AI risk management for Mauritian organisations must be practical. The goal is not to frighten teams away from AI. The goal is to make adoption safer, clearer, and more accountable.
Risk appears when AI tools touch data, decisions, customers, employees, or public communication. That means even simple generative AI use deserves basic controls.
The Four Main AI Risks
The four practical risks are:
- Privacy risk when sensitive data is entered into tools
- Accuracy risk when outputs are accepted without review
- Bias risk when systems affect people unfairly
- Vendor risk when the business depends on opaque external platforms
These risks can be managed, but they must be named.
How to Control Risk
Controls should match risk level. A low-risk marketing draft does not need the same process as a tool supporting HR screening or financial recommendations.
Good controls include approved tool lists, data rules, human review, use-case registers, vendor checks, and escalation paths.
For governance structure, read AI Governance Consultant Mauritius.
Who Owns Accountability
AI cannot be accountable. People are accountable.
Every AI use case should have a named owner who monitors performance, risk, and business value. If a tool affects customers, employees, or financial decisions, ownership must be explicit.
Risk Checklist
- Classify the use case by risk
- Identify sensitive data
- Check vendor terms
- Require human review
- Assign a named owner
- Track errors and incidents
- Review outputs for bias
- Reassess the tool regularly
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