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DAT6230 Mastering ISO 42001 for Program Managers in Critical Infrastructure Technology

$199.00
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A tailored course, built for your situation

Mastering ISO 42001 for Program Managers in Critical Infrastructure Technology

A structured path from AI governance intent to fully documented, audit-ready outputs in weeks, not months

$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.
AI governance mandates are accelerating, but most teams stall translating policy into audit-ready outputs

The situation this course is for

Program Managers in regulated technology environments are being asked to deliver ISO 42001 compliance artefacts without clear templates or repeatable methods. The gap isn't knowledge, it's execution velocity. Most teams waste cycles debating control scope, evidence formats, or AI inventory structure instead of shipping.

Who this is for

Senior Program Manager in a regulated federal systems integrator, responsible for deploying governance frameworks across enterprise asset management and technology modernization programs

Who this is not for

Entry-level auditors, standalone developers, or practitioners outside of compliance-critical technology delivery roles

What you walk away with

  • Produce a complete Statement of Applicability in under 10 days
  • Build an auditable AI inventory with ownership, risk tiering, and control links
  • Document control implementation evidence that passes internal and external review
  • Structure cross-functional inputs from engineering, security, and legal into a single narrative
  • Reduce revision cycles on governance artefacts by 70% or more

The 12 modules (with all 144 chapters)

