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DAT3185 Mastering ISO 42001 for Senior Executives in Post-Retirement Advisory Roles

$199.00
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A tailored course, built for your situation

Mastering ISO 42001 for Senior Executives in Post-Retirement Advisory Roles

Build defensible AI governance positions with framework-backed reasoning tailored to complex enterprise 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.
Even experienced leaders hesitate when asked to justify AI governance choices without concrete precedent.

The situation this course is for

You're seen as a trusted voice, but when peers challenge your AI governance stance, you need more than experience. You need on-demand access to standards-aligned reasoning, documented precedent, and specific examples that show why one approach prevails over another. Without it, influence erodes, even when you're right.

Who this is for

Senior executive leveraging post-retirement standing to advise on emerging governance challenges, particularly in AI and operational resilience. Values precision, precedent, and quiet authority over visibility or persuasion.

Who this is not for

Junior compliance staff, entry-level auditors, or professionals seeking certification prep. This is not a general awareness course. It’s for established leaders who must justify high-stakes decisions, not learn basics.

What you walk away with

  • Respond to peer challenges with specific clause references from ISO 42001 and documented implementation examples
  • Build a personal repository of AI governance reasoning anchored in the standard’s control objectives
  • Structure Statements of Applicability that reflect intentional exclusion, not oversight
  • Explain AI risk treatment decisions using precedent from regulated industries
  • Move from opinion-based discussion to standards-grounded dialogue in cross-functional reviews

The 12 modules (with all 144 chapters)

