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DAT5984 Mastering ISO 42001 for Product Leaders in Defense Technology

$199.00
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A tailored course, built for your situation

Mastering ISO 42001 for Product Leaders in Defense Technology

Build AI governance into your product lifecycle with confidence and clarity.

$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.
Stop scrambling for AI governance evidence during audit cycles.

The situation this course is for

Product leaders in regulated environments often face sudden requests for documentation that validate AI systems against standards like ISO 42001. Without a structured approach, teams burn cycles chasing artifacts, reconciling controls, and building narratives retroactively, especially when regulator scrutiny increases. The cost isn’t just time; it’s eroded credibility and missed opportunities to lead.

Who this is for

Senior product leader in a defense or federal technology firm responsible for bringing AI-enabled solutions to market with compliant, auditable governance built in.

Who this is not for

Entry-level product managers, non-AI-focused teams, or professionals outside regulated technology environments.

What you walk away with

  • Produce a complete Statement of Applicability (SoA) in under 48 hours
  • Map controls to product development phases with precision
  • Respond to auditor follow-ups with confidence and sources
  • Establish repeatable workflows for future AI product launches
  • Become the go-to internal reference for ISO 42001 in your organization

The 12 modules (with all 144 chapters)

Module 1. Introduction to ISO 42001 and AI Governance
Understand the core principles of ISO 42001 and how they apply specifically to AI product development in defense contexts. This module sets the foundation for integrating governance into your existing workflows, with real-world examples from federal contractors.
12 chapters in this module
  1. What ISO 42001 means for AI product leaders
  2. How AI governance differs from traditional compliance
  3. Key clauses every product team must address
  4. Structure of the standard and its relationship to NIST AI RMF
  5. Why ISO 42001 matters for defense technology firms
  6. Common misconceptions about AI governance audits
  7. Mapping product roles to ISO 42001 responsibilities
  8. Integrating governance into sprint planning cycles
  9. Evidence types required for each control
  10. How the firm-level programs align with the standard
  11. Timing conformance efforts with product milestones
  12. Avoiding over-documentation while staying audit-ready
Module 2. Defining the AI Governance Scope
Learn how to clearly define the boundaries of your AI governance program, ensuring that only relevant controls are applied. This module helps you avoid scope creep and focus on what truly impacts your product's compliance posture.
12 chapters in this module
  1. Identifying AI systems within your product portfolio
  2. Determining which models require formal governance
  3. Scoping decisions based on risk and impact level
  4. Documenting rationale for inclusions and exclusions
  5. Aligning scope with DOD AI Ethical Principles
  6. Using categorization frameworks to streamline decisions
  7. Handling edge cases like dual-use technologies
  8. Versioning scope documents across product cycles
  9. Getting leadership alignment on governance boundaries
  10. Integrating scoping into new product intake
  11. Common pitfalls in boundary definition
  12. Case study: Scoping an autonomy module for battlefield comms
Module 3. Asset Classification for AI Systems
Establish a consistent method for classifying AI assets based on function, data sensitivity, and operational criticality. This module provides templates and decision rules tailored to defense product environments.
12 chapters in this module
  1. Defining asset categories relevant to AI products
  2. Classifying models by autonomy level and decision impact
  3. Tagging datasets by provenance and PII exposure
  4. Linking asset type to control stringency requirements
  5. Using metadata to automate classification at scale
  6. Handling third-party AI components in your stack
  7. Versioning asset inventories across releases
  8. Integrating classification into CI/CD pipelines
  9. Auditor expectations for asset documentation
  10. Documenting exceptions and temporary deviations
  11. Mapping classifications to NIST CSF categories
  12. Example: Classifying a real-time language translation model
Module 4. Risk Assessment Methodology for AI
Implement a repeatable process for assessing AI-specific risks, from bias and hallucination to adversarial attacks and unintended escalation. This module walks through a the firm-relevant risk matrix and scoring methodology.
12 chapters in this module
  1. Adapting ISO 27005 for AI contexts
  2. Identifying AI-specific threat vectors
  3. Building a risk register for machine learning models
  4. Scoring likelihood and impact for AI incidents
  5. Incorporating red team findings into risk ratings
  6. Handling cascading failures in multi-model systems
  7. Integrating human-in-the-loop evaluation points
  8. Documenting risk treatment decisions
  9. Using risk scores to prioritize mitigation efforts
  10. Aligning with enterprise risk management frameworks
  11. Common gaps in AI risk assessments
  12. Case study: Risk assessment for an AI-enabled surveillance system
Module 5. Control Mapping and Implementation
Learn how to map ISO 42001 controls to specific product development practices, including documentation, testing, and deployment workflows unique to defense technology teams.
12 chapters in this module
  1. Translating control clauses into engineering actions
  2. Mapping A.8 controls to data preprocessing steps
  3. Applying A.9 to model training environments
  4. Embedding A.10 into evaluation and validation phases
  5. Implementing A.11 for model monitoring and drift detection
  6. Using A.12 to govern model updates and retraining
  7. Documenting control implementation in product artifacts
  8. Integrating control checks into sprint reviews
  9. Handling partial implementations with justification
  10. Auditor review expectations for control evidence
  11. Maintaining traceability from control to code