Module 1. Understanding ISO 42001 Foundation and Scope
Establish the core structure of ISO 42001, identify applicability in utility and EAM environments, and define the boundary of your AI governance program with precision.
12 chapters in this module
  1. Core principles of AI management systems under ISO 42001
  2. Differentiating AI governance from broader information security frameworks
  3. Mapping organizational AI use cases to control domains
  4. Defining the scope of the AI management system for audit compliance
  5. Identifying excluded clauses with documented justification
  6. Establishing leadership roles in AI governance deployment
  7. Linking ISO 42001 to NIST AI RMF and sector-specific regulations
  8. Aligning AI governance with enterprise risk management frameworks
  9. Integrating EAM data into AI asset inventory definition
  10. Documenting AI system lifecycles across development and deployment
  11. Setting boundaries for third-party AI model integration
  12. Creating a scope statement that survives auditor scrutiny
Module 2. Leadership and Organizational Commitment
Define leadership obligations, secure cross-functional buy-in, and establish governance ownership models that prevent delays in AI control execution.
12 chapters in this module
  1. Articulating leadership responsibility for AI governance outcomes
  2. Assigning AI governance roles to engineering and operations leads
  3. Establishing accountability for AI risk assessment and mitigation
  4. Defining internal oversight mechanisms for AI system compliance
  5. Creating escalation paths for AI incidents and control failures
  6. Documenting leadership review of AI management performance
  7. Integrating AI governance into existing executive reporting cadence
  8. Building cross-functional coordination between IT and compliance
  9. Linking AI governance to vendor management and procurement
  10. Ensuring AI policy adherence across subcontracted development
  11. Validating leadership commitment through audit evidence
  12. Avoiding common pitfalls in organizational AI governance ownership
Module 3. AI Risk Assessment and Treatment Planning
Execute a repeatable risk assessment process, prioritize AI risks by impact and likelihood, and align treatment plans with existing control architectures.
12 chapters in this module
  1. Establishing criteria for AI risk evaluation and classification
  2. Identifying AI-specific threats to confidentiality, integrity, and availability
  3. Assessing risks across AI data, models, and deployment pipelines
  4. Evaluating societal and ethical risks in AI system behavior
  5. Prioritizing risks based on organizational tolerance and regulatory exposure
  6. Selecting risk treatment options: avoid, modify, share, accept
  7. Documenting risk treatment decisions with traceable rationale
  8. Linking AI risks to existing ISO 27001 and NIST CSF controls
  9. Creating risk registers that integrate with audit workflows
  10. Updating risk assessments for new AI model deployments
  11. Validating risk treatment effectiveness through testing
  12. Maintaining risk documentation for regulator access
Module 4. AI System Inventory and Register Construction
Build a comprehensive, searchable inventory of AI systems with metadata, ownership, risk tiering, and control mapping for audit readiness.
12 chapters in this module
  1. Defining what constitutes an AI system under ISO 42001
  2. Classifying AI systems by function, data source, and decision impact
  3. Assigning ownership and stewardship for each AI system
  4. Documenting AI model versions, training data, and output logic
  5. Linking AI systems to business processes and compliance domains
  6. Establishing change control for AI model updates and retraining
  7. Integrating AI register data with enterprise asset management systems
  8. Automating inventory updates from CI/CD and model registry tools
  9. Classifying AI systems by risk tier and regulatory scrutiny level
  10. Generating AI register reports for audit and executive review
  11. Maintaining inventory completeness across hybrid and cloud environments
  12. Validating AI register accuracy through periodic sampling
Module 5. Control Implementation and Evidence Packaging
Translate ISO 42001 control clauses into actionable implementation steps and build defensible, reusable evidence packages.
12 chapters in this module
  1. Mapping ISO 42001 control clauses to specific technical safeguards
  2. Documenting AI model validation and testing procedures
  3. Establishing data quality and bias mitigation controls
  4. Implementing human oversight mechanisms for high-risk AI decisions
  5. Defining model monitoring requirements for production AI
  6. Creating logging and audit trail standards for AI system behavior
  7. Establishing cybersecurity controls specific to AI components
  8. Documenting AI system failover and recovery procedures
  9. Integrating control evidence into compliance management platforms
  10. Packaging evidence for internal and external auditor access
  11. Reducing evidence collection time through automation
  12. Ensuring evidence packages meet SoA linkage requirements
Module 6. Statement of Applicability and Control Justification
Construct a complete, defensible Statement of Applicability with rationale for inclusion or exclusion of each control clause.
12 chapters in this module
  1. Listing all ISO 42001 control clauses applicable to your organization
  2. Documenting rationale for adopting each control
  3. Justifying exclusions with organizational and technical reasoning
  4. Linking control justifications to existing architecture and policies
  5. Aligning SoA with sector-specific regulatory expectations
  6. Creating version-controlled SoA documents for audit cycles
  7. Integrating SoA updates with control implementation changes
  8. Automating SoA updates from governance tooling outputs
  9. Presenting SoA documentation to internal compliance reviewers
  10. Preparing for auditor questions on borderline control clauses
  11. Maintaining SoA integrity across multi-vendor AI integrations
  12. Avoiding common SoA pitfalls that trigger follow-up findings
Module 7. Internal Audit and Assurance Process Design
Design and execute internal audit cycles that validate AI governance controls and generate improvement insights without slowing delivery.
12 chapters in this module
  1. Defining scope and frequency of AI governance internal audits
  2. Selecting qualified auditors with AI technical understanding
  3. Developing audit checklists from ISO 42001 control clauses
  4. Conducting audits across distributed AI development teams
  5. Documenting audit findings with specific control references
  6. Tracking corrective actions to closure with ownership
