A tailored course, built for your situation
Mastering ISO 27001 for Senior AI Engineering Leaders
A structured path to control mapping, audit readiness, and cross-system alignment in AI infrastructure
$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.
Control evidence packages that require repeated validation cycles across teams
The situation this course is for
ML engineering teams at scale often face rework in compliance cycles because control mappings are built reactively. The same artefacts, SoA documentation, access logs, model update trails, are repeatedly requested by security, internal audit, and external assessors, creating bandwidth drain during critical development windows. Without a standardized approach anchored in ISO 27001, even senior practitioners spend disproportionate time reconciling evidence post-hoc, rather than designing systems with audit readiness built in.
Who this is for
Sr. Staff ML Engineer at a large-scale AI-driven tech firm, leading systems that process sensitive data and require compliance alignment. Works cross-functionally with security, privacy, and infrastructure teams. Values precision, anticipatory design, and technical authority. Sees compliance not as overhead but as a systems challenge.
Who this is not for
Junior engineers still mastering ML pipelines, compliance generalists without technical depth, or consultants seeking framework overviews without implementation specificity.
What you walk away with
- Map ISO 27001 controls directly to ML system architecture components
- Produce audit-ready documentation in under 5 hours per cycle
- Automate evidence collection for access reviews and model updates
- Speak confidently to assessors using framework-native language
- Design new model deployments with compliance baked into CI/CD
The 12 modules (with all 144 chapters)
Module 1. Why ISO 27001 Matters for AI Systems
Establish the linkage between information security controls and machine learning infrastructure. Understand how ISO 27001’s domains apply to data pipelines, model training environments, and inference endpoints. Learn why assessors now expect AI systems to meet the same control rigor as core IT systems.
12 chapters in this module
- The evolving role of ML engineers in compliance readiness
- How ISO 27001 applies to non-traditional IT environments
- Mapping domains A.5 through A.18 to AI systems
- Compliance expectations for foundation models at scale
- The shift from reactive audits to proactive design
- Case study: model deployment delayed by control gap
- What assessors look for in AI system documentation
- Integrating controls into MLOps from day one
- Control scope boundaries for research vs production
- Understanding assessor bias toward legacy systems
- Framework alignment vs checkbox compliance
- Why technical teams now lead compliance design
Module 2. Structure of the ISO 27001 Standard
Break down the standard into actionable parts: clauses 4, 10 and Annex A. Focus on how each section drives documentation, design decisions, and evidence requirements. Learn to navigate the standard without getting lost in compliance jargon.
12 chapters in this module
- Clause 4: Context and scope for ML systems
- Clause 5: Leadership commitment in technical teams
- Clause 6: Risk assessment for model infrastructure
- Clause 7: Documentation expectations for ICs
- Clause 8: Operational planning and control design
- Clause 9: Monitoring and measurement requirements
- Clause 10: Corrective action without bureaucracy
- Annex A: Control categories and their purpose
- Control groupings relevant to AI environments
- How to read control statements like an engineer
- Cross-walking controls to existing system diagrams
- Building a personal reference map of the standard
Module 3. Defining Scope for ML Infrastructure
Learn how to define a compliant and credible ISMS scope that includes training clusters, model registries, and inference APIs. Avoid over-scoping that creates drag or under-scoping that fails audit.
12 chapters in this module
- What constitutes an information asset in ML
- Defining boundaries between research and production
- Including third-party dependencies in scope
- Training data: in scope or out of scope?
- Model weights as controlled information assets
- API endpoints and their compliance obligations
- Exclusions that hold up under assessor review
- Documenting architecture decisions in scope statements
- Versioning scope declarations across model updates
- Aligning scope with data classification policies
- Handling ephemeral compute environments
- Scope maintenance during rapid iteration cycles
Module 4. Risk Assessment for AI Systems
Conduct a practical ISO 27001-aligned risk assessment tailored to ML systems. Use a structured approach to identify threats to model integrity, data confidentiality, and system availability.
12 chapters in this module
- Adapting risk methodology to high-dimensional systems
- Identifying assets unique to ML infrastructure
- Threat modeling for training data pipelines
- Vulnerability assessment in distributed compute
- Impact scoring for model drift and bias
- Likelihood estimation in probabilistic environments
- Risk register design for technical teams
- Incorporating red team findings into risk logs
- Linking risk outcomes to control selection
- Documenting residual risk for leadership review
- Automating risk assessment inputs from logs
- Updating assessments after model retraining
Module 5. Control Mapping for Machine Learning
Map ISO 27001 Annex A controls to actual ML system components: notebooks, CI/CD pipelines, model hosting environments. Learn how to justify 'not applicable' with engineering precision.
12 chapters in this module
- Access control for model training environments
- Cryptography controls for model weights
- Secure development practices in ML pipelines
- Change management for model updates
- Logging and monitoring for inference APIs
- Backup strategies for training checkpoints
- Supplier relationships in cloud AI platforms
- Data leakage prevention in shared clusters
- User endpoint protection for ML scientists
- Physical security of GPU clusters
- Incident response for model compromise
- Business continuity for model rollback
Module 6. Statement of Applicability (SoA)
Build a credible, assessor-ready SoA that reflects actual system design. Learn to document control implementation with technical specificity and avoid generic filler.
