Built for institutions that need AI governance grounded in supervisory expectations, not generic best practice.
Governance built for regulated environments
Four practice areas, one discipline: making AI adoption defensible.
AI & GenAI Governance
Frameworks, policies, and controls built to withstand regulatory and internal audit scrutiny.
Model Risk Management
Validation, monitoring, and inventory practices spanning traditional and generative models alike.
Advisory & Implementation
Hands-on consulting for institutions standing up or maturing their AI governance function.
A rail, not a roadblock — without loosening the rigor
Rigor, traceability and auditability stay non-negotiable. You keep them and still move fast by building governance into the process rather than bolting it on at the end: the routine path is decided in advance, and only the hard cases need a conversation.
Self-serves
Teams register a use case and get an immediate, rules-based answer on what's required of them.
Embedded controls
What "good" looks like is defined up front and owned by independent risk — not renegotiated case by case.
The business ships on self-serve — fully logged, traceable, auditable. Governed by pre-approved rules, not ad-hoc review.
Real validation and challenge, applied where it matters — so scarce expert capacity is spent on the decisions that actually warrant it.
Ownership is explicit, the path is deterministic, and every route — routine or exception — leaves a full audit trail.
Governance stops being a review you wait for and becomes a rail you run on.
Find your level — then invest in the next one
Most institutions already have the raw materials — data, platforms, models, a starter framework. Readiness is turning those into an operating capability.
Ad hoc
Where you areAI is already in production, but governed one case at a time. Every decision starts from scratch.
A single, honest view of what is actually running — and a consistent way to size oversight to risk.
Defined
Where you areA framework exists on paper. It is applied by hand, unevenly, and ownership is fuzzy.
Turning the framework into something repeatable, with ownership that is clear rather than assumed.
Operationalized
Where you areOversight is determined consistently rather than argued. Routine work stops needing bespoke review.
Reuse — so evidence produced once is not rebuilt every time — and a self-serve path for the routine cases.
Monitored
Where you areControls hold after launch, not just at approval. Problems surface early rather than at the next audit.
Real visibility into how systems behave in production, and a clear route when they drift.
Embedded
Where you areGovernance runs as infrastructure. The business moves quickly inside guardrails, and the whole portfolio is visible.
Oversight at portfolio level — and the culture that treats governance as an accelerant, not a gate.
You don't skip levels — each builds the evidence and the muscle the next one assumes.
Practitioner-tested, not theoretical.
AI Risk Consulting was founded by a model risk executive with nearly two decades at tier-1 financial institutions — including building enterprise AI/GenAI governance programs from the ground up. That means every framework we bring has already been stress-tested against real regulatory review, not just written for one.
- Frameworks built from inside a regulated institution, not consulting theory
- Direct, senior-level engagement — no junior staff, no bench
- Grounded in current supervisory expectations for AI and model risk
"The institutions that get this right don't govern AI to slow it down. They govern it so it can move — because a model nobody can defend never leaves the pilot."
Ready to talk through your AI governance gaps?
A short conversation is usually enough to tell you where to start.