Financial institutions already run mature model-risk-management programs, but those programs were built for statistical models — not generative or agentic AI that behaves non-deterministically and touches customer-facing decisions. Extending existing governance rather than building from scratch is usually the fastest, most defensible path.
Typical investment: CAD 90,000+ initial engagement, CAD 25,000-40,000+/month ongoing
Every sector treats AI governance differently — what counts as a real risk, who signs off on it, and which frameworks actually get referenced in a Board conversation. The breakdown below isn't generic: it reflects how AI governance plays out specifically for organizations in Financial Services, from the challenges that show up first to the people who typically need to be in the room.
Information security management system requirements and Annex A controls.
Artificial intelligence management systems — organizational accountability, AI policies, lifecycle governance, risk management and continuous improvement.
The Govern, Map, Measure and Manage approach for identifying and controlling risks arising from AI systems.
Assurance expectations and control environments, particularly SOC 2 Trust Services Criteria around security, availability, processing integrity, confidentiality and privacy.
The stakeholders typically at the table for an engagement in this sector.