A tailored course, built for your situation
Mastering ISO 27001 for Machine Learning & Data Science Analysts
A structured path to authoritative command of information security frameworks in AI-driven environments
$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.
Spending too much time translating compliance requirements into actionable controls for ML systems?
The situation this course is for
Traditional compliance training assumes static systems. But in machine learning, models iterate fast, data flows shift daily, and interpretation of controls like A.12.4 or A.13.2 can stall delivery when not adapted to agile data workflows.
Who this is for
Mid-level data science and machine learning analyst at a global systems integrator, accountable for ensuring model workflows meet information security standards without delay.
Who this is not for
Executives looking for board-level summaries, consultants wanting generic templates, or engineers focused solely on model accuracy without governance integration.
What you walk away with
- Precise interpretation of ISO 27001 controls in the context of data pipelines and model deployment
- Faster alignment between security policy and data science implementation timelines
- Reusable control mapping logic tailored to ML lifecycle phases
- Confident articulation of compliance posture during internal audits
- A documented, team-ready playbook for ISO 27001 implementation in AI projects
The 12 modules (with all 144 chapters)
Module 1. Introduction to ISO 27001 in AI and Machine Learning Contexts
Lay the foundation for applying ISO 27001 to machine learning workflows by understanding how information security principles map to data ingestion, model training, and inference environments.
12 chapters in this module
- Defining information security in data science systems
- How ISO 27001 applies across AI development lifecycles
- Identifying data assets unique to ML projects
- Mapping confidentiality integrity and availability to datasets
- Understanding regulatory overlap with GDPR and data usage
- Recognizing when model artifacts become controlled documents
- Classifying model inputs and outputs for security controls
- Setting scope boundaries for AI system audits
- Aligning ISO 27001 with internal the firm governance models
- Documenting asset inventories for model metadata
- Integrating security controls into agile data sprints
- Establishing baseline terminology for cross-functional alignment
Module 2. Control A.5.1 to A.5.2: Information Security Policies and Leadership
Learn how to document and operationalize security policies specific to machine learning teams, ensuring alignment with organizational mandates without slowing innovation.
12 chapters in this module
- Drafting security policies for model development teams
- Incorporating leadership commitments into AI charters
- Defining roles and responsibilities for data stewards
- Linking policy statements to model governance frameworks
- Maintaining policy review cycles for dynamic environments
- Integrating third-party model components securely
- Documenting policy exceptions for experimental workflows
- Aligning with the firm's internal compliance directives
- Versioning policies alongside model updates
- Training on policy adherence for data science collaborators
- Auditing policy implementation across data pipelines
- Using logs to verify policy enforcement
Module 3. Control A.6.1 to A.6.3: Organizational Roles in Data Security
Clarify ownership and handoffs between data engineers, scientists, and compliance officers when applying ISO 27001 controls to ML systems.
12 chapters in this module
- Assigning data ownership in collaborative AI projects
- Defining handoff protocols between modeling and infrastructure teams
- Establishing escalation paths for security incidents
- Documenting role boundaries for model deployment
- Creating RACI matrices for AI compliance tasks
- Managing access for external data vendors
- Securing model registry contributions
- Enforcing segregation of duties in CI/CD pipelines
- Controlling permissions for model rollback actions
- Tracking changes made by temporary team members
- Verifying separation between development and production
- Auditing role changes in cloud-based environments
Module 4. Control A.8.1 to A.8.3: Asset Management in Machine Learning
Develop techniques for maintaining an authoritative inventory of data assets, model versions, and dependencies in dynamic AI environments.
12 chapters in this module
- Identifying model assets requiring classification
- Applying metadata tags for security categorization
- Registering datasets under ISO 27001 ownership rules
- Tracking lineage across training data transformations
- Managing model version inventories
- Documenting third-party library dependencies
- Labeling datasets by sensitivity level
- Enforcing retention policies for model artifacts
- Mapping model outputs to business impact tiers
- Securing access to model interpretation reports
- Logging asset modifications for audit readiness
- Automating inventory updates in CI/CD workflows
Module 5. Control A.9.1 to A.9.4: Access Control for AI Systems
Implement granular access control strategies for model repositories, data stores, and inference endpoints in compliance with ISO 27001.
12 chapters in this module
- Defining role-based access for model deployment pipelines
- Restricting model download permissions by team tier
- Enforcing multi-factor authentication for model access
- Managing service account access securely
- Controlling access to model training environments
- Securing API keys used for inference endpoints
- Auditing access to sensitive model outputs
- Implementing time-bound access for contractors
- Monitoring for unauthorized access attempts
- Integrating access logs with central SIEM tools
- Revoking access upon team reassignment
- Automating periodic access reviews
Module 6. Control A.12.1 to A.12.7: Operations Security for ML Pipelines
Adapt ISO 27001 operations controls to model training schedules, data refresh cycles, and automated deployment workflows.
