or regulated information could enter the system?
  • What does the provider retain, use for training, share, or transfer?
  • Do contracts, notices, permissions, retention, and deletion practices match the intended use?

4. Quality, bias, and human review

Review must be designed for the context and consequence.

  • What errors, omissions, or uneven impacts are plausible?
  • Is the reviewer qualified, independent enough, and authorized to reject the output?
  • Is there a stronger control or non-AI alternative for high-consequence cases?

5. Security, vendors, and change

An AI use inherits dependencies from its provider, integrations, and updates.

  • Are access, authentication, logging, configuration, and incident responsibilities clear?
  • What happens if the provider changes the model, terms, data handling, or availability?
  • Can the organization suspend the use and recover the workflow without the tool?

6. Evidence, monitoring, and response

Approval is the start of oversight, not the end.

  • What baseline, quality, risk, and adoption measures will be reviewed?
  • How are errors, complaints, overrides, and near misses recorded and resolved?
  • Who decides whether to stop, change, continue, or expand the use?

Use the checklist proportionally

A low-consequence internal draft does not need the same process as an AI-supported decision affecting employment, health, safety, finance, legal rights, or access to essential services. Apply more scrutiny as the consequence, sensitivity, scale, or difficulty of detecting harm increases.

Frequently asked questions

What is shadow AI?

Shadow AI is AI use that occurs outside the organization’s approved visibility, policy, procurement, or oversight processes.

Does every AI use need the same governance?

No. Oversight should be proportional to the sensitivity, scale, reversibility, and possible consequence of the use.

Is this checklist legal advice?

No. It supports internal governance conversations and does not replace legal, security, privacy, or professional advice for a specific use.

Sources