A tailored course, built for your situation
Mastering ISO 27001 for Machine Learning Engineers in Regulated Environments
Build compliant, auditable AI systems with confidence and control
$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.
Audit fatigue in AI projects due to compliance rework
The situation this course is for
Machine learning teams in regulated sectors are spending 30-50% of project time retrofitting controls for audits, often scrambling to document access policies, model lineage, and data handling practices that weren't built with ISO 27001 in mind. This delays deployment, inflates costs, and limits participation in high-value engagements.
Who this is for
A senior Machine Learning Engineer at a global consulting firm, working on AI projects for clients in financial services, healthcare, or government sectors. They are technically strong but increasingly expected to navigate compliance requirements without slowing innovation. They want to lead, not just execute.
Who this is not for
This course is not for entry-level data scientists, auditors, or compliance officers without hands-on ML engineering experience. It's also not for teams working exclusively on non-regulated, internal R&D projects with no external audit or certification requirements.
What you walk away with
- Produce ISO 27001-compliant AI project documentation on the first pass
- Reduce audit preparation time for ML systems by 70% or more
- Lead client conversations about AI governance with authority and precision
- Unlock participation in premium, compliance-sensitive AI engagements
- Design reusable control templates for access, logging, and model validation
The 12 modules (with all 144 chapters)
Module 1. Why ISO 27001 Matters for Machine Learning Engineers
Understand how information security standards directly impact AI project scope, timelines, and team authority. Learn where ML intersects with control domains like access management, logging, and asset classification.
12 chapters in this module
- The growing role of ML engineers in compliance-critical projects
- How ISO 27001 defines information assets in AI systems
- Mapping model lifecycle stages to control requirements
- Understanding auditor expectations for AI documentation
- The cost of non-compliance in client-facing ML deployments
- How compliance posture influences engagement selection
- Key differences between SOC 2 and ISO 27001 for AI
- Where ML teams typically fail ISO 27001 audits
- Building credibility with internal compliance partners
- The strategic value of shipping compliant models faster
- Case study: AI fraud detection system audit outcome
- From rework to readiness: shifting left on compliance
Module 2. Foundations of ISO 27001 for Technical Teams
Break down the standard into actionable components for engineers. Focus on Clauses 4, 10 and Annex A controls most relevant to ML infrastructure and deployment.
12 chapters in this module
- Clause 4: Context of the organization in AI projects
- Clause 5: Leadership commitment and project ownership
- Clause 6: Risk assessment for model deployment
- Clause 7: Documentation and evidence requirements
- Clause 8: Operational planning and control
- Clause 9: Monitoring and measurement of controls
- Clause 10: Continual improvement in AI systems
- Annex A.5: Information security policies for ML teams
- Annex A.6: Organization of information security
- Annex A.7: Human resource security in data science roles
- Annex A.8: Asset management for models and datasets
- Annex A.9: Access control in model serving environments
Module 3. Building the Statement of Applicability for AI
Create a living SoA that maps ISO 27001 controls to specific ML system components, reducing audit friction and clarifying team responsibilities.
12 chapters in this module
- Purpose and structure of the Statement of Applicability
- Identifying AI-specific control applicability
- Justifying exclusions with technical rationale
- Documenting model access control implementations
- Capturing data handling and retention policies
- Logging and monitoring requirements for audit trails
- Versioning the SoA alongside model iterations
- Integrating SoA updates into CI/CD pipelines
- Collaborating with compliance teams on review cycles
- Using the SoA to defend design decisions under scrutiny
- Common pitfalls in SoA documentation for ML
- Example: SoA for a credit scoring model deployment
Module 4. Secure Design Patterns for ML Infrastructure
Implement reusable architectural blueprints that satisfy ISO 27001 controls by default, reducing configuration drift and evidence gaps.
12 chapters in this module
- Designing ISO-compliant model training environments
- Securing data pipelines with access logging
- Implementing role-based access for ML platforms
- Isolating sensitive datasets in development workflows
- Enforcing encryption in transit and at rest
- Audit trail requirements for model retraining
- Container security and image scanning policies
- Network segmentation for model serving endpoints
- Managing secrets and credentials in MLOps
- Hardening Jupyter environments for regulated use
- Automated compliance checks in deployment gates
- Template: Terraform module for compliant ML cluster
Module 5. Access Control and Identity Management in AI Systems
Design granular, auditable access policies for models, data, and infrastructure that meet ISO 27001 A.9 requirements without slowing development.
12 chapters in this module
- Defining roles and responsibilities in ML teams
- Implementing least privilege for model access
- Managing service accounts in MLOps pipelines
- Logging access attempts to sensitive models
- Multi-factor authentication for production access
- Time-bound access for external collaborators
- Role-based access control in Kubernetes
- Integrating with enterprise identity providers
- Auditing access changes in model registries
- Handling access revocation after team changes
- Automated access reviews using policy-as-code
- Example: Access control matrix for healthcare AI
Module 6. Data Handling and Privacy in Model Workflows
Ensure data classification, retention, and processing align with ISO 27001 A.8 and A.10, especially in cross-border AI deployments.