  12. Example: Mapping controls to a battlefield decision support tool
Module 6. Statement of Applicability (SoA) Development
Create a comprehensive, defensible SoA that clearly communicates which controls apply, why, and how they are met. This module includes a customizable template and real-world examples from federal contractors.
12 chapters in this module
  1. Structure of a compliant SoA document
  2. Justifying inclusion and exclusion of controls
  3. Linking SoA entries to product-specific evidence
  4. Writing clear rationale for auditor consumption
  5. Versioning SoA across product iterations
  6. Integrating SoA updates into release notes
  7. Aligning SoA with security categorization guides
  8. Handling classified or sensitive control mappings
  9. Using automation to maintain SoA accuracy
  10. Common reviewer comments and how to preempt them
  11. Presenting SoA to technical review boards
  12. Case study: SoA for an AI-enabled logistics optimizer
Module 7. AI Governance in Agile Product Lifecycles
Adapt ISO 42001 requirements to fast-moving, iterative development environments without sacrificing compliance. This module shows how to bake governance into sprints, not bolt it on at the end.
12 chapters in this module
  1. Integrating governance gates into sprint planning
  2. Defining 'governance done' in user story acceptance
  3. Building compliance checks into CI/CD pipelines
  4. Tracking control evidence in Jira or equivalent
  5. Scheduling lightweight reviews between sprints
  6. Managing documentation debt in agile teams
  7. Using templates to accelerate artifact creation
  8. Conducting internal audits without disrupting flow
  9. Reporting compliance status to leadership monthly
  10. Handling urgent patches and hotfixes
  11. Maintaining audit readiness during rapid iteration
  12. Example: Governance for a time-sensitive comms upgrade
Module 8. Third-Party AI Component Oversight
Ensure compliance when using or integrating third-party AI models, APIs, or platforms. This module covers due diligence, contractual expectations, and ongoing monitoring requirements.
12 chapters in this module
  1. Assessing vendor conformance to ISO 42001
  2. Reviewing third-party SoA and audit reports
  3. Defining contractual obligations for AI transparency
  4. Monitoring performance and drift in external models
  5. Handling updates and version changes from vendors
  6. Documenting reliance on third-party assurances
  7. Integrating external model logs into central monitoring
  8. Managing subcomponent supply chain risks
  9. Auditor expectations for vendor oversight
  10. Common gaps in third-party AI governance
  11. Using SIG questionnaires effectively
  12. Case study: Integrating a commercial NLP engine into a secure platform
Module 9. Monitoring, Logging, and Model Drift Detection
Establish robust monitoring practices that satisfy ISO 42001 requirements while providing real operational value. This module covers logging strategies, alerting thresholds, and drift detection tuned for defense applications.
12 chapters in this module
  1. Defining minimum logging requirements for AI systems
  2. Capturing input, output, and context for model decisions
  3. Setting up drift detection for performance degradation
  4. Monitoring for concept drift in dynamic environments
  5. Logging human override events and rationale
  6. Storing logs securely with appropriate retention
  7. Integrating logs into SIEM or SOAR platforms
  8. Creating auditor-friendly dashboards
  9. Responding to alerts without unnecessary escalation
  10. Documenting incident response workflows
  11. Using logs for model retraining triggers
  12. Example: Monitoring an AI-driven target identification system
Module 10. Internal Audit Preparation and Response
Prepare for internal and external audits with confidence by maintaining always-ready evidence and clear narratives. This module covers how to structure documentation and coordinate responses efficiently.
12 chapters in this module
  1. Organizing evidence for quick retrieval
  2. Conducting mock audits with engineering teams
  3. Training team members on auditor interactions
  4. Documenting responses to findings
  5. Prioritizing remediation based on risk tier
  6. Tracking findings to closure with evidence
  7. Using automation to reduce audit fatigue
  8. Preparing executive summaries for leadership
  9. Handling sensitive or classified findings
  10. Aligning with DOD audit frameworks
  11. Common auditor questions and how to answer
  12. Case study: Preparing for a CMMC-adjacent review
Module 11. Continuous Improvement and Version Control
Implement a sustainable process for updating governance practices as products evolve. This module focuses on version control, change management, and learning from past cycles.
12 chapters in this module
  1. Versioning governance artifacts alongside code
  2. Managing changes to SoA and control mappings
  3. Establishing a governance change advisory board
  4. Integrating lessons learned into next iterations
  5. Using feedback from audits to improve processes
  6. Updating risk assessments after operational changes
  7. Handling legacy system exceptions
  8. Communicating changes across product teams
  9. Maintaining backward compatibility in reporting
  10. Automating evidence updates for recurring controls
  11. Documenting rationale for deviations
  12. Example: Updating governance after a system upgrade
Module 12. Scaling AI Governance Across the Portfolio
Extend proven practices from one product to others, creating organizational leverage and reducing redundancy. This module shows how to build reusable patterns without sacrificing context.
12 chapters in this module
  1. Identifying common governance patterns across products
  2. Creating standardized templates and checklists
  3. Building a central governance knowledge base
  4. Training new teams on established methods
  5. Adapting frameworks to different AI domains
  6. Measuring governance maturity across products
  7. Reporting portfolio-wide status to leadership
  8. Recognizing and rewarding team contributions
  9. Avoiding one-size-fits-all pitfalls
  10. Integrating with enterprise architecture teams
  11. Planning for future ISO revisions and updates
  12. Case study: Scaling governance from comms to logistics AI

