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CMP5012 Mastering ISO 27701 for Generative AI GTM Leaders

$199.00
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A tailored course, built for your situation

Mastering ISO 27701 for Generative AI GTM Leaders

Build trusted, privacy-first GTM frameworks for AI products with verifiable governance

$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.
GTM leaders lose momentum when privacy controls stall in legal or compliance review

The situation this course is for

Even strong go-to-market plans for AI tools get delayed or diluted when privacy frameworks lack auditable structure. Teams default to generic checklists instead of product-integrated controls, leaving leaders vulnerable during regulatory scrutiny or M&A due diligence.

Who this is for

Senior GTM leader owning Generative AI product commercialization with accountability for compliance readiness and cross-functional alignment

Who this is not for

Individual contributors not owning end-to-end GTM strategy, or teams focused solely on internal AI tools without external customer-facing risk

What you walk away with

  • Handcrafted ISO 27701 privacy controls mapped to AI product features
  • A documented evidence trail that passes legal review without revisions
  • Escalation protocols recognized by peer teams during integration conflicts
  • Stakeholder briefing kits that preempt compliance friction in launch planning
  • A reusable GTM privacy playbook that survives leadership changes

The 12 modules (with all 144 chapters)

Module 1. Foundations of ISO 27701 in AI-Driven Environments
Establish core privacy principles as they apply specifically to generative AI systems, including data provenance, model inference boundaries, and consent management frameworks.
12 chapters in this module
  1. Understanding the relationship between ISO 27701 and AI data flows
  2. Mapping personal data touchpoints in training and inference pipelines
  3. Defining controller vs processor roles in third-party AI integrations
  4. How privacy by design applies to prompt engineering and RAG systems
  5. Key differences between ISO 27701 and GDPR Article 30 requirements
  6. Integrating DPIA outcomes into product development sprints
  7. Establishing accountability for model-generated personal data
  8. Documenting lawful basis for processing in multimodal systems
  9. Privacy impact thresholds that trigger executive escalation
  10. Versioning privacy decisions as models retrain and redeploy
  11. Aligning AI privacy scope with SOC 2 and ISO 27001 boundaries
  12. Creating living records of processing activities for audit readiness
Module 2. Designing Privacy Controls for LLM-Powered Workflows
Build practical, enforceable controls tailored to large language model deployments in enterprise service environments.
12 chapters in this module
  1. Identifying PII leakage risks in conversational AI interfaces
  2. Implementing input sanitization and output filtering mechanisms
  3. Role-based access patterns for AI-generated recommendations
  4. Controlling data persistence in AI session memory stores
  5. Privacy-preserving techniques for fine-tuning on customer data
  6. Establishing data retention policies for AI interaction logs
  7. Detecting and logging unauthorized access to AI components
  8. Embedding consent signals into automated workflow triggers
  9. Handling opt-out requests across AI-driven notification chains
  10. Designing for right to explanation in automated decisions
  11. Auditing model behavior against defined privacy thresholds
  12. Integrating privacy controls into CI/CD pipelines for AI
Module 3. Stakeholder Alignment in Cross-Functional GTM Launches
Navigate legal, compliance, product, and engineering expectations when bringing privacy-centric AI products to market.
12 chapters in this module
  1. Translating ISO 27701 requirements into product team language
  2. Creating joint ownership models for privacy control implementation
  3. Running effective privacy readiness workshops pre-launch
  4. Developing escalation paths for control conflicts between teams
  5. Aligning AI privacy scope with customer contract obligations
  6. Facilitating sign-off between DPO, legal, and product leadership
  7. Managing external auditor access to AI system documentation
  8. Preparing for regulator inquiries on model transparency
  9. Documenting design trade-offs when privacy and UX compete
  10. Coordinating with marketing on privacy claim validation
  11. Integrating customer support playbooks with privacy boundaries
  12. Establishing feedback loops for post-launch privacy incidents
Module 4. Building Audit-Ready Records of Processing Activities
Develop complete, defensible RoPAs tailored to dynamic AI systems that withstand internal and external scrutiny.
12 chapters in this module
  1. Structuring RoPA entries for AI model inference events
  2. Documenting data flows across cloud and on-prem AI services
  3. Capturing third-party processor agreements for AI APIs
  4. Version control practices for evolving AI data processing
  5. Linking RoPA entries to specific product features and releases
  6. Automating RoPA updates using code scanning tools
  7. Classifying risk levels for different AI processing activities
  8. Integrating RoPA maintenance into sprint planning
  9. Defining ownership for RoPA accuracy across product teams
  10. Preparing RoPA summaries for executive review
  11. Mapping RoPA content to ISO 27701 Annex A controls
  12. Generating audit evidence from RoPA for compliance reports
Module 5. Implementing Data Subject Rights in AI Systems
Enable DSAR fulfillment across complex AI architectures without compromising system integrity.
12 chapters in this module
  1. Mapping data subject requests to AI-generated content stores
  2. Designing search capabilities for unstructured AI outputs
  3. Handling deletion requests in model retraining cycles
  4. Verifying identity in AI-driven customer service channels
  5. Building DSAR workflows into service request platforms
  6. Tracking fulfillment timelines across AI component teams
