AI-RRS study plan
AI-RRS · Give each week a clear focus.
8 weeks · About 7 hours per week
AI and Algorithm Risk Fundamentals
Define core AI/ML concepts (training, inference, model types, data pipelines) in your own words
Distinguish AI risk from traditional software risk by listing five differences
Map common failure modes (bias, drift, overfitting, adversarial input) to their root causes
Classify algorithmic risk categories and give one example of each
Your checkpoint
A one-page glossary of 20 AI/algorithm risk terms with a worked example for each
AI and Algorithm Risk Fundamentals & AI Risk Identification and Assessment
Explain how model lifecycle stages create distinct risk exposures
Practice applying a risk-identification technique (e.g., structured brainstorming or checklist) to a described AI system
Draft a risk register entry with likelihood, impact, and rationale
Compare inherent versus residual risk for an AI use case
Your checkpoint
A 10-row risk register for a hypothetical AI system with scored likelihood and impact
AI Risk Governance and Frameworks
Summarize the structure of a major AI risk governance framework
Identify the roles and responsibilities typical of an AI governance structure
Draft policy language covering model approval and accountability
Trace how a framework principle translates into an operational control
Your checkpoint
A governance charter outline with roles, decision rights, and escalation paths
AI Risk Governance and Frameworks & AI Risk Identification and Assessment
Build a risk assessment workflow from intake to sign-off
Select assessment criteria appropriate to model risk tier
Document model documentation and validation expectations
Identify gaps between your draft workflow and a recognized framework
Your checkpoint
A complete AI risk assessment template with scoring rubric and sign-off fields
AI Risk Mitigation and Controls
Match mitigation strategies to specific risk categories
Design pre-deployment controls (testing, validation, review gates)
Design in-production controls (monitoring thresholds, human oversight, rollback)
Evaluate control effectiveness and document residual risk
Your checkpoint
A control matrix mapping at least 12 risks to preventive, detective, and corrective controls
AI Risk Mitigation and Controls & AI Risk Monitoring, Reporting, and Regulatory Compliance
Define monitoring metrics and alert thresholds for a deployed model
Draft a model performance and drift reporting cadence
Map regulatory and reporting obligations to internal controls
Write an incident response procedure for an AI failure
Your checkpoint
A monitoring and incident response playbook with metrics, thresholds, and escalation triggers
AI Risk Monitoring, Reporting, and Regulatory Compliance
Compare reporting expectations across relevant regulatory regimes
Assemble evidence artifacts needed for an audit or examination
Draft a board-level AI risk summary
Identify documentation gaps in your prior deliverables
Your checkpoint
An audit evidence checklist plus a one-page executive risk report
AI and Algorithm Risk Fundamentals & AI Risk Governance and Frameworks & AI Risk Identification and Assessment & AI Risk Mitigation and Controls & AI Risk Monitoring, Reporting, and Regulatory Compliance
Integrate all deliverables into a single end-to-end AI risk program narrative
Complete timed practice questions across all five domains
Review and remediate your weakest domain
Finalize a personal reference sheet for exam day
Your checkpoint
A consolidated program document plus a scored practice exam with a remediation log
Milestones
Week 2: Can explain AI risk fundamentals and produce a scored risk register without notes
Week 4: Governance and assessment materials are complete enough to walk a colleague through end to end
Week 6: Control matrix and monitoring playbook cover all five domains with no orphan risks
Week 8: Practice exam score at or above your target threshold with remediation log closed out