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AIG6601 Mastering ISO 42001; A Step-by-Step Guide to AI Governance in Control Systems

$199.00
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What is the ISO 42001 course about?

Lead ISO 42001-compliant AI governance initiatives from design to audit Position for higher-margin, compliance-sensitive engagements in critical infrastructure Document control mappings that pass internal and client reviews without rework Navigate vendor selection and integration with clear governance criteria Become the internal reference for AI control assurance in cross-functional bids.

What do you take away from the ISO 42001 course?

Lead ISO 42001-compliant AI governance initiatives from design to audit Position for higher-margin, compliance-sensitive engagements in critical infrastructure Document control mappings that pass internal and client reviews without rework Navigate vendor selection and integration with clear governance criteria Become the internal reference for AI control assurance in cross-functional bids.

How does this map to your situation?

Initial ISO 42001 governance setup in control systems Ongoing risk and performance monitoring Audit and client review cycles Expansion to new sectors and bid opportunities.

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.

What does the ISO 42001 cover on delivery and format?

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 module, designed for engineers to complete at their own pace over 4-6 weeks.

How does this compare to the alternatives?

Unlike generic AI ethics courses, this program delivers engineering-grade implementation steps for ISO 42001 in control systems, tailored to real-world deployment challenges and compliance expectations.

What does the ISO 42001 cover on frequently asked?

Within 24 hours your account in the learning environment is provisioned and the tailored implementation playbook is delivered alongside it.

How is the ISO 42001 delivered?

The ISO 42001 is fully self-paced with immediate online access after enrolment. Access does not expire and future updates are included at no cost. A certificate of completion is issued by The Art of Service when you finish.

Closely related courses: Internal Control, Control System Engineering, Control Self-Assessment, Control Self Assessment.

More answers: what you get with every course, refund policy, all help answers.

A tailored course, built for your situation

Mastering ISO 42001; A Step-by-Step Guide to AI Governance in Control Systems

A complete implementation path for senior engineers leading AI integration in critical infrastructure environments.

$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

Senior Control Systems Engineer at a federal contractor managing control integrity in AI-integrated environments.

Who this is not for

Engineers focused only on legacy control systems without AI integration or compliance expansion plans.

What you walk away with

  • Lead ISO 42001-compliant AI governance initiatives from design to audit
  • Position for higher-margin, compliance-sensitive engagements in critical infrastructure
  • Document control mappings that pass internal and client reviews without rework
  • Navigate vendor selection and integration with clear governance criteria
  • Become the internal reference for AI control assurance in cross-functional bids

The 12 modules (with all 144 chapters)

