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BCM2983 Mastering ISO 22301 for AI/ML Engineering Leaders in Ad-Tech

$199.00
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A tailored course, built for your situation

Mastering ISO 22301 for AI/ML Engineering Leaders in Ad-Tech

A proven system to align business continuity planning with AI infrastructure scale and stakeholder expectations

$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.
Stakeholder misalignment on AI system resilience

The situation this course is for

ML engineering leaders face growing scrutiny on system uptime and disaster response, but current business impact assessments lack standardization, lead to rework, and delay deployment cycles. Without a structured framework, teams default to ad-hoc narratives that fail under cross-functional review.

Who this is for

Senior AI/ML engineering leader in ad-tech at a major platform, responsible for system reliability, incident response, and cross-functional coordination under pressure

Who this is not for

Individual contributors without cross-team influence, non-technical managers, or teams focused solely on model accuracy without operational scale concerns

What you walk away with

  • Produce stakeholder-validated business impact assessments in under 6 hours
  • Establish clear escalation paths for AI system outages aligned with ISO 22301
  • Lead resilience conversations with product and infrastructure peers using standardized criteria
  • Reduce rework in continuity planning cycles by using repeatable templates
  • Position yourself as the internal reference for AI system resilience decisions

The 12 modules (with all 144 chapters)

Module 1. Foundations of ISO 22301 in AI-Driven Environments
Establish the core principles of business continuity management as they apply specifically to machine learning infrastructure, including scope definition, leadership roles, and integration with DevOps cycles.
12 chapters in this module
  1. Defining business continuity in the context of AI system failures
  2. Mapping ISO 22301 clauses to ad-tech operational realities
  3. Understanding the role of the ML engineering leader in resilience planning
  4. Differentiating availability, recoverability, and fault tolerance
  5. Integrating continuity planning into model deployment pipelines
  6. Aligning with Meta-level SRE and infrastructure teams
  7. Key terminology: BCM, BIA, RTO, RPO in AI contexts
  8. How ISO 22301 complements other standards like SOC 2 and ISO 27001
  9. Common misconceptions about resilience in AI systems
  10. Setting expectations with non-technical stakeholders
  11. Documenting assumptions in high-velocity environments
  12. Building a living continuity framework instead of a static document
Module 2. Conducting AI-Specific Business Impact Analyses
Learn how to lead a BIA tailored to machine learning systems, identifying critical functions, dependencies, and downtime thresholds with stakeholder input.
12 chapters in this module
  1. Identifying core AI services in ad delivery pipelines
  2. Mapping data dependencies for training and inference
  3. Engaging product and sales teams for impact input
  4. Quantifying financial impact of model downtime
  5. Assessing reputational risk from degraded personalization
  6. Setting RTOs and RPOs for different model types
  7. Handling model drift as a continuity concern
  8. Documenting cascading failures across services
  9. Using historical outage data to inform thresholds
  10. Validating BIA findings with infrastructure teams
  11. Creating visual impact heatmaps for leadership
  12. Avoiding over-scoping and maintaining focus
Module 3. Risk Assessment for AI Infrastructure
Apply ISO 22301 risk methodology to identify, analyze, and evaluate threats specific to AI/ML systems and supporting infrastructure.
12 chapters in this module
  1. Common threat categories for AI platforms
  2. Identifying single points of failure in model serving
  3. Assessing data pipeline vulnerabilities
  4. Evaluating third-party dependencies and vendor risks
  5. Considering adversarial attacks and data poisoning
  6. Rating likelihood and impact of AI-specific threats
  7. Involving security and privacy teams in risk workshops
  8. Linking risk findings to control objectives
  9. Creating risk registers with traceable logic
  10. Prioritizing risks with executive input
  11. Documenting risk treatment strategies
  12. Maintaining risk assessments through model updates
Module 4. Designing Resilience Controls for ML Systems
Build technical and procedural controls that meet ISO 22301 requirements while maintaining agility in AI development.
12 chapters in this module
  1. Implementing redundancy for model serving endpoints
  2. Designing fallback mechanisms for degraded operation
  3. Automating failover detection and response
  4. Creating data backup and restoration procedures
  5. Establishing model version rollback protocols
  6. Documenting manual intervention playbooks
  7. Integrating monitoring with incident management
  8. Defining thresholds for automatic alerts
  9. Securing access to recovery environments
  10. Validating control effectiveness through testing
  11. Balancing security with developer velocity
  12. Aligning controls with existing Meta engineering standards
Module 5. Incident Response Planning for AI Outages
Develop response procedures specific to AI system failures, ensuring rapid recovery and clear communication channels.
12 chapters in this module
  1. Defining activation triggers for AI incident response
  2. Establishing response team roles and responsibilities
  3. Creating communication templates for internal stakeholders
  4. Drafting public-facing statements for model issues
  5. Coordinating with legal and PR teams in crises
  6. Documenting incident timelines and decisions
  7. Integrating with Meta’s centralized incident management
  8. Conducting post-mortems with learning focus
  9. Updating runbooks based on incident findings
  10. Training team members on response protocols
  11. Running tabletop exercises for AI scenarios
  12. Maintaining response readiness across rotations
Module 6. Testing and Validation of Continuity Plans
Implement a structured approach to testing business continuity plans for AI systems, ensuring reliability without disrupting operations.
12 chapters in this module
  1. Designing test scenarios for model degradation
  2. Conducting tabletop exercises with cross-functional teams
  3. Planning technical failover tests during maintenance windows
  4. Measuring test success against recovery objectives
  5. Documenting test findings and action items
  6. Involving external auditors in validation
  7. Adjusting plans based on test results
  8. Creating evidence packages for compliance reviews
  9. Scheduling regular test cycles
  10. Integrating test results into risk assessments
  11. Communicating test outcomes to leadership
  12. Maintaining test records for audit readiness
Module 7. Stakeholder Engagement and Communication
Lead effective conversations with product, legal, and infrastructure teams about AI system resilience expectations and trade-offs.
12 chapters in this module
  1. Identifying key stakeholders in continuity planning
  2. Translating technical risks into business terms
  3. Setting realistic expectations for system availability
  4. Negotiating RTO and RPO agreements
  5. Presenting BIA findings to non-technical leaders
  6. Building trust through transparency
  7. Handling conflicting stakeholder priorities
  8. Creating executive summaries from technical data
  9. Maintaining engagement through regular updates
  10. Documenting alignment and disagreements
  11. Using ISO 22301 as a neutral reference point
  12. Positioning resilience as enabler, not blocker
Module 8. Documentation and Evidence Management
Produce audit-ready documentation that demonstrates compliance with ISO 22301 while remaining useful for operational teams.
12 chapters in this module
  1. Structuring the business continuity policy document
  2. Creating living BIA and risk assessment records
  3. Maintaining evidence of stakeholder involvement
  4. Documenting control design and implementation
  5. Organizing test results and action tracking
  6. Using version control for continuity artifacts
  7. Linking documentation to Meta’s internal systems
  8. Creating executive dashboards for status
  9. Preparing for internal and external audits
  10. Defining document ownership and review cycles
  11. Balancing completeness with readability
  12. Archiving outdated versions securely
Module 9. Integration with Existing Engineering Practices
Embed business continuity requirements into existing ML development, deployment, and monitoring workflows at scale.
12 chapters in this module
  1. Incorporating BIA into model design phase
  2. Adding resilience checks to CI/CD pipelines
  3. Integrating with Meta’s internal monitoring tools
  4. Automating evidence collection for controls
  5. Updating documentation during model refresh
  6. Training new team members on procedures
  7. Linking incident response to existing on-call
  8. Aligning with platform-wide SRE standards
  9. Using feature flags for controlled recovery
  10. Establishing review gates for high-impact models
  11. Measuring team adherence to continuity practices
  12. Feedback loops from operations to planning
Module 10. Continuous Improvement and Review
Establish cycles for reviewing and updating business continuity plans to keep pace with AI system evolution.
12 chapters in this module
  1. Scheduling regular plan reviews
  2. Triggering updates after major incidents
  3. Incorporating lessons from post-mortems
  4. Tracking changes in system architecture
  5. Updating BIA with new product requirements
  6. Reassessing risk landscape annually
  7. Measuring effectiveness of improvements
  8. Engaging leadership in review cycles
  9. Benchmarking against industry practices
  10. Identifying innovation opportunities
  11. Documenting review outcomes
  12. Planning for future scalability
Module 11. Scaling Resilience Across Model Portfolios
Extend proven continuity practices across multiple AI models and teams while maintaining consistency.
12 chapters in this module
  1. Creating standardized templates for new models
  2. Establishing center of excellence practices
  3. Training peer engineering leads
  4. Developing audit playbooks for reviewers
  5. Implementing centralized dashboards
  6. Sharing best practices across teams
  7. Handling exceptions and edge cases
  8. Maintaining consistency with autonomy
  9. Onboarding new product areas
  10. Evolving standards with technical maturity
  11. Measuring adoption across the organization
  12. Recognizing team achievements
Module 12. Leading as the Resilience Authority
Position yourself as the trusted internal expert on AI system continuity, influencing decisions beyond your immediate scope.
12 chapters in this module
  1. Building credibility through consistent delivery
  2. Contributing to cross-functional standards
  3. Mentoring other engineering leaders
  4. Presenting successes at technical forums
  5. Influencing platform-level decisions
  6. Advising on M&A technical integration
  7. Representing Meta in industry discussions
  8. Developing thought leadership content
  9. Guiding junior staff on resilience thinking
  10. Balancing innovation with responsibility
  11. Earning executive recognition
  12. Shaping the future of AI operations

