A tailored course, built for your situation
Mastering ISO 27701 for Senior Data Scientists in AI Innovation
A step-by-step guide to owning privacy-by-design implementation in LLM and RAG 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.
Struggling to align AI innovation with strict privacy standards?
The situation this course is for
Many data scientists face delays when privacy requirements are retrofitted into AI pipelines. Without early integration, projects stall during compliance review, lose stakeholder trust, or require costly rework.
Who this is for
Senior data scientists in regulated industries who lead AI innovation and need to embed compliance into technical design
Who this is not for
Entry-level analysts, non-technical compliance staff, or professionals outside AI development and data governance roles
What you walk away with
- Map data processing activities to ISO 27701 Annex D requirements
- Build privacy-preserving data flows into LLM/RAG pipelines
- Document processing records that satisfy external auditors
- Own sign-off authority on privacy control implementation
- Integrate consent architecture into model development lifecycle
The 12 modules (with all 144 chapters)
Module 1. Foundations of ISO 27701 in AI Contexts
Understand the scope, intent, and data protection principles of ISO 27701 as applied to machine learning systems.
12 chapters in this module
- Scope of ISO 27701
- Relationship to GDPR and HIPAA
- Core privacy principles
- Data subject rights
- Controller vs processor
- Processing record basics
- Consent lifecycle
- Anonymization standards
- Jurisdictional boundaries
- Data flow mapping
- Risk-based approach
- Alignment with NIST Privacy Framework
Module 2. Privacy by Design in ML Pipelines
Embed privacy controls at each stage of data ingestion, model training, and inference.
12 chapters in this module
- Privacy impact assessment
- Data minimization techniques
- Purpose limitation enforcement
- Storage limitation design
- Accuracy safeguards
- Transparency mechanisms
- Consent integration
- Access control layers
- Audit trail generation
- Model explainability
- Data provenance tracking
- Version-controlled processing records
Module 3. Data Processing Records for AI Systems
Create complete, auditable records that satisfy regulatory expectations and internal governance.
12 chapters in this module
- Processing activity inventory
- Legal basis documentation
- Data sharing disclosures
- Retention period settings
- Third-party processor review
- Subprocessor transparency
- Cross-border transfer logs
- Consent tracking schema
- Opt-in/opt-out history
- Data subject request handling
- Record automation tools
- Validation against ISO 27701 Annex D
Module 4. Consent Architecture for LLM Applications
Design and implement scalable consent structures tailored to generative AI interfaces.
12 chapters in this module
- Explicit consent patterns
- Granular permission layers
- Dynamic consent interfaces
- Consent revocation flow
- Inference data opt-out
- Training data withdrawal
- API-level consent checks
- User identity binding
- Audit trail for consent events
- Consent status APIs
- Revocation propagation
- Consent compliance testing
Module 5. Vendor and Subprocessor Governance
Evaluate and document third-party AI services under ISO 27701 obligations.
12 chapters in this module
- Vendor due diligence
- Processor agreement clauses
- Subprocessor disclosure
- Data processing addendums
- Cloud provider assessments
- API security review
- Model hosting compliance
- Penetration testing coordination
- Incident response alignment
- Data residency enforcement
- Audit rights negotiation
- Exit strategy documentation
Module 6. Information Security Integration
Align ISO 27701 controls with existing security frameworks like ISO 27001 and NIST CSF.
12 chapters in this module
- Cryptography for PII
- Access control policies
- Role-based permissions
- Data-at-rest encryption
- Data-in-transit protection
- Key management
- Logging and monitoring
- Incident classification
- Breach notification workflow
- Vulnerability scanning
- Security patch cadence
- Security audit coordination
Module 7. Data Subject Rights Implementation
Enable full lifecycle management of data access, correction, deletion, and portability requests.
12 chapters in this module
- Access request fulfillment
- Correction workflows
- Deletion automation
- Right to be forgotten
- Data portability format
- Machine-readable exports
- Request validation
- Consent history access
- Time-bound retention
- Model retraining after deletion
- Audit logging for DSARs
- Response SLA tracking
Module 8. Privacy Control Validation
Test and verify that implemented controls meet ISO 27701 requirements.
12 chapters in this module
- Control testing framework
- Automated compliance checks
- Manual review cadence
- Evidence collection
- Privacy control dashboard
- Gap identification
- Remediation tracking
- Internal audit prep
- External auditor brief
- Control maturity scoring
- Continuous monitoring
- Improvement backlog
Module 9. Cross-Border Data Transfers
Manage international data flows in compliance with regional regulations.
12 chapters in this module
- Transfer impact assessments
- Adequacy decisions
- Standard contractual clauses
- Data localization laws
- Schrems II implications
- Regional consent rules
- Data residency settings
- Cloud region selection
- Latency vs compliance tradeoffs
- Hybrid deployment models
- On-prem processing nodes
- Transfer logging
Module 10. Privacy in Model Development Lifecycle
Integrate privacy controls from concept through deployment and monitoring.
12 chapters in this module
- Privacy in model design
- Data sourcing ethics
- Bias and fairness review
- Model documentation
- Training data consent
- Inference data handling
- Model versioning
- Performance monitoring
- Drift detection
- Feedback loop privacy
- Model decommissioning
- Audit trail retention
Module 11. Building Internal Privacy Capability
Create reusable artifacts and guide cross-functional adoption.
12 chapters in this module
- Template creation
- Playbook documentation
- Workshop facilitation
- Stakeholder alignment
- Training materials
- Cross-team onboarding
- Knowledge transfer
- Governance committee
- Compliance reporting
- Feedback collection
- Continuous improvement
- Leadership briefing
Module 12. Audit and Certification Readiness
Prepare for external audits and achieve ISO 27701 certification.
12 chapters in this module
- Audit preparation
- Document organization
- Gap remediation
- Internal mock audit
- External auditor coordination
- Certification body engagement
- Statement of Applicability
- Control implementation evidence
- Nonconformity response
- Certification maintenance
- Surveillance audit prep
- Recertification cycle
How this maps to your situation
- AI system development in regulated environments
- Privacy compliance for LLM and RAG pipelines
- Data processing documentation for audit
- Cross-functional governance leadership
Before vs. after
Before
Privacy compliance is treated as a post-development check requiring rework and delays.
After
Privacy-by-design is embedded into the AI pipeline, accelerating deployment and expanding technical 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 18-24 hours over 6 weeks, with self-paced access.
If nothing changes
Projects face increased rework, audit delays, and loss of influence when privacy is not integrated early by technical leads.
How this compares to the alternatives
Unlike generic compliance courses, this program is tailored to senior data scientists building AI systems in regulated contexts, focusing on executable privacy integration rather than theoretical frameworks.
Frequently asked
Is this course suitable for someone without a formal privacy background?
Yes. It's designed for technical practitioners who need to apply privacy standards in real systems, with clear implementation steps.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Does the course include templates?
Yes. Every module includes downloadable templates and worked examples for immediate use.
$199 one-time. Approximately 18-24 hours over 6 weeks, with self-paced access..
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