Description

A focused course, tailored for you

The Head of AI Risk Oversight's Course on Building an AI Risk Operating Model When Regulatory Pressure Peaks

Turn fragmented AI risk data into a repeatable, audit-ready process that lets you protect the bank and advance your career.

Stop spending Friday evenings stitching AI risk evidence while regulator deadlines keep slipping.

$199 one-time

Tailored to your situation. Access within 24 hours. 30-day money-back.

Includes a hand-built implementation playbook delivered alongside course access, generated for your specific situation.

Why this course

Every month you receive ad-hoc requests from compliance, finance and the board for AI model risk evidence. Your team scrambles through notebooks, scattered Jupyter outputs and email threads, trying to stitch together a risk register that never matches the regulator’s checklist. The result is missed deadlines, endless rework, and a growing perception that AI risk is a reactive after-thought.

Your current tooling - separate notebooks, a shared drive, and occasional PowerPoint decks - cannot provide the traceability or governance the audit committee expects. When a new model is deployed, you lack a single source of truth for data lineage, impact scoring, and mitigation tracking, so senior leadership questions whether you can keep pace with emerging AI regulations. The stakes are a potential audit finding, delayed product launches, and a career risk for the oversight function.

What you walk away with

Produce a complete AI risk register that aligns with regulator expectations.
Run a quarterly AI risk review with ready-to-present evidence packs.
Apply a consistent impact scoring method to new model releases.
Automate data lineage capture for all AI models in production.
Communicate AI risk status to the board with a single dashboard.

The 12 modules

Module 1. Mapping AI Risk Governance

Define roles, responsibilities and decision gates for AI risk oversight.

Module 2. Building the AI Risk Register

Create a central register that captures model inventory, owners, and risk attributes.

Module 3. Data Lineage Capture

Implement automated tracing of data sources through model pipelines.

Module 4. Impact Scoring Framework

Apply a quantitative scoring system to evaluate model risk exposure.

Module 5. Evidence Collection Process

Standardize collection of test results, bias analyses, and compliance artefacts.

Module 6. Quarterly Review Cadence

Design a repeatable review meeting structure with pre-built decks.

Module 7. Dashboard Construction

Build a live dashboard that surfaces key risk metrics to leadership.

Module 8. Remediation Tracking

Set up a RACI-driven plan for addressing identified risk gaps.

Module 9. Regulatory Alignment Checklist

Map your artefacts to the regulator’s evidence requirements.

Module 10. Communication Playbook

Craft concise board and audit briefings that tell a clear risk story.

Module 11. Tool Integration Blueprint

Connect notebooks, version control and risk registers into a single workflow.

Module 12. Continuous Improvement Loop

Embed feedback loops to evolve the AI risk operating model over time.

How this addresses your situation

Specific modules that map to what you said you are dealing with.

Module 2 covers Building the AI Risk Register , exactly the fragmented spreadsheet you rely on when the audit team asks for a consolidated model inventory.

Module 5 covers Evidence Collection Process , precisely the manual pull of test logs you perform each time a new model is deployed.

Module 7 covers Dashboard Construction , the live view you need when the board asks for a single risk snapshot during quarterly reviews.

What you get with this course

A populated AI risk register template with 30 pre-filled model entries.
A data lineage capture checklist.
An impact scoring matrix with weighting guidance.
A quarterly review deck skeleton.
A live dashboard wireframe with sample visualizations.
A remediation RACI table.
A regulatory evidence mapping sheet.
A board briefing guide.
A tool-integration blueprint checklist.
A continuous improvement log template.

What you will have in hand by Day 1, Week 1, Month 1

Day 1: tailored playbook in hand, AI risk register template pre-populated for your environment, data lineage checklist ready.

Week 1: first version of the quarterly review deck live and shared with the risk council.

Month 1: live dashboard feeding from the register, quarterly review cadence established and evidence pack ready for the next audit.

Before and after

Before

You are managing dozens of AI model risk files scattered across shared drives, email threads and notebook exports. Evidence lives in multiple locations, causing version conflicts and audit gaps. When the regulator asks for a single source of truth, you spend days reconciling data, and the risk team loses credibility.

After

After the course you maintain a single AI risk register, a live dashboard, and a ready-to-share evidence pack that updates automatically. Quarterly reviews run on a defined cadence, and leadership receives a concise risk snapshot that demonstrates control and forward-looking mitigation.

What happens if you do not address this

If you ignore this gap, Q3 audit will arrive without a clean evidence pack and the audit committee will demand an emergency remediation plan. Your credibility with the board will erode, and upcoming AI product launches may be delayed pending risk clearance.

Who it is for

You are the senior leader responsible for AI model risk across the bank, juggling board reporting, regulator inquiries, and cross-functional risk workshops. Your day is split between tactical fires - pulling logs, answering audit tickets - and strategic planning, but you lack a unified operating rhythm and concrete artefacts to demonstrate control.

Who this is NOT for. This is not for someone who needs a basic introduction to AI risk concepts rather than an operating method.

How it arrives

Within 24 hours of purchase your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it. The playbook is hand-built around your specific situation, not LLM-generated boilerplate.

Time investment. 6 hours of focused work spread over a week, saving an estimated 40-60 hours of internal scaffolding work.

Why $199 is the right number

A half-day consultant would charge $2-5K for the same scope, a generic compliance certification runs $800-2K, and building the operating model yourself can consume 60+ hours. At $199 you get a complete, ready-to-use toolkit and a custom playbook that accelerates delivery by months.

FAQ

Do I need deep technical AI expertise to take this course?

No, the course focuses on governance mechanics and provides templates you can apply regardless of your technical background.

Will the materials work with the tools my team already uses?

All artefacts are format-agnostic and can be imported into your existing notebooks, version-control system or reporting platform.

How much time do I need each week to complete the program?

Plan for 6 hours of focused work spread over a week to apply the modules to your environment.

Is there support if I get stuck on a specific module?

You have access to a community forum and a weekly live Q&A where you can ask implementation questions.

30-day money-back guarantee. If after a week of working through the materials this is not what you needed, reply to the receipt email and a full refund is processed. No questions, no forms.

Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.