  7. Managing exceptions when AI outputs reference third parties
  8. Ensuring accuracy of AI-assisted DSAR responses
  9. Logging DSAR processing for audit and reporting
  10. Integrating DSAR status into customer portal experiences
  11. Scaling DSAR operations for enterprise customer volumes
  12. Training support teams on AI-specific DSAR handling
Module 6. Vendor Risk Management for AI Ecosystems
Assess and govern third-party AI providers and open-source components with confidence.
12 chapters in this module
  1. Evaluating ISO 27701 compliance in AI model vendors
  2. Assessing data handling practices in API-based AI services
  3. Conducting due diligence on foundation model providers
  4. Managing risks in open-source LLM implementations
  5. Defining scope for third-party AI penetration testing
  6. Reviewing model card and system card disclosures
  7. Establishing contractual privacy terms for AI vendors
  8. Monitoring compliance of AI-as-a-service providers
  9. Handling sub-processor transparency in AI supply chains
  10. Auditing vendor incident response capabilities
  11. Planning exit strategies for AI vendor dependencies
  12. Integrating vendor reviews into product release gates
Module 7. Privacy by Design in AI Product Development
Embed privacy principles into the earliest stages of AI product ideation and design.
12 chapters in this module
  1. Integrating privacy checkpoints into AI product roadmaps
  2. Conducting threat modeling for AI system architectures
  3. Defining data minimization rules for AI training sets
  4. Setting privacy thresholds for model accuracy trade-offs
  5. Incorporating privacy requirements into user stories
  6. Designing for explainability in AI-driven decisions
  7. Balancing personalization with privacy in AI features
  8. Establishing data protection impact assessment triggers
  9. Creating privacy design patterns for reuse
  10. Training product teams on privacy-aware development
  11. Validating privacy designs through red team exercises
  12. Documenting design decisions for compliance evidence
Module 8. Monitoring and Auditing AI Privacy Controls
Establish continuous oversight mechanisms for AI privacy that adapt to system changes.
12 chapters in this module
  1. Defining key privacy metrics for AI systems
  2. Implementing logging for AI data access and processing
  3. Using automation to detect privacy policy deviations
  4. Conducting regular privacy control effectiveness reviews
  5. Planning internal audits of AI system compliance
  6. Preparing for external auditor access to AI environments
  7. Generating compliance reports from operational data
  8. Maintaining evidence logs for control testing
  9. Integrating findings into product improvement cycles
  10. Tracking privacy incidents across AI components
  11. Benchmarking against industry AI privacy standards
  12. Reporting privacy posture to executive leadership
Module 9. Incident Response Planning for AI Systems
Prepare for and respond to privacy incidents involving AI components effectively.
12 chapters in this module
  1. Identifying AI-specific privacy incident scenarios
  2. Establishing detection mechanisms for model drift
  3. Defining escalation paths for AI-generated content breaches
  4. Conducting tabletop exercises for AI incidents
  5. Documenting response procedures for data leaks
  6. Managing communication during AI-related incidents
  7. Coordinating with legal and PR teams on AI incidents
  8. Investigating root causes in AI system failures
  9. Remediating vulnerabilities in AI models and data
  10. Reporting incidents to regulators when required
  11. Updating controls based on incident learnings
  12. Maintaining audit trail of incident response actions
Module 10. Global Data Transfer Mechanisms for AI
Ensure compliance with cross-border data transfer rules in AI system operations.
12 chapters in this module
  1. Mapping data flows for AI training and inference
  2. Assessing transfer impact under evolving regulations
  3. Implementing SCCs for AI data processing
  4. Using binding corporate rules in global AI deployments
  5. Evaluating adequacy decisions for AI hosting regions
  6. Managing data localization requirements in AI systems
  7. Documenting legal basis for international data flows
  8. Auditing compliance with transfer mechanisms
  9. Handling changes in international data laws
  10. Planning architecture for multi-region AI services
  11. Coordinating with local counsel on transfer rules
  12. Maintaining records for cross-border processing
Module 11. Executive Communication on AI Privacy
Present AI privacy posture clearly to leadership and board-level audiences.
12 chapters in this module
  1. Translating technical privacy issues into business risk
  2. Creating executive summaries of AI compliance status
  3. Reporting on key privacy metrics and trends
  4. Preparing for leadership questions on AI risk
  5. Communicating incident response readiness
  6. Demonstrating compliance investment ROI
  7. Aligning AI privacy strategy with business goals
  8. Presenting audit findings to senior management
  9. Articulating competitive advantage through privacy
  10. Educating executives on AI-specific risks
  11. Building trust through transparency initiatives
  12. Maintaining ongoing executive engagement
Module 12. Sustaining Compliance Through AI Evolution
Maintain ISO 27701 alignment as AI models and products continuously improve.
12 chapters in this module
  1. Managing version changes in AI models and data
  2. Updating privacy documentation for model updates
  3. Reassessing DPIAs after significant AI changes
  4. Communicating changes to data subjects when needed
  5. Maintaining compliance during AI technical debt reduction
  6. Handling open-source library updates in AI systems
  7. Planning for AI model retirement and data disposition
  8. Tracking regulatory changes affecting AI privacy
  9. Updating training materials for evolving AI systems
  10. Benchmarking against emerging AI privacy standards
  11. Sharing best practices across product teams
  12. Future-proofing AI privacy through modular design

