This lesson establishes the decision framework for Risk Treatment, Human Oversight, Testing, Documentation, and Third-Party Controls within Lead AI Risk Manager. The activity uses a regulated organization deploying predictive models and generative AI for customer decisions and internal productivity and contributes to the wider objective to lead AI risk governance, assessment, treatment, monitoring, and communication across the AI lifecycle.
The instructor traces a case involving a regulated organization deploying predictive models and generative AI for customer decisions and internal productivity. The demonstration links decisions to objective evidence, records the result in AI treatment and validation plan, and tests the condition where vendor model changes are deployed without validation evidence.
Complete a controlled exercise covering Risk Treatment, Human Oversight, Testing, Documentation, and Third-Party Controls. Confirm scope and responsibilities, examine the stated exception, reach a supported conclusion, and preserve the outcome in AI treatment and validation plan.
Start where expected practice and observed evidence first diverge. Separate a missing record from an ineffective process, distinguish unclear ownership from a control failure, and treat “vendor model changes are deployed without validation evidence” as a hypothesis until tested against the applicable criteria and intended outcome.
Learners can apply Risk Treatment, Human Oversight, Testing, Documentation, and Third-Party Controls, explain the reasoning used, and defend the result through AI treatment and validation plan.