AI Governance: A Complete Guide for Professionals

Name: AI Governance: A Complete Guide for Professionals
Rating: 4.6 (90 reviews)

Build a working AI governance framework with model cards, policies, risk assessments, and a real EU AI Act scorecard.

Bestseller

Role Play

Created byGeorgi Smarts

Last updated 8/2026

English

Bulgarian [Auto],English [Auto],

What you'll learn

Build a complete AI governance framework from scratch using the 6 pillars: accountability, risk, transparency, fairness, privacy, and compliance
Classify any AI system into EU AI Act risk tiers (unacceptable, high, limited, minimal) and assign the right controls for each level
Run a full AI risk assessment covering bias and fairness audits, data privacy risks, and third-party vendor due diligence
Write production-ready governance artefacts: model cards, acceptable use policy, incident response runbook, and audit trails
Govern generative AI specifically through prompt governance, output validation, and cost controls for LLM APIs
Plan a realistic 90-day implementation roadmap and get buy-in from leadership, legal, and engineering
Apply everything to a real capstone: build an AI Governance Scorecard for a fictional retailer end to end

Course content

8 sections • 35 lectures • 2h 41m total length

Introduction3:00
What is AI Governance2:06
The risks of ungoverned AI4:29
The regulatory landscape: EU AI Act, GDPR, US frameworks3:02
Explore the regulatory landscape for AI governance, including the EU AI Act’s risk-based four-tier system, GDPR implications for AI, and evolving US frameworks, with emphasis on mandatory compliance.
AI Governance vs Data Governance7:14

The 6 pillars of AI Governance9:28
Roles and responsibilities: who owns what5:17
AI risk classification: low, limited, high, unacceptable7:29
The AI inventory: documenting every AI system in use6:29
Identify all ai systems in production and establish a governance-focused inventory. Document each entry with 12 fields, addressing shadow it, embedded saas ai, and internal api calls.
AI model cards: what they are and how to write one5:01
Create and use a one-page ai model card to explain a system’s purpose, ownership, training data, performance, risks, and controls and oversight for onboarding, audits, and vendor assessments.
The AI Governance Council: setup and decision rights10:10

Bias and fairness auditing5:39
Integrate bias and fairness auditing from the start, addressing historical, representation, and proxy bias. Apply the 80% rule, per-group checks, and the audit checklist to guide documentation and deployment readiness.
Third-party and vendor AI risk checklist7:29
(Optional) Practice - Bias audit4:03
Explore a bias audit in AI governance by loading a credit decisions dataset, computing overall approval rates, and analyzing gender disparities through six practical tasks in a Python notebook.

AI Acceptable Use Policy3:32
AI Technical & Procedural Controls3:39
Explore how technical and procedural controls operationalize AI governance by enforcing policy frameworks, preventing data misuse, and ensuring human oversight through access restrictions, reviews, and templates.
Access Controls & Role-Based Permissions5:22
Input Validation & PII Scanning3:38
Apply input validation to govern data entering AI systems and strip personal data before calls. Use a proxy to scan and replace PII in text before sending to external models.
Output Validation & Guardrails4:10
Explore output validation and guardrails that govern AI outputs before delivery. Implement structural schema validation and semantic guardrails to block, log, and route deviations to safe fallbacks.
Human-in-the-Loop Requirements3:25
Audit Logging3:55
Cost Ceilings & Rate Limits3:22
Implement hard monthly cost caps and per session token limits (8,000 tokens) to prevent uncapped spend and misuse patterns, with soft alerts and real-time dashboards to monitor and respond.
Model Change Control4:54
Implement strict model change control with versioned ai artifacts, dual approvals, bias audits, and rollback capabilities to govern updates to production systems and vendor changes.
User Disclosure & Transparency4:41
Communicate clearly that users interact with AI before any message, per EU AI Act Article 50. Update privacy notices with system details and user rights.
AI incident response7:53
Learn how an AI incident response plan enables organized, legally compliant handling of failures, from detection to post-incident review, with roles, severity levels, and regulatory obligations.

Certified AI Governance Professional (AIGP) - IAPP3:09
ISACA Advanced in AI Audit (AAIA)2:47
ISO/IEC 42001 Lead Implementer / Lead Auditor2:51
EXIN AICP (AI Compliance Professional)3:17
Explore Exin ai compliance professional certification, focused on european regulation and eu ai act, six domains. Understand the exam: 40 questions in 90 minutes, open book, 65% pass, accredited training.
EC-Council CRAGE (Certified Responsible AI Governance and Ethics)2:37

Requirements

No prior AI, machine learning, or governance experience required
Basic familiarity with how organizations use software is helpful but not essential

Description

This course contains the use of artificial intelligence.

AI is everywhere in your organization. Governance usually isn't.

The EU AI Act is in force. GDPR Article 22 still applies. Your CFO wants to know how much you're spending on LLM APIs. Your legal team is asking who signs off on the new AI hiring tool. And someone in marketing just pasted a client list into a chatbot.

This course gives you a complete, working AI governance framework you can apply in your organization immediately. No theory-heavy slides. No vague principles. Just the structures, policies, and artefacts that real governance programs run on.

What you will build

In three hours, you'll work through 30 focused lectures organized into 7 sections. You'll go from "why governance matters" to writing your own AI Model Card, Acceptable Use Policy, Incident Response runbook, and a 90-day implementation roadmap. Then you'll apply everything in a capstone lab: a full governance scorecard for a fictional retailer with five AI systems across three departments.

What's inside

The 6 pillars of AI governance and how to score against them
EU AI Act risk classification, GDPR overlap, and US frameworks (NIST AI RMF, Colorado AI Act)
A repeatable risk assessment process for any AI system
Bias auditing, privacy risks, and third-party vendor due diligence
Generative AI specifics: prompt governance, output validation, cost controls
Downloadable templates you can drop into your own organization
Hands-on labs and a scorecard you keep as a portfolio piece

This course contains a promotion.

Who this course is for:

Compliance, risk, and legal professionals who have suddenly inherited AI governance
Product managers, engineers, and architects who build or buy AI systems
Data and analytics leaders accountable for responsible AI in their organisation
Founders and operators at companies adopting GenAI quickly and informally
Anyone preparing their company for the EU AI Act, GDPR Article 22, or the NIST AI RMF
Career changers looking to move into AI governance, AI risk, or responsible AI roles

AI Governance: A Complete Guide for Professionals

What you'll learn

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Course content

Introduction5 lectures • 20min

The Course Project intro1 lecture • 6min

The AI Governance Framework6 lectures • 44min

AI Risk Assessment deep dive3 lectures • 17min

AI Policies and Controls11 lectures • 49min

Building Your AI Governance Roadmap2 lectures • 8min

Top AI Governance Certifications5 lectures • 15min

Course Closure3 lectures • 3min

Requirements

Description

Who this course is for: