A tailored course, built for your situation
Mastering ISO 22301 for Machine Learning Engineers in Financial AI
Turn continuity planning into cross-functional influence for AI systems
$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 deployments stall when continuity isn’t embedded from the start
The situation this course is for
Without a formal continuity structure, ML engineers face repeated rework, audit delays, and siloed ownership, especially when compliance or operations teams intervene late in the cycle.
Who this is for
Machine Learning Engineers in financial institutions who own or contribute to AI system design and need to ensure operational resilience across regulatory and technical boundaries
Who this is not for
Incident managers, BCM consultants, or operations leads focused on general IT continuity without AI system specificity
What you walk away with
- Own end-to-end continuity documentation for AI workloads
- Align AI deployments with ISO 22301 control objectives
- Produce audit-ready business impact analyses specific to ML systems
- Lead cross-functional alignment between AI, compliance, and operations teams
- Reduce rework cycles by integrating continuity into MLOps pipelines
The 12 modules (with all 144 chapters)
Module 1. Why ISO 22301 matters for AI systems
Understand how business continuity planning applies uniquely to machine learning workloads, especially in regulated financial environments.
12 chapters in this module
- AI reliability vs IT availability
- Regulatory drivers in EU financial services
- ISO 22301 scope boundaries for ML
- Linking AI failure to business impact
- The compliance advantage of continuity
- Continuity as part of model risk
- When AI downtime becomes operational risk
- Mapping AI roles to BCM teams
- The cost of unplanned AI outages
- How continuity strengthens MLOps
- Case study: AI model rollback incident
- Designing continuity into sprints
Module 2. Defining AI system criticality
Classify machine learning systems by business impact and recovery priority using ISO 22301 logic.
12 chapters in this module
- Identifying mission-critical AI
- Recovery time objectives for ML
- Recovery point objectives for data
- Defining acceptable downtime
- Stakeholder input collection
- Risk scoring AI workloads
- Dependency mapping for ML pipelines
- Third-party model risk
- API uptime SLAs
- Data freshness thresholds
- Human-in-the-loop continuity
- Scoring model drift impact
Module 3. Conducting business impact analysis
Build a defensible, evidence-based BIA tailored to AI systems and their downstream effects.
12 chapters in this module
- BIA templates for ML systems
- Interviewing business owners
- Quantifying AI failure costs
- Uptime requirements by use case
- Customer impact scoring
- Operational ripple effects
- Reporting chain disruptions
- Compliance exposure metrics
- Financial exposure models
- Reputation risk estimation
- Documenting assumptions
- Validating BIA findings
Module 4. Mapping AI to ISO 22301 controls
Translate high-level BCM controls into specific, actionable practices for machine learning environments.
12 chapters in this module
- Control 5.2: Awareness programs
- Control 6.1: Exercise planning
- Control 6.2: Exercise types
- Control 7.1: Response structure
- Control 7.2: Crisis communication
- Control 8.1: Resource continuity
- Control 8.2: Data backup
- Control 8.3: Data replication
- Control 9.1: Supplier continuity
- Control 9.2: Third-party AI
- Control 10.1: Plan maintenance
- Control 10.2: Plan testing
Module 5. Designing AI-specific continuity plans
Create living, actionable plans that ensure AI workloads can be restored or safely degraded.
12 chapters in this module
- Failover vs fail-degrade strategies
- Model rollback procedures
- Data continuity protocols
- Feature store recovery
- Model registry backups
- API endpoint redundancy
- Monitoring during outages
- Manual override pathways
- Fallback logic design
- Graceful degradation rules
- Drift detection in downtime
- Revalidation after recovery
Module 6. Exercising AI continuity
Run effective tabletop and technical exercises that validate AI system resilience.
