A tailored course, built for your situation
Mastering ISO 27001 for AI/ML Security Engineers
Produce defensible, auditor-ready security documentation with precision and consistency.
$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.
Avoid rework on ISO 27001 evidence due to unclear scope or missing traceability in AI/ML systems.
The situation this course is for
Even strong technical work gets delayed in compliance review when controls aren't documented with clear, consistent, and audit-ready rigor. For AI/ML engineers, this means repeating documentation cycles, facing auditor pushback, or deferring deployments.
Who this is for
Senior AI/ML engineer working at a global tech firm, responsible for ensuring compliant design and deployment of machine learning systems within regulated environments.
Who this is not for
Entry-level engineers unfamiliar with ISO standards or practitioners focused solely on non-technical compliance administration.
What you walk away with
- Produce complete and accurate ISO 27001 control documentation specific to AI/ML systems on the first pass
- Apply a structured method to define scope, assets, and risk treatments for ML model pipelines
- Reduce auditor back-and-forth with documentation that is consistent, evidence-backed, and logically organized
- Align control statements with actual deployment configurations in cloud environments
- Deliver polished security narratives that reflect technical rigor without rework
The 12 modules (with all 144 chapters)
Module 1. Understanding ISO 27001 in the context of AI/ML systems
Establish a foundational grasp of ISO 27001 requirements as they apply specifically to machine learning infrastructure, model deployment, and data lifecycle management.
12 chapters in this module
- Defining information security scope for AI/ML systems
- Mapping ISO 27001 clauses to machine learning components
- Identifying asset boundaries in model training pipelines
- Classifying data types under ISO 27001 control scope
- Recognizing regulatory overlap in cloud-hosted ML systems
- Differentiating between compliance and security by design
- Using ISO 27001 to strengthen AI governance posture
- Linking security controls to MLOps workflows
- Common misinterpretations of Annex A controls
- How auditors evaluate completeness in technical contexts
- Avoiding over-scope in distributed model environments
- Integrating ISO 27001 early in model development lifecycle
Module 2. Defining asset inventory for audit-ready documentation
Learn how to document digital assets in a way that satisfies ISO 27001 requirements while reflecting the complexity of ML systems.
12 chapters in this module
- Identifying model, dataset, and pipeline components as assets
- Assigning ownership and classification levels to models
- Documenting version control systems as security assets
- Tracking ephemeral compute resources in cloud environments
- Handling metadata and feature stores in asset registers
- Mapping access rights to specific model endpoints
- Using tagging strategies to maintain asset clarity
- Automating asset discovery in CI/CD pipelines
- Documenting dependencies between models and services
- Ensuring traceability from asset list to control application
- Validating asset completeness with internal checklists
- Updating asset inventories during model retraining cycles
Module 3. Risk assessment tailored to model deployment
Conduct risk assessments that are relevant, rigorous, and directly tied to the operational reality of deploying AI/ML systems.
12 chapters in this module
- Adapting ISO 27001 risk methodology to ML contexts
- Identifying threat vectors in model serving infrastructure
- Assessing data leakage risks in training environments
- Evaluating adversarial attacks on deployed models
- Scoring likelihood and impact for ML-specific risks
- Documenting risk treatment decisions transparently
- Choosing between risk acceptance, mitigation, transfer
- Using threat modeling to inform risk statements
- Aligning risk register with cloud provider responsibilities
- Incorporating drift detection into risk treatment plans
- Maintaining risk documentation across model versions
- Producing auditor-ready risk assessment summaries
Module 4. Access control mapping for model environments
Translate technical access configurations into formal ISO 27001-compliant control narratives.
12 chapters in this module
- Defining roles in model development and deployment
- Mapping IAM policies to ISO 27001 access controls
- Documenting principle of least privilege in practice
- Tracking access provisioning and deprovisioning
- Handling shared accounts in development teams
- Securing model endpoints with authentication layers
- Reviewing access logs for compliance alignment
- Managing service account access in pipelines
- Enforcing multi-factor authentication for admin access
- Controlling access to model artifacts and weights
- Auditing access changes in cloud infrastructure
- Producing evidence of access control enforcement
Module 5. Cryptography and data protection in ML pipelines
Apply cryptographic standards appropriately across data movement, model training, and inference stages.
12 chapters in this module
- Encrypting data at rest in feature repositories
- Securing data in transit within distributed training
- Managing encryption keys for model artifacts
- Applying tokenization to sensitive inputs in inference
- Using secure enclaves for privacy-preserving ML
- Documenting cryptographic control implementation
- Aligning cipher suites with organizational policy
- Handling key rotation in automated environments
- Protecting model weights from exfiltration
- Logging cryptographic operations for audit trail
- Validating encryption settings in staging environments
- Avoiding hardcoded secrets in pipeline configurations
Module 6. Incident response planning for AI system failures
Build incident response procedures that account for model drift, data poisoning, and service outages.
12 chapters in this module
- Defining security incidents in ML system context
- Creating runbooks for model performance degradation
- Detecting data poisoning or model sabotage attempts
- Reporting incidents to compliance and legal teams
- Preserving logs and model snapshots for forensics
- Coordinating with cloud provider incident teams
- Documenting response actions for audit review
- Conducting post-incident reviews with engineering leads
- Updating controls based on incident findings
- Testing response plans with simulation exercises
- Integrating model monitoring alerts into response flows
- Maintaining incident documentation over time
Module 7. Operational security controls in CI/CD pipelines
Integrate ISO 27001 operational controls into automated ML deployment workflows.
