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CMP1344 Mastering ISO 27701 for AI Product Designers

$199.00
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A tailored course, built for your situation

Mastering ISO 27701 for AI Product Designers

Build defensible privacy-by-design patterns into AI systems with framework-backed decisions

$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 rework and skepticism when privacy teams or leads question design choices

The situation this course is for

AI product designers are increasingly asked to justify privacy decisions, not just deliver features. Without documented alignment to standards like ISO 27701, even strong designs face delays or pushback from compliance and security stakeholders.

Who this is for

Senior product designers and AI experience leads working at scale, where framework alignment impacts velocity and trust

Who this is not for

Junior designers without influence over core AI product decisions, or those not involved in privacy-sensitive AI experiences

What you walk away with

  • Map AI design choices directly to ISO 27701 control clauses with documented rationale
  • Respond confidently to privacy or compliance queries using specific examples and cited sources
  • Turn design reviews into faster approvals by preempting framework-related objections
  • Build reusable decision templates that align AI innovation with compliance expectations
  • Create artefacts that survive team changes and leadership shifts

The 12 modules (with all 144 chapters)

Module 1. Foundations of ISO 27701 in AI Context
Understand how ISO 27701 extends GDPR and CCPA principles into actionable design controls for AI systems.
12 chapters in this module
  1. Scope of ISO 27701 vs general data protection
  2. Privacy by design in machine learning pipelines
  3. Mapping PII flows in AI training data
  4. Controller vs processor roles in AI products
  5. Documentation requirements for audits
  6. Key differences from SOC 2 and ISO 27001
  7. Integrating DORA-aligned resilience thinking
  8. How ISO 27701 supports NIST AI RMF
  9. Linking to CCPA and GDPR compliance
  10. Audit expectations for AI-driven services
  11. Common misconceptions about certification
  12. Baseline assessment for your current product
Module 2. Privacy Impact Assessments for AI
Conduct PIAs that anticipate scrutiny and document design intent aligned with ISO 27701.
12 chapters in this module
  1. Scoping AI-specific privacy risks
  2. Stakeholder identification in AI systems
  3. Data subject rights in automated decisioning
  4. Bias as a privacy consideration
  5. Transparency requirements in model behavior
  6. Documenting lawful basis for processing
  7. Retention policies for training data
  8. Third-party model vendor accountability
  9. User consent design patterns
  10. Anonymization thresholds in AI
  11. Version-controlled PIA templates
  12. Peer review checklist for AI PIAs
Module 3. Designing Data Subject Rights Workflows
Implement rights fulfillment mechanisms that meet ISO 27701 control requirements.
12 chapters in this module
  1. Right to access in AI-powered interfaces
  2. Right to erasure in model training logs
  3. Opt-out mechanisms for profiling
  4. Automated response to data portability
  5. Human-in-the-loop for sensitive requests
  6. Logging fulfillment for audit trails
  7. Designing for data minimization
  8. APIs for rights automation
  9. Response time benchmarks
  10. Edge cases in federated learning
  11. Localization of rights handling
  12. Cross-border data transfer implications
Module 4. Privacy Controls in Model Development
Embed ISO 27701-aligned privacy practices in AI model development lifecycle.
12 chapters in this module
  1. Data labeling privacy safeguards
  2. Differential privacy in training
  3. Federated learning compliance
  4. Encryption of model parameters
  5. Access controls for model repositories
  6. Audit logging for model changes
  7. Version control for decision logic
  8. Bias detection as privacy control
  9. Model cards with privacy assertions
  10. Third-party dataset vetting
  11. Penetration testing for AI APIs
  12. Incident response for model leaks
Module 5. Vendor Management for AI Services
Ensure third-party AI tools and APIs comply with ISO 27701 through documented oversight.
12 chapters in this module
  1. Controller-processor agreements for AI
  2. Due diligence for AI API vendors
  3. Sub-processing restrictions
  4. Data processing addenda structure
  5. Audit rights in vendor contracts
  6. Standard contractual clauses updates
  7. Security assessments for AI partners
  8. Right to audit enforcement
  9. Vendor risk scoring framework
  10. Exit planning for AI services
  11. Multi-cloud data residency checks
  12. Certification requirements for vendors
Module 6. Security Measures for Privacy Protection
Align technical security controls with ISO 27701 privacy-specific requirements.
12 chapters in this module
  1. Encryption at rest for personal data
  2. Access control granularity
  3. Role-based permissions design
  4. Multi-factor authentication enforcement
  5. Network segmentation for PII
  6. Logging and monitoring PII access
  7. Incident detection for privacy breaches
