AI and Machine Learning Policy Oversight Course

Name: AI and Machine Learning Policy and Oversight Training Course
Availability: InStock

Enable governance leaders to set policy scope, classify AI risks, assign ownership, operate approval gates, and produce assurance evidence.

AI and Machine Learning Policy Oversight Course

At a glance

Duration: 5 days
Format: Classroom
Cities: London, Amsterdam, Chicago, Istanbul, Tokyo, Casablanca and more
Next session: 5 – 9 October 2026, London
Average fee: 5,800 €

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AI and Machine Learning Policy and Oversight Training Course

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Overview

AI and Machine Learning Policy and Oversight Training Course is a five-day advanced course for policy owners, governance and risk leaders, compliance managers, model-risk personnel, auditors, legal advisers, and executives, who leave with a Policy and Oversight Operating Framework. Participants define AI policy scope, set machine learning risk tiers, build an AI system inventory, assign accountable ownership, establish human oversight controls, and organize assurance evidence and committee reporting. Agile Leaders Training Center delivers training in organizational AI policy oversight.

Who Should Attend

Policy personnel responsible for organization-wide AI principles, rules, and exceptions
Governance and risk personnel responsible for risk classification, ownership, and approval controls
Compliance and legal personnel responsible for obligations, evidence, and policy interpretation
Model-risk and data personnel responsible for inventories, validation interfaces, and monitoring
Internal audit and executive personnel responsible for assurance, challenge, and committee reporting

The course assumes participants can evaluate policies, risks, controls, and accountability at work, and it leaves out model development, coding, prompt engineering, and vendor-platform configuration.

Departments and Industries

The course supports governance and oversight functions across financial services, healthcare administration, manufacturing, energy, technology services, and public services.

Governance, enterprise risk, and compliance
Legal, ethics, and policy management
Internal audit and independent assurance
Data, analytics, and model-risk management
Technology, procurement, and third-party oversight

Learning Objectives

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

Analyze AI policy scope and lifecycle responsibilities
Build use-case and model inventory requirements
Apply risk tiers and approval gates
Evaluate data, model, and third-party controls
Design human oversight, monitoring, and incident paths
Use assurance evidence for committee reporting

Course Agenda

Day 1: Policy Scope and Governance Context

AI Policy Scope and Applicability Canvas
AI System Lifecycle Responsibility Map
NIST AI RMF Govern-Map-Measure-Manage Crosswalk
ISO/IEC 42001:2023 Management-System Alignment Grid
Policy Principles and Prohibited-Use Register

Day 2: Inventories, Risk Tiers, and Ownership

AI Use-Case and Model Inventory Schema
Risk-Tier Classification Decision Tree
Accountable Owner and Control-Function Matrix
Approval Gate and Evidence Checklist
Exception Request and Expiry Register

Day 3: Controls and Human Oversight

Data Provenance and Quality Control Map
Model Limitation and Performance-Control Matrix
Third-Party AI Due-Diligence Checklist
Human Oversight and Decision-Rights Framework
Transparency and Documentation Requirement Table

Day 4: Monitoring, Incidents, and Assurance

AI Performance and Risk Indicator Catalogue
Control Monitoring and Evidence Calendar
AI Incident Classification and Escalation Flow
Policy Breach and Remediation Log
Assurance Evidence and Committee Reporting Pack

Day 5: Policy Oversight Practice and Capstone

Suggested Exercise: Resolve an AI Policy Scope Question
Suggested Exercise: Classify a Use Case by Risk Tier
Suggested Exercise: Test an Approval Gate and Exception
Suggested Exercise: Review an Incident and Assurance Pack
Capstone Exercise: Policy and Oversight Operating Framework

Practical Exercises

The course uses suggested activities that convert policy requirements into operational oversight artifacts.

Suggested activity: distinguish covered use cases, excluded tools, policy owners, and lifecycle responsibilities
Suggested activity: classify an AI use case and identify its approval, evidence, and human-review requirements
Suggested activity: assess data, model, and third-party controls against an identified risk tier
Suggested activity: assemble monitoring results, incident evidence, exceptions, and decisions for committee review

FAQs

Who suits the AI and Machine Learning Policy and Oversight Training Course, and what does it assume?

