Description

A focused course, tailored for you

AI Governance for Global Policy Leads

Build the multi-jurisdiction governance architecture that keeps a large AI portfolio audit-ready across regulators who do not agree with each other.

You have a high-risk classification decision sitting in your inbox from the product team. The EU AI Act says one thing. The US NIST AI RMF says another. Brazil's emerging AI bill reads differently again. Your job is not to pick one answer. Your job is to design a governance layer that satisfies all of them simultaneously, on a timeline the product team can live with.

$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

At the Global AI Policy and Governance lead level, the friction is rarely a single regulation you do not understand. It is the architecture problem: how do you build a policy position that is rigorous enough for Brussels, defensible enough for Washington, and portable enough to be restated for Singapore, India, and Brazil without triggering a new internal review each time? Most governance frameworks in circulation were designed for a single regulator. The multi-jurisdiction version requires a different architecture from the foundation up.

What you walk away with

Map prohibited-use and high-risk classification requirements across the EU AI Act, US NIST AI RMF, Singapore Model AI Governance Framework, Brazil AI bill, and India DPDP so that a single product decision produces four consistent outputs.
Design an internal AI governance documentation layer that travels across jurisdictions without restatement, so that a policy position drafted for Brussels can be adapted for Washington and Singapore in hours rather than weeks.
Build an evidence pack architecture that satisfies auditors reading different standards simultaneously, with artefact mapping that shows which document answers which regulator question.
Establish a high-risk classification logic for a large AI portfolio that is defensible to regulators who use different definitions of high-risk and who do not coordinate with each other.
Create a governance communication layer for product teams that converts regulatory constraint into actionable product guidance without requiring legal re-engagement at every decision point.
Produce a cross-jurisdiction policy position template that product teams can use as a working document and that legal teams can file with any of the covered regulators with minimal adaptation.

The 12 modules

Module 1. The Multi-Jurisdiction Architecture Problem

Why single-regulator governance frameworks fail at scale and what a portable governance system requires. This module maps the conflict points between the EU AI Act, US NIST AI RMF, Singapore MGAF, Brazil AI bill, and India DPDP: where their high-risk definitions diverge, where audit evidence requirements clash, and where a position designed for one regulator will actively undermine your standing with another. Output: a conflict map used throughout the rest of the course.

Module 2. Prohibited Use Mapping Across Five Jurisdictions

A working methodology for mapping prohibited-use determinations across the EU AI Act, US executive framework, Singapore MGAF, Brazil AI bill, and India DPDP against a live AI product portfolio. Covers the classification logic that documents a product's use-case scope in a way that satisfies each regulator's prohibited-use test without creating a contradiction in the record. Worked example: a large-scale content recommendation system with advertising targeting functionality mapped across all five frameworks.

Module 3. High-Risk Classification Logic for a Large Portfolio

The EU AI Act Annex III categories are specific. The US NIST AI RMF tiers are contextual. Brazil's framework is sector-anchored. This module builds a classification logic producing a consistent internal determination while accommodating each regulator's preferred framing. Output: a classification decision memo template your product team uses as a live working document and your legal team files with any covered regulator without triggering a new classification review.

Module 4. Evidence Pack Architecture for Multi-Standard Auditors

When the EU DPA, the FTC, and Singapore's PDPC each request evidence of AI governance practices, they ask different questions using similar language. This module builds an evidence pack that maps each artefact to each regulator's preferred evidence category, producing four audit-ready packages from a single governance documentation set. Covers the specific artefact types each regulator has historically requested and the gaps that create re-engagement cycles.

Module 5. The Policy Position Document That Travels

Most policy positions are drafted for a specific regulatory audience and cannot be adapted without a new drafting cycle. This module covers the structural design of a jurisdiction-portable policy position: how to separate core governance logic from jurisdiction-specific framing so a position drafted for the EU AI Act consultation can be adapted for the US NIST AI RMF self-assessment and the Singapore MGAF self-certification with a parameter change rather than a rewrite. Template included.

Module 6. Internal Governance Translation for Product Teams

The most common governance failure at large technology platforms is the translation layer between policy team analysis and product build decisions. This module covers the communication architecture that converts regulatory constraint into product guidance engineers can act on without legal re-engagement at every decision point. Specific focus on where EU AI Act Article 10, NIST AI RMF Govern function, and Singapore MGAF internal governance requirements produce conflicting guidance to product teams.

Module 7. Cross-Border Data Governance for AI Training and Deployment

AI governance policy is increasingly inseparable from data governance. The EU AI Act's Article 10 training data requirements, Brazil's LGPD restrictions on cross-border flows used for AI training, India's DPDP cross-border transfer rules, and Singapore's PDPA AI guidance all create a data governance layer your AI framework must accommodate. This module builds the architecture that keeps AI training and deployment practices defensible across all four data regimes, with the specific artefacts regulators request during AI system audits.

Module 8. Responding to Regulatory Inquiries at Scale

When multiple regulators send information requests about your AI systems in the same quarter, the governance framework is only as useful as your response infrastructure. This module covers the response architecture that allows a global AI policy team to answer concurrent inquiries from the EU, US, Singapore, and India without each response being a fresh drafting exercise. Covers document structure, evidence retrieval connecting regulatory questions to existing governance artefacts, and internal review keeping responses consistent across jurisdictions.

Module 9. Transparency and Disclosure Obligations Across Jurisdictions

The EU AI Act's transparency requirements, the US AI framework's disclosure guidance, and Singapore's MGAF transparency principles each require disclosure at different points in the user journey and in different forms. This module maps disclosure obligations across all five frameworks against a large consumer AI product portfolio and builds a disclosure architecture that satisfies the most demanding regulator without over-disclosing in jurisdictions where that creates a different risk. Worked example: a generative AI product across consumer and enterprise deployment.

