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BCM0363 Mastering ISO 22301 for Data & GenAI Engineering Leaders

$199.00
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A tailored course, built for your situation

Mastering ISO 22301 for Data & GenAI Engineering Leaders

Build defensible, resilient data systems with framework-backed reasoning

$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.
Peers challenge your data architecture or GenAI integration choices, and you need more than opinion to hold ground

The situation this course is for

In high-visibility roles, technical decisions face cross-functional scrutiny. Without concrete, source-backed reasoning, even sound approaches can be dismissed as subjective or risky, delaying delivery and weakening influence.

Who this is for

Senior engineering leader in data, GenAI, or quantitative systems, responsible for delivering resilient, auditable, and production-grade data products under compliance-aware governance

Who this is not for

Junior engineers, non-technical compliance staff, or practitioners without ownership of data system design or GenAI integration

What you walk away with

  • Articulate the rationale behind control implementations using ISO 22301 clauses and real-world parallels
  • Respond to peer challenges with specific examples from audit-tested configurations
  • Map data continuity requirements directly to GenAI pipeline resilience
  • Defend architecture choices using precedent from certified organizations
  • Produce clear, source-traceable documentation that survives leadership changes

The 12 modules (with all 144 chapters)

Module 1. Understanding ISO 22301 in the Context of Data Engineering
Ground the standard’s intent in data flow resilience, not generic compliance. Learn how clauses translate to data pipeline uptime, failover logic, and GenAI model continuity.
12 chapters in this module
  1. How ISO 22301 defines continuity for data-dependent systems
  2. Differences between operational resilience and disaster recovery in AI pipelines
  3. Why BCMS applies to model inference environments
  4. Mapping clause 4.1 to data ingestion and transformation workflows
  5. Case study: Airline booking system resilience during infrastructure outages
  6. Key roles in a data-focused BCMS implementation
  7. Integrating observability into business continuity planning
  8. How regulatory scrutiny shapes resilience scope
  9. Linking data product SLAs to ISO 22301 objectives
  10. Common misconceptions about compliance vs. operability
  11. Assessing criticality of GenAI-powered data services
  12. Documenting decision rationale for audit readiness
Module 2. Scoping Resilience for GenAI and Data Product Lifecycles
Define what systems require ISO 22301 alignment based on impact, not intuition. Use evidence-based criteria to justify scope to peers and auditors.
12 chapters in this module
  1. Using data lineage to trace critical service dependencies
  2. Scoring model impact on downstream operations
  3. Determining recovery time objectives for AI pipelines
  4. Classifying data products by continuity necessity
  5. Involving product and engineering stakeholders in scoping
  6. Documenting rationale for excluded systems
  7. Aligning with enterprise architecture frameworks
  8. Handling edge cases in hybrid cloud data environments
  9. Balancing agility with resilience requirements
  10. Versioning scope decisions over time
  11. Auditor expectations for documented scoping logic
  12. Common disputes over scope and how to preempt them
Module 3. Risk Assessment and Its Application to Data Infrastructure
Apply ISO 22301 risk methodology to data systems, not generic templates. Build credible, defensible assessments that withstand peer review.
12 chapters in this module
  1. Identifying threats to data availability and integrity
  2. Using historical outage data in risk likelihood scoring
  3. Impact analysis for corrupted or delayed data streams
  4. Involving data engineers in risk workshops
  5. Documenting assumptions behind risk ratings
  6. Linking risk outcomes to control implementation
  7. Avoiding overly broad or vague risk statements
  8. Testing risk register completeness with red teaming
  9. Updating assessments after pipeline changes
  10. Benchmarking risk thresholds with industry peers
  11. Role of automation in risk detection and response
  12. Common pitfalls in data-related risk assessments
Module 4. Designing Controls for Data Continuity
Translate ISO 22301 requirements into actionable, maintainable controls in data architecture and GenAI deployment.
12 chapters in this module
  1. Designing failover mechanisms for streaming data pipelines
  2. Implementing redundancy without over-engineering
  3. Using canary routing to maintain data flow during outages
  4. Control documentation for data replication strategies
  5. Validating backup data usability for AI models
  6. Detecting and alerting on data pipeline breaks
  7. Designing for human intervention during automated failures
  8. Ensuring logging continuity during disruptions
  9. Control ownership and accountability in engineering teams
  10. Version control for resilience configurations
  11. Common gaps in control design for machine learning systems
  12. Auditable evidence trails for control operation
Module 5. Documenting the Business Continuity Plan for Data Systems
Create a living BCP that engineers use, not just auditors read. Focus on clarity, relevance, and actionability.
12 chapters in this module
  1. Structuring BCPs for technical audience comprehension
  2. Integrating runbooks into the BCP framework
  3. Using diagrams to show data flow during failover
  4. Defining recovery steps for AI model reinitialization
  5. Including credentials and access paths in secure formats
  6. Versioning and change control for BCPs
  7. Linking BCPs to incident response playbooks
  8. Ensuring offline accessibility of critical documents
  9. Automating BCP validation checks
  10. Common auditor findings in BCP documentation
  11. Updating BCPs after data architecture changes
  12. Making BCPs actionable during high-stress events
Module 6. Testing Resilience in Data and GenAI Systems
Go beyond checkbox testing. Design realistic, informative exercises that validate real system behavior under failure.
12 chapters in this module
  1. Planning test scenarios based on actual risk profiles
  2. Simulating network partitions in distributed data systems
  3. Testing data integrity after failover events
  4. Measuring recovery time for AI pipeline restarts
  5. Involving engineering teams in test execution
  6. Documenting test outcomes and gaps
  7. Using test results to refine controls
  8. Scheduling tests aligned with release cycles
  9. Remote participation in resilience testing
  10. Common excuses for skipping tests and how to counter them
  11. Building executive confidence through testing
  12. Turning test findings into prioritized improvements
Module 7. Maintaining Continuity Documentation and Evidence
Keep records that pass audit and peer review, automated, accurate, and meaningful.
12 chapters in this module
  1. Scheduling periodic review of continuity documents
  2. Automating evidence collection from logging systems
  3. Tracking control exceptions and justifications
  4. Maintaining records of test results and follow-up
  5. Using configuration management tools to preserve state
  6. Documenting changes to data pipeline architecture
  7. Versioning control implementations over time
  8. Proving continuity readiness without burdening engineers
  9. Common audit findings in evidence retention
  10. Avoiding last-minute evidence scrambling
  11. Integrating evidence workflows into CI/CD pipelines
  12. Training new team members on documentation roles
Module 8. Integrating ISO 22301 with Data Governance Frameworks
Align business continuity with existing data governance to reduce duplication and increase adoption.
12 chapters in this module
  1. Mapping ISO 22301 clauses to data governance policies
  2. Coordinating with data stewardship teams
  3. Using data catalogs to support continuity planning
  4. Linking metadata to resilience requirements
  5. Shared ownership models for control maintenance
  6. Avoiding siloed compliance initiatives
  7. Harmonizing terminology across teams
  8. Reporting on joint KPIs for data resilience
  9. Common friction points between teams
  10. Building trust through joint workshops
  11. Executive messaging for integrated programs
  12. Documenting integration benefits for leadership
Module 9. Leading Cross-Functional Resilience Initiatives
Influence without authority by grounding decisions in ISO 22301 and concrete examples.
12 chapters in this module
  1. Building credibility through documented rationale
  2. Presenting control trade-offs to non-technical leaders
  3. Using peer-reviewed case studies in discussions
  4. Facilitating workshops with diverse stakeholders
  5. Negotiating scope and priorities with product teams
  6. Communicating progress without technical jargon
  7. Escalating issues with evidence-backed context
  8. Gaining buy-in for resilience investments
  9. Common objections and how to counter them
  10. Measuring influence through adoption metrics
  11. Recognizing cross-team contributions
  12. Sustaining engagement across business units
Module 10. Preparing for Internal and External Audits
Enter audits with confidence, ready to demonstrate compliance through action, not paperwork.
12 chapters in this module
  1. Organizing evidence for audit access
  2. Anticipating auditor questions on data systems
  3. Conducting pre-audit readiness checks
  4. Using internal audit as a feedback loop
  5. Responding to findings with concrete action plans
  6. Training team members on audit communication
  7. Avoiding defensiveness during audit interviews
  8. Demonstrating continuous improvement
  9. Common misinterpretations of ISO 22301 by auditors
  10. Turning audit outcomes into roadmap items
  11. Documenting corrective actions effectively
  12. Building long-term audit relationships
Module 11. Scaling Resilience Across Data and AI Platforms
Extend proven practices across platforms without losing specificity.
12 chapters in this module
  1. Identifying reusable control patterns
  2. Adapting controls for different data environments
  3. Creating templates for common pipeline architectures
  4. Training platform teams on resilience principles
  5. Standardizing documentation formats
  6. Using automation to enforce resilience standards
  7. Tracking consistency across data domains
  8. Managing exceptions at scale
  9. Sharing lessons learned across teams
  10. Measuring maturity across platforms
  11. Avoiding one-size-fits-all implementations
  12. Balancing standardization with flexibility
Module 12. Sustaining Resilience Through Organizational Change
Preserve institutional knowledge and continuity commitment despite turnover or restructuring.
12 chapters in this module
  1. Documenting tribal knowledge in BCPs
  2. Onboarding new engineers into resilience practices
  3. Maintaining ownership during leadership changes
  4. Updating plans after M&A or divestitures
  5. Preserving evidence during system migrations
  6. Communicating resilience value to new executives
  7. Integrating resilience into promotion criteria
  8. Recognizing resilience contributions publicly
  9. Auditing knowledge retention periodically
  10. Using playbooks to standardize responses
  11. Measuring program sustainability over time
  12. Handing off long-term projects without disruption