Module 1. Why ISO 42001 Is the Foundation for Defensible AI Governance
Establish the strategic importance of ISO 42001 as a credibility anchor in emerging AI governance debates. Trace how its structure supports auditable decision-making and withstands challenges from technical and executive stakeholders alike.
12 chapters in this module
  1. How ISO 42001 differs from internal AI ethics frameworks
  2. The role of clause 4.1 in scoping AI governance applicability
  3. Using organizational context to justify governance boundaries
  4. Precedent from financial services adopting ISO 42001 for AI
  5. Why regulators reference it in AI oversight discussions
  6. Mapping executive expectations to clause 4 requirements
  7. Case example: Justifying AI use in claims processing
  8. Avoiding overreach by anchoring to defined scope
  9. Documenting excluded domains with traceable rationale
  10. The value of consistency across AI and data governance
  11. How clause 4.2 aligns with stakeholder needs
  12. Building audit readiness from the first governance draft
Module 2. Clause 5 Leadership Commitment in Practice
Demonstrate how senior accountability is codified in ISO 42001 and how to evidence it credibly. Focus on real-world artifacts that show leadership engagement beyond policy signatures.
12 chapters in this module
  1. Translating clause 5.1 into actionable governance behaviors
  2. Evidence of leadership involvement in AI risk reviews
  3. Documenting strategic direction in AI governance updates
  4. Role of the governance committee under clause 5.3
  5. How to structure leadership review meetings
  6. Tracking decisions that reflect top management commitment
  7. Case example: COO-led review of AI model inventory
  8. Avoiding tokenism in governance committee charters
  9. Clause 5.1 a: Commitment to AI governance framework
  10. Clause 5.1 b: Integrating governance into business processes
  11. Clause 5.1 c: Providing necessary resources
  12. Clause 5.1 d: Communicating policy internally
Module 3. Building a Defensible Statement of Applicability
Walk through the creation of a complete SoA that justifies inclusions and exclusions with documented reasoning. Emphasize traceability to risk assessments and business context.
12 chapters in this module
  1. Structure of a robust Statement of Applicability
  2. Linking controls to identified AI risks
  3. Documenting justification for excluding control 8.2
  4. How to handle dual-use AI models in scope definition
  5. Using risk treatment plans to inform control selection
  6. Precedent from healthcare on AI transparency controls
  7. Clause 6.1 a: Policies and objectives alignment
  8. Clause 6.1 b: Risk assessment methodology
  9. Clause 6.1 c: Risk treatment process
  10. Clause 6.1 d: Statement of Applicability maintenance
  11. Version control and review cycles
  12. Auditor expectations for SoA completeness
Module 4. AI Risk Assessment Grounded in ISO 42001
Teach how to conduct AI-specific risk assessments using ISO 42001’s framework. Include how to define asset types, threat actors, and impact criteria specific to machine learning systems.
12 chapters in this module
  1. Defining AI assets in information security terms
  2. Mapping model lifecycle phases to risk exposure
  3. Identifying threat actors targeting AI systems
  4. Assessing impact of model drift on business outcomes
  5. Benchmarking likelihood using sector-specific data
  6. Documenting risk acceptance thresholds
  7. Case example: Credit scoring model risk register
  8. Integrating AI risk into enterprise risk management
  9. How clause 6.1 b applies to model retraining
  10. Linking risk treatment to control 14.1
  11. Using heat maps to prioritize AI risks
  12. Updating assessments after model deployment
Module 5. Control 8: AI System Acquisition and Development
Detail how ISO 42001’s acquisition controls apply to AI systems, including vendor selection, development standards, and lifecycle governance.
12 chapters in this module
  1. Applying control 8.1 to third-party AI platforms
  2. Requirement for documented development lifecycle
  3. Vendor due diligence on model explainability
  4. Ensuring compliance with fairness and bias requirements
  5. Contractual terms for model monitoring and updates
  6. Case example: Selecting an AI-powered underwriting tool
  7. Control 8.2: Classification of AI development approaches
  8. Managing open-source model risk
  9. Documentation requirements for custom AI
  10. Control 8.4: Securing AI development environments
  11. Independent review of model validation plans
  12. Handover procedures from dev to ops teams
Module 6. Control 14: AI-Specific Data and Model Governance
Focus on how ISO 42001’s control 14 supports governance of training data, model updates, and inference pipelines, with emphasis on auditability.
12 chapters in this module
  1. Defining data quality standards for training sets
  2. Establishing data lineage requirements
  3. Logging model inputs and outputs for traceability
  4. Version control for AI models and datasets
  5. Monitoring for concept drift and data drift
  6. Audit readiness for model decision records
  7. Case example: Fraud detection model updates
  8. Data anonymization in training pipelines
  9. Retention policies for model artifacts
  10. Access controls for model deployment environments
  11. Change management for model retraining
  12. Documenting model deprecation decisions
Module 7. Internal Audit and Monitoring of AI Governance
Equip learners to design and respond to audits focused on AI systems. Emphasize how ISO 42001 provides the structure for measurable compliance.
12 chapters in this module
  1. Planning audit scope for AI governance
  2. Sampling methods for model decision logs
  3. Evaluating adherence to documented policies
  4. Assessing effectiveness of bias mitigation controls
  5. Reporting findings to governance committees
  6. Case example: Internal audit of HR screening AI
  7. Clause 9.2 a: Audit program requirements
  8. Clause 9.2 b: Audit criteria alignment
  9. Handling non-conformities in model performance
  10. Follow-up on corrective actions
  11. Integrating AI into annual audit cycles
  12. Coordination with external auditors
Module 8. Continuous Improvement Through AI Incident Review
Show how to use incidents and near-misses to strengthen AI governance. Focus on root cause analysis aligned with ISO 42001’s continual improvement clause.
12 chapters in this module
  1. Defining reportable AI incidents
  2. Conducting root cause analysis on model errors
  3. Updating risk assessments after incidents
  4. Case example: Re-architecting a failed recommendation engine
  5. Clause 10.1: Corrective action process
  6. Documenting lessons learned in governance updates
  7. Tracking recurrence prevention measures
  8. Linking incident data to model monitoring
  9. Improving training data based on failures
  10. Updating model validation frequency
  11. Sharing anonymized learnings across teams
  12. Integrating feedback into governance policy
Module 9. Communicating AI Governance Decisions Effectively
Teach how to translate ISO 42001 reasoning into clear narratives for executives, legal teams, and technical staff , without oversimplifying or losing rigor.
12 chapters in this module
  1. Tailoring messaging to different stakeholder groups
  2. Using the SoA as a communication tool
  3. Explaining risk treatment to non-technical leaders
  4. Creating executive summaries from audit findings
  5. Case example: Justifying AI use in customer service
  6. Responding to internal inquiries about bias
  7. Building FAQ documents from governance decisions
  8. Documenting rationale for public disclosure
  9. Aligning messaging with corporate values
  10. Handling media inquiries about AI decisions
  11. Training spokespeople on governance principles
  12. Monitoring sentiment on AI-related topics
Module 10. Integrating ISO 42001 with Other Frameworks
Demonstrate how ISO 42001 aligns with NIST AI RMF, GDPR, and other standards, allowing for unified governance without duplication.
12 chapters in this module
  1. Mapping ISO 42001 to NIST AI Risk Management Framework
  2. Aligning controls with GDPR Article 22 on automated decisions
  3. Integrating with SOC 2 for service organizations
  4. Case example: Unified report for multiple compliance needs
  5. Avoiding conflicting requirements across standards
  6. Maintaining a single control inventory
  7. Cross-walking control 14.1 with NIST RMF
  8. Handling jurisdictional variations in AI law
  9. Using ISO 42001 as the baseline for AI assurance
  10. Coordinating updates across framework implementations
  11. Training teams on integrated compliance
  12. Auditor acceptance of consolidated evidence
Module 11. Governance for Generative AI Deployments
Address the specific challenges of generative AI, including hallucination risk, content provenance, and prompt engineering governance.
12 chapters in this module
  1. Scoping generative AI under ISO 42001
  2. Defining asset boundaries for LLMs
  3. Risk assessment for hallucination and misinformation
  4. Case example: Customer support chatbot governance
  5. Control 8.31: Managing prompt libraries
  6. Documentation requirements for generated content
  7. Ensuring human oversight in generative workflows
  8. Audit trails for model inputs and outputs
  9. Vendor management for API-based LLMs
  10. Compliance with copyright in training data
  11. Handling personal data in prompts
  12. Updating governance as models evolve
Module 12. Sustaining Governance Through Leadership Transitions
Ensure AI governance outlasts individual leaders. Focus on documentation, playbook maintenance, and institutionalizing decision-making.
12 chapters in this module
  1. Building governance playbooks for new leaders
  2. Documenting rationale for future reference
  3. Case example: Transitioning AI oversight after retirement
  4. Ensuring continuity in audit preparation
  5. Training successors on ISO 42001 principles
  6. Maintaining SoA updates across tenures
  7. Archiving governance decisions for legal readiness
  8. Succession planning for governance roles
  9. Onboarding materials for new team members
  10. Versioning control for policy documents
  11. Knowledge transfer sessions with stakeholders
  12. Long-term preservation of implementation evidence

