AI & Data Governance Unveiled

• CEOs, Board members, executives and CXO teams.
• Business leaders and operational leaders responsible for AI-enabled transformation.
• Chief Data Officers, data owners, data stewards and data governance teams.
• AI, digital, IT, architecture and product leaders.
• Risk, compliance, legal, audit and cybersecurity teams.
• PMO and transformation teams responsible for implementation.
• Organizations moving from isolated AI pilots to governed, scalable and accountable AI execution.

No advanced technical background is required.
A general understanding of digital transformation, data, AI, governance, compliance, risk management or business operations is recommended.
Participants are encouraged to bring one or more AI use cases, data initiatives or governance challenges from their organization to use during the workshops.

This 5-day program enables executives, business leaders, data teams, IT, risk, compliance, audit and transformation teams to understand, design and implement a practical AI and Data Governance operating model.

By the end of the program, participants will be able to:

• Connect AI ambition to measurable business value, risk control, accountability, compliance and trust.
• Classify AI use cases according to business impact, operational dependency, data sensitivity, regulatory exposure and oversight needs.
• Understand Data Governance as a core control layer within AI Governance.
• Define a target operating model covering roles, responsibilities, RACI, decision rights, governance bodies and escalation paths.
• Design governance architecture covering the data layer, AI lifecycle, control plane, compliance evidence and monitoring.
• Build an AURORA Command Centre dashboard to monitor AI portfolio, value, risk, compliance, data readiness, incidents and control effectiveness.
• Create a 90-day implementation backlog and a Board / CXO-ready decision pack.

The program is an intensive 5-day pathway combining executive framing, practical tools, standards-based governance, working sessions and company-specific blueprint development.

• Days 1 and 2 build the strategic, regulatory and data governance foundation.
• Days 3 and 4 convert that foundation into architecture, processes, controls and compliance evidence.
• Day 5 consolidates the work into a company blueprint.

Day 1 – AI Governance Strategy, Standards and Risk Classification

Participants explore AI Governance as a business discipline connecting value, risk, trust, accountability and execution. The day introduces the Demeester stance model, AI risk classification, use-case criticality, data sensitivity, human oversight, external impact and the standards backbone, including NIST AI RMF, ISO/IEC 42001, EU AI Act, GDPR, DAMA and COBIT.

Day 2 – Data Governance, Lifecycle, Roles and Readiness

This day positions Data Governance as the foundation of trusted AI. Participants work on data ownership, stewardship, metadata, lineage, data quality, privacy, security, retention, access controls and auditability. The session also covers the AI, model and agent lifecycle, RACI design, policy stack and initial AURORA dashboard concepts.

Day 3 – Architecture and Target Operating Model

Participants move from principles to implementation architecture. The day focuses on the target operating model, Board and CXO responsibilities, AI governance councils, data governance, legal, risk, cyber, audit and PMO roles. It also covers governance control planes, data layer architecture, AI lifecycle architecture, AURORA architecture and architecture stress testing.

Day 4 – Processes, Compliance Evidence, Controls and Assurance

The fourth day converts architecture into operating processes and evidence. Participants define lifecycle gates, control libraries, monitoring flows, incident processes, EU AI Act evidence, GDPR / DPIA / AIA evidence, ISO 42001 alignment and assurance planning. The day produces a structured evidence bundle and internal control rhythm.

Day 5 – Company Workshop, AURORA Dashboard, 90-Day Backlog and Board Pitch

The final day is a practical company workshop. Participants integrate the governance stance, target operating model, architecture, lifecycle gates, controls and evidence into a company blueprint. They build an AURORA dashboard specification, define a 90-day backlog, prepare an executive decision pack and present a Board / CXO-ready pitch.

• AI Governance as a system for value, risk, trust and execution.
• AI and Data Governance strategy for executives and management teams.
• Demeester stance model: impact, operational dependency and governance intensity.
• AI use-case classification and risk appetite.
• NIST AI RMF, ISO/IEC 42001, EU AI Act, GDPR, DAMA-DMBOK, COBIT, ISO 27001 and NIST CSF.
• Data Governance for AI: ownership, stewardship, domains, metadata, lineage and data quality.
• Privacy, security, access, retention and auditability.
• AI, model and agent lifecycle governance.
• Human oversight, transparency, accountability and explainability.
• RACI, decision rights and governance bodies.
• Target operating model and governance control plane.
• Lifecycle gates from intake to retirement.
• Control library for data, models, privacy, security, fairness, explainability, vendors and incidents.
• Vendor, GenAI and agentic AI controls.
• Monitoring, incidents, escalation and remediation.
• EU AI Act, GDPR, DPIA, AIA and ISO 42001 evidence packs.
• AURORA Command Centre dashboard design.
• 90-day implementation roadmap.
• Board / CXO decision pack and executive pitch.

The program combines executive learning, practical workshops and structured deliverables.

Assessment is based on participation, practical outputs and final presentation.

An attendance certificate may be issued to participants who complete the full program.

Participants receive practical course materials and templates

The training can be delivered in person, remotely or in hybrid format.

Training designed and delivered by DEMEESTER S.a r.l.-S – AI & Data Governance Academy.

The program can be adapted in partnership with the client organization to reflect its strategic priorities, regulatory environment, AI maturity, data governance .

This program is designed to help organizations convert AI ambition into governed execution.

Key outputs produced during the 5-day program include:

• Governance stance and risk appetite.
• AI use-case classification.
• Target operating model.
• Governance control-plane blueprint.
• Data Governance and AI lifecycle architecture.
• Lifecycle gates and policy stack.
• Internal control library.
• Compliance evidence bundle.
• AURORA Command Centre dashboard specification.
• 90-day implementation backlog.
• Board / CXO-ready executive decision pack.

The program is especially relevant for organizations seeking to scale AI responsibly while strengthening value delivery, risk management, compliance, data quality, transparency, accountability and stakeholder trust.

Training Options

AI & Data Governance Unveiled can be delivered in three modular formats, depending on the organization’s maturity, urgency and implementation needs:

Executive Briefing – 4 hours
A focused session for the Board, CEO and CXO team to create fast alignment on AI value, risk, accountability, regulatory expectations and the first governance decisions. The session introduces the AI & Data Governance challenge, the Demeester stance model, key standards, the AURORA Command Centre concept and a first 90-day action sheet.

Essentials – 2 days
A practical foundation program for leaders, legal, risk, data, technology, audit and business owners. It builds common governance literacy and introduces the essential toolkit: AI use-case classification, Data Governance for AI, data readiness, lifecycle controls, RACI, policy gaps, vendor and agent controls, readiness scorecard and AURORA dashboard canvas.

Operations Blueprint Lab – 3 days
A hands-on implementation lab for the core execution team. It is designed to produce a company-specific AI & Data Governance blueprint, including the target operating model, governance architecture, lifecycle gates, risk register, control library, compliance evidence pack, AURORA dashboard specification and 90-day implementation backlog.

These options are modular: the 2-day Essentials program builds capability and shared understanding, while the 3-day Operations Blueprint Lab converts that foundation into a practical operating model. Together, they form the complete 5-day integrated pathway from AI ambition to governed execution.