Module 10. Board and Executive Governance Reporting

AI governance policy leads at large platforms are increasingly required to produce board-level reporting on AI risk and governance posture. This module covers the reporting architecture that translates a multi-jurisdiction compliance picture into a board-level risk narrative executives can act on. Covers the specific metrics and artefacts that EU, US, and Singapore regulators have indicated they expect to see in board-level AI governance reporting during the current regulatory cycle.

Module 11. Managing Regulatory Divergence as Policy Evolves

The five frameworks covered in this course are in active development. Secondary legislation is still being written. Federal agencies are operationalising executive frameworks. Brazil and India have not finalised their AI-specific rules. This module covers governance architecture for managing regulatory divergence: how to accommodate future regulatory changes without a full rebuild, how to track developments across five jurisdictions with a lean team, and how to communicate regulatory uncertainty to product teams so they can build rather than stall.

Module 12. The Governance Artefact Set: What You Build and File

This module assembles the complete governance artefact set from the previous eleven modules: the classification decision memo, evidence pack, policy position template, cross-jurisdiction disclosure architecture, and board reporting framework. Each artefact is described at the level of detail needed to build it for a large AI portfolio, with the specific fields and evidence anchors each regulator has historically requested. The implementation playbook delivered alongside this course is hand-built for the Global AI Policy and Governance Lead role.

How this addresses your situation

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

Modules 1-3 address the architecture and classification problem: how do you build a governance framework that works across five jurisdictions whose definitions do not agree.

Modules 4-6 address the evidence and communication layer: how do you document governance decisions in a way that satisfies auditors reading different standards and product teams who need actionable guidance.

Modules 7-9 address the data governance and regulatory response infrastructure: how do you keep your AI practices defensible across data regimes and respond to concurrent regulatory inquiries without rebuilding each response.

Modules 10-12 address governance reporting, future-proofing, and artefact assembly: how do you produce board-level reporting, manage regulatory divergence, and assemble the complete documentation set.

What you get with this course

12 written modules covering multi-jurisdiction AI governance architecture from classification logic to board reporting
Downloadable templates for each governance artefact: classification decision memo, evidence pack, policy position document, disclosure architecture, and regulatory response framework
Worked examples mapped across EU AI Act, US NIST AI RMF, Singapore MGAF, Brazil AI bill, and India DPDP
Hand-built implementation playbook tailored to the Global AI Policy and Governance Lead role, covering sequencing and prioritisation for your specific governance build
Access within 24 hours of purchase

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

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

Before and after

Before

Each new regulator inquiry triggers a new drafting cycle. Classification decisions are made jurisdiction by jurisdiction. The product team treats each regulatory requirement as a fresh negotiation. Your policy positions are not portable across jurisdictions.

After

A governance architecture that produces four regulator-ready outputs from a single policy decision. Classification logic consistent across all five frameworks. Product guidance that travels without legal re-engagement. Policy positions that adapt to each jurisdiction as a parameter change, not a rewrite.

What happens if you do not address this

As the EU AI Act moves from framework to enforcement, US federal agencies operationalise the executive framework, and Brazil and India finalise their AI rules, the window for building a portable governance architecture before the first enforcement actions narrows. The platforms that build the architecture correctly this cycle handle concurrent regulatory inquiries in weeks. The ones that build jurisdiction by jurisdiction handle them in quarters.

Who it is for

This course is for senior AI policy professionals at large technology platforms, global AI developers, or major AI-deploying enterprises who hold cross-border governance accountability. You are the person your product teams call when a new regulator asks a question about your AI systems. You need governance documentation that works across jurisdictions without requiring you to rebuild it from scratch for every filing.

Who this is NOT for. This course is not for entry-level compliance analysts learning a single framework. It is not for legal counsel who need jurisdiction-specific legal advice. It is not for product managers building their first AI risk checklist. It is for senior policy leads who already understand the individual regulations and need the architecture layer that makes them work together.

How it arrives

Text-based course in the Art of Service learning environment, plus downloadable templates and worked examples for every module, plus the hand-built implementation playbook delivered alongside course access.

Time investment. Each module is designed to be completed in 45-90 minutes. The full course runs approximately 12-15 hours of focused reading and template work, designed to be spread across a working week.

Why $199 is the right number

External counsel per jurisdiction handles legal risk but does not produce a portable governance architecture. Internal governance teams built jurisdiction by jurisdiction produce artefacts that do not travel. This course builds the architecture layer that makes existing legal and governance work portable.

FAQ

Is this course specific to a particular AI product type?

The course covers consumer AI, enterprise AI, and large model deployment scenarios across the five frameworks. Worked examples draw on content recommendation, generative AI, and advertising AI use cases. The classification logic is applicable to any AI system that needs to be positioned across multiple regulatory jurisdictions.

How current is the regulatory content?

The course covers the EU AI Act as enacted with secondary legislation current to the delivery date, the US NIST AI RMF as published, the Singapore Model AI Governance Framework, Brazil's LGPD and current AI bill status, and India's DPDP Act and current AI framework guidance. The implementation playbook includes notes on active regulatory developments.

How is this different from a legal compliance course?

This course is not about understanding each individual regulation. It is about the governance architecture that makes five regulations work together. It assumes you already understand the regulations and builds the layer above them: the classification logic, evidence architecture, documentation design, and product team communication framework.

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.