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BCM9722 Mastering ISO 22301 for Senior ML/AI Engineers in Global Tech

$199.00
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A tailored course, built for your situation

Mastering ISO 22301 for Senior ML/AI Engineers in Global Tech

A step-by-step system to align AI infrastructure with business continuity standards and gain peer influence in technical governance decisions.

$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.
End the churn in business continuity documentation cycles for AI systems.

Who this is for

Senior ML/AI Engineers in large tech orgs who own system uptime and need to influence beyond their immediate team.

Who this is not for

Junior engineers, non-technical compliance staff, or teams focused solely on model accuracy without infrastructure ownership.

What you walk away with

  • Produce ISO 22301-compliant documentation that passes cross-functional review without iteration
  • Gain peer credibility in vendor selection and architecture reviews involving resilience
  • Anchor AI infrastructure decisions in continuity standards that leadership trusts
  • Reduce business continuity documentation cycles from weeks to days
  • Position yourself as the go-to voice on AI system resilience in planning forums

The 12 modules (with all 144 chapters)

Module 1. Understanding ISO 22301 in the context of AI infrastructure
Lays the foundation for applying business continuity management principles specifically to AI and ML systems, focusing on uptime, failover, and incident response alignment.
12 chapters in this module
  1. Defining business continuity in AI-driven environments
  2. How ISO 22301 differs from general reliability engineering
  3. Mapping AI system components to BCMS requirements
  4. Key clauses relevant to machine learning infrastructure
  5. Interpreting 'continuity' in distributed AI systems
  6. Linking model serving uptime to organizational resilience
  7. Common misconceptions about AI and BCMS
  8. Why resilience isn't just about hardware redundancy
  9. Integrating incident response workflows with BCMS
  10. Assessing single points of failure in AI pipelines
  11. Establishing minimum viable continuity for ML services
  12. Aligning AI uptime goals with business impact thresholds
Module 2. Scoping AI systems under ISO 22301
Covers how to define boundaries and applicability for AI systems within a BCMS framework, avoiding overreach while maintaining compliance.
12 chapters in this module
  1. Identifying which AI services fall under BCMS scope
  2. Defining criticality thresholds for AI workloads
  3. Documenting service-level continuity expectations
  4. Classifying AI systems by recovery time objectives
  5. Managing scope creep in large AI environments
  6. Aligning AI infrastructure scope with network policies
  7. Handling experimental vs. production AI systems
  8. Exclusion justification for non-critical AI models
  9. Integrating scope decisions with change management
  10. Using RTO and RPO to prioritize AI continuity efforts
  11. Stakeholder input in AI system scoping
  12. Maintaining scope documentation for audits
Module 3. Risk assessment for AI infrastructure
Guides engineers through identifying and documenting risks unique to AI systems, including data pipeline failures and model drift.
12 chapters in this module
  1. Threats specific to AI infrastructure components
  2. Identifying single points of failure in AI pipelines
  3. Assessing impact of training data unavailability
  4. Model deployment rollback risks
  5. Dependency mapping for AI service chains
  6. Third-party risk in AI model hosting platforms
  7. Human-in-the-loop failure scenarios
  8. Monitoring blind spots in AI operations
  9. Adversarial attack surfaces in model serving
  10. Data poisoning and latency risks
  11. Scoring AI risks using ISO 22301 criteria
  12. Prioritizing risks based on business impact
Module 4. Business impact analysis for machine learning systems
Teaches how to quantify downtime impact for AI services using financial, operational, and reputational metrics.
12 chapters in this module
  1. Defining downtime cost per minute for AI services
  2. Measuring opportunity cost of delayed AI inference
  3. Impact of model drift on downstream processes
  4. Customer experience degradation from AI failure
  5. Internal stakeholder reliance on AI outputs
  6. Regulatory exposure from interrupted AI services
  7. Reputation risk from public-facing AI outages
  8. Calculating RTO for AI services using BIA data
  9. RPO alignment with data pipeline recovery
  10. Documenting BIA findings for audit readiness
  11. Updating BIA with model lifecycle changes
  12. Cross-functional validation of BIA assumptions
Module 5. Designing continuity strategies for AI infrastructure
Covers engineering approaches to ensure AI systems remain functional or recover quickly during disruptions.
12 chapters in this module
  1. Architectural patterns for resilient AI systems
  2. Failover design for model serving endpoints
  3. Data pipeline redundancy options
  4. Caching strategies to maintain AI service uptime
  5. Graceful degradation in AI systems
  6. Multi-region deployment for AI models
  7. Container orchestration during disruption
  8. Automated rollback triggers for AI deployments
  9. Human override mechanisms in critical AI systems
  10. Backup and restore procedures for model artifacts
  11. Monitoring continuity strategy effectiveness
  12. Cost-benefit analysis of different resilience levels
Module 6. Developing incident response for AI systems
Builds an incident management framework tailored to AI outages, data corruption, and model performance degradation.
12 chapters in this module
  1. Defining incident severity levels for AI systems
  2. AI-specific incident classification and triage
  3. Notification workflows during AI outages
  4. Escalation paths for unresolved AI incidents
  5. Incident response team roles in AI recovery
  6. Playbooks for model performance degradation
  7. Handling data pipeline interruptions
  8. Root cause analysis for AI system failures
  9. Post-mortem documentation for AI outages
  10. Integrating AI incident data into BCMS
