Training by theme
Governance
Usage policy, risk matrix, traceability, oversight of high-impact decisions: the path for those who govern AI across the organization.
Why this path
Documented evidence is your best defense.
An overly restrictive policy drives shadow AI; without a risk matrix, controls scatter; without a register, you cannot demonstrate diligence in an investigation. This path covers writing a policy that sticks, risk classification, traceability and mandatory human oversight.
The modules in this path
Writing an AI usage policy that will be followed
M19
Design a balanced, co-built and documented policy.
An effective policy gives concrete examples of allowed tasks and approved tools. Too restrictive, it drives shadow AI; documented rollout demonstrates diligence.
RegisterClassifying uses by risk level
M20
Build a risk matrix based on data, impact and autonomy.
Classifying uses by risk focuses controls where impact is high. Risk rises with decision automation and its effect on people's rights.
RegisterTracking and documenting: building the evidence file
M21
Keep a compliance register and a verifiable audit trail.
Tracking tools, purposes, risk levels, training and incidents is the best defense in an investigation. Documented evidence demonstrates due diligence.
RegisterHigh-impact decisions: mandatory human oversight
M22
Guarantee explainability, fairness and the right to human review.
A high-impact decision is never fully delegated to AI. The person concerned is entitled to an explanation of the criteria and to human review.
RegisterThe regulatory framework: Law 25, AI Act, ISO 42001
M23
Place the three frameworks and their extraterritorial reach.
The framework combines the AI Act (AI literacy), ISO 42001 (governance) and Law 25 (transparency, PIA). A Québec company selling in the EU may be fully concerned.
RegisterReady to equip your team?
Enroll your teams in our AI-literacy and governance training paths, from the common core to function-specific use cases.
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