How this maps to your situation

  • AI/ML Engineering Manager
  • ad-tech environment
  • high-availability systems
  • cross-functional stakeholder alignment

Before vs. after

Before
Spending cycles on rework of resilience documentation and reacting to stakeholder challenges without a standardized framework
After
Confidently leading alignment on AI system continuity with stakeholder-accepted evidence and clear decision pathways

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: 90 minutes of focused learning, designed to be consumed in one Sunday morning with immediate applicability to current work.

If nothing changes
Continuing without a structured approach risks repeated rework, misalignment during incidents, and diminished influence in technical resilience decisions as Meta faces increasing scrutiny on ad system reliability.

How this compares to the alternatives

Unlike generic ISO 22301 training, this course is tailored to AI/ML engineering leaders in ad-tech, focusing on real-world decisions, stakeholder dynamics, and integration with existing Meta engineering practices.

Frequently asked

Is this relevant if I don’t own infrastructure directly?
Yes. The course focuses on influence, decision-making, and cross-team coordination, skills that matter whether you manage infrastructure or lead modeling teams.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help with internal audits?
Yes. You’ll learn to create evidence packages that satisfy reviewers while remaining useful for operational teams.
$199 one-time. 90 minutes of focused learning, designed to be consumed in one Sunday morning with immediate applicability to current 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
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