How this maps to your situation

  • Post-retirement advisory roles requiring credible governance positions
  • AI governance in regulated enterprise environments
  • Executive-level decision justification under scrutiny
  • Sustainability of governance frameworks beyond individual leadership

Before vs. after

Before
Relies on experience and intuition when defending AI governance choices, risking erosion of influence when challenged.
After
Responds to peer pushback with specific ISO 42001 clauses, documented precedents, and structured reasoning , maintaining authority without escalation.

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 week over eight weeks, designed for experienced leaders balancing advisory commitments.

If nothing changes
Without a standards-backed foundation, even experienced leaders can find their positions challenged when AI governance comes under scrutiny. The absence of documented, defensible reasoning increases vulnerability to second-guessing, undermines credibility, and exposes advisory roles to reputational risk , especially in post-retirement contexts where formal authority is absent.

How this compares to the alternatives

Unlike generic AI governance overviews or certification prep courses, this program is built for seasoned executives who must defend high-stakes decisions. It doesn't teach compliance checklists , it builds the ability to reason aloud with precision, using ISO 42001 as a scaffold for unshakeable positions.

Frequently asked

Is this course technical?
No. It's designed for executives and advisors who need to justify decisions, not implement systems. The focus is on reasoning, not code or infrastructure.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will I receive a certification?
No. This is a mastery-building course, not a certification prep. You gain concrete reasoning tools, not a credential.
$199 one-time. Approximately 90 minutes per week over eight weeks, designed for experienced leaders balancing advisory commitments..

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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