A tailored course, built for your situation
Mastering ISO 22301; A Step-by-Step Guide to Business Continuity Automation
A 12-module deep dive into resilient systems design for senior engineering leaders in AI-driven organizations.
$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.
Continuity planning still runs on spreadsheets and last-minute updates, even in AI-first orgs.
The situation this course is for
The quarterly continuity validation cycle consumes hundreds of engineering hours across teams, with manual runbook updates, fragmented ownership, and audit rework. At scale, this slows incident recovery and strains cross-functional bandwidth, even when the framework is well-defined.
Who this is for
Senior ML and systems engineers in AI-driven enterprises who own or influence business continuity automation, resilience testing, and operational risk readiness.
Who this is not for
Entry-level compliance coordinators, consultants selling maturity assessments, or teams without active ISO 22301 or SOC 2 audit exposure.
What you walk away with
- Ship a fully automated ISO 22301-aligned continuity pipeline in under 90 days
- Reduce manual runbook updates by 85% through AI-driven evidence synchronization
- Own the validation cycle without cross-team chasing or audit rework
- Turn continuity documentation into a version-controlled, CI/CD-integrated artefact
- Demonstrate technical leadership in resilience that scales with AI infrastructure
The 12 modules (with all 144 chapters)
Module 1. Understanding ISO 22301 in AI-Driven Environments
Lay the foundation for applying ISO 22301 principles specifically within machine learning and distributed systems environments, focusing on how resilience frameworks intersect with AI infrastructure.
12 chapters in this module
- Introduction to ISO 22301 and its relevance in tech enterprises
- Core clauses of ISO 22301 and their application to AI systems
- Mapping business continuity to machine learning operations
- Integrating ISO 22301 with existing AI governance frameworks
- Key differences between traditional and AI-driven continuity planning
- How resilience supports infrastructure reliability at scale
- Common misconceptions about ISO 22301 in software engineering
- The role of automation in modern business continuity
- Establishing ownership across distributed engineering teams
- Linking continuity planning to incident response workflows
- Benchmarking current maturity against ISO 22301 expectations
- Preparing for the first internal continuity assessment
Module 2. Identifying Critical Functions in ML Systems
Define which components of AI and ML infrastructure are essential for business continuity, using real-world failure scenarios to prioritize resilience efforts.
12 chapters in this module
- Defining mission-critical systems in AI organizations
- Using failure mode analysis to identify key dependencies
- Mapping data pipelines to business continuity requirements
- Evaluating model serving infrastructure for continuity risk
- Prioritizing components based on user impact and revenue
- Documenting decision criteria for system classification
- Engaging stakeholders in criticality assessments
- Aligning with product and infrastructure leadership
- Avoiding over-scoping the continuity plan
- Establishing review cycles for critical function updates
- Integrating findings into incident response playbooks
- Validating criticality assumptions with real outages
Module 3. Business Impact Analysis for AI Workloads
Conduct a tailored BIA that accounts for AI-specific risks such as data drift, model degradation, and compute dependency.
12 chapters in this module
- Introduction to business impact analysis in tech
- Designing BIA questionnaires for machine learning teams
- Measuring downtime impact on AI model performance
- Estimating financial and reputational risk of outages
- Setting realistic recovery time objectives for AI systems
- Determining data loss tolerance in ML pipelines
- Incorporating latency and availability SLAs into BIA
- Collaborating with finance and product on impact metrics
- Documenting BIA findings for audit readiness
- Updating BIA based on system changes and scale
- Automating BIA input collection from observability tools
- Aligning BIA scope with ISO 22301 requirements
Module 4. Designing Automated Continuity Runbooks
Shift from manual, document-based runbooks to version-controlled, executable continuity scripts integrated into CI/CD.
12 chapters in this module
- From static documents to executable runbooks
- Choosing the right automation framework for runbooks
- Version controlling runbook logic in Git repositories
- Integrating runbooks with monitoring and alerting
- Building conditional logic for incident escalation
- Using templates to standardize runbook structure
- Testing runbook execution in staging environments
- Incorporating feedback loops from incident reviews
- Securing access to automated runbook systems
- Documenting fallback procedures when automation fails
- Measuring runbook effectiveness through metrics
- Reducing mean time to recovery with automation
Module 5. Integrating ISO 22301 with DevOps Pipelines
Embed business continuity checks into CI/CD workflows to ensure resilience is maintained through deployment cycles.
12 chapters in this module
- Understanding DevOps and CI/CD in AI environments
- Mapping ISO 22301 requirements to deployment gates
- Automating evidence collection during builds
- Embedding continuity validation in pre-deployment checks
- Generating compliance reports from pipeline outputs
- Alerting on continuity gaps before deployment
- Maintaining audit trails through automation
- Coordinating with platform and security teams
- Reducing manual review with pipeline integration
- Updating pipeline rules as systems evolve
- Scaling continuity checks across service boundaries
- Measuring compliance velocity improvements
Module 6. AI-Driven Risk Assessment and Monitoring
Leverage machine learning to predict continuity risks and monitor system health in real time.
