AI-RRS study guide
AI-RRS · Work through your exam, one topic at a time.
The AI-RRS (AI and Algorithm Risk Certification) is an independent credential for professionals who assess, govern, and control risks arising from artificial intelligence and algorithmic systems. It suits risk managers, compliance officers, model validators, auditors, and technology leaders who need a structured, domain-neutral vocabulary for AI risk. The AI-RRS study guide has 5 sections and about 1,476 words.
01AI and Algorithm Risk Fundamentals
AI and algorithm risk is the potential for an AI system to cause harm, loss, or regulatory breach through its behavior, outputs, or the way it is built and operated. Candidates must distinguish model risk (error, bias, overfitting, drift), data risk (quality, provenance, representativeness, privacy), operational risk (availability, latency, integration failure), security risk (adversarial inputs, data poisoning, model theft), and third-party risk (vendor, foundation-model, and API dependencies).
307 words, about 2 min02AI Risk Governance and Frameworks
Governance is the system of accountability, policy, and oversight that makes AI risk management repeatable. Candidates should know the three-lines-of-defense model: the first line owns and operates AI, the second provides independent risk and compliance oversight, and the third (internal audit) provides independent assurance.
317 words, about 2 min03AI Risk Identification and Assessment
Risk identification is the disciplined discovery of what could go wrong; assessment is estimating likelihood and impact and prioritizing. Candidates must know risk taxonomies (e.g., harm to individuals, harm to the organization, societal harm), use-case tiering based on autonomy, data sensitivity, population affected, and reversibility of harm.
294 words, about 1 min04AI Risk Mitigation and Controls
Controls reduce risk to within appetite. Candidates must distinguish preventive , detective , and corrective controls, and automated versus manual controls.
284 words, about 1 min05AI Risk Monitoring, Reporting, and Regulatory Compliance
Monitoring detects drift, degradation, and emerging risk after deployment. Candidates must know data drift , concept drift , model performance decay , output distribution shifts , and behavioral anomalies .
274 words, about 1 min