Establish clear ownership, lifecycle controls and oversight so AI models remain trusted, accountable and fit for purpose.
Book ConsultationModel governance establishes the ownership, controls and lifecycle processes needed to manage AI models responsibly. It provides a structured approach for governing how models are designed, approved, deployed, monitored, changed and retired.
Effective model governance goes beyond technical performance. It defines who is accountable for each model, how decisions are documented, when validation is required, how changes are approved and how ongoing performance, drift, limitations and exceptions are monitored over time.
INTAI helps organisations design practical model governance arrangements that connect model ownership, validation evidence, monitoring, change control and reporting. The result is clearer accountability, stronger lifecycle oversight and better evidence for risk, compliance and assurance activity.
Identify the models in scope, their business purpose, owners, users, decision impact and governance responsibilities.
Define controls for model development, approval, validation, deployment, monitoring, change, retirement and evidence retention.
Establish what evidence is required before deployment, including validation results, limitations, assumptions and approval decisions.
Track model performance, drift, exceptions, changes in use, data quality issues and any deterioration against agreed thresholds.
Control updates, version changes, revalidation, escalation and retirement so models remain governed throughout their operational life.
Practical model governance outputs that define ownership, lifecycle controls, validation evidence, monitoring expectations and change management.
Take the AI Governance Assessment™ to evaluate how effectively your organisation manages AI ownership, accountability, risk, controls, lifecycle oversight and assurance, and receive practical recommendations and a personalised 90-day roadmap.
Take Assessment Book ConsultationModel governance defines how AI models are owned, approved, controlled, monitored, changed and retired throughout their lifecycle. It creates clear accountability and helps ensure models remain appropriate for their intended use.
Governance should be proportionate to risk and cover AI models that influence important business processes, decisions, customer outcomes or regulated activity. This can include internally developed models, adapted foundation models and third-party AI services.
Models should have clearly defined business accountability alongside appropriate technical ownership. Responsibilities for approval, operation, monitoring, change and retirement should be documented rather than assumed.
Model validation assesses whether a model performs appropriately for its intended purpose. Model governance is broader and covers the structures and controls surrounding the model, including ownership, approvals, documentation, monitoring, change management and retirement.
Reviews should be triggered by defined events such as significant retraining, material data changes, changes in intended use, major configuration changes, deployment into new processes or evidence that performance or risk has changed materially.
Using a third-party AI service does not remove organisational accountability. Governance should include proportionate supplier due diligence, clear contractual responsibilities, change notifications, performance oversight and ongoing review of the risks created by its use.
Documentation may include inventory records, ownership, intended use, risk classification, approval history, validation and testing evidence, version history, performance reviews, material changes and retirement decisions. The level of documentation should reflect the model's risk and impact.
Retirement should be considered when a model is replaced, no longer serves its intended purpose, cannot be maintained effectively, consistently falls outside agreed performance thresholds or creates risks that can no longer be managed appropriately.
Yes. Effective model governance supports accountability, traceability, documentation, monitoring and lifecycle oversight. These provide important foundations for regulatory readiness, internal assurance and independent review, although governance alone does not guarantee compliance.
Start with the AI Model Risk & Assurance Assessment™ to identify gaps across model risk, ownership, validation, lifecycle controls and assurance, or book a consultation to discuss your model governance requirements in more detail.
Strengthen ownership, approval, validation, change control and ongoing oversight so your AI models remain governed from initial development through to retirement.
Continue strengthening AI assurance with related services covering model governance, explainability, bias and fairness, audit readiness and risk assessment.