The course suits experienced governance, risk, compliance, legal, audit, data, and executive personnel who evaluate policies and controls without developing AI models.

How does AI policy and oversight training differ from machine learning development training?

AI policy and oversight training focuses on scope, inventories, risk tiers, accountability, controls, monitoring, incidents, assurance, and reporting rather than algorithms, coding, data pipelines, or model implementation.

What should an AI and machine learning policy define?

An AI and machine learning policy should define scope, principles, prohibited uses, inventory duties, risk tiers, owners, approval gates, human-review requirements, control expectations, monitoring, incident paths, exceptions, evidence, and review cadence.

How should organizations assign AI oversight responsibilities?

Organizations should assign accountable business owners, policy custodians, control functions, technical evidence providers, independent challenge roles, approval authorities, incident responders, and committees with clear decision and escalation rights.

What evidence supports AI policy assurance?

AI policy assurance uses current inventories, classifications, approvals, control tests, monitoring results, incidents, exceptions, remediation records, ownership decisions, and committee actions that can be traced to policy requirements.

Conclusion

Participants take back a Policy and Oversight Operating Framework linking policy scope, inventories, risk tiers, ownership, controls, monitoring, incidents, and assurance evidence. It changes how governance teams convert policy statements into repeatable decisions and review records. The framework supports accountable approvals, visible exceptions, human oversight, independent challenge, and committee reporting.

credits: 5 credit per day

Course Mode: full-time

Provider: Agile Leaders Training Center

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Showing 1-20 of 76 events

Location	Dates	Duration	Mode	Price
London	Week 41, 2026 5 – 9 October 2026	5 Days	Onsite	€5,700
Amsterdam	Week 41, 2026 5 – 9 October 2026	5 Days	Onsite	€5,700
Chicago	Week 41, 2026 11 – 15 October 2026	5 Days	Onsite	€12,000
Istanbul	Week 42, 2026 12 – 16 October 2026	5 Days	Onsite	€4,500
Tokyo	Week 43, 2026 19 – 23 October 2026	5 Days	Onsite	€10,000
Casablanca	Week 44, 2026 26 – 30 October 2026	5 Days	Onsite	€4,100
Prague	Week 44, 2026 26 – 30 October 2026	5 Days	Onsite	€6,000
Sharm El-Sheikh	Week 45, 2026 2 – 6 November 2026	5 Days	Onsite	€4,100
Vienna	Week 45, 2026 2 – 6 November 2026	5 Days	Onsite	€5,700
Zanzibar	Week 45, 2026 8 – 12 November 2026	5 Days	Onsite	€5,500
Milan	Week 47, 2026 16 – 20 November 2026	5 Days	Onsite	€5,700
Tashkent	Week 47, 2026 22 – 26 November 2026	5 Days	Onsite	€4,500
Dubai	Week 48, 2026 23 – 27 November 2026	5 Days	Onsite	€4,500
Athens	Week 49, 2026 30 November – 4 December 2026	5 Days	Onsite	€6,700
Bangkok	Week 49, 2026 6 – 10 December 2026	5 Days	Onsite	€6,000
Paris	Week 50, 2026 7 – 11 December 2026	5 Days	Onsite	€5,700
Cairo	Week 51, 2026 14 – 18 December 2026	5 Days	Onsite	€4,100
Abu Dhabi	Week 51, 2026 14 – 18 December 2026	5 Days	Onsite	€4,700
London	Week 52, 2026 21 – 25 December 2026	5 Days	Onsite	€5,700
Madrid	Week 52, 2026 21 – 25 December 2026	5 Days	Onsite	€5,700

Frequently asked questions

What does this course cover?

OverviewAI and Machine Learning Policy and Oversight Training Course is a five-day advanced course for policy owners, governance and risk leaders, compliance managers, model-risk personnel, auditors, legal advisers, and executives, who leave with a Policy and Oversight Operating Framework. Participants define AI policy scope, set machine learning risk tie…

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 and Machine Learning Policy Oversight Course