A tailored course, built for your situation
Mastering ISO 27701; A Step-by-Step Guide to Privacy Implementation
Build institutional-grade privacy practices within your current Gen AI leadership role
$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.
Privacy in AI isn’t just about compliance, it’s about credibility, speed, and scope.
The situation this course is for
Without a structured approach, privacy efforts in AI become reactive, fragmented, and inconsistent, leading to rework, delayed launches, and missed influence.
Who this is for
Senior AI product and design leaders who own privacy standards in fast-moving environments but lack formal frameworks to scale their impact.
Who this is not for
Individual contributors not in decision-shaping roles, or practitioners focused solely on post-deployment audits.
What you walk away with
- Lead ISO 27701 implementation tailored to Gen AI workflows
- Establish clear ownership over privacy decision logs in design sprints
- Accelerate stakeholder alignment by speaking to an accepted institutional standard
- Produce artifact-ready documentation that scales across model versions
- Strengthen cross-functional authority without requiring role changes
The 12 modules (with all 144 chapters)
Module 1. Understanding ISO 27701 in the Context of AI Systems
Ground your privacy leadership in the specific clauses of ISO 27701 that apply to AI-driven design and data flows, distinguishing between general PII and AI-specific personal data processing.
12 chapters in this module
- How ISO 27701 extends beyond GDPR and CCPA compliance
- Mapping privacy scope in generative AI user interactions
- Differentiating controller vs processor roles in AI pipelines
- Key definitions: personal data, PII, and identifiable outputs
- Why privacy by design matters in latent space modeling
- Linking ISO 27701 to AI fairness and transparency standards
- Understanding the role of data protection impact assessments
- Integrating privacy into model training data curation
- Accountability for inference-time personal data use
- Traceability requirements for dynamic AI outputs
- Cross-border data flows in distributed AI systems
- Aligning with Meta’s internal data governance expectations
Module 2. Privacy Governance Structures for AI Teams
Define roles and responsibilities within your team to ensure consistent privacy decision-making, documentation, and escalation paths under ISO 27701.
12 chapters in this module
- Establishing a privacy lead within Gen AI product squads
- Documenting internal approval chains for data use cases
- Creating decision logs for model data inclusions
- Defining boundaries between design and engineering privacy ownership
- Integrating legal and compliance reviewers into sprint cycles
- Building cross-functional privacy working groups
- Assigning accountability for third-party data integrations
- Managing escalation paths for ambiguous privacy edge cases
- Maintaining up-to-date records of processing activities
- Versioning privacy governance decisions across releases
- Ensuring leadership continuity in privacy standards
- Benchmarking team structure against ISO 27701 clause 4.3
Module 3. Defining the Scope of AI Processing Activities
Learn how to document the precise boundaries of AI systems that process personal data, a core requirement under ISO 27701.
12 chapters in this module
- Identifying AI components that process personal information
- Mapping data inputs from user prompts and behavior
- Tracking embedded PII in training datasets
- Defining system boundaries for multimodal models
- Documenting data flows in retrieval-augmented generation
- Scoping inference-time personalization features
- Excluding non-personal data from formal recording
- Handling synthetic data derived from real individuals
- Clarifying scope for internal vs external-facing AI tools
- Integrating scope documentation into product specs
- Using diagrams to visualize data movement in AI systems
- Auditing scope definitions for consistency across teams
Module 4. Privacy Risk Assessment for Generative AI
Apply ISO 27701-aligned risk assessment methods tailored to generative AI’s unique data exposure points.
12 chapters in this module
- Identifying high-risk data use cases in AI workflows
- Assessing re-identification risks from AI outputs
- Evaluating prompt leakage and context retention
- Scoring privacy risks across model deployment tiers
- Involving diverse stakeholders in risk evaluation
- Documenting risk treatment plans in standard format
- Integrating risk assessments into model review boards
- Prioritizing mitigation efforts based on impact and likelihood
- Using templates to standardize risk documentation
- Updating assessments with model version changes
- Benchmarking risk thresholds across product lines
- Aligning risk appetite with organizational standards
Module 5. Data Minimization and Purpose Limitation in AI
Implement operational controls that ensure personal data use in AI systems is necessary, limited, and aligned with intended purposes.
12 chapters in this module
- Defining legitimate purposes for AI-driven personalization
- Designing prompts to avoid unnecessary PII collection
- Implementing data retention policies for AI interactions
- Avoiding secondary use of user inputs beyond scope
- Building system defaults that limit data persistence
- Training teams on minimizing data in mockups and testing
- Auditing logs for unintended personal data capture
- Using anonymization techniques in model development
- Documenting exceptions to data minimization rules
- Balancing innovation with proportionality in data use
- Introducing data stewardship checks in design sprints
- Measuring compliance with purpose limitation principles
Module 6. Transparency and User Communication for AI Systems
Develop clear, actionable communication strategies that inform users about how their data is used in AI interactions.
12 chapters in this module
- Writing effective notices for AI-powered features
- Disclosing model training data sources to users
- Explaining how prompts influence output generation
- Providing accessible opt-out and correction mechanisms
- Designing just-in-time privacy disclosures in UI flows
- Localizing transparency content across regions
- Ensuring consistency between marketing and privacy claims
- Handling user rights requests in AI-driven systems
- Documenting response workflows for DSARs
- Training support teams on AI-specific privacy queries
- Using layered notices to improve comprehension
- Measuring user understanding of AI data practices
Module 7. Third-Party and Vendor Privacy Oversight
Extend your privacy leadership to external partners and suppliers involved in AI development and deployment.
