Defining high-risk output criteria drawing on risk taxonomy.
Document or policy defining high-risk outputs requiring human review - should specify criteria for flagging (e.g. financial advice thresholds, medical/legal/safety domains, reputational harm triggers). Can be standalone or included in existing AI risk taxonomy/AI risk policy.
Implementing automated detection mechanisms for high-risk outputs. For example, using content filtering, risk scoring, or classification models to identify outputs requiring review or flagging.
Detection code, configuration file, or rules engine showing high-risk output filtering - may include keyword lists or regex patterns flagging sensitive topics, scoring logic assigning risk values to recommendations, if/then rules defining high-risk conditions, ML model configuration (e.g., classification thresholds in config.yaml), or API response showing confidence scores with risk thresholds.
Establishing human review workflows for flagged high-risk outputs. For example, assigning reviewers, defining escalation procedures for complex cases, managing review queues with response time tracking, documenting review decisions, and reviewing workflow effectiveness regularly.
Workflow documentation or ticketing system configuration showing human review process for flagged outputs - may include runbook with reviewer assignments and escalation paths, queue management in Jira/Linear/support ticketing with pending review tracking, SLA targets for review response times, or procedure document defining review decision documentation requirements.
Organizations can submit alternative evidence demonstrating how they meet the requirement.