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DAT9869 Mastering ISO 42001 for DevOps Technical Leaders in Global Systems Integration

$199.00
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A tailored course, built for your situation

Mastering ISO 42001 for DevOps Technical Leaders in Global Systems Integration

Build defensible, audit-ready AI governance artefacts with precision and consistency

$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.
Avoid rework cycles on AI governance deliverables due to misalignment with ISO 42001 expectations

The situation this course is for

DevOps teams are increasingly responsible for demonstrating compliance in AI-enabled systems, yet many artefacts fail internal or client audit rounds due to inconsistent control mapping, vague scope definitions, or incomplete documentation trails. This leads to delayed project sign-offs, repeated effort, and erosion of technical credibility.

Who this is for

Senior DevOps leader in a global systems integrator, accountable for delivering compliant, production-grade AI infrastructure within complex client environments

Who this is not for

Junior engineers, non-technical compliance analysts, or professionals outside of technology delivery roles in regulated environments

What you walk away with

  • Produce fully compliant AI governance documentation that passes internal and client review the first time
  • Map ISO 42001 controls directly to CI/CD pipelines and MLOps workflows
  • Author precise Statements of Applicability (SoA) with defensible rationale and evidence trails
  • Integrate governance checkpoints into sprint cycles without slowing delivery
  • Confidently lead cross-functional audits with structured, reusable artefacts

The 12 modules (with all 144 chapters)