12 chapters in this module
- Exercise planning calendar
- Scenario design for AI
- Red team vs blue team roles
- Simulating model drift
- Testing data pipeline breaks
- API outage drills
- Alert fatigue evaluation
- Decision logging during drills
- Post-exercise review templates
- Improvement tracking
- Regulator-ready exercise reports
- Integrating lessons into MLOps
Module 7. Documentation for audit and review
Produce clean, consistent, defensible artifacts for internal and external auditors.
12 chapters in this module
- SoA alignment for AI
- Control mapping evidence
- Version-controlled playbooks
- Audit trail for decisions
- Change logs for continuity plans
- Evidence of testing
- Roles and responsibilities register
- Third-party attestation
- Compliance crosswalks
- Management sign-off process
- Review cycle documentation
- Continuous monitoring logs
Module 8. Integrating with MLOps pipelines
Embed continuity checks directly into deployment workflows and CI/CD processes.
12 chapters in this module
- Pre-deployment continuity gate
- Automated BIA triggers
- Model rollback scripting
- Data lineage for recovery
- Drift detection integration
- Failover configuration as code
- Documentation generation
- Audit readiness pipeline
- Staging environment testing
- Production drift monitoring
- Automated alerting
- Self-healing triggers
Module 9. Leading cross-functional alignment
Drive consensus and accountability across AI, operations, compliance, and risk teams.
12 chapters in this module
- Stakeholder onboarding
- Continuity ownership model
- Escalation pathways
- Joint exercise planning
- Shared KPIs
- Communication protocols
- Conflict resolution
- Reporting to leadership
- Translating tech to business
- Building executive trust
- Standardizing language
- Feedback integration
Module 10. Managing third-party AI risk
Ensure vendor-provided models and tools meet internal continuity standards.
12 chapters in this module
- Vendor continuity requirements
- Due diligence checklists
- Contractual SLAs
- Model ownership clarity
- API reliability terms
- Fallback options for SaaS
- Vendor exercise participation
- Audit rights clauses
- Exit strategy planning
- Continuity documentation access
- Penalty enforcement
- Multi-vendor fallback
Module 11. Maintaining continuity over time
Keep plans relevant as models evolve, infrastructure changes, and regulations shift.
12 chapters in this module
- Change impact analysis
- Model update reviews
- Infrastructure change triggers
- Regulatory change monitoring
- Quarterly review cycle
- Automated drift alerts
- Model registry sync
- Playbook versioning
- Stakeholder revalidation
- Lessons learned integration
- Toolchain updates
- Continuity debt tracking
Module 12. Scaling across the AI portfolio
Replicate and standardize continuity practices across multiple models and teams.
12 chapters in this module
- Template library creation
- Centralized oversight
- Standardized scoring
- Cross-team playbooks
- Shared tooling
- Continuity champions network
- Enterprise reporting
- Benchmarking progress
- Maturity assessment
- Roadmap development
- Leadership dashboards
- Scaling without centralization
How this maps to your situation
- When launching a new AI product
- During regulatory audit prep
- After an AI system outage
- While expanding AI to new business units
Before vs. after
Before
AI continuity planning is reactive, siloed, and inconsistent, leading to audit findings and deployment delays.
After
You own a standardized, auditable, and repeatable method to design and document AI system continuity that earns trust across teams.
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 to be completed in parallel with ongoing work.
If nothing changes
Without structured continuity, AI systems remain fragile, audits take longer, and your influence is limited to technical circles.
How this compares to the alternatives
Unlike generic BCM courses, this is tailored to ML engineers in financial services, focusing on AI-specific risks, MLOps integration, and ISO 22301 compliance in regulated environments.
Frequently asked
Is this course relevant if I don’t work in BCM or risk?
Yes. It’s designed for ML engineers who own system reliability and need to speak the language of compliance and operations.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Does this cover other standards like ISO 27001 or DORA?
Focus is on ISO 22301, but crosswalks to DORA and ISO 27001 are included where relevant.
$199 one-time. Approximately 3 hours per module, designed to be completed in parallel with ongoing work..
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