A tailored course, built for your situation
Board-Level AI Risk Officer Capabilities for Senior Leaders
Master the strategic, governance, and risk leadership skills needed to guide AI adoption at scale
$199 one-time
24-hour access provisioning
30-day money-back guarantee
Hand-built implementation playbook
12 modules. 12 chapters per module. 144 chapters total.
12 modules, each with 12 chapters (144 chapters total), text-based, plus downloadable templates and a hand-built implementation playbook delivered alongside course access.
Senior leaders are expected to govern AI, but most lack structured frameworks to do so confidently
The situation this course is for
AI initiatives are advancing quickly, yet executive teams struggle to establish clear oversight, risk boundaries, and board-level reporting. Without a formalized approach, leaders face ambiguity in accountability, compliance, and strategic alignment, even as expectations rise.
Who this is for
Senior business and technology leaders stepping into or preparing for board-level AI governance, risk, and compliance responsibilities
Who this is not for
Individual contributors without strategic influence, entry-level professionals, or those seeking technical AI engineering training
What you walk away with
- Apply a proven governance framework for AI risk oversight at the board level
- Communicate AI risk posture clearly to executives and directors
- Integrate compliance requirements from major standards and regulations
- Design AI risk thresholds aligned with organizational strategy
- Lead cross-functional AI governance initiatives with confidence
The 12 modules (with all 144 chapters)
Module 1. The Evolving Role of the AI Risk Officer
Understand the emergence of the AI Risk Officer as a strategic leadership function.
12 chapters in this module
- Defining the AI Risk Officer mandate
- From IT risk to enterprise AI governance
- Board expectations in the current cycle
- Key responsibilities and reporting lines
- Strategic vs operational oversight
- Case study: Global financial institution
- Regulatory drivers shaping the role
- Skills and competencies required
- Organizational placement options
- Stakeholder mapping for AI governance
- Building credibility with the C-suite
- Future evolution of the role
Module 2. AI Governance Frameworks and Standards
Master leading governance models and their practical application.
12 chapters in this module
- Overview of ISO, NIST, and OECD AI guidelines
- Mapping frameworks to organizational size
- Adapting principles to industry context
- Customizing governance for scale
- Integrating with existing ERM frameworks
- Benchmarking against peer organizations
- Gap analysis techniques
- Prioritizing framework adoption
- Executive communication of framework choice
- Maintaining framework agility
- Third-party assessment readiness
- Continuous improvement loops
Module 3. Risk Taxonomy for AI Systems
Develop a structured classification of AI-specific risks.
12 chapters in this module
- Identifying unique AI risk categories
- Bias, fairness, and representation risks
- Transparency and explainability gaps
- Model drift and performance decay
- Data provenance and quality risks
- Security and adversarial attack vectors
- Reputational and brand exposure
- Legal and contractual liabilities
- Operational disruption scenarios
- Third-party and supply chain risks
- Emerging risk indicators
- Creating a living risk register
Module 4. Board Communication and Reporting
Craft clear, actionable reports for non-technical directors.
12 chapters in this module
- Understanding board information needs
- Translating technical risk into business terms
- Designing executive dashboards
- Setting risk tolerance thresholds
- Reporting frequency and cadence
- Escalation protocols for critical issues
- Preparing for board Q&A
- Balancing transparency and confidentiality
- Using scenario planning in briefings
- Incorporating external benchmark data
- Documenting oversight decisions
- Evaluating board engagement effectiveness
Module 5. AI Compliance and Regulatory Landscape
Navigate evolving global and sector-specific requirements.
12 chapters in this module
- Overview of EU AI Act implications
- US federal and state-level developments
- Financial services regulatory expectations
- Healthcare and privacy considerations
- Cross-border data and model deployment
- Sector-specific compliance nuances
- Preparing for audits and inspections
- Engaging with regulators proactively
- Maintaining compliance documentation
- Tracking regulatory change signals
- Aligning with internal policies
- Demonstrating due diligence
Module 6. AI Risk Assessment Methodology
Implement a repeatable process for evaluating AI projects.
12 chapters in this module
- Pre-deployment risk screening
- Impact assessment frameworks
- Stakeholder consultation techniques
- Scoring risk severity and likelihood
- Determining risk treatment options
- Documenting assessment rationale
- Third-party model risk evaluation
- Ongoing monitoring triggers
- Reassessment intervals and criteria
- Integrating with project lifecycle
- Automating assessment workflows
- Quality assurance for assessments
Module 7. AI Oversight Controls and Monitoring
Design and deploy effective control mechanisms.
