A tailored course, built for your situation
Deeper command of the AI governance frameworks shaping financial services
Master the standards, controls, and implementation patterns defining responsible AI in regulated banking environments
$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.
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The situation this course is for
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Who this is for
Senior engineering leader in a regulated financial institution driving AI/ML adoption under formal risk and control scrutiny
Who this is not for
Individuals looking for high-level AI policy overviews or non-technical governance summaries
What you walk away with
- Fluency in the NIST AI RMF and how it maps to internal control frameworks
- Ability to translate model risk management expectations into engineering requirements
- Confidence deploying governance controls that satisfy examiners and developers alike
- Reputation as the go-to technical authority on AI governance decisions
- Articulation of governance trade-offs with precision, not abstraction
The 12 modules (with all 144 chapters)
Module 1. Understanding the AI governance landscape in financial services
Establish context for how AI risk is being framed by regulators, standards bodies, and internal audit functions in banking.
12 chapters in this module
- What defines AI governance in finance
- Regulatory drivers shaping the space
- Core principles from NIST AI RMF
- Model risk management basics
- Internal audit expectations
- How control frameworks apply
- Emerging standards to track
- Mapping governance to use cases
- Stakeholder roles defined
- Engineering constraints to anticipate
- Baseline assessment tool
- Self-evaluation for readiness
Module 2. Integrating NIST AI RMF into engineering workflows
Translate the NIST framework into actionable steps for development, testing, and deployment processes.
12 chapters in this module
- NIST RMF structure breakdown
- Mapping to software lifecycle
- Risk categories explained
- Harm pathways identified
- Assessment goals defined
- Tailoring for scale
- Documentation requirements
- Integration with CI/CD
- Team responsibilities assigned
- Version control for models
- Audit trail design
- Feedback loop mechanisms
Module 3. Designing model risk controls for production AI
Build technical controls that align with FFIEC and SR 11-7 expectations while maintaining innovation velocity.
12 chapters in this module
- Model risk lifecycle stages
- Validation plan components
- Performance monitoring specs
- Drift detection implementation
- Bias assessment protocols
- Explainability requirements
- Output evaluation design
- Retraining triggers defined
- Change management rules
- Access controls enforced
- Model inventory structure
- Sunset process planning
Module 4. Mapping AI governance to internal control frameworks
Align AI controls with existing SOX, RCSA, and operational risk frameworks.
12 chapters in this module
- Control framework fundamentals
- Mapping AI risks to RCSA
- SOX-relevant AI systems
- Control ownership models
- Evidence collection methods
- Testing frequency rules
- Exception handling process
- Documentation alignment
- Audit preparation checklist
- Cross-functional alignment
- Control automation options
- Scalable oversight design
Module 5. Creating governance-ready documentation
Produce artefacts examiners accept and engineers trust.
12 chapters in this module
- What examiners look for
- Model documentation standards
- Version control logs
- Data provenance tracking
- Training data summaries
- Testing protocols recorded
- Performance benchmarks documented
- Bias mitigation reports
- Explainability outputs
- Change history logs
- Approval workflow records
- Retention policy alignment
Module 6. Operationalizing AI oversight committees
Structure cross-functional governance bodies that make timely, credible decisions.
12 chapters in this module
- Committee purpose definition
- Membership criteria
- Tiered review model
- Gate review design
- Escalation paths defined
- Decision logging
- Charter development
- Meeting rhythm setup
- Reporting templates
- Stakeholder alignment
- Feedback integration
- Performance review
Module 7. Implementing monitoring and alerting for AI systems
Deploy technical observability that meets both engineering and risk standards.
12 chapters in this module
- Key metrics to track
- Model performance dashboards
- Drift detection thresholds
- Bias monitoring alerts
- Data quality checks
- Output consistency checks
- Human-in-the-loop triggers
- Anomaly detection rules
- Incident response workflow
- Root cause analysis process
- Remediation tracking
- Audit integration
Module 8. Building ethical AI review into development
Institutionalize ethical design checks without slowing delivery.
12 chapters in this module
- Ethical design principles
- Harm typology application
- Stakeholder impact mapping
- Use case risk tiers
- Pre-deployment checklist
- Bias testing integration
- Transparency standards
- Stakeholder consultation
- Red teaming process
- Ethics escalation path
- Documentation requirements
- Audit trail creation
Module 9. Establishing AI model inventory and registry
Create a single source of truth for all AI assets across the enterprise.
12 chapters in this module
- Model taxonomy design
- Registry data fields
- Ownership assignment
- Lifecycle stage tracking
- Risk tier classification
- Integration with CMDB
- Access control rules
- Search and discovery
- Reporting capabilities
- Automated ingestion
- Maintenance workflow
- Audit integration
Module 10. Scaling AI governance across teams and platforms
Extend governance consistently without centralizing all decisions.
12 chapters in this module
- Governance operating model
- Center of excellence design
- Embedded roles defined
- Standards dissemination
- Tooling standardization
- Training rollout plan
- Compliance automation
- Audit coordination
- Feedback loop design
- Change management
- Metrics for success
- Maturity assessment
Module 11. Navigating audits and examinations
Prepare for scrutiny with confidence and clarity.
12 chapters in this module
- Examination scope understanding
- Document request readiness
- Interview preparation
- Evidence organization
- Deficiency response process
- Coordination with legal
- Position paper drafting
- Follow-up workflow
- Lessons learned capture
- Process improvement
- Relationship management
- Proactive disclosure
Module 12. Advancing your influence as a technical governance leader
Position yourself as the trusted voice between engineering and oversight.
12 chapters in this module
- Building cross-functional credibility
- Communicating risk clearly
- Translating policy to practice
- Leading without authority
- Mentoring junior staff
- Shaping strategy inputs
- Presenting to leadership
- Writing thoughtfully
- Speaking with precision
- Earning discretionary trust
- Expanding scope
- Defining the future
How this maps to your situation
- When launching a new AI initiative
- Before regulatory examination cycle
- During model risk framework review
- After control deficiency finding
Before vs. after
Before
Reacting to governance asks with incomplete frameworks and fragmented documentation
After
Proactively shaping AI governance with full command of standards, controls, and implementation patterns
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, designed to fit around executive engineering schedules.
If nothing changes
Continuing to treat AI governance as a compliance hurdle rather than a technical leadership opportunity risks ceding influence to non-technical teams and slowing innovation due to rework.
How this compares to the alternatives
Unlike generic AI ethics courses or high-level policy summaries, this program delivers technical precision tailored to the compliance expectations of global financial institutions.
Frequently asked
Who is this course designed for?
Senior engineering leaders in regulated financial institutions who are responsible for implementing or overseeing AI systems with formal risk and control expectations.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Does this cover actual implementation?
Yes , every module includes templates, checklists, and real-world examples used in top-tier banks to operationalize AI governance.
$199 one-time. Approximately 3 hours per module, designed to fit around executive engineering schedules..
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