A tailored course, built for your situation
Mastering ISO 27001 for Machine Learning Engineers
Build compliance-ready AI systems with confidence and precision
$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.
Who this is for
Senior machine learning engineers working in regulated AI environments who are expected to deliver secure, auditable models without sacrificing innovation speed.
Who this is not for
Entry-level engineers, compliance auditors without technical background, or professionals outside AI/ML development roles.
What you walk away with
- Identify and map ISO 27001 controls relevant to ML pipelines and model deployment
- Produce audit-ready documentation that satisfies security reviews without slowing iteration
- Lead cross-functional alignment on security requirements early in project lifecycles
- Position yourself for higher-visibility engagements with security and governance teams
- Deliver repeatable compliance patterns across AI projects
The 12 modules (with all 144 chapters)
Module 1. Understanding ISO 27001 in AI Contexts
Foundational principles of ISO 27001 as applied to machine learning environments. Learn how to interpret controls in ways that support agile development and data-centric workflows.
12 chapters in this module
- Origins of ISO 27001
- Core objectives for information security
- Relevance to AI and ML systems
- Control families overview
- Mapping to engineering workflows
- Risk assessment baseline
- Security policy alignment
- Role of documentation
- Audit expectations
- Regulatory overlap awareness
- Integration with SDLC
- Common misconceptions
Module 2. Classifying Data in ML Workflows
Apply data classification frameworks to training datasets, model outputs, and intermediate artifacts to meet ISO 27001 requirements.
12 chapters in this module
- Data sensitivity levels
- Identifying personal data
- Model input classification
- Output handling rules
- Metadata tagging strategies
- Encryption triggers
- Access control tiers
- Retention classification
- Jurisdiction mapping
- Cross-border data flows
- Storage labeling
- Audit trail scope
Module 3. Access Control Design for ML Systems
Implement ISO 27001-compliant access structures in model repositories, training infrastructure, and deployment environments.
12 chapters in this module
- Principle of least privilege
- Role-based access mapping
- Service account controls
- Model registry permissions
- Pipeline authorization
- Break-glass procedures
- Audit logging integration
- Temporary access workflows
- Identity federation
- Access revocation triggers
- Monitoring privilege escalation
- Access review cadence
Module 4. Secure Development Lifecycle Integration
Embed ISO 27001 practices into CI/CD pipelines and model development workflows without sacrificing velocity.
12 chapters in this module
- Security gates in CI
- Code scanning integration
- Model signing process
- Artifact provenance tracking
- Dependency checks
- Vulnerability scanning
- Change control alignment
- Peer review standards
- Automated compliance checks
- Versioning discipline
- Rollback protocols
- Incident linkage
Module 5. Risk Assessment for ML Projects
Conduct ISO 27001-aligned risk assessments tailored to AI development , balancing innovation with security rigor.
12 chapters in this module
- Asset identification
- Threat modeling ML systems
- Likelihood versus impact
- Risk register structure
- Control applicability filtering
- Residual risk evaluation
- Approval workflows
- Risk treatment options
- Third-party risk input
- Model drift considerations
- Data poisoning risks
- Adversarial attack vectors
Module 6. Documentation Standards for Audits
Produce clear, concise, and reusable documentation that passes ISO 27001 audits and reduces reviewer follow-up.
12 chapters in this module
- Statement of Applicability drafting
- Control implementation evidence
- Policy exception justification
- Compliance mapping tables
- Audit trail formatting
- Version history retention
- Reviewer-ready summaries
- Glossary standardization
- Cross-reference linking
- Template reuse strategy
- Review cycles
- Update triggers
Module 7. Third-Party Vendor Security Alignment
Evaluate and govern third-party tools and platforms used in ML workflows under ISO 27001 requirements.
12 chapters in this module
- Vendor due diligence
- Contractual security clauses
- API security review
- Cloud provider alignment
- SOC 2 report analysis
- Penetration testing access
- Compliance certification checks
- Incident response coordination
- Exit strategy planning
- Subprocessor oversight
- Audit rights negotiation
- Continuous monitoring
Module 8. Incident Response for ML Systems
Apply ISO 27001 incident management controls to machine learning infrastructure, models, and data breaches.
12 chapters in this module
- Incident classification tiers
- Detection mechanisms
- Response team activation
- Model compromise scenarios
- Data leak containment
- Forensic readiness
- Notification workflows
- Regulatory reporting triggers
- Post-mortem process
- Control improvements
- Legal liaison protocols
- Recovery validation
Module 9. Physical and Environmental Security
Understand how physical security and infrastructure controls apply to cloud-hosted and on-premise ML environments.
12 chapters in this module
- Data center access
- Server rack security
- Network segregation
- Cloud region selection
- Backup storage security
- Environmental monitoring
- Disaster recovery plans
- Redundancy requirements
- Power supply controls
- Fire suppression systems
- Access logging
- Third-party audits
Module 10. Business Continuity in AI Operations
Design resilient ML systems that meet ISO 27001 continuity expectations during disruptions.
12 chapters in this module
- Critical system identification
- Recovery time objectives
- Model redeployment plans
- Data availability safeguards
- Failover testing
- Personnel redundancy
- Communication plans
- Dependency mapping
- External provider roles
- Stress testing
- Documentation access
- Review frequency
Module 11. Continuous Monitoring and Improvement
Implement ongoing compliance validation and improvement mechanisms for sustained ISO 27001 alignment.
12 chapters in this module
- Control performance metrics
- Automated checks
- Anomaly detection
- Review cadence planning
- Audit readiness checks
- Feedback integration
- Gap remediation
- Internal audit coordination
- Management review input
- Policy update process
- Training effectiveness
- Benchmarking progress
Module 12. Strategic Influence and Cross-Functional Leadership
Position yourself as a trusted partner in security and governance conversations across your organization.
12 chapters in this module
- Building trust with compliance teams
- Translating controls to engineering impact
- Influencing architecture choices
- Gaining early project involvement
- Presenting to leadership
- Documenting value
- Mentoring peers
- Shaping policy input
- Cross-team collaboration
- Speaking the auditor's language
- Reference examples
- Sustaining influence
How this maps to your situation
- Starting a new AI project with security review requirements
- Responding to audit findings in ML systems
- Designing a secure ML platform
- Advancing into leadership or cross-functional roles
Before vs. after
Before
Navigating ISO 27001 requirements as an ML engineer feels like overhead , slowing down innovation while demands increase.
After
You confidently lead compliance-integrated ML projects, deliver audit-ready artefacts, and get picked for high-impact work.
What's included with your purchase
- 12 modules with 12 chapters each (144 chapters total)
- 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 3 hours per week over 12 weeks to complete all modules and apply templates to real projects.
If nothing changes
Without structured knowledge of ISO 27001, engineers risk being sidelined from strategic initiatives, facing repeated audit friction, and missing opportunities to lead in the growing field of secure AI.
How this compares to the alternatives
Unlike generic compliance courses, this program is tailored specifically to machine learning engineers , focusing on real-world implementation, not abstract theory. It delivers actionable frameworks, not just awareness.
Frequently asked
Is this course only for compliance officers?
No , it’s designed specifically for machine learning engineers working in environments with ISO 27001 requirements.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Can I apply this to non-ISO 27001 frameworks?
Yes , the patterns align with NIST CSF and SOC 2, but ISO 27001 is the primary anchor for structure and clarity.
$199 one-time. Approximately 3 hours per week over 12 weeks to complete all modules and apply templates to real projects..
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