A tailored course, built for your situation
Mastering ISO 27701 for AI/ML Senior Software Engineers
Build a privacy-by-design engineering practice that compounds across AI/ML 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.
Spending too much time reinventing privacy controls for each new AI/ML model?
The situation this course is for
Engineers often rebuild compliance artifacts from scratch per project, wasting time, creating inconsistency, and slowing deployment. Without standardized, reusable components, privacy becomes a repeat tax, not a strategic asset.
Who this is for
Senior AI/ML software engineers in regulated environments who ship models requiring privacy compliance and want to reduce rework while increasing influence.
Who this is not for
Entry-level developers, non-technical compliance staff, or professionals outside AI/ML engineering roles.
What you walk away with
- Produce ISO 27701-aligned privacy documentation in under two hours per model
- Maintain a personal library of reusable code templates for data anonymization and consent handling
- Automate 80% of privacy impact assessment inputs using structured model metadata
- Standardize architecture patterns that satisfy both engineering velocity and compliance scrutiny
- Ship audit-ready AI/ML deployments without rework loops
The 12 modules (with all 144 chapters)
Module 1. Understanding ISO 27701 in AI/ML Contexts
Map core clauses of ISO 27701 to real AI/ML system components and data flows.
12 chapters in this module
- Scope of privacy extensions
- Data controller vs processor roles
- Linking PII to model inputs
- Privacy-by-design principles
- AI-specific data lifecycle stages
- Consent handling in training data
- Data minimization patterns
- Anonymization thresholds
- Third-party processor risks
- Model explainability and privacy
- Cross-border data flows
- Audit trail requirements
Module 2. Privacy Impact Assessments for ML Models
Build repeatable PIAs tailored to classification, regression, and clustering systems.
12 chapters in this module
- PIA triggers in AI workflows
- Stakeholder mapping
- Risk scoring for inference
- Data subject rights impact
- Model drift and privacy
- Bias as a privacy risk
- Automated decision-making disclosures
- Retention policies for embeddings
- Model versioning implications
- Input data provenance
- Output disclosure risks
- Template customization
Module 3. Data Mapping for Training Pipelines
Trace personally identifiable information through ingestion, labeling, and preprocessing.
12 chapters in this module
- Identifying PII in raw datasets
- Labeling pipeline controls
- Synthetic data compliance
- Data sharing agreements
- Annotator access policies
- On-prem vs cloud processing
- Metadata classification rules
- Shadow data detection
- Encryption in transit
- Access logging standards
- Data subject request handling
- Automated data flow diagrams
Module 4. Consent Architecture Patterns
Design modular, revocable consent handling for dynamic AI systems.
12 chapters in this module
- Explicit vs implied consent
- Consent in automated labeling
- Granular opt-ins
- Blockchain for consent logs
- Revocation propagation
- Inference-time checks
- Model rollback triggers
- Consent metadata schema
- UI patterns for transparency
- API-level enforcement
- Audit-ready consent trails
- Third-party integration risks
Module 5. Anonymization Techniques for ML
Apply k-anonymity, differential privacy, and perturbation at scale.
12 chapters in this module
- k-anonymity in feature sets
- l-diversity enhancements
- Differential privacy budgets
- Noise addition strategies
- Federated learning setups
- Homomorphic encryption use cases
- Synthetic data generation
- Evaluation of anonymization quality
- Re-identification risk scoring
- Model performance trade-offs
- Toolchain integration
- Compliance verification steps
Module 6. Processor Agreements for AI Vendors
Structure contracts with clarity on data usage, model ownership, and audit rights.
12 chapters in this module
- Defining processor scope
- Sub-processor restrictions
- Model ownership clauses
- Audit rights definition
- Data deletion requirements
- Incident response timelines
- Geographic constraints
- Performance vs privacy balance
- SLA for privacy compliance
- Exit strategy terms
- IP rights preservation
- Liability caps and carveouts
Module 7. Model Documentation Standards
Generate standardized, auditor-friendly model cards and data sheets.
12 chapters in this module
- Model card structure
- Training data descriptions
- Intended use cases
- Performance metrics by subgroup
- Bias assessment methods
- Fairness thresholds
- Version control details
- Update frequency disclosures
- Known limitations
- Error mode documentation
- Security vulnerabilities
- Third-party components
Module 8. Audit-Ready Artifact Generation
Produce evidence packages that satisfy external and internal auditors.
12 chapters in this module
- Evidence mapping to ISO 27701
- Automated log collection
- Access control reports
- Data processing records
- Consent verification
- Anonymization validation
- Processor compliance checks
- Incident response logs
- Model change history
- Stakeholder communication logs
- Retention policy enforcement proofs
- Gap closure documentation
Module 9. Cross-Functional Alignment Tactics
Bridge engineering, legal, and compliance teams with shared artefacts.
12 chapters in this module
- Common glossary development
- Joint review meetings
- Risk escalation paths
- Change approval workflows
- Ambassador roles
- Document ownership models
- Feedback loops
- Training for non-engineers
- Centralized policy hub
- Escalation playbooks
- Metrics for collaboration
- Conflict resolution frameworks
Module 10. Privacy Testing in MLOps
Integrate privacy validation into CI/CD pipelines and model monitoring.
12 chapters in this module
- Pre-commit hooks for PII
- Data leakage detection
- Consent policy checks
- Anonymization verification
- Bias detection triggers
- Model drift alerts
- Automated PIA updates
- Re-training triggers
- Security scanning tools
- Integration with Databricks
- Monitoring in production
- Incident response automation
Module 11. Scaling Privacy Across AI Portfolios
Extend individual model compliance to multi-team, multi-product environments.
12 chapters in this module
- Centralized policy library
- Shared anonymization services
- Cross-team governance
- Standardized onboarding
- Knowledge transfer mechanisms
- Internal certification programs
- Toolchain standardization
- Vendor assessment programs
- Metrics for maturity
- Leadership reporting
- Budget alignment
- Roadmap integration
Module 12. Building a Compounding Privacy Practice
Turn individual project work into enduring, reusable assets.
12 chapters in this module
- Personal IP library curation
- Template versioning
- Knowledge documentation
- Internal workshops
- Mentorship programs
- Case study compilation
- Speaking engagements
- Publication strategies
- Leadership visibility
- Promotion readiness
- Influence expansion
- Legacy artefact preservation
How this maps to your situation
- Starting a new AI project with privacy requirements
- Responding to internal audit findings
- Onboarding a new vendor with data access
- Preparing for external regulator inquiry
Before vs. after
Before
Rebuilding privacy controls from scratch on every AI/ML project, leading to delays and inconsistency.
After
Leveraging a growing library of reusable, ISO 27701-aligned artefacts that accelerate delivery and strengthen compliance.
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 fit around project deadlines.
If nothing changes
Without standardized, reusable privacy implementations, engineers waste time on repetitive tasks, increase compliance risk, and miss opportunities to scale their impact across the organization.
How this compares to the alternatives
Unlike generic compliance courses, this program is tailored to AI/ML engineers and focuses on actionable, reusable artefacts rather than theoretical frameworks.
Frequently asked
Who is this course for?
Senior AI/ML software engineers working in regulated environments who want to streamline privacy compliance and build reusable assets.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help me pass an audit?
Yes, the course includes templates and checklists designed to produce audit-ready documentation for ISO 27701.
$199 one-time. Approximately 3 hours per module, designed to fit around project deadlines..
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