  11. Training engineers on AI incident response
  12. Testing incident response for AI scenarios
Module 7. Documenting recovery procedures for ML services
Provides templates and best practices for creating clear, actionable recovery steps for AI infrastructure components.
12 chapters in this module
  1. Writing recovery procedures for model serving
  2. Data pipeline restoration steps
  3. AI model version rollback instructions
  4. Authentication system recovery for AI access
  5. Monitoring system recovery after disruption
  6. Database recovery for AI metadata
  7. Feature store availability during recovery
  8. API gateway recovery for AI services
  9. Rate limiting adjustments post-recovery
  10. Validation steps after AI system recovery
  11. Documentation standards for recovery procedures
  12. Maintaining up-to-date recovery documentation
Module 8. Testing and exercising AI continuity plans
Teaches how to validate AI continuity measures through realistic testing without disrupting production systems.
12 chapters in this module
  1. Designing safe tests for AI system continuity
  2. Tabletop exercises for AI incident scenarios
  3. Simulating data pipeline failures
  4. Testing model rollback procedures
  5. Measuring recovery time objectively
  6. Documenting test results for compliance
  7. Improving plans based on test outcomes
  8. Frequency of AI continuity testing
  9. Involving cross-functional teams in exercises
  10. Remote testing options for distributed teams
  11. Automated validation of continuity readiness
  12. Reporting test results to technical leads
Module 9. Maintaining AI continuity documentation
Covers ongoing management of BCMS documentation as AI systems evolve through iterations and updates.
12 chapters in this module
  1. Tracking changes to AI system architecture
  2. Updating continuity plans after model updates
  3. Version control for recovery procedures
  4. Change management integration with BCMS
  5. Audit trail requirements for plan modifications
  6. Regular reviews of AI continuity documentation
  7. Stale documentation detection
  8. Automated checks for documentation accuracy
  9. Handling temporary changes to AI systems
  10. Documentation ownership in AI teams
  11. Archiving outdated continuity plans
  12. Ensuring documentation accessibility
Module 10. Management review and continual improvement
Shows how to present AI continuity performance to technical leadership and drive ongoing enhancements.
12 chapters in this module
  1. Metrics for AI system continuity performance
  2. Reporting uptime and recovery data
  3. Presenting BCMS findings to engineering leads
  4. Identifying improvement opportunities
  5. Tracking corrective actions for AI issues
  6. Benchmarking against industry standards
  7. Incident trend analysis for AI systems
  8. Resource planning for continuity improvements
  9. Feedback loops with incident response
  10. Aligning AI continuity goals with org strategy
  11. Planning for AI system growth and scaling
  12. Reviewing BCMS effectiveness quarterly
Module 11. Internal audit of AI continuity measures
Prepares engineers to conduct or support audits of their AI systems' compliance with ISO 22301 requirements.
12 chapters in this module
  1. Planning internal audits for AI systems
  2. Audit criteria for AI infrastructure
  3. Sampling methods for AI continuity checks
  4. Conducting interviews with AI team members
  5. Reviewing documentation completeness
  6. Testing recovery procedure accuracy
  7. Identifying non-conformities in AI BCMS
  8. Reporting audit findings to technical leads
  9. Tracking corrective actions post-audit
  10. Preparing for external certification audits
  11. Using audit results to improve AI systems
  12. Maintaining audit records for compliance
Module 12. Implementing ISO 22301 in AI-first organizations
Closes with real-world integration strategies for embedding continuity thinking into AI development culture.
12 chapters in this module
  1. Introducing BCMS concepts to AI engineering teams
  2. Building continuity into AI development lifecycle
  3. Training programs for AI engineers
  4. Automating BCMS evidence collection
  5. Integrating BCMS with CI/CD pipelines
  6. Leadership engagement in AI continuity
  7. Scaling BCMS across multiple AI projects
  8. Balancing innovation with resilience
  9. Sharing best practices across AI teams
  10. Measuring BCMS maturity in AI orgs
  11. Future trends in AI system resilience
  12. Closing the gap between policy and practice

How this maps to your situation

  • AI infrastructure ownership
  • Technical governance influence
  • Cross-functional documentation alignment
  • Resilience standards adoption

Before vs. after

Before
Producing business continuity documentation for AI systems that requires multiple revisions and stakeholder alignment delays.
After
Shipping ISO 22301-aligned continuity plans in one draft, backed by engineering rigor and accepted 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 90 minutes per week over six weeks to complete all modules and apply templates.

If nothing changes
Without structured continuity planning, AI systems remain vulnerable to prolonged outages, compliance gaps, and erosion of peer trust during incident reviews.

How this compares to the alternatives

Generic BCMS courses ignore AI-specific risks; internal templates lack standardization; consultants charge $15k+ for fragmented advice. This course delivers a complete, field-tested system tailored to AI engineers.

Frequently asked

Is this relevant for AI/ML engineers without formal compliance roles?
Yes. This course focuses on the technical artefacts and documentation you already own, framed in continuity language that earns peer influence.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Does the course assume prior knowledge of ISO 22301?
No. It starts with fundamentals and builds to advanced implementation, using AI-specific examples throughout.
$199 one-time. Approximately 90 minutes per week over six weeks to complete all modules and apply templates..

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
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