Regulated AI Governance and Assurance Enablement
Anonymised sector pattern. Client identity is not disclosed. Outcome language describes qualitative operating improvements only — no invented metrics.
Challenge
AI and generative use cases were emerging across functions faster than the institution could inventory, classify and oversee them. Boards and control teams needed a practical governance operating model, not only policy language.
Context
A regulated financial environment required a shared system for use-case intake, risk classification, approvals and lifecycle monitoring — spanning traditional analytics and newer generative applications.
Agrayian approach
We established an AI governance operating model with clear risk tiers, approval pathways and evidence expectations. The AI Governance Command Centre became the inventory and oversight surface, aligned to CoE intake and assurance reporting needs.
Solution architecture
A governance platform tracked proposed and in-production AI systems, risk classifications, approval status and monitoring ownership. Dashboards supported control and leadership reporting. Integration points linked intake from business functions to CoE and risk review workflows.
Governance approach
High-impact and customer-facing systems required elevated approval and monitoring. Roles were separated across proposers, builders, control owners and approvers. Lifecycle status and exceptions were retained as evidence for internal oversight discussions.
Outcome category — Responsible AI Oversight
- • A single inventory view of AI use cases across business functions.
- • Clearer risk-tiered approval and monitoring expectations.
- • Improved readiness for board and control conversations on AI oversight.
- • A durable foundation for scaling AI under institutional accountability.