12 chapters in this module
- Scheduling secure model training runs
- Protecting data during preprocessing stages
- Securing temporary files in distributed environments
- Monitoring resource usage for anomalies
- Logging model training parameters securely
- Controlling changes to inference logic
- Validating inputs to prevent model poisoning
- Securing model checkpoint storage
- Applying change management to model updates
- Enforcing configuration baselines in production
- Ensuring clock synchronization across clusters
- Maintaining operational logs for audit trails
Module 7. Control A.13.1 to A.13.3: Data Protection in Transit and at Rest
Apply encryption and data protection measures to model artifacts and datasets throughout their lifecycle.
12 chapters in this module
- Encrypting model weights during storage
- Securing model container images in registries
- Applying TLS for inference API calls
- Protecting data in intermediate processing layers
- Managing encryption keys for AI systems
- Implementing storage-level encryption for datasets
- Validating end-to-end encryption in pipelines
- Classifying data flows by sensitivity
- Documenting data residency requirements
- Enforcing data minimization in training sets
- Auditing encryption compliance across environments
- Responding to data exposure incidents
Module 8. Control A.14.1 to A.14.3: Secure Development for Machine Learning
Embed security into the development lifecycle of AI models, from code commits to deployment.
12 chapters in this module
- Applying secure coding practices to Python scripts
- Scanning model code for vulnerabilities
- Validating third-party library licenses
- Integrating SAST tools into CI pipelines
- Enforcing code review standards for models
- Securing model parameter tracking systems
- Documenting model assumptions and limitations
- Assessing model drift as a security risk
- Validating model inputs against expected ranges
- Protecting model explainability reports
- Managing technical debt in AI systems
- Maintaining secure documentation repositories
Module 9. Control A.15.1 to A.15.2: Supplier Security for AI Tools
Evaluate and manage third-party risks associated with pre-trained models, cloud AI platforms, and open-source libraries.
12 chapters in this module
- Assessing security posture of AI platform vendors
- Reviewing contracts for model usage rights
- Auditing third-party model training practices
- Managing updates for pre-trained components
- Tracking compliance obligations for API use
- Evaluating data handling in cloud inference
- Validating model bias disclosures
- Requiring security attestation from vendors
- Documenting third-party risk decisions
- Establishing exit strategies for vendor tools
- Monitoring supply chain for vulnerabilities
- Applying due diligence to open-source AI
Module 10. Control A.16.1 to A.16.2: Incident Management for AI Systems
Prepare to detect, respond, and recover from security incidents involving machine learning models and data.
12 chapters in this module
- Detecting anomalous model behavior
- Logging model prediction outliers
- Responding to data poisoning attempts
- Isolating compromised model endpoints
- Documenting incident timelines for audits
- Notifying stakeholders of model breaches
- Preserving evidence from training jobs
- Recovering from model rollback events
- Conducting post-incident reviews for AI
- Improving detection via feedback loops
- Integrating AI incidents into SOC workflows
- Testing incident playbooks with simulations
Module 11. Control A.17.1 to A.17.2: Business Continuity for Machine Learning
Ensure critical AI systems can be restored and maintained during disruptions.
12 chapters in this module
- Identifying mission-critical model services
- Establishing backup procedures for model artifacts
- Testing model recovery in isolated environments
- Maintaining versioned copies of training data
- Safeguarding model access during outages
- Documenting failover procedures for inference APIs
- Coordinating with infrastructure teams during recovery
- Validating model performance after restoration
- Updating business impact analyses periodically
- Aligning recovery timelines with SLAs
- Communicating status during AI system outages
- Reviewing continuity plans after real events
Module 12. From Framework to Field: Implementing Your Playbook
Synthesize all controls into a living implementation guide tailored to your role and current projects.
12 chapters in this module
- Reviewing completed control mappings
- Customizing templates for internal use
- Integrating with existing the firm workflows
- Presenting findings to compliance reviewers
- Gathering feedback from cross-functional peers
- Updating documentation for audit cycles
- Sharing playbook with project stakeholders
- Scheduling periodic control reviews
- Automating control checks where possible
- Aligning with client-specific requirements
- Maintaining version history of the playbook
- Scaling the approach to additional projects
How this maps to your situation
- Initial scoping of AI security responsibilities
- Policy integration with data science workflows
- Model lifecycle compliance alignment
- Audit preparation and evidence assembly
Before vs. after
Before
Spending unplanned time adapting generic compliance templates to data science workflows, with inconsistent results and audit delays.
After
Producing structured, repeatable ISO 27001 control mappings that align with agile ML development and pass review faster.
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: 90 minutes of focused work, designed to fit within a single Sunday morning.
If nothing changes
Continuing without a structured approach risks recurring friction during audits, inconsistent control application across projects, and missed opportunities to position as a go-to practitioner in AI governance.
How this compares to the alternatives
Generic ISO 27001 training covers enterprise IT but skips AI-specific nuances. This course is tailored to data science workflows, packed with concrete examples from ML environments, and designed to generate usable outputs immediately.
Frequently asked
Is this course relevant for someone in a data science role?
Yes. It’s built specifically for machine learning analysts who need to apply ISO 27001 controls to data pipelines and model deployments.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will I get templates I can use at work?
Yes. Every module includes downloadable, field-tested templates and a final implementation playbook.
$199 one-time. 90 minutes of focused work, designed to fit within a single Sunday morning..
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