AI Risk Management and Governance Implementation

A tiered, risk-based approach that uses the EU AI Act as the ceiling for high-risk systems while applying proportionate controls (ISO/IEC 42001) for lower-risk systems optimally balances stringent compliance with operational feasibility. Country-specific addendums address local variations without duplicating the entire framework. This approach aligns with international best practices for managing AI risk across multiple jurisdictions while avoiding over-engineering low-risk systems or under-protecting high-risk ones.

These metrics directly measure control effectiveness at preventing or detecting harm in practice. Control override rates reveal when humans bypass safeguards (indicating either control design flaws or emerging risks). Time-to-detect failures shows monitoring responsiveness. Risk severity distribution when controls activate demonstrates whether controls catch the right cases. False positive/negative rates of controls themselves reveal whether safeguards are calibrated correctly—together these create actionable feedback for improving the control environment rather than just documenting its existence.

This is risk reduction (mitigation) because face data IS temporarily captured by the camera sensor and processed by the blurring algorithm before being discarded—the risk exists for milliseconds. The control significantly reduces the risk by preventing storage/transmission of identifiable data, but doesn't avoid the risk entirely since capture occurs. True avoidance would mean faces are never captured at all (e.g., camera angle/placement preventing face capture). Understanding this distinction is critical for accurate risk classification and selecting appropriate additional controls if needed.

Significant distributional shift in the population the model acts upon (demographic changes from geographic expansion) directly impacts model performance and fairness, potentially causing the model to perform differently on new populations than it did on training data. This is a material change to the risk profile that can introduce bias and fairness issues even without model changes. Impact assessments should be triggered by changes to input data distributions, use contexts, or affected populations—not just model updates. This factor creates the strongest case for more frequent reassessment because the operating context has fundamentally changed.

Mandatory business owner attestation creates accountability at the decision-maker level and forces regular, documented consideration of whether each model should continue operating. This human governance control catches cases that automated monitoring might miss: changing clinical standards, new treatment options, regulatory updates, strategic shifts, or subtle degradation in decision quality that doesn't trigger metric thresholds. The requirement for documented justification creates an audit trail and forces active decision-making rather than passive continuation—strongest defense against organizational inertia keeping obsolete models running.

This two-pronged criterion correctly captures both technical incidents (performance outside specifications indicates system malfunction/degradation) AND harm-based incidents (actual harm can occur even when system performs 'as designed' if the design was inadequate or context changed). The 'OR' structure is critical—either condition triggers response. This prevents both false negatives (harm occurring within spec) and false positives (expected limitations documented during validation). It aligns with risk-based governance principles where incidents are defined by departures from expected behavior OR realization of harm, regardless of technical conformance.