A tailored course, built for your situation
Mastering ISO 42001 for Corporate & Investment Banking Analysts
Build AI governance frameworks that stand up to executive review and internal scrutiny
$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 governance work getting lost in technical detail or buried in silos
The situation this course is for
Practitioners deliver strong technical work, but it doesn't rise to the level where leadership sees or values it, especially in regulated financial institutions where AI use is under increasing scrutiny
Who this is for
Corporate & Investment Banking Analyst at a global financial institution, involved in compliance-adjacent AI governance initiatives, working to align technical standards with business risk and regulatory expectations
Who this is not for
Executives seeking board-level summaries, data scientists implementing AI models, or consultants without banking sector exposure
What you walk away with
- Produce ISO 42001 statements of applicability that reflect both technical rigor and business context
- Map AI governance controls to existing risk reporting lines used by senior leadership
- Create audit-ready documentation packages that reduce follow-up cycles
- Anticipate leadership questions and embed responses directly in framework outputs
- Build credibility as a go-to contributor on AI governance across compliance, risk, and innovation teams
The 12 modules (with all 144 chapters)
Module 1. Understanding ISO 42001 in Financial Services Context
Learn how ISO 42001 applies specifically to AI systems in banking, with emphasis on risk classification, model transparency, and auditability.
12 chapters in this module
- Scope of AI governance in CIB
- Regulatory drivers in banking
- AI use cases at BBVA
- Defining AI system boundaries
- Mapping to internal risk frameworks
- Identifying AI stakeholders
- Documentation expectations
- Risk-based approach to AI
- Control objectives in practice
- Human oversight requirements
- Performance monitoring needs
- Lifecycle management phases
Module 2. Establishing Governance Structure and Roles
Define clear roles for AI oversight that align with existing compliance hierarchies and executive reporting lines.
12 chapters in this module
- AI governance committee design
- Executive sponsor identification
- Internal auditor engagement
- Control owner assignment
- Escalation pathways
- Reporting cadence setup
- Cross-functional alignment
- Documentation stewardship
- Accountability mapping
- Sign-off authority flow
- Change review process
- Version control standards
Module 3. Asset Management for AI Systems
Track AI components, data flows, and dependencies using templates aligned with enterprise architecture practices.
12 chapters in this module
- Inventory of AI systems
- Data lifecycle mapping
- Model version tracking
- Dependency logs
- Access control matrix
- Metadata standards
- Ownership documentation
- Change history logs
- Integration points
- Decommissioning process
- Storage location tracking
- Third-party model oversight
Module 4. Risk Assessment and Treatment Planning
Conduct AI-specific risk assessments that produce actionable treatment plans and prioritized remediation steps.
12 chapters in this module
- Threat identification for AI
- Bias and fairness evaluation
- Security risk profiling
- Privacy impact considerations
- Model drift detection
- Adversarial attack vectors
- Risk scoring methodology
- Treatment options matrix
- Mitigation implementation
- Residual risk reporting
- Risk acceptance criteria
- Independent review triggers
Module 5. Data, Model, and System Quality Assurance
Implement quality checks for training data, model outputs, and deployment environments that meet ISO 42001 standards.
12 chapters in this module
- Training data integrity
- Data provenance tracking
- Feature engineering review
- Model validation process
- Bias testing protocols
- Accuracy benchmarks
- Stability monitoring
- Output consistency checks
- Model interpretability
- Fallback mechanisms
- Re-training triggers
- Model retirement criteria
Module 6. Transparency and Explainability in AI Decisions
Design reporting and documentation that make AI decisions interpretable to non-technical stakeholders.
12 chapters in this module
- Decision rationale capture
- Model explanation standards
- User communication templates
- Stakeholder briefing packs
- Audit trail requirements
- Log retention policies
- Access request procedures
- Right to explanation
- Human-in-the-loop design
- Escalation to review process
- Complaint handling workflow
- Transparency reporting
Module 7. Human Oversight Mechanisms
Build processes that ensure meaningful human review of high-risk AI decisions, especially in client-facing contexts.
12 chapters in this module
- Oversight trigger definition
- Review frequency standards
- Escalation thresholds
- Human override protocol
- Intervention documentation
- Reviewer competency
- Training for oversight
- Performance metrics
- Bias detection triggers
- Model justification
- Decision logging
- Audit readiness
Module 8. Robustness, Security, and Safety Controls
Apply security best practices to AI systems to prevent misuse, breaches, and unintended consequences.
12 chapters in this module
- Model security posture
- Access control enforcement
- Input validation rules
- Adversarial testing
- Model poisoning prevention
- Escape scenario planning
- Fail-safe design
- Incident response plan
- Breach detection
- Recovery procedures
- Penetration testing
- Vulnerability management
Module 9. Record Keeping and Audit Readiness
Create comprehensive, organized records that support internal and external audits.
12 chapters in this module
- Documentation checklist
- Version control system
- Storage compliance
- Retention periods
- Access logs
- Audit trail structure
- Change request tracking
- Approval workflows
- Evidence collection
- Regulator readiness
- Internal audit response
- Third-party review support
Module 10. Conformity Assessment and Certification Path
Navigate the ISO 42001 certification process with confidence and clarity.
12 chapters in this module
- Certification scope definition
- Gap analysis method
- Internal audit process
- Corrective action tracking
- External auditor coordination
- Statement of Applicability
- Control evidence packages
- Readiness assessment
- Certification timeline
- Surveillance audit prep
- Continuous improvement
- Recertification planning
Module 11. Integration with Existing Compliance Programs
Align ISO 42001 efforts with existing SOX, GDPR, and other compliance frameworks.
12 chapters in this module
- SOX control mapping
- GDPR integration
- Operational risk overlap
- BCP alignment
- Internal audit coordination
- Regulatory reporting
- Policy harmonization
- Training synergy
- Vendor oversight
- Third-party assurance
- Compliance dashboard
- Executive reporting
Module 12. Sustaining AI Governance Over Time
Ensure long-term effectiveness and relevance of AI governance through continuous monitoring and improvement.
12 chapters in this module
- Ongoing monitoring plan
- Performance metrics
- Feedback loops
- Change management
- Policy updates
- Technology refresh
- Stakeholder engagement
- Lessons learned
- Benchmarking against peers
- Innovation pipeline
- Regulatory alert system
- Maturity model tracking
How this maps to your situation
- AI governance in banking
- ISO 42001 implementation
- Executive visibility
- Audit and compliance
Before vs. after
Before
AI governance work remains technical, detailed, and under-recognized, with limited executive visibility
After
AI governance outputs are structured, strategic, and consistently brought into leadership conversations
What's included with your purchase
- 12 modules with 12 chapters each (144 chapters total)
- 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, or 36 hours total, designed for flexible, self-paced learning
If nothing changes
Without structured ISO 42001 alignment, AI initiatives may face delayed approvals, increased audit friction, or reputational exposure due to perceived governance gaps
How this compares to the alternatives
Unlike generic AI ethics courses or university programs, this course delivers actionable, ISO 42001-specific implementation steps tailored to financial services practitioners, focused on documentation, control mapping, and executive communication rather than theory
Frequently asked
Who is this course designed for?
It's tailored for compliance, risk, and governance professionals in financial services who are responsible for implementing AI governance frameworks, particularly those preparing for ISO 42001 alignment.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Does this course prepare me for ISO 42001 certification?
Yes, it provides the roadmap, templates, and implementation guidance needed to lead or support a certification effort within a financial institution.
$199 one-time. Approximately 3 hours per module, or 36 hours total, 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