AI governance policy gives leaders a practical way to direct artificial intelligence, assign ownership, manage risk, and decide when a use should advance, change, pause, or stop.
This guide is written for Canadian organizations. It separates widely useful governance practice from rules that apply only to specific governments, sectors, provinces, contracts, or activities.
Table of contents
- Purpose, scope, and definitions
- Permitted and prohibited use
- Accountability and decision rights
- Risk classification
- Data, privacy, and security
- Testing and human oversight
- Vendor and change controls
- Training, exceptions, and enforcement
- Practical checklist
- Frequently asked questions
- References
“A useful AI policy gives employees a safe path to act, not only a list of reasons to say no. It makes responsible use clearer and easier than ungoverned improvisation.”
Mehrzad Verdizadegan,
CEO, Praevion Consulting Inc.
Purpose, scope, and definitions
An AI governance policy should explain why it exists, who and what it covers, and how the organization defines AI and related roles.
Include systems built, purchased, embedded in other software, or reached through public tools.
Permitted and prohibited use
State approved environments, sensitive information rules, prohibited activities, and uses that require specialist or executive approval.
Use practical examples. Employees need to make decisions during work, not interpret broad principles.
Accountability and decision rights
Name business, technical, data, and control roles. Define who can approve, reject, pause, change, and retire an AI system.
Human accountability should remain clear when a vendor supplies the model.
Risk classification
Set risk tiers and triggers using affected people, decision impact, data, scale, autonomy, reversibility, and possible harm.
Tie each tier to evidence and approval requirements.
Data, privacy, and security
Cover lawful authority, data minimization, access, retention, prompts, outputs, training use, security, logging, and incident response.
The Canadian privacy commissioners’ generative-AI principles explain that obligations vary by organization and activity under applicable privacy law.
Testing and human oversight
Require evaluation for intended use, difficult cases, affected groups, reliability, bias, security, and human review.
Define who reviews what, with which skill, and what happens when the person disagrees with the output.
Vendor and change controls
Address data use, subcontractors, model updates, service changes, audit evidence, incidents, exit, and business continuity.
Reassess when purpose, model, data, vendor, or operating context changes.
Training, exceptions, and enforcement
Require role-based learning, safe reporting, documented exceptions, consequences for misuse, and regular policy review.
Keep stable responsibilities in policy and detailed tool lists or procedures in faster-changing standards.
Questions for the next governance review
Ask whether the purpose is still valid, the owner still has authority, the evidence reflects current operation, and the controls work in practice. Review model, data, vendor, workflow, user, and legal changes. Then record the decision: continue, improve, limit, pause, or retire. This short discipline prevents yesterday’s approval from becoming permanent permission.
AI governance policy checklist
- Define the purpose, affected people, business outcome, and accountable owner.
- Record the use in an inventory and classify risk using clear evidence.
- Apply privacy, security, data, testing, human-oversight, and vendor controls.
- Document approval, limits, exceptions, residual risk, and stop conditions.
- Monitor value, performance, adoption, incidents, complaints, and major changes.
- Reassess after changes and retire systems that no longer justify cost or risk.
Related Praevion guidance
- Read the related Praevion governance guide
- Explore the next related article
- Explore Praevion Consulting Inc. digital transformation services
Frequently asked questions
How often should the policy be updated?
Review at least annually and after material legal, technology, incident, or business change.
Should the policy name specific tools?
Keep approved-tool lists in a separate standard so they can change without rewriting the whole policy.
Who approves the policy?
Executive leadership should approve it, with board oversight where the organization’s risk model requires it.
Executive takeaway
What Should an AI Governance Policy Include? The practical answer is to place the right decision with a named owner, require evidence that matches the possible impact, and keep governance active after launch. Strong governance protects people and the organization while giving delivery teams a clear route to responsible use.
To discuss your needs, contact Praevion Consulting Inc..
References
- NIST, Artificial Intelligence Risk Management Framework
- NIST, Generative AI Profile, 2024
- ISO/IEC 42001:2023, AI management systems
- Office of the Privacy Commissioner of Canada, Principles for responsible, trustworthy and privacy-protective generative AI
- Government of Canada, Guide on Departmental AI Responsibilities