12 chapters in this module
- Structure of the SoA document
- Writing control implementation statements
- Justifying exclusions with technical rationale
- Versioning the SoA across model releases
- Automating SoA updates from CI/CD triggers
- Linking SoA entries to architecture diagrams
- Using code comments to support SoA claims
- Documenting 'partially implemented' controls
- Maintaining audit trail for SoA changes
- Integrating peer review into SoA updates
- Generating SoA excerpts for specific assessors
- Storing SoA history in version control
Module 7. Evidence Collection Automation
Design systems that auto-generate audit evidence: access logs, configuration snapshots, dependency checks. Reduce manual effort while increasing accuracy.
12 chapters in this module
- Defining evidence requirements per control
- Automated screenshots of access reviews
- Scheduled configuration snapshots
- Logging model input/output for traceability
- Exporting IAM policies in standard format
- Generating network diagram exports
- Automated software inventory reporting
- Integrating evidence collection with CI/CD
- Storing evidence in assessor-accessible formats
- Version-locking evidence for audit cycles
- Alerting on evidence generation failures
- Retention policies for automated logs
Module 8. Internal Audit Preparation
Prepare for internal audits with confidence. Understand common findings, anticipate assessor questions, and produce documentation that closes loops quickly.
12 chapters in this module
- Typical audit timelines for ML systems
- Preparing walkthrough materials
- Common findings in AI compliance reviews
- Responding to auditor requests efficiently
- Scheduling evidence review windows
- Conducting pre-audit self-assessments
- Training team members on audit readiness
- Mapping auditor questions to control evidence
- Building a rapid-response evidence package
- Handling findings with engineering precision
- Documenting corrective actions technically
- Post-audit knowledge transfer
Module 9. Security Awareness for ML Teams
Implement role-specific security training that sticks. Move beyond generic modules to context-rich content tied to daily workflows.
12 chapters in this module
- Why generic training fails in ML teams
- Phishing risks in research environments
- Secure collaboration on public cloud platforms
- Model theft and data leakage scenarios
- Incident reporting pathways for scientists
- Password hygiene in notebook environments
- Multi-factor authentication for cluster access
- Training on model card documentation
- Secure sharing of experimental results
- Handling sensitive data in development
- Annual refresh with technical updates
- Tracking completion without bureaucracy
Module 10. Continuous Improvement Loop
Integrate ISO 27001 compliance into ongoing development cycles. Use audits, incidents, and changes as inputs to improve system design.
12 chapters in this module
- Turning audit findings into roadmap items
- Incorporating incidents into control updates
- Change-driven control reviews
- Metrics for compliance health
- Quarterly management review inputs
- Updating risk assessments post-incident
- Tracking control effectiveness over time
- Feedback loops from assessors
- Engineering debt and control gaps
- Automated compliance health dashboards
- Cross-team learning from findings
- Celebrating compliance wins in team culture
Module 11. Vendor and Supply Chain Controls
Assess and manage third-party risk in AI development: cloud providers, open-source libraries, pre-trained models. Document due diligence with technical depth.
12 chapters in this module
- Classifying vendor relationships
- Due diligence for cloud AI platforms
- Open-source library risk assessment
- Pre-trained model provenance checks
- API dependency risk management
- Contractual obligations for data handling
- Right-to-audit clauses for cloud vendors
- Security questionnaires for new tools
- Monitoring vendor security posture
- Incident response coordination with vendors
- Exit strategies for critical dependencies
- Documenting supply chain decisions
Module 12. Sustaining Compliance at Scale
Design processes that survive team growth, leadership changes, and architectural shifts. Build institutional knowledge that outlives individuals.
12 chapters in this module
- Onboarding new engineers to compliance design
- Documenting decisions for future maintainers
- Architecture review board integration
- Compliance handoff during team rotation
- Preserving knowledge in technical debt logs
- Succession planning for compliance champions
- Automated alerts for control drift
- Versioned runbooks for incident response
- Cross-training on audit processes
- Scaling documentation with system growth
- Integrating compliance into promotion criteria
- Building organizational memory beyond individuals
How this maps to your situation
- Control mapping for AI systems
- Audit readiness in ML infrastructure
- Evidence automation for compliance
- Sustaining standards through team changes
Before vs. after
Before
Spending cycles manually assembling control evidence, responding to repeated auditor questions, and reconciling gaps between system design and compliance expectations.
After
Producing tightly scoped, technically rigorous compliance artefacts on demand, with systems designed to generate audit readiness by default.
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 module, designed to be consumed incrementally. Total course time: 18, 20 hours, paced over 4, 6 weeks.
If nothing changes
Without deliberate design, compliance becomes a tax on innovation, slowing deployments, increasing rework, and exposing systems to avoidable findings. Teams that treat controls as engineering problems, not paperwork, gain leverage across audits, M&A due diligence, and cross-functional trust.
How this compares to the alternatives
Generic ISO 27001 courses focus on policy templates and checklist compliance. This course is built for engineers who need to map controls to actual system components, automate evidence, and justify design decisions to assessors, without slowing innovation.
Frequently asked
Is this course relevant to non-security roles?
Yes. It’s designed for senior engineers and architects who own system design and need to align with compliance standards without becoming auditors.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Can I use this for team training?
Yes. The implementation playbook includes guidance for scaling the content across engineering teams.
$199 one-time. Approximately 90 minutes per module, designed to be consumed incrementally. Total course time: 18, 20 hours, paced over 4, 6 weeks..
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