Module 1. Understanding ISO 42001 Scope and Applicability in AI-Driven DevOps
This module breaks down the core structure of ISO 42001, focusing on how its principles apply specifically to AI-infused systems in DevOps environments. It clarifies the boundary between general AI management and operational implementation, ensuring you define scope accurately and avoid over-engineering or under-covering critical areas. You’ll learn to identify which controls are mandatory, which are situational, and how to justify omissions defensibly.
12 chapters in this module
  1. Defining AI system boundaries for ISO 42001 compliance
  2. Differentiating between AI governance and implementation controls
  3. Mapping organisational context to control applicability
  4. Using risk assessments to inform scope decisions
  5. Documenting legal and regulatory dependencies in client engagements
  6. Establishing roles and responsibilities for AI management
  7. Integrating ISO 42001 with existing DevOps governance models
  8. Avoiding common scope creep in multi-vendor projects
  9. Aligning with NIST AI RMF and other complementary frameworks
  10. Documenting assumptions and constraints for audit readiness
  11. Preparing for client-specific deviations from baseline controls
  12. Creating a living compliance boundary document
Module 2. Building the AI Governance Leadership Structure
This module focuses on structuring governance accountability within technical delivery teams. It covers how to assign ownership of AI-related controls, define escalation paths, and integrate governance roles into existing engineering leadership models. You’ll learn to design a governance framework that scales across client projects without creating bureaucratic overhead.
12 chapters in this module
  1. Defining leadership roles for AI governance in DevOps
  2. Integrating AI stewards into sprint planning cycles
  3. Establishing cross-project governance coordination
  4. Documenting decision rights for model deployment
  5. Creating escalation paths for AI control violations
  6. Balancing agility with compliance in fast-moving teams
  7. Training technical leads on governance expectations
  8. Using RACI matrices for AI control ownership
  9. Integrating governance into incident response plans
  10. Measuring governance engagement across teams
  11. Handling governance in offshore-onshore delivery models
  12. Maintaining role clarity during team rotations
Module 3. AI Risk Assessment Process Design
This module teaches you how to conduct rigorous AI-specific risk assessments that meet ISO 42001 requirements while remaining practical for engineering teams. You’ll learn to identify, evaluate, and prioritize AI-related risks in ways that align with technical workflows and client compliance expectations.
12 chapters in this module
  1. Identifying AI-specific risk sources in DevOps pipelines
  2. Classifying risks by impact and likelihood
  3. Integrating risk assessments into CI/CD workflows
  4. Documenting risk treatment decisions with defensible rationale
  5. Using threat modelling for AI system components
  6. Applying risk scoring to model training data sources
  7. Integrating third-party AI vendor risks
  8. Maintaining risk registers across project phases
  9. Aligning risk assessments with client audit requirements
  10. Automating risk flagging in development environments
  11. Conducting periodic risk reassessments
  12. Producing audit-ready risk documentation packages
Module 4. Data Management for AI Systems
This module addresses ISO 42001’s data requirements in the context of AI model development and operation. It covers data quality, provenance, labeling practices, and lifecycle management, ensuring your data handling meets audit expectations and supports reliable AI outcomes.
12 chapters in this module
  1. Defining data quality criteria for AI training sets
  2. Establishing data provenance tracking mechanisms
  3. Managing data labeling consistency and bias checks
  4. Implementing data versioning for model reproducibility
  5. Documenting data retention and disposal policies
  6. Ensuring compliance with data privacy regulations
  7. Integrating data lineage into MLOps pipelines
  8. Validating data representativeness for model fairness
  9. Handling data drift detection in production models
  10. Auditing data access and usage patterns
  11. Securing training data storage and transfer
  12. Producing data governance artefacts for client review
Module 5. AI System Documentation and Transparency
This module guides you in creating comprehensive, audit-ready documentation for AI systems. It covers model cards, data cards, system architecture diagrams, and transparency reports that meet ISO 42001 expectations while remaining useful to engineering teams.
12 chapters in this module
  1. Creating model cards with technically accurate details
  2. Documenting system architecture for audit clarity
  3. Producing data cards with lineage and quality metrics
  4. Writing transparency reports for non-technical stakeholders
  5. Maintaining up-to-date documentation in agile environments
  6. Using automated tools to generate documentation drafts
  7. Versioning documentation alongside code releases
  8. Ensuring documentation accessibility across teams
  9. Aligning documentation depth with risk level
  10. Integrating documentation into CI/CD gates
  11. Preparing documentation packages for client audits
  12. Avoiding common documentation pitfalls in AI projects
Module 6. AI Model Development Lifecycle Controls
This module maps ISO 42001 controls to each phase of the AI model lifecycle, from planning to deployment. It ensures your development process includes necessary checkpoints without sacrificing delivery speed.
12 chapters in this module
  1. Integrating governance into model planning phases
  2. Establishing model design review checkpoints
  3. Validating training data suitability before model build
  4. Implementing bias and fairness testing protocols
  5. Conducting model performance validation
  6. Preparing models for explainability requirements
  7. Integrating security testing into model development
  8. Documenting model assumptions and limitations
  9. Creating deployment approval checklists
  10. Establishing model rollback procedures
  11. Managing model versioning and lifecycle states
  12. Producing audit trails for model development
Module 7. AI System Deployment and Monitoring
This module focuses on governance during AI system deployment and operation. It covers continuous monitoring, performance tracking, and incident response to ensure ongoing compliance and system reliability.
