A tailored course, built for your situation
Mastering ISO 27701 for Senior Data Scientists and AI Engineers
Build privacy-first AI systems with confidence and precision
$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.
Avoid last-minute compliance fixes in AI deployments
The situation this course is for
AI models often ship without embedded privacy controls, leading to rework, delayed launches, and audit vulnerabilities. Privacy is treated as a bolt-on, not a foundation.
Who this is for
Senior data and AI engineers in regulated or cloud-first environments who bridge analytics and production systems
Who this is not for
Entry-level analysts, pure data engineers without AI/ML scope, or compliance auditors without technical implementation roles
What you walk away with
- Produce AI model documentation that satisfies ISO 27701 privacy control requirements out of the gate
- Integrate data anonymization and consent tracking into pipelines with zero performance degradation
- Map model data flows to Annex D.5 and D.6 controls without external consulting support
- Deliver audit-ready records for PII processing activities tied directly to model behavior
- Reduce time spent on compliance remediation by 70% through upfront framework alignment
The 12 modules (with all 144 chapters)
Module 1. Foundations of ISO 27701 in Data Systems
Understand how ISO 27701 extends ISO 27001 for privacy, with a focus on data classification, PII handling, and scope definition in AI and cloud environments.
12 chapters in this module
- What ISO 27701 adds to ISO 27001
- PII vs non-PII data boundaries
- Role of the PII Controller and Processor
- Mapping AI systems to privacy roles
- Cloud architecture compliance obligations
- Jurisdictional scope triggers
- Data inventory methods for AI
- Automated discovery tools
- Classification sensitivity tiers
- Labelling conventions in pipelines
- Storage location tracking
- Retention rule enforcement
Module 2. Privacy by Design in AI Development
Embed privacy controls into the AI lifecycle from ideation through deployment, ensuring compliance is built in, not bolted on.
12 chapters in this module
- Intake form privacy checks
- Model purpose specification
- Lawful basis documentation
- Consent vs legitimate interest
- Minimization in feature selection
- Bias-risk and privacy overlap
- Model card requirements
- Data provenance chaining
- Anonymization thresholds
- K-anonymity in training sets
- Differential privacy integration
- Privacy budget tracking
Module 3. Data Mapping for AI Workloads
Create clear, auditable data flow diagrams that trace PII from source to insight, with tooling and notation standards that stand up to scrutiny.
12 chapters in this module
- Identifying PII touchpoints
- Source-to-destination mapping
- Third-party processor tracking
- Data sharing agreements
- Cross-border transfer flags
- Encryption-in-transit verification
- Cloud provider responsibility matrix
- Region-specific data laws
- Audit trail requirements
- Mapping visualization tools
- Automated lineage capture
- Version-controlled diagrams
Module 4. Consent and Lawful Basis Management
Operationalize consent tracking and lawful basis documentation in AI systems that process personal data at scale.
12 chapters in this module
- Consent as a data field
- Opt-in vs implied consent
- Withdrawal mechanisms
- Audit trail for consent changes
- Preference center integration
- Lawful basis justification
- Legitimate interest assessments
- Processor agreements
- Data subject access rights
- Right to explanation
- Model transparency scope
- Consent expiration rules
Module 5. Anonymization and Pseudonymization Techniques
Apply tested methods to de-identify data in AI pipelines while preserving utility for modeling and analytics.
12 chapters in this module
- K-anonymity implementation
- L-diversity for attribute protection
- T-closeness thresholds
- Generalization techniques
- Suppression rules
- Re-identification risk scoring
- Noise injection levels
- Differential privacy parameters
- Utility vs privacy tradeoffs
- Validation testing methods
- Model accuracy benchmarks
- Re-identification red teaming
Module 6. Privacy Controls in Cloud Architecture
Implement ISO 27701 controls in AWS, Azure, or GCP environments with configuration templates and monitoring standards.
