A tailored course, built for your situation
Mastering ISO 42001 for Windows System Engineers in Regulated Environments
A structured path to owning AI governance implementation in enterprise IT operations
$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.
Spending 80+ hours pulling together AI governance evidence for audits?
The situation this course is for
Windows system engineers in regulated services firms spend cycles scrambling to align IAM logs, change tickets, and configuration snapshots into audit-ready ISO 42001 evidence, often with cross-team chases and version drift.
Who this is for
Mid-career Windows System Engineer in a global IT services firm, responsible for maintaining compliant infrastructure under audit cycles, seeking to transition from execution to ownership of governance deliverables
Who this is not for
Executives looking for board-level summaries, software developers focused on model tracing, or consultants selling ISO 42001 programs
What you walk away with
- Produce ISO 42001-compliant AI governance evidence packages without cross-team escalation
- Automate recurring data calls from logging, patch, and access review systems
- Own the narrative when regulators ask about AI system lineage or change control
- Shift from being a support role to the named owner of AI governance implementation artefacts
- Reduce evidence assembly from days to under four hours
The 12 modules (with all 144 chapters)
Module 1. Understanding ISO 42001 and Its Relevance to Windows Infrastructure
Establish foundational knowledge of ISO 42001 clauses and how they map directly to system logging, access control, and change management practices in Windows environments.
12 chapters in this module
- Defining AI governance in the context of enterprise IT systems
- Key differences between ISO 42001 and traditional security standards
- How AI system registration applies to Windows-hosted applications
- Mapping control objectives to existing group policy configurations
- Understanding the scope boundary for AI lifecycle tracking
- Integrating ISO 42001 with existing change advisory boards
- Identifying AI-enabled services in legacy Windows environments
- Role of system engineers in maintaining governance metadata
- Audit expectations for AI system documentation completeness
- Common gaps in evidence between DevOps and IT operations
- How DORA resilience requirements intersect with AI governance
- Preparing for internal audit scoping conversations
Module 2. Establishing AI System Inventory and Registry Practices
Learn how to build and maintain a living inventory of AI-enabled services running on Windows systems, including automated discovery techniques.
12 chapters in this module
- Defining what constitutes an AI system in a Windows environment
- Using PowerShell scripts to detect AI inference endpoints
- Integrating registry updates into standard deployment pipelines
- Linking service accounts to AI system ownership records
- Version tracking for model-hosting services on IIS
- Documenting training data sources for hosted models
- Automating registry updates from configuration management
- Handling decommissioning of AI-enabled Windows services
- Audit trail requirements for registry modifications
- Integrating registry with existing CMDB practices
- Role-based access for registry updates and reviews
- Monthly attestation cycles for inventory accuracy
Module 3. Designing AI Data Governance for Logging and Access
Implement data governance controls that satisfy ISO 42001 requirements for transparency and auditability in Windows system logs.
12 chapters in this module
- Defining minimum logging standards for AI-enabled services
- Configuring event forwarding for AI system activity
- Securing access to logs containing model decision trails
- Retention policies aligned with AI system lifecycle
- Integrating Windows logs with centralized SIEM tools
- Documenting data lineage for AI input and output streams
- Validating log integrity through scheduled checksums
- Handling PII in AI inference logs on Windows servers
- Role-based access for log review and extraction
- Preparing logs for regulator walkthroughs
- Automating log completeness checks before audits
- Linking log entries to specific model version deployments
Module 4. Implementing Change Control for AI System Updates
Adapt standard change control processes to cover AI model updates, retraining, and configuration drift on Windows platforms.
12 chapters in this module
- Classifying AI-related changes in the Windows environment
- Integrating model deployment into change advisory board
- Defining rollback requirements for failed model updates
- Documenting training data changes as part of change tickets
- Validating model signature integrity during deployment
- Tracking model provenance in change records
- Automating pre-deployment checks for AI services
- Handling emergency changes to AI inference services
- Post-implementation review for AI system performance
- Linking change tickets to AI system registry entries
- Auditing change control compliance for regulator reviews
- Reducing review cycle time through templated evidence
Module 5. Managing Access Controls for AI System Operations
Apply least-privilege principles to service accounts, administrators, and applications interacting with AI systems on Windows platforms.
12 chapters in this module
- Defining roles for AI system administration on Windows
- Separating model deployment from inference hosting
- Securing service accounts used by AI inference services
- Implementing Just Enough Administration for AI hosts
- Monitoring privileged access to model configuration
- Documenting access review cycles for regulator reporting
- Integrating access reviews with existing IAM tools
- Handling third-party vendor access to AI systems
- Automating access certification evidence generation
- Tracking access changes related to AI deployments
- Validating access controls through scheduled attestation
- Preparing access review packs for audit cycles
Module 6. Ensuring AI System Availability and Resilience
Apply ISO 42001 resilience requirements to high-availability Windows environments hosting AI services.
