AI risk management is the structured way to identify, assess, treat, and monitor AI risks—such as bias, security threats, transparency gaps, and compliance exposure—through governance, controls, and evidence.
In practice, AI risk management turns 'responsible AI' into repeatable decisions and measurable controls. It starts by defining the organizational context: what the AI system does, who is impacted, what data is used, and what obligations apply.
Risk identification then focuses on AI-specific categories such as bias and fairness, model security vulnerabilities, transparency and explainability limits, privacy issues, and regulatory compliance. Teams analyze likelihood and impact, then prioritize risks and define treatment plans.
Mitigation can include technical controls (testing, monitoring, guardrails), procedural controls (review gates, documentation, change management), and incident response measures. Ongoing monitoring and reporting ensure risks are tracked over time as models drift, data changes, and regulations evolve.
Most failures come from weak lifecycle controls: no ownership, no monitoring, and no documentation for changes. Strong AI risk programs treat model updates like production releases.
“AI risk management makes AI governance operational.”
Expert Trainer
Expert Trainer
They reduce failures from bias, privacy breaches, security issues, and non-compliance, and they help ensure AI stays aligned with business objectives over time.
The exam is domain-based, covering AI risk concepts and regulations, governance, identification and analysis, evaluation/treatment/monitoring, and organizational learning and performance improvement.
Day 1 covers AI risk fundamentals; Day 2 covers context, governance, and risk identification; Day 3 covers analysis, evaluation, and treatment; Day 4 covers monitoring, reporting, awareness, and continual improvement.
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