12 chapters in this module
  1. Establishing deployment pre-checks for compliance
  2. Integrating monitoring into production environments
  3. Setting up performance and drift detection alerts
  4. Documenting model performance baselines
  5. Handling model retraining triggers
  6. Establishing human-in-the-loop protocols
  7. Creating incident response plans for AI failures
  8. Monitoring for ethical boundary violations
  9. Auditing model decision patterns over time
  10. Managing model retirement and data disposal
  11. Producing operational reports for governance bodies
  12. Integrating feedback loops into model improvement
Module 8. Third-Party and Vendor AI Governance
This module addresses how to govern AI systems that incorporate third-party components or services. It ensures you can assess vendor compliance and integrate external AI responsibly.
12 chapters in this module
  1. Assessing third-party AI vendor compliance posture
  2. Integrating vendor documentation into SoA
  3. Establishing contractual requirements for AI vendors
  4. Monitoring third-party model performance
  5. Handling vendor model updates and changes
  6. Conducting due diligence on open-source AI components
  7. Managing dependencies on external AI APIs
  8. Documenting vendor risk treatment decisions
  9. Establishing vendor audit rights clauses
  10. Creating vendor escalation paths for issues
  11. Ensuring supply chain transparency for AI
  12. Producing consolidated governance reports
Module 9. AI Incident Management and Learning
This module builds robust processes for handling AI-related incidents, from detection to resolution and organisational learning. It ensures your response is compliant, timely, and contributes to continuous improvement.
12 chapters in this module
  1. Defining AI incident classification levels
  2. Establishing incident detection mechanisms
  3. Creating incident response playbooks
  4. Documenting incident details and root causes
  5. Integrating ethics review into incident analysis
  6. Communicating incidents to stakeholders
  7. Implementing corrective actions effectively
  8. Sharing learnings across DevOps teams
  9. Updating controls based on incident patterns
  10. Auditing incident response effectiveness
  11. Reporting incidents to governance bodies
  12. Integrating incident data into risk assessments
Module 10. Internal Audit and Continuous Improvement
This module prepares you to lead internal audits of AI governance practices and drive continuous improvement. It covers audit planning, evidence collection, and follow-up actions that strengthen compliance over time.
12 chapters in this module
  1. Planning internal AI governance audits
  2. Collecting evidence from technical systems
  3. Conducting interviews with development teams
  4. Evaluating control effectiveness
  5. Documenting audit findings clearly
  6. Prioritizing audit recommendations
  7. Tracking remediation progress
  8. Reporting audit outcomes to leadership
  9. Integrating audit insights into planning
  10. Benchmarking against industry standards
  11. Preparing for external certification audits
  12. Using audit data for maturity assessment
Module 11. Preparing for External Certification Audits
This module guides you through preparing for formal ISO 42001 certification audits. It covers evidence gathering, documentation packaging, and communication strategies to ensure a smooth audit process.
12 chapters in this module
  1. Understanding certification audit requirements
  2. Assembling the evidence portfolio
  3. Preparing documentation for auditor review
  4. Conducting pre-audit readiness assessments
  5. Identifying critical control gaps
  6. Developing remediation plans for findings
  7. Coordinating audit logistics
  8. Training team members for audit interviews
  9. Responding to auditor questions effectively
  10. Handling non-conformity reports
  11. Implementing post-audit improvements
  12. Maintaining certification over time
Module 12. Sustaining AI Governance at Scale
This module focuses on institutionalizing AI governance practices across multiple projects and teams. It covers knowledge sharing, tooling, and organisational structures that ensure long-term compliance and quality.
12 chapters in this module
  1. Creating reusable governance templates
  2. Building internal expertise communities
  3. Integrating governance into onboarding
  4. Developing governance training programs
  5. Standardizing tools and platforms
  6. Automating compliance checks
  7. Measuring governance maturity
  8. Sharing best practices across teams
  9. Conducting periodic governance reviews
  10. Updating policies based on experience
  11. Scaling governance in multi-client environments
  12. Ensuring sustainability through leadership support

How this maps to your situation

  • When client audit requirements land on your desk
  • During AI system integration into existing infrastructure
  • Before launching a new AI-enabled service offering
  • When governance standards are revised or updated

Before vs. after

Before
Spending extra cycles refining governance artefacts, facing last-minute audit requests, and explaining rework due to misaligned controls.
After
Confidently producing ISO 42001-compliant outputs the first time, with clear documentation, defensible rationale, and audit-ready evidence.

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 six weeks, designed to fit around delivery commitments.

If nothing changes
Continuing with inconsistent governance practices increases exposure to audit findings, client escalations, and delivery delays, especially as AI adoption accelerates in regulated contexts.

How this compares to the alternatives

Unlike generic AI ethics courses or high-level compliance overviews, this course delivers specific, actionable guidance tailored to DevOps technical leaders implementing ISO 42001 in real-world client engagements.

Frequently asked

Is this course focused on technical implementation or executive-level policy?
It’s designed for technical leaders who need to translate governance standards into working systems, bridging compliance requirements with engineering execution.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help with client-specific compliance demands?
Yes, the course teaches how to adapt ISO 42001 to client contexts while maintaining defensible, auditable outputs.
$199 one-time. Approximately 90 minutes per week over six weeks, designed to fit around delivery 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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