A tailored course, built for your situation
Mastering ISO 27001 for Machine Learning Engineers
Build security into ML systems with command of the ISO 27001 framework
$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.
Struggling to align ML system design with compliance expectations?
The situation this course is for
Machine learning engineers often inherit security and compliance as afterthoughts, forcing rework, delaying deployments, and creating friction with audit teams. The lack of early integration means control mapping feels like translation work, not engineering.
Who this is for
Senior ML engineers working in regulated or standards-conscious environments who need to embed compliance into system design without sacrificing velocity.
Who this is not for
Engineers focused solely on research prototypes, or those without responsibility for production system integrity or audit readiness.
What you walk away with
- Map ISO 27001 controls to ML data lifecycle stages with precision
- Anticipate auditor questions on access control, encryption, and change management
- Produce a Statement of Applicability that reflects actual system architecture
- Document risk treatment plans that pass internal review on first submission
- Lead cross-functional alignment between ML teams and compliance stakeholders
The 12 modules (with all 144 chapters)
Module 1. Introduction to ISO 27001 in ML Contexts
Understand how ISO 27001 applies to machine learning systems, including data pipelines, model hosting, and access governance.
12 chapters in this module
- What ISO 27001 protects in ML systems
- Why ML increases attack surface
- Control relevance to training data
- Framework structure overview
- Annex A vs. control objectives
- Mapping scope to model deployment
- Common misalignments in AI teams
- Audit frequency for ML environments
- Integrating controls early
- Documentation expectations
- Role of the ML engineer in compliance
- Case example: fraud detection system
Module 2. Scope Definition for ML Systems
Define the boundaries of your ISO 27001 scope with precision, covering data sources, storage, processing, and deployment endpoints.
12 chapters in this module
- Identifying asset owners
- Classifying data types
- Mapping data flow paths
- Exclusions with justification
- Model versioning considerations
- API endpoints in scope
- Cloud provider responsibilities
- Training vs. inference separation
- Third-party dependencies
- Logging and monitoring inclusion
- User access boundaries
- Scope sign-off process
Module 3. Risk Assessment for ML Pipelines
Conduct a risk assessment tailored to machine learning workflows, identifying threats specific to data ingestion, model retraining, and inference APIs.
12 chapters in this module
- Threat modelling for data poisoning
- Model inversion risks
- Access control failure modes
- Unauthorised inference detection
- Bias as a security concern
- Data leakage vectors
- Adversarial attack surface
- Overfitting and data memorization
- Shadow model risks
- Third-party model components
- Risk scoring methodology
- Linking risks to controls
Module 4. Control Selection and Customization
Select and adapt ISO 27001 Annex A controls to fit ML-specific risks, avoiding generic checklists.
12 chapters in this module
- A.5.1 Policies for ML systems
- A.6.2 Remote ML work
- A.7.4 Data labelling security
- A.8.1 Asset inventory for models
- A.8.2 Data classification schemes
- A.8.3 Handling encrypted models
- A.9.1 Access control for model APIs
- A.9.2 Role definitions for ML teams
- A.9.4 Self-service access risks
- A.10.1 Model code encryption
- A.12.6 Logging model access
- A.13.2 Secure ML pipelines
Module 5. Statement of Applicability Creation
Build a defensible SoA that reflects real engineering decisions, not checklist compliance.
12 chapters in this module
- Justifying inapplicable controls
- Mapping controls to system features
- Documenting technical exemptions
- Versioning the SoA
- Reviewer expectations
- Linking to architecture diagrams
- Model retraining triggers
- Update frequency
- Automating SoA updates
- Cross-team review process
- Audit trail for changes
- SoA sign-off authority
Module 6. Access Control Implementation
Design and document access governance for ML systems that meets ISO 27001 while enabling agile development.
12 chapters in this module
- Role-based access for data scientists
- Just-in-time access patterns
- Model access tokens
- API key lifecycle
- Service account hardening
- Privileged access review
- Break-glass procedures
- Access revocation automation
- Multi-factor for model deployment
- Identity provider integration
- Access logging standards
- Audit-ready access reports
Module 7. Data Protection in ML Workflows
Apply encryption, pseudonymization, and retention policies across the ML data lifecycle.
