About the Course

As artificial intelligence becomes embedded in decisions, workflows and customer-facing services, organizations face a new class of governance, operational, legal, ethical and reputational risks. Uncontrolled use of generative AI, opaque models, third-party dependencies, weak data controls and unmanaged automated decisions can expose institutions to material harm. Governance capability therefore needs to mature alongside technical adoption rather than being added after systems have already scaled.

This advanced programme develops practical capability to govern AI across its lifecycle. It integrates leading concepts from AI management systems, risk-management practice, responsible AI principles and organizational compliance. Participants examine AI inventories, risk taxonomies, governance structures, impact assessments, control design, third-party risk, model oversight, generative AI risk, Shadow AI, documentation, incident response, monitoring and assurance. The programme is designed for organizations using both internally developed and externally procured AI systems.

Participants apply the material to a continuing governance case and develop tangible institutional tools such as an AI use-case register, risk taxonomy, governance charter, risk-assessment template, acceptable-use standard, third-party due-diligence checklist, monitoring dashboard and implementation plan. This creates a practical bridge between policy commitments and operational controls, enabling sponsoring organizations to strengthen accountability, oversight and evidence of responsible AI management.

Target Participants

This advanced course is intended for risk managers, compliance officers, internal auditors, governance professionals, legal and regulatory staff, data protection officers, information-security managers, AI and digital-transformation leads, technology managers, procurement professionals, policy makers, regulators, assurance specialists and senior managers responsible for AI oversight.

What You Will Learn

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

• Map organizational AI use across systems, vendors, functions and decision processes.
• Develop an AI governance framework with defined roles, committees, decision rights and escalation mechanisms.
• Construct an AI risk taxonomy covering strategic, model, data, cyber, privacy, legal, ethical, operational and third-party risks.
• Apply AI risk-assessment and impact-assessment techniques across the system lifecycle.
• Design controls for generative AI, automated decisions, third-party models and Shadow AI.
• Integrate AI risk into enterprise risk, compliance, audit and information-security processes.
• Establish monitoring, documentation, incident-response and assurance requirements for AI systems.
• Evaluate organizational alignment with recognized AI governance frameworks and standards.
• Prepare an institutional AI governance implementation roadmap.
• Advise senior management on risk-based scaling of AI initiatives.

Course Duration

• Classroom – 10 Days
• Online – 14 Days

Course Outline

AI Governance Foundations

• Purpose and scope of AI governance
• AI lifecycle responsibilities
• Governance versus management controls
• Accountability in human-AI decision systems
• Common governance failure patterns

AI Inventory

• Identifying AI systems and embedded capabilities
• Mapping generative AI use across functions
• Capturing third-party and vendor AI
• Classifying business criticality and decision impact
• Maintaining an enterprise AI register

Governance Architecture

• Board and executive oversight
• AI governance committees
• Three-lines accountability
• Business ownership and technical ownership
• Escalation and exception-management structures

AI Risk Taxonomy

• Strategic and business risks
• Model and performance risks
• Data and privacy risks
• Cybersecurity and resilience risks
• Legal, ethical and reputational risks

Risk Assessment

• Inherent-risk assessment
• Likelihood and impact criteria
• Risk-tiering methods
• Control effectiveness assessment
• Residual-risk evaluation

Impact Assessment

• Affected stakeholders and rights
• Decision consequences and materiality
• Bias and discrimination exposure
• Transparency and explainability needs
• Documenting mitigation and approvals

Generative AI Risk

• Hallucination and reliability risk
• Prompt-injection and data leakage
• Copyright and intellectual-property concerns
• Synthetic-content misuse
• Human-review controls for generative outputs

Shadow AI

• Drivers of unmanaged AI adoption
• Discovery methods for Shadow AI
• Acceptable-use boundaries
• Employee tool governance
• Containment and remediation approaches

Data Governance

• Data provenance and lineage
• Training and inference data controls
• Sensitive-data restrictions
• Data minimization and access controls
• Retention, deletion and recordkeeping

Model Oversight

• Model purpose and intended-use definition
• Performance thresholds and validation
• Explainability requirements
• Change control and versioning
• Model retirement and decommissioning

Third-Party Risk

• AI vendor due diligence
• Contractual control requirements
• Subprocessor and supply-chain risk
• Service-level and performance monitoring
• Exit, continuity and portability planning

Policy Framework

• Enterprise AI policy structure
• Acceptable-use standard
• Prohibited and restricted uses
• Human oversight requirements
• Policy exception management

Monitoring Framework

• Key risk indicators for AI
• Operational performance monitoring
• Bias and fairness monitoring
• Control testing and issue tracking
• Management reporting and dashboards

Incident Response

• AI incident classification
• Detection and reporting triggers
• Containment and kill-switch concepts
• Investigation and root-cause analysis
• Remediation and lessons learned

Regulatory Landscape

• Risk-based regulatory approaches
• Privacy and data-protection considerations
• Transparency obligations
• Sector-regulatory expectations
• Cross-border compliance considerations

Standards Alignment

• ISO/IEC 42001 management-system concepts
• NIST AI RMF functions and outcomes
• OECD trustworthy AI principles
• Mapping frameworks to organizational controls
• Selecting an appropriate governance baseline

Assurance Model

• AI control self-assessment
• Internal audit coverage
• Independent validation concepts
• Evidence and documentation requirements
• Management attestation and assurance reporting

Governance Maturity

• AI governance maturity models
• Capability gap assessment
• Prioritizing control improvements
• Resourcing governance functions
• Continuous improvement mechanisms

Executive Reporting

• Board-level AI risk reporting
• Portfolio risk visualization
• Material issue escalation
• Decision papers for AI approvals
• Communicating uncertainty and limitations

Implementation Capstone

• Governance target-state design
• Phased control implementation
• Roles, owners and milestones
• Resource and capability planning
• Capstone presentation and peer review

Practical Outputs

• Enterprise AI inventory template
• AI risk taxonomy
• AI risk-assessment matrix
• AI governance charter
• Acceptable-use standard
• Third-party AI due-diligence checklist
• AI monitoring dashboard specification
• Governance implementation roadmap

Training Approach

This 10-day programme is workshop-intensive and uses governance case studies, policy critique, risk-assessment labs, control-design exercises, vendor scenarios, incident simulations and executive reporting practice. Participants progressively build a complete AI governance toolkit around a realistic institutional case. The delivery emphasizes evidence, documentation and control design rather than abstract ethics discussion alone.

Organizational Benefits

• Stronger accountability for organizational AI use
• Improved visibility of AI systems and Shadow AI exposure
• More consistent assessment of AI-related risk
• Better integration of AI controls into existing GRC structures
• Improved third-party AI oversight
• Clearer management reporting on AI risk
• Stronger readiness for regulatory, audit and stakeholder scrutiny
• More sustainable scaling of AI initiatives

Certification

Participants who successfully complete this course will receive a Certificate of Course Completion from Devimpact Institute, recognizing the knowledge, practical skills and competencies developed through the programme.

Tailor-Made Course

This course can be customized and delivered as an in-house programme to address the specific policies, systems, sector context and capacity-development priorities of an organization. The course duration, modules, case studies and practical exercises may be adjusted following a training needs assessment.