A tailored course, built for your situation
AI Governance for Compliance and Risk Teams
Align emerging AI initiatives with compliance, risk, and governance frameworks
$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.
AI is moving fast, but compliance can't play catch-up, it needs to lead.
The situation this course is for
Organizations are adopting AI faster than policies can keep up. Compliance and risk teams are expected to enable innovation while preventing exposure. Without a structured governance approach, teams face regulatory blind spots, ethical missteps, and operational risk. The pressure is on to define guardrails that don't stifle progress, especially in public-facing or highly regulated functions.
Who this is for
A compliance, risk, or governance professional in a transformation-led organization, responsible for ensuring emerging technologies like AI are deployed responsibly and in alignment with policy, ethics, and oversight requirements.
Who this is not for
This course is not for data scientists building AI models or executives seeking high-level AI strategy. It’s for those who must operationalize governance where policy meets practice.
What you walk away with
- Establish a risk-based AI governance framework aligned with compliance mandates
- Integrate ethical review processes into AI project lifecycles
- Lead cross-functional alignment between tech teams, legal, and compliance
- Document controls and audit trails for regulatory scrutiny
- Anticipate and mitigate AI-specific compliance risks before deployment
The 12 modules (with all 144 chapters)
Module 1. Foundations of AI Governance
Understand the core principles of AI governance, including accountability, transparency, and regulatory alignment. Learn how governance differs from traditional compliance and why proactive oversight is critical. Explore real-world examples of AI failures and the governance gaps that enabled them. Define your role within the governance ecosystem.
12 chapters in this module
- What is AI governance?
- Why governance differs from compliance
- Key regulatory drivers
- Accountability frameworks
- Transparency requirements
- Ethical oversight models
- Risk-based prioritization
- Stakeholder mapping
- Governance vs innovation tension
- Internal control design
- Audit readiness basics
- Governance maturity levels
Module 2. Regulatory Landscape for AI
Navigate global and regional AI regulations including the EU AI Act, NIST AI RMF, and sector-specific rules. Understand enforcement trends and how they apply to different use cases. Learn to classify AI systems by risk tier and map obligations accordingly. Stay ahead of upcoming mandates.
12 chapters in this module
- EU AI Act overview
- NIST AI Risk Management Framework
- Sector-specific rules
- Risk classification tiers
- High-risk use cases
- Enforcement precedents
- Cross-border implications
- Compliance deadlines
- Regulator expectations
- AI and data protection
- Algorithmic accountability laws
- Future regulatory signals
Module 3. AI Risk Assessment Frameworks
Build a repeatable process for evaluating AI risks across ethical, legal, and operational dimensions. Learn to assess bias, explainability, data integrity, and model drift. Apply scoring models to prioritize governance attention. Integrate assessments into project intake workflows.
12 chapters in this module
- Risk dimensions in AI
- Bias detection methods
- Explainability standards
- Data quality checks
- Model validation steps
- Drift monitoring setup
- Human oversight triggers
- Third-party risk factors
- Incident escalation paths
- Risk scoring models
- Assessment documentation
- Integration with project intake
Module 4. Ethical Oversight and Review Boards
Design and implement an AI ethics review process. Understand the role of ethics boards, define membership and mandates, and establish review criteria. Learn how to operationalize ethical principles like fairness and autonomy in technical design.
12 chapters in this module
- Purpose of ethics boards
- Board composition models
- Review criteria design
- Fairness definitions
- Autonomy considerations
- Human-in-the-loop rules
- Red teaming AI systems
- Bias mitigation plans
- Ethics checklist creation
- Review meeting workflows
- Documentation standards
- Escalation protocols
Module 5. Compliance Integration with Development
Embed compliance checks into AI development lifecycles. Learn to align with SDLC and DevOps practices. Define governance checkpoints, documentation requirements, and handoff protocols between teams. Ensure audit readiness from day one.
12 chapters in this module
- Governance in SDLC
- DevOps integration points
- Compliance checkpoints
- Documentation templates
- Model cards explained
- Data lineage tracking
- Version control policies
- Change approval workflows
- Audit trail setup
- Staging environment rules
- Go/no-go decision gates
- Post-deployment reviews
Module 6. AI Auditing and Assurance
Prepare for internal and external AI audits. Understand what auditors look for in AI systems. Build documentation packages, control evidence, and remediation plans. Learn to respond to findings and demonstrate continuous improvement.