How this maps to your situation

  • Preparing for increased scrutiny on AI systems in defense contracts
  • Reducing time spent on last-minute audit evidence gathering
  • Establishing clear ownership of AI governance within product teams
  • Building reusable artifacts that survive leadership changes

Before vs. after

Before
Spending weeks compiling evidence for AI governance reviews, reacting to auditor questions without sources, and struggling to align engineering teams around compliance expectations.
After
Producing complete, defensible ISO 42001 artifacts in under 48 hours, leading internal reviews with confidence, and being sought out as the go-to expert on AI governance across product lines.

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 of focused work, designed to fit into a single Sunday morning.

If nothing changes
Without a structured approach, AI governance remains reactive, consuming disproportionate team bandwidth during audit cycles and increasing the risk of non-compliance findings that could impact contract renewals or new award eligibility.

How this compares to the alternatives

Unlike generic compliance courses, this program is tailored to product leaders in defense technology, focusing on practical, actionable steps to implement ISO 42001 within existing development workflows, not just theoretical overviews.

Frequently asked

Is this course only for security or compliance roles?
No. It's designed specifically for product leaders who need to integrate AI governance into development workflows without slowing innovation.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Can I apply this to non-AI products?
While focused on AI, the control mapping and documentation methods can be adapted to other governed technologies.
$199 one-time. Approximately 90 minutes of focused work, designed to fit into a single Sunday morning..

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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