12 chapters in this module
- Introduction to AI in risk monitoring
- Training models to detect infrastructure degradation
- Using anomaly detection for early warning signals
- Integrating predictive models into continuity planning
- Setting thresholds for automated risk escalation
- Validating model accuracy with historical outages
- Avoiding false positives in automated alerts
- Maintaining model fairness and interpretability
- Updating risk models as systems change
- Documenting AI use for ISO 22301 compliance
- Monitoring model drift in risk prediction
- Balancing automation with human oversight
Module 7. Continuity Testing and Validation at Scale
Design and execute realistic continuity tests that validate resilience without disrupting production systems.
12 chapters in this module
- Planning regular continuity testing cycles
- Designing realistic failure scenarios for AI systems
- Running automated chaos experiments
- Measuring test coverage against ISO 22301
- Involving cross-functional teams in test execution
- Capturing lessons from test outcomes
- Reducing test overhead with simulation
- Automating test reporting and follow-up
- Integrating test results into incident reviews
- Adjusting plans based on test findings
- Scaling tests across global infrastructure
- Demonstrating audit readiness through testing
Module 8. Evidence Management and Audit Readiness
Automate the collection and presentation of ISO 22301 evidence to reduce audit preparation time.
12 chapters in this module
- Understanding auditor expectations for ISO 22301
- Identifying required evidence across clauses
- Automating evidence collection from logs and systems
- Storing evidence in audit-ready formats
- Linking evidence to control objectives
- Reducing manual documentation effort
- Versioning and timestamping evidence files
- Preparing for internal and external audits
- Responding to auditor inquiries efficiently
- Updating evidence workflows as systems change
- Demonstrating continuous compliance
- Reducing audit cycle time with automation
Module 9. Cross-Team Coordination and Communication
Establish clear roles, escalation paths, and communication protocols for continuity events.
12 chapters in this module
- Defining roles in continuity response teams
- Establishing communication channels for outages
- Creating incident command structures
- Coordinating with legal and PR during crises
- Documenting decision logs during events
- Integrating with existing incident management tools
- Conducting tabletop exercises with stakeholders
- Improving response coordination through practice
- Reducing mean time to acknowledge and resolve
- Measuring team performance during events
- Updating playbooks based on coordination gaps
- Scaling coordination across time zones
Module 10. Continuous Improvement and Feedback Loops
Institutionalize learning from outages and tests to continuously refine the continuity program.
12 chapters in this module
- Conducting effective post-incident reviews
- Capturing action items and tracking closure
- Integrating feedback into runbooks and plans
- Measuring program maturity over time
- Benchmarking against internal and external peers
- Adjusting priorities based on risk trends
- Automating maturity assessments
- Reporting progress to leadership
- Engaging teams in improvement initiatives
- Recognizing contributions to resilience
- Scaling best practices across the organization
- Maintaining momentum in continuity efforts
Module 11. Advanced Automation Patterns for Resilience
Implement advanced patterns such as self-healing systems and predictive scaling to enhance continuity.
12 chapters in this module
- Designing self-healing mechanisms for ML systems
- Using AI to predict and prevent outages
- Automating failover across regions
- Implementing canary-based recovery strategies
- Scaling infrastructure based on continuity risk
- Integrating with service mesh for resilience
- Applying policy-as-code to continuity rules
- Monitoring automation effectiveness
- Reducing human intervention in recovery
- Documenting automated decisions for compliance
- Handling edge cases in automated recovery
- Ensuring safety and reliability in automation
Module 12. Sustaining Resilience in Evolving Architectures
Adapt continuity practices to new technologies, cloud services, and organizational changes.
12 chapters in this module
- Managing continuity in multi-cloud environments
- Updating plans for new AI and ML services
- Integrating third-party services into continuity
- Handling acquisitions and divestitures
- Scaling resilience practices with growth
- Maintaining compliance across reorganizations
- Updating documentation for new team structures
- Onboarding new teams to continuity practices
- Preserving knowledge across team changes
- Adapting to new regulatory requirements
- Future-proofing continuity with modularity
- Leading resilience in a changing tech landscape
How this maps to your situation
- ML systems resilience at scale
- Automated continuity validation
- Audit-integrated DevOps
- AI-driven risk monitoring
Before vs. after
Before
Manual runbook updates, cross-team delays, and last-minute audit prep consume engineering cycles.
After
Automated, version-controlled continuity pipelines that validate themselves and scale with infrastructure.
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 9 hours of focused learning, designed to be completed in short sessions over a weekend or across evenings.
If nothing changes
Without automated continuity practices, engineering bandwidth will continue to be drained by manual updates and audit cycles, slowing innovation and increasing outage recovery time.
How this compares to the alternatives
Unlike generic ISO 22301 training or auditor-led gap assessments, this course is built specifically for senior engineers who must implement and automate resilience in AI-driven systems , not just understand the standard.
Frequently asked
Is this course focused on compliance or engineering implementation?
It's focused on engineering implementation. You'll learn how to build and automate systems that satisfy ISO 22301 requirements, not just document compliance.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help with internal audit cycles?
Yes. The course teaches how to design systems that generate their own audit evidence, reducing manual prep work by over 80%.
$199 one-time. Approximately 9 hours of focused learning, designed to be completed in short sessions over a weekend or across evenings..
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