A tailored course, built for your situation
Mastering ISO 42001 for AI Governance Practitioners
Build authoritative control frameworks that reflect your technical depth and expand your influence in AI governance.
$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.
Who this is for
Senior individual contributor in AI/ML or data platform governance, operating at a cloud-scale tech firm with growing AI compliance obligations.
Who this is not for
Entry-level engineers, consultants selling governance as a service, or executives seeking board-level narratives.
What you walk away with
- Define the scope and boundaries of AI governance within your current role using ISO 42001 controls
- Document a fully implementable AI governance framework tailored to your organization’s AI deployment patterns
- Lead cross-functional alignment on AI risk thresholds without escalation
- Establish formal sign-off pathways for AI system registration and monitoring
- Produce a reusable governance playbook that persists beyond team changes
The 12 modules (with all 144 chapters)
Module 1. Understanding ISO 42001 and Its Strategic Importance
Lay the foundation by exploring the structure, intent, and organizational impact of ISO 42001. Learn how it differs from other AI governance efforts and why it’s becoming the standard for technical practitioners leading internal frameworks.
12 chapters in this module
- What ISO 42001 regulates
- Linking AI systems to governance domains
- Differentiating from NIST AI RMF
- Organizational triggers for adoption
- Mapping to real-world AI use cases
- Global recognition of certification
- Timeline for implementation
- Integration with existing compliance
- Executive expectations
- Common misconceptions
- Scope definition principles
- First steps in internal rollout
Module 2. Defining the AI Governance Boundary
Clarify which AI systems fall under governance based on risk, deployment context, and data sensitivity. Build a scoping model that prevents overreach while ensuring accountability.
12 chapters in this module
- Identifying AI system types
- Classifying model risk levels
- Deployed vs experimental systems
- Data provenance considerations
- Human oversight thresholds
- Autonomous decision criteria
- Generative AI inclusion rules
- Third-party model tracking
- Open-source inclusion policy
- Internal tooling boundaries
- Versioning and rollback scope
- Change triggers for re-scope
Module 3. Establishing Governance Roles and Responsibilities
Design clear ownership across development, operations, security, and compliance. Avoid ambiguity in decision rights and escalation paths.
12 chapters in this module
- RACI for AI systems
- Defining governance leads
- Cross-team coordination
- Escalation protocols
- Sign-off authorities
- Change advisory boards
- Vendor oversight rules
- Incident reporting flow
- Documentation custodians
- Audit readiness owners
- Policy enforcement roles
- Training responsibility matrix
Module 4. AI Risk Assessment Framework Design
Build a repeatable, evidence-based risk assessment method aligned with ISO 42001 controls, tailored to your platform and deployment patterns.
12 chapters in this module
- Risk criteria definition
- Likelihood scales
- Impact categories
- Bias and fairness scoring
- Transparency thresholds
- Security exposure levels
- Model drift triggers
- Data quality checks
- Third-party dependency risks
- Reputational exposure levels
- Legal compliance mapping
- Risk tolerance documentation
Module 5. Building the AI System Register
Create a living inventory of AI systems with metadata, risk classification, and control coverage, forming the backbone of audit readiness.
12 chapters in this module
- Fields to include
- Ownership tracking
- Risk classification tags
- Model version linkage
- Data source documentation
- Human oversight status
- Change history tracking
- External model identification
- Access control settings
- Monitoring cadence
- Audit trail requirements
- Integration with MLOps
Module 6. Designing Transparency and Explainability Controls
Implement practical methods to ensure AI decisions are interpretable, auditable, and defensible without slowing innovation.
12 chapters in this module
- Defining explainability tiers
- Model documentation standards
- Input-output traceability
- Feature importance reporting
- Counterfactual explanations
- User-facing disclosures
- Internal transparency portals
- Bias mitigation documentation
- Model confidence reporting
- Uncertainty communication
- Audit log requirements
- Feedback loop integration
Module 7. Human Oversight and Intervention Design
Establish clear, scalable rules for human involvement in AI decisions, including thresholds, escalation, and override mechanisms.
12 chapters in this module
- High-risk decision markers
- Override capability design
- Monitoring dashboards
- Alert thresholds
- Review cadence rules
- Escalation workflows
- Documentation of intervention
- Training for human reviewers
- False positive tolerance
- Auto-approval conditions
- Periodic validation
- Audit readiness checks
Module 8. AI System Lifecycle Controls
Map governance controls across development, testing, deployment, monitoring, and decommissioning phases for full lifecycle coverage.
12 chapters in this module
- Pre-development governance
- Model design review
- Testing gate criteria
- Deployment approvals
- Monitoring setup
- Incident response plan
- Model retraining rules
- Version change process
- Decommission policy
- Data retention rules
- Audit trail preservation
- Post-mortem requirements
Module 9. Data Management and Quality Assurance
Ensure data feeding AI systems is governed, documented, and fit for purpose, aligning with ISO 42001's data integrity expectations.
12 chapters in this module
- Data provenance tracking
- Bias in training data
- Data quality metrics
- Labeling process controls
- Data drift detection
- Anonymization standards
- Third-party data sourcing
- Data access controls
- Data retention policies
- Data lineage mapping
- Data versioning
- Data refresh rules
Module 10. Security and Resilience for AI Systems
Apply robust security practices to AI infrastructure and models, addressing unique risks like model theft, poisoning, and evasion.
12 chapters in this module
- Model access controls
- Encryption in transit
- Model signing standards
- Adversarial testing
- Model integrity checks
- API security rules
- Incident response for AI
- Resilience testing
- Fail-safe modes
- Threat modeling
- Penetration testing
- Model rollback capability
Module 11. Audit Readiness and Continuous Monitoring
Prepare for internal and external audits with continuous monitoring, evidence collection, and real-time compliance dashboards.
12 chapters in this module
- Control mapping to ISO 42001
- Evidence collection
- Automated monitoring
- Compliance dashboards
- Audit trail structure
- Documentation standards
- Internal review cycles
- External auditor prep
- Findings remediation
- Continuous improvement
- Metrics for maturity
- Certification roadmap
Module 12. Sustaining AI Governance Over Time
Ensure governance evolves with technology, personnel, and business needs through documentation, training, and adaptive policies.
12 chapters in this module
- Change management process
- Training for new hires
- Policy review cadence
- Framework updates
- Lessons learned capture
- Cross-team feedback
- Succession planning
- Knowledge transfer
- External benchmarking
- Regulatory horizon scanning
- Stakeholder communication
- Governance maturity model
How this maps to your situation
- AI system scoping and classification
- Cross-functional governance ownership
- Risk assessment and documentation
- Audit-ready evidence production
Before vs. after
Before
AI governance decisions require multiple approvals and lack a single source of truth.
After
You define and own the governance framework, with direct authority over scope, control, and compliance.
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 hours per module, designed for integration into real-time projects.
If nothing changes
Without a structured approach, AI governance remains reactive, fragmented, and dependent on others’ timelines, limiting your ability to lead.
How this compares to the alternatives
Unlike generic compliance courses, this program delivers a fully tailored, implementable ISO 42001 framework specific to your AI environment and role authority.
Frequently asked
Is this course technical or policy-focused?
It bridges both, designed for technical practitioners who lead governance, with concrete implementation steps, templates, and control mappings.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help me get certified?
This course prepares you to implement ISO 42001 effectively and leads toward audit readiness, though certification is awarded by accredited bodies.
$199 one-time. Approximately 3 hours per module, designed for integration into real-time projects..
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