AI Audit Training: Governance, Risk & Ethics Course

Name: AI Audit Mastery: Governance, Risk & Ethics
Availability: InStock

Master AI audit training to evaluate machine learning models, governance structures, ethical controls, and regulatory risk across enterprise automated systems.

AI Audit Training: Governance, Risk & Ethics Course

At a glance

Duration: 1 Week
Format: Classroom
Cities: Lisbon, Dubai, Johannesburg, Cairo, Abu Dhabi, Chicago and more
Next session: 16 – 20 November 2026, Lisbon
Average fee: 5,700 €

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AI Audit Mastery: Governance, Risk & Ethics

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Overview

Automated decision-making and machine learning architectures introduce operational, ethical, and oversight demands that traditional IT controls cannot address alone. This AI audit training provides internal auditors, risk managers, and technology leaders with systematic methods to evaluate algorithms, establish oversight structures, and test automated workflows. Participants examine structured techniques for AI risk control strategies, model validation, algorithmic bias testing, and data privacy safeguards across the development lifecycle. Through practical audit programmes, risk matrices, and case analysis, practitioners learn how to assess automated systems against governance standards and produce defensible assurance reports. This course is delivered by Agile Leaders Training Center.

Who Should Attend

Internal and external auditors seeking specialized procedures to assess machine learning and automated workflows.
IT auditors and compliance professionals responsible for verifying supervisory controls and data governance.
Risk management and governance leaders establishing oversight policies for automated technologies.
Cybersecurity professionals evaluating automated threat surfaces, access boundaries, and model integrity.
Data scientists and technical leads preparing model documentation for independent assurance reviews.
Governance consultants advising enterprise clients on ethical controls and algorithmic risk protocols.

Departments and Industries

This programme supports assurance and oversight specialists across data-intensive sectors adopting machine learning platforms.

Internal Audit and Assurance Departments in Financial Services and Banking
Risk Management and Compliance Divisions in Healthcare and Pharmaceuticals
Technology Governance and Security Teams in Telecommunications and Software
Governance and Ethics Review Committees in Government and Public Administration
Quality Assurance and Systems Control Units in Industrial Manufacturing
Advisory and Independent Review Practices in Professional Services

Learning Objectives

By the end of this course, participants will be able to:

Conduct structured AI audits aligned with recognized governance frameworks and internal control principles.
Identify and evaluate operational, legal, and ethical exposures across machine learning workflows.
Evaluate AI model risk management practices and determine compliance with supervisory standards.
Perform systematic assessments for algorithmic bias, fairness, transparency, and data privacy.
Apply specialized AI risk assessment tools to verify data lineage, model drift, and system security.
Trace machine learning lifecycles from data collection to active deployment to confirm supervisory controls.

Course Agenda

Day 1: AI Governance and Audit Frameworks

Structural foundations of governance and accountability in automated systems
Audit compliance parameters and internal control expectations for machine learning
Overview of AI risk assessment tools and assurance methodologies
Ethical dimensions and organizational values in automated decision review
Auditing system lifecycles from model design to operational deployment
Structured review of assurance frameworks and audit charter alignment

Day 2: Risk Management and Ethical Controls

Organizational governance models, oversight committees, and operational roles
Risk identification, impact scoring, and threat mitigation in intelligent systems
Data privacy in auditing: data provenance, processing records, and consent verification
Establishing control baselines to satisfy evolving regulatory obligations
Evaluating algorithmic bias, demographic parity, and accountability measures
Review session on embedding risk controls into operational business units

Day 3: Risk Assessment Tools and Model Auditing

Deploying standardized assessment templates across complex data pipelines
Verification techniques for technical security, access controls, and data integrity
Audit procedures for validating machine learning model design and data inputs
Practical case evaluation: conducting an assurance review on clinical automation
Quantitative testing methods for detecting bias and evaluating fairness
Review session on evidence collection standards and technical testing logs

Day 4: Model Governance and Decision Transparency

Governance frameworks for model evaluation, validation, and change management
Continuous monitoring mechanisms to detect model drift and performance anomalies
Assurance roles in pre-deployment model sign-off and operational supervision
Auditing explainability mechanisms and automated decision traceability
Legal liability, supervisory expectations, and ethical risk containment
Simulated walkthrough of internal control testing on predictive algorithms

Day 5: Audit Execution and Assurance Reporting

Consolidated review of technical testing tools and control evaluation methods
Practical integration of model audits into enterprise risk management frameworks
Simulation exercise: preparing and conducting an automated workflow audit
Assessing organizational control maturity, escalation paths, and supervisory boards
Drafting the final assurance report: findings, risk ratings, and remediation plans
Action planning session: operationalizing audit work programmes within the organization

Course Toolbox

Standardized audit programmes and testing workpapers for automated systems
Checklists for evaluating governance structures and ethical control measures
Risk assessment matrix templates for data quality, model drift, and security
Sample workbooks for testing algorithmic bias, fairness metrics, and explainability
Reporting templates with findings classification matrices and remediation tracking sheets

Frequently Asked Questions

What specific qualifications or prerequisites are needed for participants before enrolling in the course?