12 chapters in this module
- Encryption key management
- Access control policies
- Role-based permissions
- Audit logging setup
- Data residency constraints
- Private subnets configuration
- VPC flow logging
- Zero-trust integration
- Secrets rotation schedules
- SaaS vendor compliance checks
- API gateway privacy rules
- Serverless data handling
Module 7. Data Subject Rights Fulfillment
Design AI systems to respond to DSARs, deletions, and access requests without disrupting core operations.
12 chapters in this module
- DSAR intake workflows
- Data location discovery
- Automated response templates
- Right to erasure scope
- Model retraining impact
- Anonymization vs deletion
- Exemption justification
- Response time tracking
- Audit trail for actions
- Third-party coordination
- Global request routing
- Expiry-based auto-deletion
Module 8. Incident Response and Breach Management
Prepare for and respond to PII incidents in AI systems with clear protocols and regulatory timelines.
12 chapters in this module
- PII breach definition
- 72-hour notification rule
- Internal escalation paths
- Regulatory contact lists
- Breach impact scoring
- Evidence preservation
- Forensic data capture
- Communication templates
- Legal counsel engagement
- Post-mortem documentation
- Regulatory reporting format
- Corrective action tracking
Module 9. Internal Audit and Compliance Verification
Conduct self-assessments and prepare for external audits using ISO 27701 control checklists tailored to AI systems.
12 chapters in this module
- Control mapping templates
- Evidence collection standards
- Automated compliance checks
- Control testing scripts
- Audit readiness scoring
- Gap identification methods
- Remediation tracking
- Stakeholder interviews
- Documentation completeness
- Third-party audit prep
- Internal reporting format
- Compliance dashboard design
Module 10. Vendor and Third-Party Risk
Evaluate and manage privacy compliance in external AI tools, APIs, and data providers.
12 chapters in this module
- Processor due diligence
- Contractual obligations
- Right to audit clauses
- Sub-processing restrictions
- API data handling
- Model inference logging
- Cloud service addenda
- Penetration testing rights
- Breach notification terms
- Compliance certification review
- Oversight frequency
- Exit strategy planning
Module 11. Privacy Impact Assessments for AI
Conduct PIAs that meet ISO 27701 standards for new or modified AI systems processing personal data.
12 chapters in this module
- PIA trigger events
- Stakeholder identification
- Data processing description
- Necessity and proportionality
- Risk identification
- Mitigation strategies
- DPIA thresholds
- Consultation requirements
- Approval workflows
- Documentation retention
- Update triggers
- Model change review
Module 12. Sustaining Compliance Over Time
Maintain ISO 27701 alignment as AI models evolve, teams change, and regulations shift.
12 chapters in this module
- Annual review cycles
- Change control processes
- Model version tracking
- Retraining triggers
- Documentation updates
- Staff onboarding modules
- Training frequency
- Audit trail maintenance
- Policy refresh schedule
- Regulatory monitoring
- Compliance playbook updates
- Lessons learned integration
How this maps to your situation
- Starting a new AI initiative with PII
- Responding to internal compliance audit
- Preparing for external certification
- Scaling AI systems across regions
Before vs. after
Before
Compliance is reactive, bolted on late in AI projects, leading to rework and uncertainty.
After
Privacy is embedded by design, AI outputs meet ISO 27701 standards the first time, with full traceability and audit readiness.
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: 6, 8 hours total, self-paced, with modular design to fit around production cycles.
If nothing changes
Without structured privacy integration, AI projects face delayed launches, audit findings, or regulatory penalties, especially as enforcement of frameworks like ISO 27701 becomes more common in cloud and education sectors.
How this compares to the alternatives
Unlike generic compliance training, this course is tailored to AI engineers, focusing on technical implementation, code-level controls, and cloud architecture alignment with ISO 27701, not just policy awareness.
Frequently asked
Is this course relevant for AI engineers outside highly regulated industries?
Yes, privacy expectations are rising everywhere. This course prepares you to build systems that meet the highest standards, even if your organization isn't under strict regulation yet.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Does the course cover technical implementation in Python or cloud platforms?
Yes, each module includes downloadable code snippets, configuration templates, and implementation examples for AWS, Azure, and GCP.
$199 one-time. 6, 8 hours total, self-paced, with modular design to fit around production cycles..
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