How this maps to your situation

  • Defining scope for data and GenAI systems under ISO 22301
  • Implementing controls in production data environments
  • Demonstrating compliance during audit cycles
  • Leading resilience initiatives across engineering teams

Before vs. after

Before
Peers question data system design choices, and you rely on experience rather than documented precedent to justify decisions.
After
You respond with ISO 22301-aligned reasoning, specific examples, and traceable sources, turning technical decisions into trusted, defensible outcomes.

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 week over 4 weeks to complete all modules, with flexible pacing allowed.

If nothing changes
Without defensible reasoning, sound technical decisions risk rejection, rework, or erosion of influence, especially under scrutiny from compliance, audit, or cross-functional peers.

How this compares to the alternatives

Unlike generic compliance trainings, this course is tailored to data and GenAI engineering leaders, focusing on defensible decision-making with ISO 22301 as the foundation, not checklists, but concrete, applicable depth.

Frequently asked

Is this course technical or managerial?
It’s for technical leaders who must defend design decisions to both engineers and non-engineers using structured, source-backed reasoning.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Can I use this if my organization isn’t certified?
Yes. The course teaches defensible reasoning, even without formal certification, you’ll gain the depth to justify choices under scrutiny.
$199 one-time. Approximately 3 hours per week over 4 weeks to complete all modules, with flexible pacing allowed..

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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