12 chapters in this module
- Assessing vendor compliance with ISO 27701 requirements
- Mapping data flows in API-driven AI integrations
- Establishing contractual terms for AI model providers
- Auditing third-party annotation and labeling services
- Managing open-source model components with privacy risk
- Tracking sub-processor relationships in AI pipelines
- Requiring evidence of data governance from vendors
- Enforcing data use limitations in external collaborations
- Integrating vendor assessments into procurement workflows
- Updating vendor oversight with model version changes
- Building centralized records of third-party processing
- Conducting periodic reviews of high-risk AI vendors
Module 8. Privacy by Design in AI Development Lifecycle
Integrate privacy considerations into every phase of the AI product lifecycle, from concept to deprecation.
12 chapters in this module
- Embedding privacy reviews in sprint planning
- Conducting privacy threat modeling for new features
- Building automated checks for PII in test environments
- Incorporating privacy metrics into CI/CD pipelines
- Creating design patterns for privacy-preserving AI
- Using red teaming to uncover data exposure risks
- Introducing privacy criteria into model approval gates
- Documenting design trade-offs in architecture reviews
- Involving privacy leads in feature deprecation planning
- Tracking privacy debt alongside technical debt
- Standardizing privacy documentation across repositories
- Aligning with ISO 27701 requirements at each stage
Module 9. Internal Audit and Continuous Monitoring
Establish ongoing review practices to ensure sustained compliance with ISO 27701 in dynamic AI environments.
12 chapters in this module
- Developing audit checklists for AI privacy controls
- Scheduling regular assessments of model data use
- Using logging tools to monitor personal data flows
- Integrating audit findings into product roadmaps
- Assigning owners for corrective action plans
- Measuring improvement over time with privacy KPIs
- Preparing for internal and external audit cycles
- Generating reports for leadership review
- Benchmarking against peer AI teams
- Documenting evidence for clause-specific compliance
- Updating monitoring practices with new regulations
- Building audit-readiness into team rituals
Module 10. Incident Response and Data Breach Preparedness
Prepare for potential privacy incidents in AI systems with proactive response planning and communication protocols.
12 chapters in this module
- Defining what constitutes a privacy incident in AI
- Mapping data exposure scenarios specific to models
- Creating response playbooks for prompt leakage events
- Establishing thresholds for incident escalation
- Coordinating legal and communications teams in crises
- Documenting breach timelines and root causes
- Notifying regulators and users under GDPR and CCPA
- Reviewing incident data to improve model safety
- Conducting tabletop exercises for AI-specific scenarios
- Storing incident records for audit purposes
- Integrating lessons learned into model updates
- Training teams on incident reporting workflows
Module 11. Training and Awareness for AI Teams
Equip designers, engineers, and product managers with practical knowledge to uphold privacy standards daily.
12 chapters in this module
- Developing role-specific privacy training modules
- Onboarding new team members on ISO 27701 expectations
- Creating just-in-time guidance for design decisions
- Building internal knowledge bases for reference
- Using case studies from real AI incidents
- Integrating privacy awareness into team rituals
- Measuring training effectiveness through assessments
- Reinforcing expectations during performance reviews
- Sharing updates on regulatory changes
- Promoting privacy champions across squads
- Tracking completion and engagement metrics
- Updating materials with new project needs
Module 12. Certification Readiness and External Validation
Prepare your team’s documentation and practices for third-party validation against ISO 27701.
12 chapters in this module
- Gathering evidence for each control clause
- Organizing documentation for auditor access
- Conducting pre-certification readiness assessments
- Addressing gaps in policy or implementation
- Scheduling internal mock audits
- Preparing team members for auditor interviews
- Demonstrating continuous improvement efforts
- Responding to auditor findings and recommendations
- Maintaining compliance post-certification
- Leveraging certification for external credibility
- Updating materials with organizational changes
- Building a sustainable posture beyond the audit
How this maps to your situation
- Current privacy ambiguity in AI design
- Need for standardized internal practices
- Cross-functional alignment challenges
- Demonstrating leadership maturity externally
Before vs. after
Before
Privacy decisions are ad hoc, reactive, or dependent on external teams.
After
You lead consistent, documented privacy implementation that scales with your AI product vision.
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: 90 minutes per week over six weeks, designed for practitioners leading AI initiatives.
If nothing changes
Without a structured approach, privacy efforts remain fragmented, slowing approvals, weakening cross-functional influence, and increasing exposure to scrutiny.
How this compares to the alternatives
Unlike generic compliance courses, this program is tailored to AI product leaders, focusing on practical implementation of ISO 27701 within fast-moving design and development environments.
Frequently asked
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is prior certification in ISO 27701 required?
No. The course is designed for practitioners building privacy programs from the ground up.
Can I apply this to non-AI systems?
Yes. While optimized for AI, the ISO 27701 implementation method applies across data systems.
$199 one-time. 90 minutes per week over six weeks, designed for practitioners leading AI 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