12 chapters in this module
- Audit scope definition
- Evidence collection methods
- Control testing techniques
- Documentation packages
- Remediation planning
- Regulator communication
- Internal audit prep
- Third-party audit support
- Findings response templates
- Corrective action tracking
- Continuous monitoring
- Audit readiness checklist
Module 7. AI Incident Response and Escalation
Develop protocols for detecting, reporting, and resolving AI incidents. Define thresholds for model failure, bias events, and security breaches. Establish cross-functional response teams and communication plans.
12 chapters in this module
- Incident classification
- Detection mechanisms
- Bias event protocols
- Model failure thresholds
- Security breach response
- Escalation paths
- Cross-team coordination
- Communication templates
- Root cause analysis
- Remediation workflows
- Post-mortem documentation
- Regulatory reporting
Module 8. Third-Party and Vendor Oversight
Govern AI systems developed or hosted by third parties. Learn to assess vendor compliance, manage contractual obligations, and monitor ongoing performance. Ensure oversight extends beyond internal systems.
12 chapters in this module
- Vendor risk assessment
- Contractual clauses
- Due diligence steps
- Audit rights negotiation
- Performance monitoring
- Data handling rules
- Sub-processor oversight
- Compliance certifications
- Vendor incident response
- Exit strategy planning
- Ongoing review cycles
- Relationship governance
Module 9. AI Policy Development and Communication
Write clear, enforceable AI policies tailored to your organization. Learn to communicate expectations across technical and non-technical teams. Establish training and awareness programs to ensure adoption.
12 chapters in this module
- Policy drafting principles
- Scope definition
- Enforceability criteria
- Stakeholder alignment
- Training program design
- Awareness campaigns
- Policy version control
- Exception handling
- Compliance monitoring
- Feedback loops
- Policy review cycles
- Executive endorsement
Module 10. Cross-Functional Leadership in AI Governance
Lead without authority in AI governance. Build influence across data science, legal, compliance, and business units. Navigate competing priorities and align on shared objectives.
12 chapters in this module
- Stakeholder influence
- Conflict resolution
- Alignment workshops
- Shared KPIs
- Governance council setup
- Decision rights mapping
- Communication cadence
- Meeting facilitation
- Progress reporting
- Escalation frameworks
- Coalition building
- Change leadership
Module 11. AI Governance Metrics and Reporting
Define and track KPIs for AI governance effectiveness. Report to executives and regulators with confidence. Demonstrate value and maturity over time.
12 chapters in this module
- KPI selection
- Governance maturity metrics
- Compliance rate tracking
- Incident frequency
- Audit pass rates
- Risk exposure trends
- Stakeholder satisfaction
- Board reporting templates
- Dashboard design
- Benchmarking data
- Improvement tracking
- Regulatory submissions
Module 12. Scaling AI Governance Organization-Wide
Evolve from ad-hoc oversight to an enterprise-wide AI governance function. Build teams, define roles, and institutionalize practices. Ensure sustainability and adaptability as AI adoption grows.
12 chapters in this module
- Governance function design
- Team structure options
- Role definitions
- Center of excellence model
- Federated governance
- Budget justification
- Tooling investment
- Knowledge management
- Continuous improvement
- Adaptation to new tech
- Leadership engagement
- Long-term roadmap
How this maps to your situation
- Responding to AI-driven transformation
- Strengthening compliance in digital innovation
- Leading cross-functional AI oversight
- Preparing for regulatory scrutiny on AI
Before vs. after
Before
Uncertain how to govern AI systems without slowing innovation or increasing risk.
After
Confident in leading structured, compliant, and ethical AI deployment across teams and use cases.
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 3-4 hours per module, designed for flexible, self-paced learning.
If nothing changes
Without structured AI governance, organizations face regulatory penalties, reputational damage, and loss of stakeholder trust, especially when AI systems impact public services or sensitive data.
How this compares to the alternatives
Unlike generic AI ethics courses, this program focuses on actionable governance frameworks, compliance integration, and real-world implementation, built for risk and compliance professionals, not technologists.
Frequently asked
Who is this course for?
Compliance, risk, and governance professionals responsible for overseeing AI systems in regulated or public-sector environments.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is this about building AI models?
No. This course focuses on governance, risk, compliance, and oversight, not technical development.
$199 one-time. Approximately 3-4 hours per module, designed for flexible, self-paced learning..
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