AI Risk, Audit & Assurance for Internal Auditors
Audit algorithms, data and controls with defensible rigour.
The executive challenge
The problem this program solves
Your competitors have industrialised their ai risk capability into pricing, service and operations while your organisation still debates the pilot. Every quarter of hesitation now compounds. The talent you hired leaves, the data you hold decays, and the gap between your ai risk ambition and your capability widens every quarter. This programme replaces tool curiosity with an investment logic: where ai risk creates value, what it should automate, what must remain human — and how to prove the return.
Context
Why this matters now
Capability
What you will be able to do
- Evaluate where ai risk creates economic value across the organisation — and where it reliably does not.
- Govern ai risk models and data with controls that satisfy both boards and regulators.
- Price the build, buy and partner options for ai risk capability with real economics.
- Distinguish ai risk automation opportunities from decisions where human judgement remains strategically important.
The IPAT Course Standard
The five-day journey
Each day is a deliberate progression in managerial capability — from strategic context, to analytical frameworks, to applied practice, to executive judgement under pressure, to a capstone that produces something your organisation keeps.
IPAT Signature Case™
The AI Risk Model Nobody Can Explain
A a regional telecommunications group deployed a ai risk model into daily operations. It performs — until a customer dispute, a journalist and the regulator arrive in the same week, and no one can reconstruct why the model decided what it decided.
Your decision: Pause the model pending an explainability rebuild, defend it as deployed, or restrict it to advisory use — each response signals something different to the regulator, the market and your own staff.
IPAT Data Lab™
Three functions at a regional telecommunications group report three different truths about the same ai risk question.
Dataset: Synthetic multi-source ai risk dataset — finance, operations and customer tables that disagree.
Your task: Reconcile the sources, build the single defensible view, and decide which dashboard the executive committee keeps.
A reconciled ai risk decision view and the data architecture behind it.
Data → Analysis → Decision, not Data → Code → Certificate
Day 5 · The Capstone
What will you change when you return to work?
Build a twelve-month analytical and AI roadmap for your organisation: the value case, the data foundations, the governance, and the first 90 days.
Methodology
Frameworks you will master
- • IPAT Data-to-Decision Framework™
- • IPAT Executive Capability Model™
- • Value-versus-feasibility portfolio analysis
- • CRISP-DM analytical lifecycle
- • Model risk management principles (SR 11-7 adapted)
Cohorts
Upcoming cohorts
New dates are being scheduled
Register with your preferred start date and our admissions team will match you to the next available cohort — or contact us to arrange an in-house delivery for your organisation.
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