  8. Data loss prevention in AI outputs
  9. Secure deletion techniques
  10. Physical security for data centers
  11. Cloud provider compliance checks
  12. Penetration testing scope for privacy
Module 7. Internal Audits and Compliance Reviews
Prepare for audits with documented alignment between design and ISO 27701.
12 chapters in this module
  1. Audit planning for AI products
  2. Sampling techniques for AI decisions
  3. Evidence collection from logs
  4. Interview guides for product teams
  5. Control testing methodology
  6. Gap assessment framework
  7. Remediation tracking system
  8. Audit communication strategy
  9. Preparing documentation packets
  10. Mock audit exercises
  11. Cross-functional alignment tactics
  12. Audit follow-up response templates
Module 8. Training and Awareness for AI Teams
Scale privacy knowledge across product and engineering using ISO 27701 as anchor.
12 chapters in this module
  1. Onboarding privacy training
  2. Role-specific learning paths
  3. AI ethics discussion guides
  4. Scenario-based learning modules
  5. Privacy decision playbooks
  6. Knowledge retention assessments
  7. Leadership messaging framework
  8. Feedback loops from support teams
  9. Privacy champion programs
  10. Incident simulation drills
  11. Quarterly refresh content
  12. Metrics for training effectiveness
Module 9. Documentation and Record Keeping
Create audit-ready records that demonstrate continuous ISO 27701 alignment.
12 chapters in this module
  1. Register of processing activities
  2. Data flow diagram standards
  3. Control implementation evidence
  4. Policy version history
  5. Meeting minutes for privacy reviews
  6. Decision rationale documentation
  7. Evidence storage structure
  8. Retention schedule for records
  9. Access controls for documentation
  10. Automation of record updates
  11. Cross-team documentation sync
  12. Searchable knowledge base design
Module 10. Certification Readiness Preparation
Navigate the path to ISO 27701 certification with practical, product-integrated steps.
12 chapters in this module
  1. Selecting a certification body
  2. Stage 1 audit preparation
  3. Gap analysis reporting
  4. Evidence compilation workflow
  5. Internal audit rehearsal
  6. Management review meeting prep
  7. Corrective action planning
  8. Stage 2 audit simulation
  9. Certification decision follow-up
  10. Maintaining certified status
  11. Surveillance audit readiness
  12. Public communication of certification
Module 11. Privacy in International AI Deployments
Design for global compliance using ISO 27701 as a unifying framework.
12 chapters in this module
  1. GDPR alignment in AI products
  2. CCPA and CPRA implementation
  3. Brazilian LGPD considerations
  4. Canada PIPEDA compliance
  5. Japan APPI alignment
  6. India DPDPA readiness
  7. China PIPL challenges
  8. Cross-border transfer mechanisms
  9. Localization vs centralization trade-offs
  10. Language-specific consent design
  11. Regional audit expectation mapping
  12. Global incident response coordination
Module 12. Continuous Improvement of Privacy Design
Establish feedback loops that evolve AI products in line with ISO 27701 principles.
12 chapters in this module
  1. Privacy key performance indicators
  2. User feedback analysis methods
  3. Audit finding trend tracking
  4. Framework update monitoring
  5. Lessons learned from incidents
  6. Benchmarking against peers
  7. Privacy maturity assessments
  8. Roadmap integration techniques
  9. Leadership reporting templates
  10. Resource allocation for privacy
  11. Innovation within compliance guardrails
  12. Long-term defensible design strategy

How this maps to your situation

  • Designing AI experiences with embedded privacy controls
  • Responding to compliance queries with documented evidence
  • Preparing for internal and external audits
  • Scaling privacy practices across product teams

Before vs. after

Before
Privacy decisions are made reactively, with limited documentation to support design choices during reviews.
After
Every AI design decision is backed by ISO 27701 rationale, with sources and examples ready for scrutiny.

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 3-4 hours per module, designed to be completed alongside active product work.

If nothing changes
Without documented alignment to privacy frameworks, even innovative AI designs face delays, rework, or rejection during compliance reviews.

How this compares to the alternatives

Unlike generic privacy courses, this program is tailored to AI product design and grounded in ISO 27701 with real-world examples and templates you can apply immediately.

Frequently asked

Who is this course for?
AI product designers and technical leads responsible for privacy-conscious design decisions in production AI systems.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Does this course cover other standards?
ISO 27701 is the primary anchor, with contextual links to GDPR, CCPA, NIST AI RMF, and SOC 2 where relevant.
$199 one-time. Approximately 3-4 hours per module, designed to be completed alongside active product work..

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
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