12 chapters in this module
- Defining service level objectives for AI inference hosts
- Integrating AI workloads into existing DR runbooks
- Validating failover procedures for model hosting clusters
- Monitoring model performance during failover events
- Documenting backup strategies for model artifacts
- Testing recovery of AI-enabled Windows services
- Integrating AI systems into existing availability dashboards
- Handling configuration drift in standby environments
- Preparing availability evidence for ISO 42001 audits
- Aligning AI resilience with existing DORA requirements
- Documenting recovery time objectives for regulators
- Automating availability test reporting
Module 7. Documenting AI System Performance Monitoring
Establish performance monitoring practices that satisfy ISO 42001 requirements for ongoing AI system oversight.
12 chapters in this module
- Defining KPIs for AI inference service health
- Monitoring model drift through performance metrics
- Setting thresholds for automatic alerting
- Integrating model monitoring with existing SCOM dashboards
- Documenting model degradation response procedures
- Validating monitoring coverage across environments
- Handling model performance during peak loads
- Auditing monitoring configuration changes
- Preparing performance reports for governance reviews
- Automating evidence for regulatory cycles
- Linking monitoring alerts to incident management
- Reviewing model performance across versions
Module 8. Integrating AI Governance into Incident Response
Adapt incident response plans to address AI-specific failure modes in Windows-hosted environments.
12 chapters in this module
- Identifying AI-specific incident scenarios
- Updating runbooks for model inference failures
- Handling model corruption incidents
- Documenting model rollback procedures
- Integrating AI incidents with existing ticketing
- Defining escalation paths for AI system issues
- Training L1 teams on AI incident identification
- Validating incident response through tabletop exercises
- Auditing incident records for governance compliance
- Preparing incident data for regulator inquiries
- Automating incident reporting for audits
- Reviewing post-mortem documentation completeness
Module 9. Building Audit-Ready Evidence Packages
Streamline the production of ISO 42001 evidence packages by integrating automated data collection into existing workflows.
12 chapters in this module
- Defining minimum evidence requirements by control
- Automating log collection for AI system activity
- Generating access review attestations programmatically
- Validating change control documentation completeness
- Assembling evidence packages from distributed sources
- Verifying evidence integrity before submission
- Reducing manual effort in evidence compilation
- Integrating evidence checks into release pipelines
- Preparing evidence for internal audit cycles
- Documenting evidence collection procedures
- Training team members on evidence standards
- Reviewing evidence quality before audit
Module 10. Communicating AI Governance to Regulators
Develop clear narratives for regulator interactions based on documented implementation practices.
12 chapters in this module
- Preparing for regulator inquiries about AI systems
- Documenting control implementation for auditors
- Using evidence packages to demonstrate compliance
- Handling follow-up questions from reviewers
- Aligning terminology with ISO 42001 requirements
- Demonstrating control effectiveness through data
- Presenting change control for model updates
- Explaining access controls to audit teams
- Verifying narrative consistency across evidence
- Training engineers on regulator communication
- Preparing for remote audit sessions
- Closing audit findings efficiently
Module 11. Sustaining Compliance Through Automation
Implement scripting and tooling to maintain ISO 42001 compliance with minimal manual intervention.
12 chapters in this module
- Identifying repetitive evidence collection tasks
- Building PowerShell scripts for log extraction
- Automating registry updates from deployment tools
- Integrating evidence checks with CI/CD pipelines
- Scheduling automated access reviews
- Validating configuration drift controls
- Alerting on missing evidence components
- Generating monthly compliance dashboards
- Reducing audit preparation time
- Documenting automation procedures
- Training team on automated workflows
- Reviewing automation reliability quarterly
Module 12. Scaling AI Governance Across Environments
Extend proven practices to additional business units and geographies while maintaining consistency.
12 chapters in this module
- Assessing readiness for new environment rollout
- Adapting controls to regional regulatory needs
- Training teams on standardized practices
- Validating implementation through spot checks
- Documenting global consistency in governance
- Handling localization requirements
- Integrating new teams into evidence cycles
- Reducing onboarding time for engineers
- Auditing cross-environment compliance
- Preparing global evidence for central review
- Sharing best practices across regions
- Reviewing scalability of automation tools
How this maps to your situation
- Before an ISO 42001 audit cycle
- When rolling out AI governance in a legacy environment
- After a regulator inquiry
- During a system consolidation
Before vs. after
Before
Spending weeks assembling evidence manually, chasing logs and change tickets, and preparing for audit cycles reactively.
After
Producing regulator-ready ISO 42001 documentation in under four hours, with automated evidence collection and clear ownership.
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 6-8 hours total, designed to be completed in short sessions over a weekend or across two evenings.
If nothing changes
Continuing with manual evidence assembly increases the risk of delayed audits, findings related to incomplete documentation, and unnecessary engineering bandwidth spent on compliance overhead rather than innovation.
How this compares to the alternatives
Unlike generic compliance courses, this program focuses specifically on implementing ISO 42001 in Windows environments, with templates and automation scripts tailored to system engineers in regulated services firms.
Frequently asked
Is this course relevant if my organization hasn't formally adopted ISO 42001?
Yes. The practices taught are increasingly expected by regulators and clients, and provide a structured way to demonstrate responsible AI governance regardless of formal certification status.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this course help me pass an ISO 42001 audit?
It provides the implementation guidance and evidence practices needed to succeed in an audit, focusing on the technical artefacts system engineers control.
$199 one-time. Approximately 6-8 hours total, designed to be completed in short sessions over a weekend or across two evenings..
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