Independent evidence for accountable AI

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Professional course

AI Risk Officer

Professional training for designing, operating, and reporting an enterprise AI risk program across portfolios, suppliers, lifecycle decisions, and incidents.

Curriculum

From principle to operating evidence

Lessons use concise analysis and realistic cases. Enrolled learners can open the full material and record completion.

Module 1

AI risk program design

Translate enterprise objectives and risk appetite into accountable AI decisions.

  1. Mandate, authority, and lines of accountability · 35 minutes · Article
  2. Risk appetite and taxonomy · 35 minutes · Article
  3. Case study: the board wants one number · 35 minutes · Case study

Module 2

Assessment and treatment

Run consistent assessments that connect evidence to proportionate risk treatment.

  1. Assessment workflow and challenge · 40 minutes · Article
  2. Control library and residual risk · 40 minutes · Article
  3. Case study: the enterprise assistant · 40 minutes · Case study

Module 3

Third-party and operational risk

Control dependencies that persist beyond contracting and initial validation.

  1. Supplier and value-chain risk · 40 minutes · Article
  2. Monitoring and key risk indicators · 40 minutes · Article
  3. Case study: the silent model update · 35 minutes · Case study

Module 4

Incidents, reporting, and improvement

Prepare for adverse events and turn portfolio evidence into leadership action.

  1. AI incident command and escalation · 40 minutes · Article
  2. Executive reporting and assurance · 40 minutes · Article
  3. Case study: the exception backlog · 35 minutes · Case study

Learning outcomes

What you will be able to do

  • Establish AI risk appetite, governance forums, accountable roles, and defensible escalation paths.
  • Design an inventory, tiering method, assessment workflow, and control library connected to business decisions.
  • Evaluate third-party, data, model, security, human, and operational risk across the system lifecycle.
  • Build key risk indicators, incident readiness, exception management, and executive reporting.
  • Lead proportionate risk treatment and challenge residual-risk decisions with evidence.