Emerging Roles in
AI Governance & Trusted AI

Validates AI/ML models for accuracy, bias, robustness, and regulatory compliance before and after deployment. Reviews model documentation, conducts independent challenge, and signs off on model risk.

Develops and operationalises AI governance frameworks, policies, and review processes. Acts as the bridge between technical AI teams and compliance/legal functions to ensure responsible deployment.

Senior role responsible for embedding ethical AI principles across the organisation. Chairs AI ethics review boards, defines red lines for model deployment, and reports to C-suite on AI risk posture.

Identifies, quantifies, and mitigates risks introduced by AI systems — covering model risk, data risk, operational risk, and third-party AI vendor risk. Works alongside Model Risk Management (MRM) teams.

Tests AI systems for reliability, safety, and trustworthiness using red-teaming, adversarial probing, and structured evaluation frameworks. Ensures outputs meet defined quality thresholds before production.

Monitors evolving AI regulation and translates legal requirements into operational controls. Ensures the organisation stays ahead of the EU AI Act, UK AI White Paper, NIST AI RMF, and sector-specific guidance.