A tailored course, built for your situation
Mastering ISO 27701; A Step-by-Step Guide to Privacy Implementation
Build a self-reinforcing privacy program that compounds across audits, acquisitions, and AI deployments
$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.
Who this is for
Senior data and AI leader in a global enterprise, responsible for data governance, measurement integrity, and algorithmic transparency
Who this is not for
Entry-level compliance staff, consultants selling one-off audits, or teams focused only on check-the-box privacy
What you walk away with
- Produce reusable data protection impact assessment templates aligned with ISO 27701
- Build a version-controlled library of consent management workflows
- Design audit-ready compliance matrices that carry forward across AI deployments
- Integrate privacy controls into automated model validation pipelines
- Document a living data map that evolves with data cloud changes
The 12 modules (with all 144 chapters)
Module 1. Foundations of ISO 27701 in AI-Centric Organizations
Understand how ISO 27701 extends beyond GDPR and CCPA to structure privacy for AI and algorithmic decision systems. Learn what separates symbolic compliance from operational leverage.
12 chapters in this module
- Scope of ISO 27701 vs GDPR
- AI workloads and PII overlap
- Controller vs processor clarity
- Data subject rights in ML systems
- Automated decision rights mapping
- Consent lifecycle stages
- Legal basis for AI training
- Cross-border data flow risks
- Accountability principle breakdown
- Documentation thresholds
- Privacy by design triggers
- Role mapping for AI teams
Module 2. Data Mapping for Algorithmic Accountability
Build dynamic data maps that feed directly into model documentation and privacy impact assessments. Focus on lineage from source to inference.
12 chapters in this module
- Identifying AI-relevant PII
- Data source tagging strategy
- Processing purpose alignment
- Third-party data flow diagrams
- API gateway tracking
- Model input provenance
- Output retention rules
- Data minimisation in feature sets
- Anonymisation thresholds
- Versioning data maps
- Automated data discovery
- Integration with metadata tools
Module 3. Privacy by Design in Model Development
Embed privacy controls directly into ML development workflows, from feature selection to inference monitoring.
12 chapters in this module
- Privacy-aware feature engineering
- Bias and privacy overlap
- Data masking in training sets
- PII leakage testing
- Model card requirements
- Explainability as privacy tool
- Consent linkage in predictions
- Retention-aware inference
- Differential privacy basics
- Federated learning scenarios
- Privacy testing checklist
- Audit trail generation
Module 4. Data Protection Impact Assessments for AI Systems
Generate DPIAs that are not just compliant but operationally useful, used by legal, risk, and engineering teams alike.
12 chapters in this module
- Trigger events for AI DPIAs
- Stakeholder identification
- Risk scoring methodology
- Bias and fairness linkage
- Third-party model risks
- Data sharing disclosures
- Retention policy integration
- Human oversight design
- Model drift thresholds
- Automated reassessment
- Version control for DPIAs
- Cross-jurisdictional analysis
Module 5. Consent and Legal Basis Management
Design scalable consent workflows that align with ISO 27701 and support AI model training, inference, and data sharing.
12 chapters in this module
- Consent vs legitimate interest
- Granular consent capture
- AI-specific consent layers
- Consent revocation workflows
- Audit trail requirements
- SDK consent syncing
- Preference center integration
- Model retraining triggers
- Cross-channel consent
- Consent versioning
- Legal basis documentation
- Processor agreement alignment
Module 6. Vendor and Third-Party Oversight
Manage third-party AI vendors with ISO 27701-aligned due diligence and ongoing monitoring.
12 chapters in this module
- Processor due diligence
- Sub-processor tracking
- Data processing agreements
- Security control mapping
- AI model audit rights
- Compliance verification
- Incident response clauses
- Data return obligations
- Model transparency expectations
- Penalty clauses
- Renewal review checklists
- Termination workflows
Module 7. Data Subject Rights Automation
Design automated workflows to handle data access, deletion, and correction requests across AI systems.
12 chapters in this module
- DSAR intake channels
- Identity verification
- Data location discovery
- Model retraining implications
- Deletion scope definition
- Exemption documentation
- Response time tracking
- Appeal process design
- Cross-system coordination
- Automation tools selection
- Audit logging
- Training data removal
Module 8. Compliance Matrices and Audit Readiness
Build self-updating compliance matrices that map ISO 27701 controls to internal policies and technical implementations.
12 chapters in this module
- Control-to-policy mapping
- Evidence collection automation
- AI-specific control gaps
- Internal audit workflows
- External auditor prep
- Remediation tracking
- Version-controlled matrices
- Control ownership
- Exception management
- Integration with GRC tools
- Real-time status dashboards
- Audit trail archiving
Module 9. Incident Response and Breach Management
Structure incident response workflows that meet ISO 27701 requirements while minimizing disruption to AI operations.
12 chapters in this module
- Breach detection triggers
- AI data exposure risks
- Legal reporting timelines
- Cross-team coordination
- Regulatory notification
- Public statement drafting
- Model revalidation need
- Data recovery planning
- Root cause analysis
- Remediation workflows
- Post-incident review
- Lessons documented
Module 10. Privacy in M&A and System Integration
Apply ISO 27701 principles during M&A integration to streamline data governance across newly combined AI systems.
12 chapters in this module
- Due diligence scope
- Data inventory comparison
- Consent harmonisation
- Policy alignment
- System integration risks
- Model governance merging
- Audit trail continuity
- Control gap analysis
- Vendor consolidation
- Team integration
- Timeline for compliance
- Executive reporting
Module 11. Continuous Monitoring and Improvement
Implement feedback loops that use audit findings, incidents, and system changes to improve privacy controls.
12 chapters in this module
- Metrics for privacy effectiveness
- Audit finding trends
- Automated control testing
- Model drift monitoring
- Privacy debt tracking
- Remediation velocity
- Stakeholder feedback
- Policy update workflows
- Training effectiveness
- Benchmarking against peers
- Annual review cycle
- Improvement backlog
Module 12. Building a Compounding Privacy IP Library
Systematise artefacts from prior projects into a reusable IP library that accelerates future privacy and AI governance work.
12 chapters in this module
- Template repository design
- Version control workflow
- Access control setup
- Cross-project search
- AI model documentation
- DPIA reuse rules
- Consent template library
- Compliance matrix patterns
- Incident playbook versioning
- Audit trail reusability
- Knowledge transfer process
- Retention and archiving
How this maps to your situation
- Implementing new AI governance process
- Preparing for regulatory audit
- Onboarding third-party AI vendor
- Merging data systems after acquisition
Before vs. after
Before
Privacy efforts restart from scratch with each project, audit, or AI initiative.
After
Each new cycle leverages prior work, consent workflows, DPIAs, compliance matrices, all compounding in value and reducing time-to-compliance.
What's included with your purchase
- 12 modules with 12 chapters each (144 chapters total)
- 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 for integration with ongoing work, applies directly to current initiatives.
How this compares to the alternatives
Unlike generic privacy training, this course builds assets that compound: every template, map, and matrix becomes more valuable with reuse. Unlike tool-specific courses, it focuses on ownership of process, not vendor features.
Frequently asked
How is this different from general privacy compliance courses?
It focuses on building reusable artefacts and IP that compound across AI projects and audits, not just passing a single assessment.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Can I use this with my team?
Yes, each purchase includes access for one learner, but the templates and playbook are designed for team-wide adoption.
$199 one-time. Approximately 3 hours per module, designed for integration with ongoing work, applies directly to current initiatives..
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