12 chapters in this module
- Classifying data sensitivity in training sets
- Documenting data sources and lineage
- Anonymization techniques for model development
- Data retention policies for model artifacts
- Cross-border data transfer compliance
- Consent management in model inputs
- Secure disposal of training data copies
- Logging data access and transformations
- Third-party data provider assessments
- Privacy by design in feature engineering
- Compliance with GDPR and CCPA in ML
- Template: Data handling policy for AI projects
Module 7. Logging, Monitoring, and Audit Trails
Build tamper-resistant, comprehensive logging systems that satisfy ISO 27001 A.12 and support rapid audit response.
12 chapters in this module
- Critical events to log in ML systems
- Centralized logging for model deployments
- Immutable log storage configurations
- Monitoring for unauthorized access attempts
- Alerting on policy violations in real time
- Retention periods for audit logs
- Log integrity verification mechanisms
- Correlating logs across model lifecycle stages
- Integrating with SIEM tools for compliance
- Automated log summarization for auditors
- Handling log access requests securely
- Example: Audit trail for model update process
Module 8. Incident Response for AI Systems
Prepare for security incidents involving models or data with response plans that meet ISO 27001 A.16 and protect client trust.
12 chapters in this module
- Defining incidents specific to AI deployments
- Model poisoning and data manipulation risks
- Response roles for ML engineering teams
- Containment strategies for compromised models
- Forensic data collection from training runs
- Notification procedures for clients and regulators
- Post-mortem analysis for model failures
- Integrating with enterprise incident management
- Documentation requirements for regulators
- Testing incident response with tabletop exercises
- Rebuilding trust after an AI-related breach
- Template: AI incident response playbook
Module 9. Vendor and Third-Party Risk in ML Ecosystems
Assess and manage risks from third-party tools, datasets, and cloud platforms used in AI development and deployment.
12 chapters in this module
- Evaluating MLOps platform compliance posture
- Assessing third-party model marketplace risks
- Data licensing and usage rights verification
- Contractual clauses for AI vendor agreements
- Right-to-audit provisions for cloud providers
- Managing open-source component risks
- Supply chain security in model dependencies
- Continuous monitoring of vendor compliance
- Escalation paths for vendor-related incidents
- Documentation for third-party attestations
- Example: Vendor risk assessment for LLM API
- Template: Third-party risk questionnaire
Module 10. Automating Compliance Evidence Collection
Use code and tooling to generate audit-ready artifacts automatically, reducing manual effort and human error.
12 chapters in this module
- Identifying evidence requirements per control
- Scripting control validation checks in Python
- Generating ISO 27001 evidence from CI/CD logs
- Automated screenshot capture for access reviews
- Policy-as-code with Open Policy Agent
- Integrating compliance checks into pull requests
- Version-controlled evidence repositories
- Dynamic evidence dashboards for auditors
- Automated SoA updates from infrastructure state
- Testing compliance automation reliability
- Handling false positives in automated checks
- Template: GitHub Actions workflow for evidence
Module 11. Preparing for ISO 27001 Certification Audits
Navigate the audit process confidently, from scoping to closing findings, with engineering-led evidence and clear narratives.
12 chapters in this module
- Understanding audit scope for AI projects
- Preparing the audit evidence package
- Common auditor questions for ML teams
- Conducting internal mock audits
- Responding to findings with technical clarity
- Managing auditor access to systems
- Leveraging automation outputs in reviews
- Demonstrating continual improvement
- Maintaining audit readiness year-round
- Working with internal audit teams
- Post-audit follow-up and reporting
- Case study: Passing ISO 27001 audit for AI platform
Module 12. Scaling Compliance Across AI Engagements
Replicate compliant patterns across projects, reduce onboarding time, and position your team as the preferred partner for high-stakes work.
12 chapters in this module
- Creating reusable compliance templates
- Onboarding new ML projects to the framework
- Training junior engineers on compliance basics
- Measuring compliance maturity over time
- Benchmarking against peer teams
- Sharing best practices across client engagements
- Reducing time-to-compliance for new clients
- Marketing compliant AI capabilities to sales
- Building a library of evidence artifacts
- Tracking compliance debt in technical backlog
- Integrating with enterprise GRC platforms
- Roadmap: From project-level to program-level compliance
How this maps to your situation
- Regulated AI deployments in financial services
- Healthcare AI projects with data privacy requirements
- Global consulting engagements with compliance expectations
- Internal AI platforms serving multiple client teams
Before vs. after
Before
Spending weeks retrofitting controls and scrambling for audit evidence after model development is complete.
After
Shipping models with compliance built in, leading high-budget engagements with documented, reusable standards.
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 week over 12 weeks, or accelerate at your own pace.
If nothing changes
Without structured compliance practices, ML teams risk project delays, audit failures, and exclusion from premium engagements that require certified governance frameworks. This limits career growth and team impact.
How this compares to the alternatives
Unlike generic compliance courses, this program is tailored to ML engineers working in consulting environments, with concrete templates, code samples, and project structures that align with ISO 27001 without sacrificing agility.
Frequently asked
Is this course suitable for someone without a security background?
Yes. It's designed for ML engineers who need to meet compliance requirements without becoming security experts. Concepts are explained in technical, practical terms.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Can I use the templates in client work?
Yes. The templates are licensed for professional use and can be adapted to your client engagements.
$199 one-time. Approximately 90 minutes per week over 12 weeks, or accelerate at your own pace..
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