12 chapters in this module
- Control types: preventive, detective, corrective
- Model performance monitoring
- Bias detection and mitigation controls
- Access and change management
- Logging and audit trail requirements
- Alerting and anomaly detection
- Human-in-the-loop design
- Red teaming and challenge processes
- Third-party control validation
- Control testing and validation
- Metrics for control effectiveness
- Updating controls as AI evolves
Module 8. AI Incident Response and Escalation
Prepare for and manage AI-related failures or breaches.
12 chapters in this module
- Defining AI incidents and near misses
- Incident classification and severity tiers
- Response team composition and roles
- Communication protocols during crisis
- Regulatory reporting obligations
- Public relations and stakeholder messaging
- Root cause analysis methods
- Remediation and model correction
- Lessons learned integration
- Testing response plans
- Insurance and liability considerations
- Post-incident review with board
Module 9. AI Ethics and Responsible Innovation
Embed ethical considerations into AI governance.
12 chapters in this module
- Establishing ethical principles
- Ethics review board formation
- Impact on vulnerable populations
- Fairness metrics and benchmarks
- Community and public engagement
- Whistleblower and reporting channels
- Balancing innovation and caution
- Ethical training for developers
- Vendor ethics assessment
- Public disclosure expectations
- Handling ethical dilemmas
- Long-term societal implications
Module 10. Cross-Functional AI Governance
Lead collaboration across legal, risk, tech, and business units.
12 chapters in this module
- Building the AI governance council
- Defining roles and responsibilities
- Establishing decision rights
- Conflict resolution mechanisms
- Facilitating interdepartmental alignment
- Change management for governance adoption
- Training and awareness programs
- Incentivizing compliance
- Measuring cross-functional effectiveness
- Managing resistance to oversight
- Scaling governance across divisions
- Global coordination challenges
Module 11. AI Risk in Mergers and Acquisitions
Assess AI liabilities during transactions.
12 chapters in this module
- Due diligence for AI assets
- Evaluating target organization's AI maturity
- Identifying hidden model risks
- Reviewing training data provenance
- Assessing compliance posture
- Valuation implications of AI risk
- Integration planning for AI systems
- Post-merger governance harmonization
- Contractual protections and warranties
- Disclosure requirements
- Third-party audit rights
- Exit strategies for high-risk models
Module 12. Future-Proofing AI Governance
Anticipate next-generation challenges and opportunities.
12 chapters in this module
- Tracking emerging AI capabilities
- Preparing for generative AI evolution
- Autonomous systems and accountability
- AI and workforce transformation
- Long-term liability models
- Insurance and risk transfer options
- Global governance coordination
- Public trust and perception trends
- Scenario planning for extreme risks
- Sustainable AI practices
- Leadership succession planning
- Continuous learning and adaptation
How this maps to your situation
- Preparing for board-level AI discussions
- Responding to increased regulatory scrutiny
- Leading enterprise AI governance rollout
- Advising on AI risk in strategic decisions
Before vs. after
Before
Uncertain how to structure AI risk oversight or communicate it effectively to executives and boards
After
Equipped with a comprehensive, board-ready framework to lead AI governance with confidence and clarity
What's included with your purchase
- 12 modules with 12 chapters each (144 chapters)
- Downloadable templates and worked examples for every module
- Hand-built implementation playbook delivered alongside course access
- 30-day money-back guarantee
Delivery and format
- Course and learning environment access provisioned within 24 hours of purchase
- Hand-built implementation playbook delivered alongside course access
Format: Text-based modules and chapters in the Art of Service learning environment, plus downloadable templates and worked examples for every chapter, plus the hand-built implementation playbook delivered alongside course access.
Time investment: Approximately 45, 60 hours total, designed for completion over 8, 12 weeks with flexible pacing.
If nothing changes
Without a structured approach, leaders may face increased scrutiny, misaligned AI initiatives, regulatory exposure, and erosion of board trust, even as organizational dependence on AI grows.
How this compares to the alternatives
Unlike generic AI ethics courses or technical risk trainings, this program is specifically designed for senior leaders needing to operationalize AI governance at the board level, with implementation-grade tools, not just theory.
Frequently asked
Who is this course designed for?
Senior business and technology leaders responsible for AI governance, risk, compliance, or strategy who engage with executive teams or boards.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is there a certificate upon completion?
Yes, a certificate of completion is issued through the Art of Service learning platform.
$199 one-time. Approximately 45, 60 hours total, designed for completion over 8, 12 weeks with flexible pacing..
Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.
30-day money-back guarantee·
144 chapters·
Hand-built playbook included·
Account access within 24 hours