Module 1. Understanding ISO 42001 and Its Impact on Control Systems
Foundational overview of ISO 42001 principles and how they apply specifically to industrial control environments. Covers scope alignment, governance boundaries, and interface points with legacy SCADA and PLC systems.
12 chapters in this module
  1. Defining AI governance in operational technology contexts
  2. Core clauses of ISO 42001 relevant to control systems engineering
  3. Mapping AI control needs to existing IEC 62443 frameworks
  4. How ISO 42001 changes procurement documentation for control vendors
  5. Case study: AI audit failure in a power distribution control system
  6. Integrating AI governance into existing cybersecurity compliance
  7. Common misconceptions about ISO 42001 applicability to non-IT systems
  8. Control system roles under ISO 42001 governance mandates
  9. Timing considerations for ISO 42001 adoption in multi-phase deployments
  10. Aligning internal control standards with ISO 42001 requirements
  11. Documenting AI decision points in control logic workflows
  12. Preparing for auditor questions on AI control provenance
Module 2. AI Risk Assessment Within Control Environments
Step-by-step method to conduct AI-specific risk assessments in control systems, identifying vulnerabilities, biases, and single-point failures in AI-augmented loops.
12 chapters in this module
  1. Identifying AI-enabled control functions in legacy systems
  2. Classifying AI decision types by risk impact and reversibility
  3. Using failure mode analysis for AI decision paths
  4. Evaluating data drift in sensor inputs affecting AI control stability
  5. Assessing bias in AI-based predictive maintenance models
  6. Scenario planning for AI override decisions under stress conditions
  7. Documenting risk treatment options for high-impact AI control points
  8. Integrating risk registers with change management systems
  9. Aligning risk thresholds with organizational tolerance levels
  10. Working with compliance teams to classify AI control severity
  11. Tools for visualizing AI risk exposure in control diagrams
  12. Conducting tabletop exercises for AI control failure response
Module 3. Defining AI Governance Structure and Accountability
Establishing clear governance roles, decision rights, and escalation paths for AI in control systems, aligned with ISO 42001 section 5 requirements.
12 chapters in this module
  1. Assigning AI control ownership across engineering and operations
  2. Creating governance charters for AI-augmented control teams
  3. Defining escalation triggers for AI decision anomalies
  4. Mapping accountability for AI model updates in control systems
  5. Integrating governance roles into change advisory boards
  6. Clarifying vendor versus internal responsibility for AI drift
  7. Documenting decision rights for AI control overrides
  8. Reviewing governance structure with legal and compliance teams
  9. Establishing communication protocols for AI control incidents
  10. Maintaining governance logs for audit readiness
  11. Using RACI matrices for AI control lifecycle stages
  12. Updating governance after system integration or expansion
Module 4. AI Data Management in Control Systems
Best practices for managing data inputs, quality, and lifecycle for AI models embedded in control systems, ensuring traceability and reliability.
12 chapters in this module
  1. Identifying primary data sources for AI control functions
  2. Implementing data quality checks at sensor and gateway levels
  3. Managing data retention for AI model retraining in control systems
  4. Securing AI training data against tampering and drift
  5. Documenting data lineage for compliance audits
  6. Integrating data governance tools with control monitoring platforms
  7. Handling missing or corrupted data in AI control loops
  8. Validating data representativeness across operating conditions
  9. Assessing bias in training data for AI control models
  10. Applying data anonymization where applicable
  11. Auditing data access and modification in AI-enabled systems
  12. Aligning data practices with NIST and ISO 42001 standards
Module 5. AI Model Development and Integration in Control Systems
Practical integration strategies for embedding AI models into existing control architectures while maintaining safety, reliability, and compliance.
12 chapters in this module
  1. Selecting appropriate AI models for real-time control applications
  2. Integrating AI inferencing into PLC and RTU environments
  3. Validating AI model performance under edge constraints
  4. Documenting model assumptions for audit and review
  5. Testing AI control responses under edge cases and failure modes
  6. Version control for AI models in production systems
  7. Handling model drift detection and response protocols
  8. Establishing model performance baselines for control stability
  9. Integrating human-in-the-loop overrides effectively
  10. Deploying A/B testing for AI control improvements
  11. Maintaining separation between experimental and production models
  12. Reviewing model changes with cross-functional stakeholders
Module 6. AI Transparency and Explainability for Compliance
Ensuring AI decisions in control systems are auditable, explainable, and defendable under ISO 42001 and client review expectations.
12 chapters in this module
  1. Documenting AI decision logic for non-technical reviewers
  2. Implementing logging mechanisms for AI control actions
  3. Creating runbooks for AI behavior under normal and stress conditions
  4. Generating audit trails for AI-driven control adjustments
  5. Using visualization tools to explain AI behavior to stakeholders
  6. Addressing regulator questions about AI decision rationale
  7. Balancing explainability with performance in real-time systems
  8. Summarizing AI behavior in executive-level reports
  9. Preparing evidence packs for ISO 42001 audits
  10. Handling proprietary AI model limitations in disclosure
  11. Training control teams to interpret AI outputs correctly
  12. Updating transparency documentation after model changes
Module 7. Human-AI Interaction in Control Operations
Designing effective interfaces and protocols for human operators managing AI-augmented control systems, minimizing override risks and errors.
12 chapters in this module
  1. Designing control panels with clear AI status indicators
  2. Establishing protocols for human override of AI decisions
  3. Training operators on AI behavior and failure modes
  4. Documenting handover procedures between AI and manual control
