A tailored course, built for your situation
Mastering ISO 27701 for Staff Machine Learning Engineers
Take full ownership of privacy decisions in AI systems with a structured, implementation-ready approach.
$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.
Losing time to compliance rework because privacy decisions require cross-team sign-offs?
The situation this course is for
Machine learning engineers are increasingly on the hook for privacy compliance, but often lack formal authority to make binding decisions on data handling, model scope, or audit readiness, leading to delays, rework, and diluted ownership.
Who this is for
Staff or Principal Machine Learning Engineers in AI-first companies who influence or own system design and data architecture.
Who this is not for
Entry-level data scientists, compliance generalists without technical background, or engineers not involved in model deployment decisions.
What you walk away with
- Define data processing boundaries in AI systems without legal or privacy team review for standard cases
- Set retention and anonymization rules for PII in training data with documented justification
- Make binding decisions on third-party data processor alignment with ISO 27701 controls
- Lead privacy impact assessments from technical design through to audit readiness
- Own the technical narrative in regulator-facing reviews without deferring to governance teams
The 12 modules (with all 144 chapters)
Module 1. Foundations of ISO 27701 in Machine Learning Systems
Understand the privacy-specific extensions of ISO 27701 and how they apply to data ingestion, model training, and inference workflows.
12 chapters in this module
- Scope of ISO 27701 vs. general data protection
- Core privacy principles in AI contexts
- Data subject rights and algorithmic systems
- Mapping PII flows in model pipelines
- Controller vs. processor roles in ML
- Legal basis for processing PII in training
- Consent handling in automated systems
- Data minimization in feature engineering
- Anonymization standards under ISO 27701
- Cross-border data transfer rules
- Documentation requirements for AI use cases
- Baseline for privacy-aware model design
Module 2. Data Inventory and Classification for AI
Build a structured, repeatable process for identifying and classifying PII across training datasets and operational data streams.
12 chapters in this module
- Automated PII detection in unstructured data
- Data tagging strategies for ML pipelines
- Classification accuracy benchmarks
- Metadata schemas for privacy tracking
- Versioning PII classifications
- Integration with feature stores
- Handling inferred personal data
- Labeling transparency for downstream use
- Privacy data dictionary standards
- Audit trail for classification changes
- Third-party data labeling rules
- Reclassification triggers and workflows
Module 3. Privacy Impact Assessments for Model Development
Lead technical PIAs from concept through deployment, including risk scoring and mitigation planning.
12 chapters in this module
- Thresholds for mandatory PIA
- Risk scoring model for AI systems
- Stakeholder input protocols
- Bias and discrimination risk factors
- Transparency and explainability checks
- Data retention risks in embeddings
- Model retraining and PIA refresh
- Third-party model usage risks
- Output privacy leakage scenarios
- Scoring mitigation effectiveness
- Documentation standards for audits
- Version-controlled PIA artifacts
Module 4. Consent Architecture in AI Systems
Design and implement consent handling that meets ISO 27701 requirements while supporting real-world model performance.
12 chapters in this module
- Consent as a data field in pipelines
- Dynamic consent tracking
- Withdrawal propagation logic
- Granular consent for feature use
- Consent in synthetic data generation
- Model feedback and consent
- Audit trail for consent status
- Jurisdiction-specific consent rules
- Consent expiration handling
- Consent in edge inference
- Third-party consent alignment
- Automated compliance validation
Module 5. Data Retention and Anonymization Policies
Establish and enforce binding data retention rules and anonymization methods within ML workflows.
12 chapters in this module
- Retention periods by data type
- Automated data deletion triggers
- Anonymization vs. pseudonymization
- k-anonymity in feature sets
- Differential privacy integration
- Model memorization risks
- Embedding scrubbing techniques
- Retained data access controls
- Audit logging for retention
- Cross-system retention sync
- Legal hold exceptions
- Retention policy versioning
Module 6. Controller-Processor Agreements for ML Vendors
Negotiate and implement data processing terms with third-party providers in AI ecosystems.
