Lead AI Risk Manager study plan

Map the full AI risk management domain into a personal outline covering governance, risk identification, assessment, treatment, monitoring, and incident response, and label each area with your current confidence level

Define the core vocabulary of AI risk (inherent versus residual risk, risk appetite and tolerance, model risk, third-party risk, data risk) and write one-sentence definitions you can defend without notes

Draft a one-page risk taxonomy for an AI system you know well, grouping risks by harm type (safety, fairness, privacy, security, transparency, accountability) and citing where each would surface in a lifecycle

Identify the primary standards and regulatory frameworks that shape AI risk practice and note for each what it actually requires versus what it merely recommends

Build a repeatable risk identification method: enumerate sources of AI risk across data, model, deployment context, users, and vendors, and explain when each source dominates

Practice turning vague concerns into specific risk statements with cause, event, and consequence, producing at least fifteen examples

Compare qualitative, semi-quantitative, and quantitative risk assessment approaches and justify which fits low, medium, and high-stakes AI use cases

Explain how risk appetite statements translate into concrete thresholds and escalation triggers for AI deployments

Design a tiered AI risk classification scheme that routes low, medium, and high-risk systems to proportionate review depth

Specify the evidence an AI risk assessment must produce (documentation, testing results, evaluation reports) and how to judge whether that evidence is sufficient

Outline treatment options in order of preference (avoid, mitigate, transfer, accept) and construct mitigation plans for three distinct AI risk scenarios

Define residual risk acceptance criteria and describe who may accept which level of residual risk and why

Assemble a governance structure for AI risk covering board oversight, an accountable executive, a cross-functional review body, and first/second/third-line responsibilities

Draft the terms of reference for an AI risk review committee, including membership, quorum, decision rights, and conflict-of-interest handling

Map the policy hierarchy needed for AI risk (policy, standards, procedures, guidelines) and write the outline of a top-level AI risk policy

Describe how to embed AI risk requirements into existing enterprise risk, procurement, and change-management processes rather than running them in parallel

Design monitoring and assurance for deployed AI: define leading and lagging indicators, drift and performance thresholds, and review cadence by risk tier

Specify an AI incident response process including detection, triage, containment, notification obligations, root-cause analysis, and lessons learned

Plan how to evaluate third-party and foundation-model dependencies, including due diligence questions, contractual risk allocation, and ongoing vendor monitoring

Explain model documentation and lineage requirements and how they support auditability and regulatory examination

Analyze how emerging AI regulation and voluntary standards allocate obligations across providers, deployers, and users, and translate those obligations into internal controls

Evaluate the assurance options available for AI (internal audit, independent review, third-party certification, red teaming) and select the right mix for different risk tiers

Build a stakeholder communication approach for AI risk, including how to brief executives, regulators, customers, and affected individuals on the same risk in appropriate depth

Identify failure modes of AI risk programs (paper compliance, unowned risks, stale assessments) and design safeguards against each

Run a full end-to-end AI risk assessment on a realistic scenario under time pressure and produce a defensible recommendation

Diagnose and correct weak risk statements, unsupported ratings, and missing treatment owners in sample artifacts

Rehearse defending risk decisions to skeptical executives and auditors, including how to handle disagreement about residual risk acceptance

Close remaining knowledge gaps identified in week one by revisiting the lowest-confidence areas with targeted study

Synthesize the full domain into a one-page mental model you can reproduce from memory, linking governance, identification, assessment, treatment, monitoring, and response

Complete at least two full-length practice sets under exam conditions and analyze every error by root cause rather than by topic label

Drill the highest-frequency weak points until you can explain each in plain language without notes

Finalize an exam-day strategy covering question triage, time allocation, and how to handle scenario questions with ambiguous best answers