12 chapters in this module
- Securing code repositories for machine learning projects
- Validating pipeline integrity with signing mechanisms
- Applying least privilege to CI/CD service accounts
- Scanning for vulnerabilities in container images
- Auditing pipeline changes and deployment triggers
- Enforcing approval gates before production release
- Logging deployment events for traceability
- Protecting secrets used in automated workflows
- Monitoring for unauthorized configuration changes
- Integrating static analysis into model pipeline steps
- Documenting pipeline controls for auditor review
- Maintaining separation of duties in deployment roles
Module 8. Physical and environmental security for cloud AI
Document compliance with physical security clauses when using third-party cloud infrastructure.
12 chapters in this module
- Understanding shared responsibility for physical controls
- Leveraging cloud provider SOC 2 and ISO reports
- Documenting data center locations for compliance
- Assessing environmental threats to cloud regions
- Verifying physical access restrictions to servers
- Handling cross-border data transfer implications
- Reviewing provider documentation for completeness
- Mapping physical controls to ISO 27001 Annex A
- Communicating physical security posture to auditors
- Updating documentation when regions change
- Managing egress costs as a control consideration
- Documenting network resilience design choices
Module 9. Supplier relationship management for AI services
Manage third-party risk in AI tooling, APIs, and infrastructure through structured documentation.
12 chapters in this module
- Identifying AI-related service providers in stack
- Assessing vendor compliance with ISO 27001
- Documenting contractual security obligations
- Monitoring vendor security posture over time
- Handling subcontractor arrangements in cloud AI
- Requiring evidence of security controls from vendors
- Managing API key exposure across integrations
- Evaluating open-source library risks in models
- Maintaining inventory of external dependencies
- Planning for vendor exit or service discontinuation
- Conducting due diligence on AI model APIs
- Producing audit-ready supplier risk summaries
Module 10. Building audit-ready documentation packages
Assemble documentation that is coherent, complete, and structured to pass review without iteration.
12 chapters in this module
- Organizing documentation using ISO 27001 structure
- Writing clear control implementation statements
- Including evidence references in narrative sections
- Formatting documents for readability and consistency
- Using consistent terminology across submissions
- Avoiding over-documentation while being thorough
- Cross-referencing between policies and controls
- Aligning tone with auditor expectations
- Preparing cover letters for submission packages
- Versioning documentation for multiple cycles
- Indexing files for quick auditor navigation
- Producing summarized overviews for reviewers
Module 11. Internal audit readiness and walkthrough prep
Prepare for internal reviews with confidence through structured rehearsal and evidence alignment.
12 chapters in this module
- Anticipating auditor questions on ML systems
- Rehearsing walkthroughs with technical narratives
- Organizing evidence files for fast retrieval
- Using checklists to verify control completeness
- Aligning team members on response roles
- Handling auditor follow-up within deadlines
- Documenting unresolved findings constructively
- Updating documentation based on feedback
- Maintaining composure during deep-dive reviews
- Explaining technical decisions in plain language
- Tracking open items with resolution timelines
- Building institutional memory from past audits
Module 12. Sustaining compliance through model iterations
Maintain ISO 27001 alignment across continuous model updates and infrastructure changes.
12 chapters in this module
- Applying change control to model retraining
- Updating documentation for new model versions
- Reassessing risks after pipeline modifications
- Conducting mini-audits after major updates
- Automating documentation updates with CI/CD
- Scheduling recurring control reviews
- Archiving outdated documentation securely
- Transferring compliance knowledge to new team members
- Linking model lineage to control records
- Ensuring policy adherence in automated workflows
- Using templates to maintain consistency
- Measuring documentation quality over time
How this maps to your situation
- AI/ML system compliance under ISO 27001
- Audit readiness for cloud-based models
- Documentation efficiency for engineering teams
- Cross-functional alignment with security teams
Before vs. after
Before
Spending extra cycles revising documentation due to auditor feedback, unclear scope, or missing traceability in AI/ML systems.
After
Producing accurate, complete, and polished ISO 27001 evidence the first time , aligned with actual deployment architecture and ready for audit.
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 six weeks, with self-paced access to all materials.
If nothing changes
Continued rework cycles, delayed deployments, and inconsistent documentation that undermines confidence in your team’s compliance rigor.
How this compares to the alternatives
Generic ISO 27001 trainings focus on broad principles but miss the nuances of AI/ML systems. This course delivers role-specific methods to document controls that reflect actual technical implementation , not theoretical checklists.
Frequently asked
Is this course relevant for engineers working on cloud-hosted AI systems?
Yes, the course is specifically designed for AI/ML engineers operating in cloud environments, with examples from AWS, GCP, and Azure patterns.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help me reduce back-and-forth with auditors?
Yes, the course teaches how to produce documentation that is accurate, complete, and auditor-ready the first time.
$199 one-time. Approximately 90 minutes per week over six weeks, with self-paced access to all materials..
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