A tailored course, built for your situation
Mastering ISO 42001 for DevOps Engineers in Global Technology Services
Build AI governance into core delivery pipelines with confidence
$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.
Invisible work in AI governance despite being on the front lines of implementation
The situation this course is for
Skilled DevOps engineers implement controls that align with standards like ISO 42001 but remain unseen by senior leadership, limiting recognition and upward influence.
Who this is for
DevOps Engineer in a global IT services firm, working at the intersection of automation, compliance, and emerging AI governance requirements
Who this is not for
Entry-level developers, non-technical compliance staff, or consultants without hands-on deployment experience
What you walk away with
- Own end-to-end implementation of ISO 42001 controls within CI/CD pipelines
- Produce audit-ready artefacts directly from version-controlled infrastructure
- Lead cross-functional alignment on AI accountability without requiring governance escalation
- Anticipate and resolve control gaps before they reach compliance review stages
- Embed compliance as code patterns that scale across teams
The 12 modules (with all 144 chapters)
Module 1. Understanding ISO 42001 in the Context of DevOps
Ground the standard in engineering workflows, not policy abstractions.
12 chapters in this module
- Scope of ISO 42001 for AI systems
- Mapping clauses to DevOps responsibilities
- AI system lifecycle phases
- Governance vs operational roles
- Control ownership models
- Integration with existing DevOps KPIs
- Difference from ISO 27001
- AI-specific risk domains
- Role of logging and traceability
- Versioning AI control frameworks
- Linking controls to incident response
- Compliance as part of definition of done
Module 2. Control Identification for Automated Systems
Identify mandatory and discretionary controls applicable to AI-enabled services.
12 chapters in this module
- Deterministic vs probabilistic outputs
- Bias assessment frequency
- Documentation depth per control
- Human oversight triggers
- Fallback mechanism design
- Data lineage requirements
- Model drift detection thresholds
- Explainability integration
- Audit trail scope
- Transparency automation
- Third-party model risk
- Control prioritization matrix
Module 3. Integrating AI Governance into CI/CD
Embed compliance checks directly into build and deployment pipelines.
12 chapters in this module
- Pre-commit hooks for AI metadata
- Linting for model documentation
- Automated fairness checks
- Security scanning for AI models
- Model signing and attestation
- Policy-as-code frameworks
- Enforcement at pull request
- Drift monitoring in staging
- Canary release compliance gates
- Rollback triggers based on control failure
- Pipeline audit logging
- Version lock for audit cycles
Module 4. Building Audit-Ready Artefacts Automatically
Generate compliant outputs without manual effort or last-minute scrambles.
12 chapters in this module
- Auto-generating system descriptions
- Control mapping from code comments
- Dynamic evidence collection
- Machine-readable compliance reports
- Traceability from code to clause
- Automated gap reporting
- Versioned attestation records
- Log aggregation for AI behavior
- Snapshot preservation strategies
- Time-stamped control validation
- Immutable storage integration
- Audit trail export format
Module 5. AI Risk Assessment in Deployment Contexts
Tailor risk evaluation to actual system impact, not theoretical scoring.
12 chapters in this module
- Impact level definitions
- Data sensitivity tiers
- Autonomy level classification
- Human intervention points
- Failure consequence modeling
- Geographic risk variation
- Regulatory overlap identification
- Third-party dependency risks
- Model update urgency bands
- Incident escalation thresholds
- Risk register automation
- Dynamic risk scoring
Module 6. Managing Model Lifecycle Compliance
Apply ISO 42001 across development, deployment, monitoring, and retirement.
12 chapters in this module
- Model registration workflow
- Development sandbox controls
- Pre-deployment validation
- Staging environment requirements
- Monitoring baseline setup
- Retraining triggers
- Model version rollback process
- Deprecation notification
- Documentation retention period
- Model sunsetting checklist
- License compliance tracking
- Audit trail preservation
Module 7. Human Oversight Integration Patterns
Design escalation paths and review points that scale with system autonomy.
12 chapters in this module
- Defining oversight scope
- Escalation trigger conditions
- Review frequency bands
- Role-based approval chains
- Automated alert routing
- Human-in-the-loop thresholds
- Exception logging
- Override justification capture
- Review cycle documentation
- Feedback loop to model
- Oversight role rotation
- Audit trail for decisions
Module 8. Data Governance for Training and Inference
Secure and govern data flows specific to AI systems.
12 chapters in this module
- Training data provenance
- Inference data handling
- PII filtering in datasets
- Synthetic data compliance
- Data refresh protocols
- Bias mitigation in data
- Data version control
- Labeling process controls
- Third-party data vetting
- Data retention policies
- Data subject rights fulfillment
- Data poisoning defenses
Module 9. Third-Party AI Component Assurance
Verify compliance of external models, APIs, and libraries.
12 chapters in this module
- Vendor due diligence checklist
- Model card evaluation
- API compliance validation
- Open source license review
- Supply chain integrity
- Model drift responsibility
- Incident response coordination
- Compliance evidence exchange
- Contractual control commitments
- Penetration testing scope
- Post-breach notification terms
- Exit strategy for vendors
Module 10. Incident Management for AI Systems
Respond to anomalies, drift, and failures with structured rigor.
12 chapters in this module
- Incident classification tiers
- Drift detection thresholds
- Model rollback procedures
- Bias incident response
- Transparency request handling
- Model retraining triggers
- Stakeholder notification
- Regulatory reporting triggers
- Post-mortem templates
- Control gap analysis
- Evidence preservation
- Communication plan
Module 11. Continuous Monitoring and Improvement
Institutionalize feedback loops that evolve controls over time.
12 chapters in this module
- Performance decay detection
- Control effectiveness review
- Stakeholder feedback channels
- Audit finding tracking
- Benchmarking against peers
- Lessons learned database
- Control refinement cycle
- Version update coordination
- Training refresh cadence
- Tooling upgrade planning
- Compliance debt tracking
- Maturity assessment
Module 12. Internal Audit and Readiness Preparation
Prepare for formal assessments with confidence and precision.
12 chapters in this module
- Audit scope definition
- Evidence collection timeline
- Interview preparation
- Gap remediation plan
- Control validation checklist
- Stakeholder alignment
- Compliance dashboard
- External auditor coordination
- Follow-up evidence submission
- Remediation tracking
- Audit report response
- Post-audit improvement
How this maps to your situation
- Implementing ISO 42001 in a global IT services environment
- Aligning DevOps practices with emerging AI governance standards
- Gaining visibility in leadership discussions on AI accountability
- Transitioning from task execution to strategic ownership
Before vs. after
Before
Working behind the scenes to implement AI governance without recognition from leadership
After
Your contributions in ISO 42001 implementation are visible and valued in strategic discussions
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: 6-8 hours of focused learning, designed to fit around project cycles
If nothing changes
Continuing to deliver critical governance work that remains invisible to decision-makers, limiting career growth and impact.
How this compares to the alternatives
Unlike generic compliance courses, this is tailored to DevOps engineers implementing AI governance controls, focusing on concrete deliverables, not abstract theory.
Frequently asked
Is this course suitable for someone without a formal compliance background?
Yes, this course is designed for engineers implementing controls, not policy writers. It focuses on practical execution.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help me get recognized for my work?
Yes, by mastering ISO 42001 implementation in engineering contexts, your contributions become visible in leadership discussions.
$199 one-time. 6-8 hours of focused learning, designed to fit around project cycles.
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