A tailored course, built for your situation
Mastering ISO 27701 for Senior Data Science Practitioners
A step-by-step guide to implementing privacy-by-design in machine learning systems
$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 momentum because privacy reviews stall at senior levels
The situation this course is for
Even strong ML initiatives slow down when privacy decisions require multiple approvals. Practitioners with deep technical insight end up waiting for sign-off from teams less familiar with data pipeline nuances.
Who this is for
Senior data scientists leading machine learning projects in regulated environments who need to drive privacy compliance without bottlenecking delivery
Who this is not for
Junior analysts, non-technical compliance staff, or teams using off-the-shelf models with no customization
What you walk away with
- Own final determination on privacy impact assessment scope for ML use cases
- Make binding decisions on data anonymization thresholds without escalation
- Control documentation sign-off for ISO 27701 compliance artefacts
- Lead exemption requests and justifications independently
- Standardize privacy-by-design patterns across predictive modeling projects
The 12 modules (with all 144 chapters)
Module 1. Understanding ISO 27701 in Data Science Contexts
Grounds the standard in real data pipeline decisions, focusing on roles, responsibilities, and compliance boundaries specific to ML workflows.
12 chapters in this module
- What ISO 27701 means for data science
- Scope of PII in training data
- Role of the data controller vs processor
- Linking GDPR to model inputs
- Privacy thresholds in feature selection
- Compliance ownership in team structures
- Data lineage documentation
- Mapping processing activities
- Purpose limitation in ML
- Retention rules for model data
- Cross-border data flow checks
- Audit readiness basics
Module 2. Privacy by Design in Model Development
Teaches integration of privacy controls at each stage of the ML lifecycle, from data intake to deployment.
12 chapters in this module
- Embedding privacy at project start
- Data minimization techniques
- Feature masking strategies
- Synthetic data use cases
- Anonymization level decisions
- Re-identification risk scoring
- Model explainability links
- Bias-privacy tradeoffs
- Validation set governance
- Privacy-aware algorithm choice
- Hyperparameter privacy impact
- Model output filtering
Module 3. Conducting Privacy Impact Assessments
Enables independent execution of PIAs tailored to machine learning applications with clear ownership of scope and conclusions.
12 chapters in this module
- Trigger points for PIA
- Stakeholder mapping for ML
- Risk scoring methodology
- Data flow diagramming
- Third-party risk inclusion
- Public harm potential
- Documentation templates
- Exemption justification
- Internal review workflow
- Version control for PIA
- Sign-off authority levels
- Audit trail maintenance
Module 4. Data Subject Rights in ML Systems
Covers operational handling of access, correction, and deletion rights within model environments.
12 chapters in this module
- Right to access model data
- Locating personal data in pipelines
- Correction request workflows
- Deletion vs model integrity
- Retraining impact analysis
- Explainability on demand
- Automated decision challenge
- Human review integration
- Response time compliance
- Logging subject requests
- Cross-system coordination
- Audit preparation
Module 5. Vendor and Third-Party Oversight
Empowers decision-making on external data sources, APIs, and cloud tools used in ML development.
12 chapters in this module
- Vendor due diligence
- Processor agreements review
- Cloud provider compliance
- API data leakage risks
- Open-source model audits
- Third-party model risk
- Sub-processing controls
- Data location transparency
- Contractual clauses
- Oversight frequency
- Penalty triggers
- Exit strategy planning
Module 6. Anonymization and Pseudonymization Techniques
Provides technical depth on implementation methods and decision thresholds for de-identifying model data.
12 chapters in this module
- k-anonymity in datasets
- l-diversity application
- t-closeness criteria
- Differential privacy basics
- Noise injection levels
- Re-identification testing
- Feature suppression rules
- Tokenization methods
- Encryption in use
- Data masking tools
- Performance tradeoffs
- Validation protocols
Module 7. Compliance Documentation and Artefacts
Builds repeatable templates and workflows for evidence generation that stand up to internal and external review.
12 chapters in this module
- Register of processing activities
- Data flow maps
- PIA templates
- Exemption logs
- Review meeting minutes
- Policy version tracking
- Evidence collection
- Internal audit packs
- External assessor prep
- Cross-functional sign-offs
- Update cycles
- Retention schedules
Module 8. Incident Response for ML Data Breaches
Equips practitioners to lead breach assessments and notifications specific to model data exposure.
12 chapters in this module
- Breach detection in ML
- Data leakage indicators
- Risk severity scoring
- Notification timelines
- Regulator reporting
- Public communication
- Model retraining needs
- Data deletion verification
- Forensic data preservation
- Lessons learned integration
- Process updates
- Team coordination
Module 9. Cross-Functional Collaboration Models
Establishes authority in joint projects with legal, privacy, and security teams while maintaining delivery pace.
12 chapters in this module
- Engagement playbooks
- Decision boundary mapping
- Escalation criteria
- Legal team alignment
- Security review paths
- Compliance checkpoint design
- Stakeholder communication
- Conflict resolution
- Timeline negotiation
- Resource coordination
- Documentation sharing
- Joint audit prep
Module 10. Ongoing Compliance Monitoring
Enables continuous oversight of deployed models for drift, data changes, and compliance decay.
12 chapters in this module
- Model monitoring basics
- Data drift detection
- Privacy threshold alerts
- Automated compliance checks
- Manual review cycles
- Version comparison
- Change impact analysis
- Re-PIA triggers
- Logging framework updates
- Alert response workflows
- Remediation tracking
- Audit trail enrichment
Module 11. Exemption and Deviation Management
Provides tools to independently justify and document exceptions to privacy controls with full traceability.
12 chapters in this module
- Exemption criteria
- Business justification
- Risk acceptance thresholds
- Senior approval bypass
- Documentation standards
- Time-bound exceptions
- Review trigger design
- Stakeholder notification
- Public reporting needs
- Legal opinion integration
- Escalation paths
- Audit evidence
Module 12. Implementing Scalable Privacy Frameworks
Closes with a blueprint for institutionalizing privacy decisions across teams and future projects.
12 chapters in this module
- Playbook creation
- Template library
- Training materials
- Onboarding integration
- Peer review system
- Quality assurance
- Feedback loops
- Leadership reporting
- Continuous improvement
- Framework evolution
- External benchmarking
- Maturity assessment
How this maps to your situation
- Model design phase
- Data sourcing and preparation
- Privacy review cycle
- Deployment and monitoring
Before vs. after
Before
Waiting for approvals on privacy decisions that slow down model delivery
After
Making final calls on PIA scope, anonymization thresholds, and documentation independently
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 3 hours per module, designed to be completed alongside active projects.
If nothing changes
Projects continue to stall at review stages, missing opportunities to lead in privacy-aware machine learning innovation.
How this compares to the alternatives
Unlike generic compliance courses, this program is tailored to data scientists implementing ISO 27701 in machine learning contexts, with specific decision rights, artefacts, and sign-off authority.
Frequently asked
Who is this course for?
Senior data scientists leading machine learning initiatives in regulated environments who need to own privacy compliance decisions.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Can I use this if my organization uses a different privacy framework?
Yes, ISO 27701 is designed to extend other standards like GDPR and APRA CPS 234, this course teaches transferable decision-making patterns.
$199 one-time. Approximately 3 hours per module, designed to be completed alongside active 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