A tailored course, built for your situation
Mastering ISO 27701 for Data Scientists in Regulated AI Deployments
Build privacy-compliant machine learning systems with confidence and clarity
$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.
Generic AI governance courses don’t speak to the data scientist’s reality, they’re too broad, too theoretical, and miss the regulatory nuances baked into real-world deployments.
The situation this course is for
Most data scientists are expected to 'figure out' privacy compliance on the fly, often after models are already in development. This leads to rework, last-minute documentation, and missed escalations from peer teams who need trusted reviewers.
Who this is for
Data scientists in regulated industries who are increasingly responsible for privacy-compliant AI/ML model design and deployment, especially in energy, utilities, and critical infrastructure.
Who this is not for
This is not for data analysts, junior developers, or professionals outside regulated AI environments. It’s not for those seeking high-level compliance overviews without implementation depth.
What you walk away with
- Own the privacy control mapping for Gen AI and ML models under ISO 27701
- Produce regulator-ready documentation that stands up to audit scrutiny
- Lead cross-functional escalations from peer teams on AI privacy implementation
- Ship privacy-by-design artefacts that reduce review cycles by 40%
- Become the internal reference for ISO 27701 in machine learning contexts
The 12 modules (with all 144 chapters)
Module 1. Foundations of ISO 27701 in AI Systems
Understand how ISO 27701 extends ISO 27001 to personal data in machine learning pipelines, including model training data and inference outputs.
12 chapters in this module
- Scope definition for AI projects
- Personal data lifecycle mapping
- Privacy risk assessment entry points
- Control applicability screening
- Documentation baseline setup
- Stakeholder alignment checklist
- Data subject rights integration
- Consent handling in training sets
- Anonymization thresholds
- Data minimization in feature engineering
- Third-party data vendor risks
- Privacy impact timing gates
Module 2. Privacy by Design in Model Development
Embed privacy controls early in the ML pipeline, from data sourcing to model validation, ensuring compliance doesn’t slow innovation.
12 chapters in this module
- Privacy-aware data collection
- Bias and fairness linkage
- Feature selection guardrails
- Model interpretability standards
- Data retention triggers
- Purpose limitation checks
- Consent verification layers
- Synthetic data use cases
- Federated learning alignment
- Edge case documentation
- Version-controlled privacy logs
- Audit trail automation
Module 3. Data Subject Rights in ML Systems
Operationalize data subject rights like access, deletion, and correction within live ML environments, not just static databases.
12 chapters in this module
- Right to explanation frameworks
- Model retraining triggers
- Data deletion propagation
- Access request routing
- Model rollback protocols
- Anonymized audit paths
- Subject verification workflows
- Deletion impact assessment
- Consent renewal cycles
- Opt-out propagation to inference
- Training data lineage tracking
- Incident escalation paths
Module 4. Third-Party Data Vendor Management
Ensure vendor data inputs meet ISO 27701 standards, with clear contractual and technical safeguards.
12 chapters in this module
- Vendor pre-screening checklist
- Data provenance verification
- Contractual compliance clauses
- Security audit rights
- Subprocessor oversight
- Data transfer mechanisms
- Breach notification timelines
- Penalty enforcement terms
- Data quality benchmarks
- Retention policy alignment
- Right to audit execution
- Exit strategy requirements
Module 5. Privacy Controls for Model Training
Apply ISO 27701 controls specifically to the model training phase, where most privacy risks emerge.
12 chapters in this module
- Training data access logs
- Data usage policy enforcement
- Labeling team protocols
- Data augmentation risks
- Cross-validation privacy leaks
- Feature engineering logs
- Model checkpoint security
- Re-training audit trails
- Data leakage checks
- Bias mitigation documentation
- Fairness metric tracking
- Model drift privacy triggers
Module 6. Inference-Time Privacy Safeguards
Protect personal data during model inference, including real-time predictions and feedback loops.