12 chapters in this module
- Encrypting training data at rest
- Tokenization for PII
- Data anonymization techniques
- Retention for model inputs
- Secure data sharing
- Encryption key management
- Data provenance tracking
- Differential privacy integration
- Data deletion automation
- Cross-border data flows
- Vendor data handling
- Data breach detection
Module 8. Incident Response for ML Systems
Develop incident response procedures that account for model drift, data poisoning, and unauthorised access.
12 chapters in this module
- Detecting model poisoning
- Response to bias spikes
- Model rollback procedures
- Inference API compromise
- Alerting on anomalous outputs
- Forensic data preservation
- Drift as security event
- Red teaming ML systems
- Post-incident review
- Reporting to compliance
- Regulator notification triggers
- Public disclosure thresholds
Module 9. Vendor and Third-Party Risk
Evaluate and govern third-party components in ML systems, including pre-trained models and cloud services.
12 chapters in this module
- Due diligence for model providers
- Licensing obligations
- Open-source model risks
- Cloud ML service compliance
- Contractual control commitments
- Subprocessor transparency
- Model provenance verification
- Penetration testing clauses
- Right to audit provisions
- Security assurance documentation
- Incident escalation paths
- Exit strategy documentation
Module 10. Audit Preparation and Evidence
Generate evidence artefacts that anticipate auditor questions and demonstrate real implementation.
12 chapters in this module
- Preparing access logs
- User permission reports
- Change management records
- Model lineage documentation
- Risk treatment plan updates
- Penetration test results
- Security awareness for ML team
- Internal audit findings
- Control testing samples
- SoA cross-references
- Executive sign-off records
- Evidence retention policy
Module 11. Continuous Compliance Automation
Integrate ISO 27001 compliance into CI/CD pipelines and monitoring systems for sustained adherence.
12 chapters in this module
- Automated control checks
- Policy-as-code for ML
- Drift detection scripts
- Automated SoA updates
- Compliance dashboards
- Alerting on control failure
- Integration with ticketing
- Audit logging completeness
- Model registry controls
- Pipeline scanning
- Automated evidence collection
- Remediation workflows
Module 12. Leadership and Governance Integration
Position ML compliance as a strategic capability that elevates engineering contributions.
12 chapters in this module
- Reporting compliance to leadership
- Linking controls to business goals
- Compliance as competitive advantage
- Investor-facing disclosures
- Ethics and compliance alignment
- Board-level communication templates
- Strategic risk narratives
- Compliance roadmap planning
- Resource allocation cases
- Cross-functional influence
- Public case studies
- Continuous improvement cycle
How this maps to your situation
- ML system design under regulatory scrutiny
- Preparing for ISO 27001 audit in AI team
- Building secure ML pipelines from scratch
- Leading compliance integration in engineering
Before vs. after
Before
Compliance feels like an external requirement, controls are mapped reactively, and audit prep is stressful.
After
You lead control integration, produce evidence effortlessly, and shape ML architecture with confidence.
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 4 hours per module, with self-paced progress and immediate access to implementation tools.
If nothing changes
Without deep command of ISO 27001, ML systems remain vulnerable to audit findings, rework cycles, and loss of stakeholder trust , slowing deployment and limiting engineering autonomy.
How this compares to the alternatives
Unlike generic ISO 27001 courses, this training is specifically tailored to machine learning engineers, with examples from real ML systems, control mappings relevant to AI/ML risks, and templates that reflect actual engineering workflows , not generic IT.
Frequently asked
Is this course relevant if I don’t work in a regulated industry?
Yes. The principles apply to any ML team that values security, reproducibility, and audit readiness, even in unregulated contexts.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Can I use this for team training?
Yes. Licensing is available for team access , reply for details.
$199 one-time. Approximately 4 hours per module, with self-paced progress and immediate access to implementation tools..
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