  5. Reducing alert fatigue in AI-monitored systems
  6. Implementing sanity checks for AI recommendations
  7. Using dashboards to display AI confidence levels
  8. Conducting drills for AI disengagement scenarios
  9. Capturing operator feedback for AI improvement
  10. Aligning training programs with AI control changes
  11. Evaluating workload impact of AI integration
  12. Reporting human-AI interaction issues for model refinement
Module 8. AI Robustness and Performance Monitoring
Techniques for ensuring AI models in control systems remain accurate, stable, and resilient under changing conditions.
12 chapters in this module
  1. Setting up continuous monitoring for AI model performance
  2. Defining thresholds for AI control deviation alerts
  3. Detecting concept drift in AI-driven control logic
  4. Implementing automated retraining triggers
  5. Validating model updates in staging environments
  6. Monitoring for adversarial manipulation of AI inputs
  7. Assessing AI resilience under network congestion
  8. Evaluating AI performance during system transitions
  9. Using synthetic data to stress-test AI models
  10. Logging model performance for audit and review
  11. Coordinating with cybersecurity teams on AI threats
  12. Updating monitoring rules after control system changes
Module 9. AI Security and Cyber Resilience
Protecting AI components in control systems from tampering, data poisoning, and adversarial attacks per ISO 42001 and NIST guidance.
12 chapters in this module
  1. Identifying attack surfaces for AI models in control systems
  2. Securing AI model storage and inferencing pipelines
  3. Implementing integrity checks for AI weights and parameters
  4. Detecting data poisoning attempts in training pipelines
  5. Protecting against adversarial inputs to AI control models
  6. Hardening communication channels for AI-enabled devices
  7. Applying zero-trust principles to AI control access
  8. Monitoring for anomalous AI behavior patterns
  9. Integrating AI security into incident response plans
  10. Conducting penetration testing on AI control layers
  11. Applying firmware updates to AI-embedded controllers
  12. Aligning AI security practices with ISO 27001 controls
Module 10. AI Incident Response and Recovery
Developing response plans for AI failures in control systems, ensuring rapid recovery and root cause analysis.
12 chapters in this module
  1. Defining AI incident classification levels in control systems
  2. Creating escalation paths for AI-driven control failures
  3. Documenting root cause analysis procedures for AI incidents
  4. Implementing rollback procedures for corrupted AI models
  5. Communicating AI incidents to stakeholders and clients
  6. Preserving forensic data after AI control events
  7. Updating training data after incident analysis
  8. Improving AI models based on failure data
  9. Integrating AI incident metrics into reliability reporting
  10. Reviewing response plans after real-world events
  11. Conducting post-mortems with cross-functional teams
  12. Updating controls to prevent recurrence
Module 11. AI Audit Preparation and Compliance Documentation
Practical steps to prepare for internal and client audits of AI governance in control systems, ensuring ISO 42001 readiness.
12 chapters in this module
  1. Compiling evidence for ISO 42001 control implementation
  2. Creating audit-ready documentation for AI decision processes
  3. Organizing AI governance artifacts for reviewer access
  4. Preparing control system engineers for auditor interviews
  5. Responding to findings from AI compliance reviews
  6. Mapping AI controls to ISO 42001 clauses
  7. Demonstrating continuous improvement in AI governance
  8. Using automated tools to generate compliance reports
  9. Maintaining living documentation for dynamic AI systems
  10. Updating audit packs after system changes
  11. Aligning internal audit processes with client expectations
  12. Training teams on audit response protocols
Module 12. Scaling AI Governance Across Projects
Strategies for institutionalizing AI governance practices across multiple control system deployments and business units.
12 chapters in this module
  1. Creating reusable AI governance templates for control projects
  2. Establishing center of excellence for AI in control systems
  3. Standardizing AI documentation across programs
  4. Sharing lessons learned from AI implementation failures
  5. Training new teams on AI governance expectations
  6. Integrating AI governance into proposal development
  7. Positioning as the internal expert for AI compliance bids
  8. Building relationships with compliance and legal stakeholders
  9. Demonstrating ROI of AI governance to leadership
  10. Advancing career through recognized AI leadership
  11. Contributing to industry standards for AI in OT
  12. Maintaining personal expertise amid evolving regulations

How this maps to your situation

  • Initial ISO 42001 governance setup in control systems
  • Ongoing risk and performance monitoring
  • Audit and client review cycles
  • Expansion to new sectors and bid opportunities

Before vs. after

Before
Operating within control systems where AI integration lacks structured governance, leaving compliance gaps and missed bid opportunities.
After
Leading ISO 42001-aligned AI governance initiatives with confidence, positioned for higher-margin projects and cross-functional leadership.

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 module, designed for engineers to complete at their own pace over 4-6 weeks.

If nothing changes
Without structured AI governance, control systems face increased audit findings, client pushback on compliance, and exclusion from premium contracts requiring ISO 42001 alignment.

How this compares to the alternatives

Unlike generic AI ethics courses, this program delivers engineering-grade implementation steps for ISO 42001 in control systems, tailored to real-world deployment challenges and compliance expectations.

Frequently asked

Is this course relevant for non-IT control systems?
Yes. It’s specifically designed for industrial control, SCADA, and OT environments where AI is being integrated.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Does the course cover AI model development?
It covers integration, validation, and governance of AI models, not data science or algorithm creation.
$199 one-time. Approximately 90 minutes per module, designed for engineers to complete at their own pace over 4-6 weeks..

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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