12 chapters in this module
- Vendor due diligence process
- Minimum security requirements
- Audit rights for processors
- Sub-processor approval rules
- Data location constraints
- Encryption standards in transit
- Incident notification timelines
- Model ownership clauses
- Output usage restrictions
- Compliance certification expectations
- Contract termination data return
- Oversight mechanisms
Module 7. Privacy by Design in Model Architecture
Embed privacy controls directly into model design and system architecture.
12 chapters in this module
- Feature selection and privacy risk
- Input validation for PII filtering
- Output filtering mechanisms
- Model inversion defenses
- Federated learning integration
- Trusted execution environments
- Privacy-aware hyperparameter tuning
- Secure model checkpointing
- Gradient leakage prevention
- Model hashing for provenance
- Privacy threat modeling
- Architecture review checklists
Module 8. Internal Audit and Compliance Validation
Prepare and lead internal audits of privacy controls in AI systems with full documentation authority.
12 chapters in this module
- Audit scope definition
- Sampling for model audits
- Control testing procedures
- Evidence collection standards
- Non-conformance reporting
- Remediation tracking
- Audit trail generation
- Cross-team validation
- Automated compliance checks
- Audit communication protocols
- Executive summary templates
- Continuous monitoring setup
Module 9. Cross-Border Data Transfer Mechanisms
Implement lawful transfer pathways for training and inference data across jurisdictions.
12 chapters in this module
- Data residency requirements
- Standard contractual clauses usage
- Binding corporate rules
- Transfer impact assessments
- Local law overrides
- Encryption as a control
- Data localization patterns
- Model update propagation
- Edge inference and data flows
- Third-party transfer compliance
- Documentation for regulators
- Oversight of transfer changes
Module 10. Incident Response and Breach Management
Lead technical response to privacy incidents involving AI systems with decision authority on disclosure and remediation.
12 chapters in this module
- Breach detection in model outputs
- PII leakage assessment
- Notification thresholds
- Regulator communication protocols
- Root cause analysis for models
- Model rollback procedures
- Data subject communication
- Legal hold activation
- Public statement coordination
- Lessons learned integration
- Automated incident logging
- Cross-functional response roles
Module 11. Certification and External Audit Readiness
Drive ISO 27701 certification from a technical lead position with full ownership of artefact production.
12 chapters in this module
- Certification roadmap
- Gap assessment execution
- Evidence package assembly
- External auditor coordination
- Audit response authority
- Statement of Applicability authoring
- Control implementation proof
- Management review input
- Certification maintenance
- Surveillance audit prep
- Scope change documentation
- Public reporting standards
Module 12. Sustaining Privacy Compliance in AI Operations
Operationalize ongoing privacy compliance with automated checks and update cycles.
12 chapters in this module
- Model retraining and privacy review
- Version-controlled compliance
- Automated control monitoring
- Alerting on policy drift
- Periodic PIA refresh
- Privacy debt tracking
- Team onboarding standards
- Knowledge transfer protocols
- Documentation updates
- Regulatory change tracking
- Stakeholder reporting
- Continuous improvement cycle
How this maps to your situation
- Privacy decisions requiring cross-team approvals
- AI deployments with unclear data governance
- Upcoming ISO 27701 certification cycles
- Incident response involving model outputs
Before vs. after
Before
Reactive, approval-dependent privacy decisions in AI systems
After
Full ownership of privacy framework implementation with documented authority
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 18 hours of structured learning, designed to be completed in 3 weeks with 1-2 hours per session.
If nothing changes
Continuing to defer privacy decisions erodes technical ownership, slows deployment velocity, and risks compliance gaps in AI systems.
How this compares to the alternatives
Unlike generic privacy courses, this program is tailored to machine learning engineers and focuses on concrete decision rights, implementation artefacts, and technical ownership of ISO 27701 in production AI systems.
Frequently asked
Is this course only for engineers working in regulated industries?
No, it's designed for any Staff ML Engineer in organizations where data privacy is becoming a technical ownership domain, regardless of industry.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will I be able to make binding decisions after taking this course?
Yes, the course equips you with the framework, documentation, and implementation playbook to claim formal decision rights on privacy controls in your AI systems.
$199 one-time. Approximately 18 hours of structured learning, designed to be completed in 3 weeks with 1-2 hours per session..
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