AI risk, made operational.
Model risk management, ethics review boards, third-line assurance and incident response for deployed AI systems.
An AI model is a live control environment.
Deployed AI is not a static deliverable - its behaviour evolves with inputs, usage and underlying data. Our AI risk practice treats deployed models as live controls: monitored, challenged, updated and, where necessary, decommissioned on a formal schedule.
Before work begins, we clarify the operating context, governance expectations, and commercial pressures behind the brief. That gives the engagement a clear purpose before technical analysis starts.
The result is a more complete advisory view: what matters now, where risk may surface next, and how recommendations can be implemented without creating unnecessary hand-offs or ambiguity.
Scope
Clarify the decision, deadline, stakeholders, and evidence standard before work begins.
Delivery
Combine partner judgement, technical review, and practical implementation planning in one workstream.
Follow-through
Convert findings into owners, actions, and next steps that leadership can track after the session.
Inventory & tiering
Every model is inventoried, tiered by risk and assigned an accountable executive.
Pre-deployment review
Review of data lineage, performance, fairness and documentation before go-live.
In-life monitoring
Drift detection, fairness metrics, and performance degradation monitoring.
Incident response
Incident playbooks covering performance failure, adversarial attack and regulatory escalation.
Third-line assurance
Independent model assurance with board-level reporting.
An AI governance framework is only as good as the first incident it handles.
Deployed AI without a risk framework?
A 30-day model-risk uplift produces an inventory, a tiering, and a credible monitoring plan for every deployed model.