  7. Integrating audit results into management review meetings
  8. Using audit data to improve AI governance maturity
  9. Automating evidence collection for audit efficiency
  10. Reducing audit disruption through continuous monitoring
  11. Preparing for external auditor validation of internal processes
  12. Maintaining audit documentation for compliance reporting
Module 8. Management Review and Continuous Improvement
Run effective management review meetings that drive AI governance improvement and ensure ongoing compliance relevance.
12 chapters in this module
  1. Scheduling regular management review of AI governance performance
  2. Aggregating key metrics from audits, incidents, and risk assessments
  3. Presenting AI governance status to executive leadership
  4. Identifying improvement opportunities in control effectiveness
  5. Updating AI governance scope for new technology adoption
  6. Reviewing resource allocation for AI compliance activities
  7. Assessing changing regulatory requirements and their impact
  8. Validating leadership commitment through review documentation
  9. Integrating management review findings into roadmap planning
  10. Creating action items with owners and deadlines
  11. Maintaining review records for auditor access
  12. Avoiding tokenistic management review processes
Module 9. AI Incident Response and Lifecycle Management
Establish procedures for detecting, reporting, and resolving AI system incidents while maintaining compliance throughout the lifecycle.
12 chapters in this module
  1. Defining what constitutes an AI incident under ISO 42001
  2. Establishing AI incident detection and alerting mechanisms
  3. Documenting incident classification and escalation workflows
  4. Assigning response ownership for different AI system types
  5. Conducting root cause analysis for AI failures and biases
  6. Implementing remediation actions for recurring AI issues
  7. Reporting incidents to regulators when required
  8. Updating AI models and controls post-incident
  9. Integrating AI incident data into risk assessment updates
  10. Conducting post-mortems with technical and governance teams
  11. Maintaining incident documentation for audit access
  12. Ensuring incident response aligns with organizational policies
Module 10. Third-Party AI Vendor Governance
Extend ISO 42001 controls to third-party AI providers and manage compliance across vendor boundaries.
12 chapters in this module
  1. Assessing AI governance maturity of potential vendors
  2. Including ISO 42001 compliance in procurement contracts
  3. Defining vendor evidence requirements for control validation
  4. Auditing third-party AI systems remotely or on-site
  5. Managing risks in SaaS and API-based AI services
  6. Ensuring data governance compliance in vendor relationships
  7. Documenting vendor oversight in the Statement of Applicability
  8. Tracking vendor compliance throughout contract lifecycle
  9. Managing transition risks when replacing AI vendors
  10. Integrating vendor AI systems into internal inventory
  11. Establishing exit strategies for non-compliant vendors
  12. Maintaining audit trail for third-party AI governance
Module 11. Integration with Existing Compliance Frameworks
Align ISO 42001 with other compliance programs like ISO 27001, NIST CSF, and SOX to avoid duplication and increase efficiency.
12 chapters in this module
  1. Mapping ISO 42001 controls to ISO 27001 security controls
  2. Aligning AI governance with NIST AI Risk Management Framework
  3. Integrating AI controls into existing SOX compliance evidence
  4. Leveraging SOC 2 reports for AI governance validation
  5. Consolidating audit evidence across compliance domains
  6. Avoiding conflicting control interpretations across frameworks
  7. Creating unified dashboards for cross-standard compliance
  8. Training auditors on multi-framework control mapping
  9. Streamlining control updates across overlapping standards
  10. Reducing compliance burden through integrated governance
  11. Documenting integration approaches for auditor review
  12. Maintaining framework-specific reporting requirements
Module 12. Sustaining Compliance and Scaling Governance
Build self-sustaining AI governance practices that scale across programs and survive leadership changes.
12 chapters in this module
  1. Designing onboarding programs for new AI governance staff
  2. Documenting governance processes in accessible formats
  3. Automating recurring compliance tasks through tooling
  4. Establishing metrics for ongoing governance effectiveness
  5. Incorporating governance into AI development lifecycle
  6. Scaling governance to support AI expansion initiatives
  7. Reducing time to compliance for new AI projects
  8. Maintaining compliance during organizational transitions
  9. Ensuring knowledge transfer across team changes
  10. Updating governance for new AI legislation and standards
  11. Creating feedback loops from audits to process improvement
  12. Building organizational resilience in AI compliance

How this maps to your situation

  • AI governance rollout in federal technology programs
  • Integration of EAM systems with AI compliance tracking
  • Control mapping under ISO 42001 for utility-scale operations
  • Audit preparation for AI systems in regulated environments

Before vs. after

Before
Spending weeks assembling control narratives and audit evidence with no repeatable method
After
Producing complete, defensible ISO 42001 artefacts in under 10 days with reusable templates

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, structured to deliver immediate application value.

If nothing changes
Without a structured approach, AI governance efforts stall in review cycles, delay compliance deadlines, and increase exposure to audit findings and regulatory scrutiny.

How this compares to the alternatives

Generic compliance courses lack field-specific ISO 42001 implementation sequences. This course delivers a step-by-step path from policy intent to audit-ready artefact, built for Program Managers in regulated technology delivery.

Frequently asked

How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is this relevant if I don’t lead an AI team?
Yes. If you’re responsible for delivering AI governance artefacts, especially in EAM or infrastructure technology, this course accelerates your execution.
Do I get templates I can use immediately?
Yes. Every module includes downloadable templates and worked examples, plus a hand-built implementation playbook tailored to utility-scale programs.
$199 one-time. 90 minutes of focused learning, structured to deliver immediate application value..

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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