A tailored course, built for your situation
Mastering ISO 42001 for Network Engineers in Industrial Operations
Build defensible AI governance practices grounded in your operational reality
$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.
Who this is for
Network Engineers in industrial sectors managing AI-integrated network infrastructure with accountability for governance alignment
Who this is not for
Entry-level IT staff, non-technical compliance roles, or consultants without hands-on network operations experience
What you walk away with
- Map ISO 42001 controls directly to network architecture decisions
- Cite specific industrial implementations when justifying design choices
- Reference authoritative sources for AI governance in OT environments
- Walk through control-by-control reasoning with confidence in cross-team reviews
- Build a playbook of examples from mining and resource-sector deployments
The 12 modules (with all 144 chapters)
Module 1. Understanding ISO 42001 in Operational Technology Contexts
Ground the standard in real-world OT environments, focusing on AI integration points in mining and industrial networks.
12 chapters in this module
- What ISO 42001 means for OT
- AI governance vs functional safety
- Control domains overview
- Mapping to network layers
- Physical-layer implications
- Case: AI monitoring in belt conveyors
- Case: Predictive maintenance systems
- Regulatory drivers in mining
- Cross-reference with NIST AI RF
- Integration with existing NOC workflows
- AI asset inventory structure
- Control ownership models
Module 2. AI Risk Assessment for Networked Industrial Systems
Learn how to assess AI risk where network stability impacts physical safety and uptime.
12 chapters in this module
- Defining AI system boundaries
- Threat modeling for AI-enabled sensors
- Failure mode analysis
- Network segmentation strategies
- Latency tolerance thresholds
- Fail-safe vs fail-secure
- Data provenance tracking
- Human-in-the-loop requirements
- Incident escalation paths
- Testing under brownout conditions
- Vendor AI model transparency
- Audit trail expectations
Module 3. Designing AI Governance Controls for Network Resilience
Build governance into network design, not as an afterthought.
12 chapters in this module
- Embedding ISO 42001 at procurement
- Vendor AI security questionnaires
- Model update approval workflows
- Zero-trust for AI services
- Network performance baselines
- AI-driven traffic anomalies
- Bandwidth reservation policies
- Failover protocol integration
- Secure over-the-air updates
- Logging AI-generated network events
- Time-synchronization requirements
- Control loop timing integrity
Module 4. Documenting AI System Design Decisions
Create defensible, source-backed documentation for every major choice.
12 chapters in this module
- SoA structure for AI systems
- Justifying control exceptions
- Referencing NIST 800-218
- OT-specific risk acceptance
- Change review documentation
- Design trade-off examples
- Vendor documentation gaps
- Peer-reviewed design notes
- Version control for AI configs
- Network topology overlays
- Failure scenario narratives
- Lessons from prior incidents
Module 5. Implementing Monitoring and Incident Response for AI Systems
Operationalize monitoring and response within existing NOC frameworks.
12 chapters in this module
- AI behavior baselines
- Anomaly detection thresholds
- False positive tuning
- Human override mechanisms
- Incident classification schema
- Response playbook structure
- Drill scenarios for AI failures
- Post-incident review process
- Root cause documentation
- Cross-team communication paths
- Regulator-facing summaries
- Lessons-learned integration
Module 6. Validating AI System Performance in Mining Environments
Ensure AI models perform reliably under real-world mining conditions.
12 chapters in this module
- Environmental stress testing
- Dust and vibration impact
- Temperature extremes
- Power fluctuation tolerance
- Model drift detection
- Retraining triggers
- Field validation protocols
- Sensor degradation handling
- Edge compute limitations
- Latency during peak load
- Fallback strategy execution
- Performance benchmarking
Module 7. Managing Third-Party AI Vendors in Industrial Networks
Hold vendors accountable to governance and network integration standards.
12 chapters in this module
- Vendor selection criteria
- AI model transparency demands
- Data handling expectations
- Update frequency negotiation
- Emergency patch processes
- Penetration testing access
- Audit rights clauses
- Service-level agreements
- Right-to-repair provisions
- Source code escrow
- Exit strategy planning
- Knowledge transfer requirements
Module 8. Maintaining AI System Integrity Across Network Upgrades
Preserve governance and control integrity during infrastructure changes.
12 chapters in this module
- Upgrade compatibility checks
- AI model revalidation
- Configuration drift detection
- Legacy system interactions
- Firmware update sequencing
- Bandwidth impact analysis
- Security policy migration
- Monitoring continuity
- Failover testing
- Documentation updates
- Stakeholder communication
- Rollback preparedness
Module 9. Auditing AI Governance in OT Environments
Prepare for audits with clear, evidence-backed control narratives.
12 chapters in this module
- Audit scope definition
- Evidence collection strategies
- Control testing procedures
- Sampling methodologies
- Non-conformance handling
- Management response drafting
- Follow-up timelines
- Corrective action tracking
- External auditor coordination
- Internal audit readiness
- Pre-audit walkthroughs
- Post-audit improvement loops
Module 10. Scaling AI Governance Across Global Sites
Replicate defensible practices across multiple operational locations.
12 chapters in this module
- Central governance model
- Local adaptation rules
- Cross-site consistency
- Knowledge sharing frameworks
- Training standardization
- Incident cross-learning
- Vendor contract harmonization
- Benchmarking site performance
- Escalation routing
- Change approval delegation
- Cultural considerations
- Time zone coordination
Module 11. Integrating AI Governance with ESG and Safety Programs
Align AI practices with broader corporate responsibility initiatives.
12 chapters in this module
- ESG reporting linkages
- Safety case documentation
- Environmental impact metrics
- Community engagement
- Transparency commitments
- Human rights assessments
- Safety override validation
- Incident disclosure policies
- Stakeholder communication
- Regulatory alignment
- Audit trail retention
- Third-party assurance
Module 12. Building a Living AI Governance Program
Ensure the program evolves with technology and operational needs.
12 chapters in this module
- Feedback loop design
- Lessons-learned integration
- Control review cycles
- Emerging threat monitoring
- Staff training updates
- Tooling improvements
- Benchmarking against peers
- Executive reporting
- Strategic roadmap alignment
- Budget justification
- Success metrics
- Program maturity assessment
How this maps to your situation
- New AI system rollout in processing plant
- Vendor AI solution audit
- Internal ISO 42001 gap assessment
- Regulatory inquiry preparation
Before vs. after
Before
Reacting to AI governance questions with general principles
After
Walking into reviews with specific examples, sources, and documented reasoning
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 over 6-8 weeks with real-world application.
If nothing changes
Without a defensible foundation, AI governance decisions may be reversed or overridden by teams with stronger documentation, delaying projects and weakening your influence.
How this compares to the alternatives
Unlike generic compliance courses, this program is built specifically for network engineers in industrial settings, with examples from mining and resource operations, not hypothetical scenarios.
Frequently asked
Is this course relevant if I’m not in a leadership role?
Yes. This course is designed for hands-on practitioners who need to defend technical and governance decisions in cross-functional settings.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Does the course cover other standards like NIST or IEC 62443?
We reference NIST AI RF and IEC 62443 where they intersect with ISO 42001, but the core framework is ISO 42001 as applied to industrial network operations.
$199 one-time. Approximately 3 hours per module, designed to be completed over 6-8 weeks with real-world application..
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