Participants should ideally have experience in IT auditing, risk management, compliance, or information security. Basic familiarity with technology infrastructure and core internal audit principles is recommended to fully engage with the technical assurance concepts.

How long is each day's session, and is there a total number of hours required for the entire course?

Each session lasts approximately 4 to 5 hours, incorporating interactive lectures, exercises, and discussions. The full course spans five consecutive days, delivering 20 to 25 hours of focused professional instruction.

Can AI auditing tools be used in real-time auditing?

Yes, specialized monitoring and testing tools can interface with operational data pipelines to observe model performance, flag unexpected drift, and track algorithmic decisions continuously alongside scheduled periodic audits.

How This Course Compares

This course focuses on practical internal audit execution, model governance structures, and ethical control testing. Unlike high-level compliance overviews or purely technical data science programmes, this curriculum bridges organizational oversight with concrete audit workpapers. Participants gain actionable testing methods to assess automated algorithms, verify data privacy, and deliver defensible audit reports that satisfy executive committees and supervisory bodies.

credits: 5 credit per day

Course Mode: full-time

Provider: Agile Leaders Training Center

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Showing 21-40 of 80 events

Location	Dates	Duration	Mode	Price
Milan	Week 07, 2027 15 – 19 February 2027	1 Week	Onsite	€5,700
Kuala Lumpur	Week 08, 2027 22 – 26 February 2027	1 Week	Onsite	€5,200
Amman	Week 08, 2027 28 February – 4 March 2027	1 Week	Onsite	€4,100
Prague	Week 10, 2027 8 – 12 March 2027	1 Week	Onsite	€6,000
Accra	Week 10, 2027 8 – 12 March 2027	1 Week	Onsite	€4,850
Dubai	Week 11, 2027 15 – 19 March 2027	1 Week	Onsite	€4,500
Amsterdam	Week 11, 2027 15 – 19 March 2027	1 Week	Onsite	€5,700
Montreux	Week 12, 2027 22 – 26 March 2027	1 Week	Onsite	€7,500
Cairo	Week 13, 2027 29 March – 2 April 2027	1 Week	Onsite	€4,100
Tbilisi	Week 13, 2027 29 March – 2 April 2027	1 Week	Onsite	€5,000
Manama	Week 13, 2027 4 – 8 April 2027	1 Week	Onsite	€4,700
Sharm El-Sheikh	Week 15, 2027 12 – 16 April 2027	1 Week	Onsite	€4,100
Madrid	Week 15, 2027 12 – 16 April 2027	1 Week	Onsite	€5,700
Kuala Lumpur	Week 16, 2027 19 – 23 April 2027	1 Week	Onsite	€5,200
Rome	Week 16, 2027 19 – 23 April 2027	1 Week	Onsite	€5,700
Paris	Week 17, 2027 26 – 30 April 2027	1 Week	Onsite	€5,700
Istanbul	Week 18, 2027 3 – 7 May 2027	1 Week	Onsite	€4,500
Amsterdam	Week 19, 2027 10 – 14 May 2027	1 Week	Onsite	€5,700
Vienna	Week 20, 2027 17 – 21 May 2027	1 Week	Onsite	€5,700
Athens	Week 20, 2027 17 – 21 May 2027	1 Week	Onsite	€6,700

Frequently asked questions

What does this course cover?

OverviewAutomated decision-making and machine learning architectures introduce operational, ethical, and oversight demands that traditional IT controls cannot address alone. This AI audit training provides internal auditors, risk managers, and technology leaders with systematic methods to evaluate algorithms, establish oversight structures, and test autom…

Are training dates available?

Yes. Available dates and destinations are listed in the course dates section on this page.

How can I register?

Choose an available date on this page and complete the registration form, or send a programme enquiry.

Can I download the course brochure?

Yes. Use the brochure download link provided on this page.

AI Audit Training: Governance, Risk & Ethics Course