How this maps to your situation

  • Aligning AI product launches with ISO 27701 requirements
  • Preparing for regulatory review of AI systems
  • Managing third-party risk in AI supply chains
  • Building executive confidence in AI privacy posture

Before vs. after

Before
Approaching AI privacy as a compliance hurdle with fragmented controls and reactive documentation.
After
Leading with a structured, verifiable ISO 27701 framework that earns trust in M&A, regulatory, and executive reviews.

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 2.5 hours per module; designed to be completed in under four weeks with weekly sprints.

If nothing changes
Without a structured approach, AI privacy efforts remain reactive, exposing GTM timelines to delays, creating vulnerabilities during due diligence, and limiting leadership credibility when escalations arise.

How this compares to the alternatives

Unlike generic privacy courses, this program is tailored to AI product leaders, focusing on ISO 27701 implementation in dynamic, model-driven environments with real-world templates and escalation playbooks.

Frequently asked

Is this course technical or strategic?
It's designed for GTM leaders, strategic with concrete implementation guidance. No coding required, but deep enough to earn trust from engineering and compliance teams.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help with audits or M&A due diligence?
Yes, modules include templates and playbooks specifically designed to streamline auditor inquiries and due diligence requests.
$199 one-time. Approximately 2.5 hours per module; designed to be completed in under four weeks with weekly sprints..

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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