12 chapters in this module
- Input data logging
- Prediction retention rules
- Feedback loop anonymization
- User identification risks
- Real-time consent checks
- Inference access controls
- Model output scrubbing
- PII detection filters
- Anonymization at scale
- Drift-triggered re-evaluation
- Inference audit sampling
- Model explainability delivery
Module 7. Documentation for Regulatory Reviews
Create clear, regulator-ready documentation that anticipates follow-up questions and demonstrates proactive compliance.
12 chapters in this module
- Privacy control registry
- Model-specific SoA templates
- Compliance evidence indexing
- Regulator Q&A prep
- Version-controlled artefacts
- Cross-team sign-off logs
- Audit trail formatting
- Gap remediation tracking
- Control implementation proofs
- Third-party attestation collection
- Data flow diagram standards
- Risk register updates
Module 8. Internal Escalation and Peer Review
Lead privacy reviews for peer ML teams, becoming the go-to expert for ISO 27701 in AI contexts.
12 chapters in this module
- Escalation intake process
- Peer review scoring
- Control gap identification
- Remediation tracking
- Cross-team alignment
- Urgent review protocols
- Documentation feedback
- Best practice sharing
- Escalation routing logic
- Priority triage framework
- Review cycle benchmarks
- Post-review validation
Module 9. Audit Preparation and Response
Prepare for internal and external audits with confidence, producing evidence that stands up to scrutiny.
12 chapters in this module
- Audit scope definition
- Evidence collection checklist
- Interview prep materials
- Control mapping verification
- Gap closure proof
- Regulator communication
- On-site audit support
- Findings response drafting
- Corrective action planning
- Follow-up audit prep
- Audit outcome reporting
- Lessons learned integration
Module 10. Continuous Monitoring and Improvement
Maintain compliance over time with automated checks and periodic reviews.
12 chapters in this module
- Control effectiveness metrics
- Automated alerting
- Quarterly review cycles
- Model re-certification
- Privacy KPI tracking
- Incident response drills
- Policy update workflows
- Stakeholder updates
- Benchmarking against peers
- Regulatory change tracking
- Control refinement process
- Lessons learned database
Module 11. Privacy Incident Response
Respond to data incidents in ML systems with clear, compliant protocols.
12 chapters in this module
- Incident detection
- Notification timelines
- Regulator reporting
- Internal escalation
- Data recovery steps
- Model rollback procedures
- Public statement prep
- Root cause analysis
- Remediation proof
- Stakeholder comms
- Audit trail preservation
- Post-incident review
Module 12. Strategic Influence and Leadership
Leverage your expertise to shape AI privacy strategy across the organization.
12 chapters in this module
- Executive briefing prep
- Policy influence pathways
- Cross-functional alignment
- Budget justification
- Team training delivery
- Mentorship frameworks
- Industry engagement
- Thought leadership
- Standards body input
- Regulatory consultation
- Public speaking topics
- Innovation advocacy
How this maps to your situation
- When your team starts a new ML project
- When peer teams escalate privacy concerns
- Before an internal audit cycle
- When onboarding third-party data
Before vs. after
Before
Generic AI governance knowledge that doesn’t translate to real-world compliance in regulated environments.
After
Confident ownership of ISO 27701 in machine learning systems, with peer teams escalating to you as the trusted reviewer.
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, or 36 hours total , designed for efficient, just-in-time learning around your workload.
If nothing changes
Without clear privacy-by-design implementation, data scientists risk delays, rework, and missed opportunities to lead on high-impact AI governance work.
How this compares to the alternatives
Unlike broad AI ethics courses or generic compliance overviews, this course delivers specific, regulator-tested frameworks for ISO 27701 in machine learning contexts , the exact skills needed to lead real-world privacy implementation.
Frequently asked
Is this course relevant for non-privacy specialists?
Yes. It’s designed for data scientists and ML engineers who need to implement privacy controls in real projects, not just compliance officers.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Does it cover AI-specific privacy risks?
Yes. Every module addresses risks unique to machine learning, including bias, explainability, and inference privacy.
$199 one-time. Approximately 3 hours per module, or 36 hours total , designed for efficient, just-in-time learning around your workload..
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