AI governance 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
- AI governance in plain language
- Governance is broader than compliance
- Start with an AI inventory
- Classify risk by context
- Assign decision rights
- Require lifecycle evidence
- Keep Canadian scope accurate
- Practical checklist
- Frequently asked questions
- References
“AI governance turns principles into decisions people can trace. If nobody can show who approved a use, what evidence was considered and how harm will be detected, governance exists only on paper.”
Mehrzad Verdizadegan,
CEO, Praevion Consulting Inc.
AI governance in plain language
AI governance is the system used to decide where artificial intelligence may be used, who owns the result, what evidence is required, and how performance and harm will be monitored.
It covers systems built internally, bought from vendors, or accessed through features and public tools. Governance begins before procurement and continues until retirement.
Governance is broader than compliance
Compliance addresses laws, regulations, contracts, and sector duties that apply. Governance also connects AI choices to strategy, values, risk tolerance, and business outcomes.
A system can meet a narrow legal test and still be unsuitable because its cost, reliability, workforce effect, or impact on trust is unacceptable.
Start with an AI inventory
Record each use case, owner, purpose, users, data, model or vendor, affected people, risk tier, status, and last review.
Without an inventory, leaders cannot see shadow use, duplicated spending, overdue controls, or systems that should be retired.
Classify risk by context
Risk depends on the decision, affected people, data sensitivity, scale, autonomy, reversibility, and possible harm. The same model may be low risk for drafting and high risk for hiring.
Use tiers to set review depth. A low-risk aid should not face the same process as a system influencing employment, credit, safety, or access to essential services.
Assign decision rights
Name the business owner, technical owner, data owner, and control specialists. State who may approve, reject, pause, change, and retire the system.
A committee can review evidence, but it should not blur individual accountability.
Require lifecycle evidence
Set requirements for data, testing, privacy, security, fairness, human oversight, vendor risk, documentation, monitoring, incidents, and change control.
The NIST AI RMF organizes risk work around Govern, Map, Measure, and Manage. ISO/IEC 42001 provides an AI management-system approach.
Keep Canadian scope accurate
Canadian organizations should map applicable federal or provincial privacy, human-rights, employment, consumer, security, intellectual-property, and sector rules.
Federal-government guidance can offer useful practice, but its application should not be presented as automatic for every private organization.
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 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
What is the main goal of AI governance?
To enable useful AI while keeping ownership, evidence, risk, and accountability clear throughout the lifecycle.
Does a small company need AI governance?
Yes, but it can use a lighter model with fewer forums and proportionate controls.
Is an AI policy enough?
No. Governance also needs roles, processes, evidence, decisions, monitoring, incident response, and improvement.
Executive takeaway
What Is AI Governance? 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
