AI Risk Framework

Methodology

AI risk taxonomy (model, data, bias, security, operational, reputational, regulatory) with assessment, controls and monitoring.

Components

AI risk taxonomy; Model risk assessment; Bias & fairness control; Security & robustness; Operational controls; Monitoring & escalation.

Governance

Model owners assess; second-line AI risk oversees; AI governance committee and board receive reporting. L1 Initial L2 Developing L3 Defined L4 Managed L5 Optimised Ad hoc Basic, siloed Standardised & Quantified & integrated Predictive & embedded governed

Maturity levels

Implementation roadmap

Diagnose (assess maturity) → Design (tailor framework & governance) → Build (policies, standards, controls, pipelines) → Embed (training, culture, adoption) → Assure (test, benchmark, re-score).

Deliverables

Framework document, governance & RACI, policy/standard templates, maturity score & roadmap, board reporting pack.

Advisory opportunities

AI risk review; Model risk programme; Bias & robustness testing.