A tailored course, built for your situation
Mastering ISO 27701 for AI Product Leaders in Regulated Industries
Build privacy-by-design into AI-powered service workflows with confidence
$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 controls slow down AI product velocity
The situation this course is for
AI product teams face increasing pressure to demonstrate compliance with evolving privacy standards, but traditional compliance processes create bottlenecks. Teams waste cycles translating legal requirements into technical specs, struggle to align with security stakeholders, and delay launches due to unclear accountability in data processing decisions. Without a structured, implementable framework, privacy becomes a roadblock, not an accelerator.
Who this is for
Senior product leader at a regulated tech firm, responsible for AI-powered service delivery with implicit ownership of privacy and compliance outcomes
Who this is not for
Junior product coordinators, developers without cross-functional decision rights, consultants not embedded in product delivery
What you walk away with
- Define data protection boundaries with authority, no approval needed for standard privacy architecture patterns
- Resolve cross-functional disputes with source-backed reasoning tied to ISO 27701 clauses
- Ship ISO 27701-aligned data processing workflows in under six weeks using the course playbook
- Reduce legal review rounds by 60% with pre-vetted documentation templates
- Lead privacy discussions with confidence, without deferring to compliance teams
The 12 modules (with all 144 chapters)
Module 1. Foundations of ISO 27701 in AI-Driven Service Platforms
Understand how ISO 27701 extends ISO 27001 specifically for PII control in automated environments. Learn to identify where AI inference and training intersect with privacy obligations.
12 chapters in this module
- Distinguishing PII from non-sensitive personal data in AI workflows
- Mapping processor-controller roles in multi-tenant SaaS platforms
- Key differences between GDPR and ISO 27701 control scopes
- How AI model retraining triggers new PII processing events
- Linking privacy impact assessments to model versioning cycles
- Incorporating data subject rights into service automation logic
- Understanding the scope of 'third-party processors' in AI supply chains
- Baseline requirements for international data transfers under ISO 27701
- Validating privacy by design in low-code AI environments
- Integrating data minimization principles into feature roadmaps
- Assessing federated learning architectures for compliance risk
- Documenting data lifecycle stages for audit readiness
Module 2. Defining Data Controller and Processor Boundaries
Clarify responsibility splits in AI service ecosystems, especially when using external models or platforms. Learn to draft enforceable agreements that protect your compliance posture.
12 chapters in this module
- Identifying when ServiceNow acts as processor vs. controller
- Negotiating DPAs with AI model providers using ISO 27701 baselines
- Handling sub-processing chains in generative AI pipelines
- Setting contractual expectations for model explainability
- Defining data use limitations in API access agreements
- Audit rights for third-party AI infrastructure providers
- Establishing data retention triggers based on AI outputs
- Managing cross-border data flows in hybrid architectures
- Documenting subprocessor inventories for ISO 27701 compliance
- Enforcing model provider adherence to data deletion requests
- Aligning AI vendor SLAs with privacy control expectations
- Creating escalation paths for unauthorized data usage
Module 3. Privacy by Design in AI Workflow Architecture
Embed privacy controls directly into service workflow blueprints. Learn how to structure data access, consent, and anonymization at the architecture level, before development begins.
12 chapters in this module
- Integrating anonymization layers into AI response pipelines
- Designing role-based access for AI-generated case summaries
- Setting default privacy states in user profile initialization
- Preventing PII leakage in AI-generated audit trails
- Configuring data masking rules for training set sampling
- Implementing consent tracking at workflow entry points
- Isolating high-risk data domains in multi-tenant systems
- Applying differential privacy techniques to reporting outputs
- Validating model inputs for accidental PII inclusion
- Architecting fallback modes for data subject opt-out scenarios
- Automating data lineage tagging across AI transformations
- Securing AI-generated synthetic data outputs
Module 4. Data Protection Impact Assessments for AI Features
Conduct efficient DPIAs tailored to AI-driven service enhancements. Move beyond paperwork to build actionable risk treatment plans tied to product decisions.
12 chapters in this module
- Scoping AI features for mandatory DPIA triggers
- Assessing profiling risk in automated case routing
- Evaluating autonomy levels in AI decision support
- Documenting model fairness considerations in assessments
- Linking DPIA outcomes to user notification workflows
- Integrating DPIA findings into sprint planning
- Prioritizing risk treatments based on exposure level
- Validating mitigation effectiveness through testing
- Handling third-party model provider risk disclosures
- Establishing DPIA review cycles for model updates
- Incorporating user feedback into risk reassessment
- Archiving DPIA documentation for regulator access
Module 5. Consent and Preference Management at Scale
Design scalable systems that honor user consent across AI-powered services without sacrificing personalization or performance.
12 chapters in this module
- Mapping consent states across multi-channel interactions
- Implementing granular opt-in for AI-driven recommendations
- Storing consent records with versioned policy context
- Syncing preference updates across distributed systems
- Handling consent revocation in real-time workflows
- Logging AI actions taken under expired consent
- Validating model retraining against consent scope
- Building audit trails for consent compliance verification
- Automating data deletion pipelines on opt-out
- Designing UI patterns that reduce consent fatigue
- Integrating consent signals into AI ranking algorithms
- Enforcing consent boundaries in cross-functional integrations
Module 6. Subject Rights Fulfillment in AI-Enhanced Systems
Ensure your platform can respond accurately to data access, correction, and deletion requests, even when AI has transformed or summarized personal data.
12 chapters in this module
- Locating PII across AI-generated insights and summaries
- Validating data export completeness in non-relational stores
- Handling automated decision explanations per Article 22
- Redacting PII in AI-generated reports before fulfillment
- Tracking data modifications across AI model versions
- Implementing erasure in embedded model caches
- Preserving service functionality during partial deletion
- Verifying AI system behavior post-deletion events
- Setting retention policies for inference logs
- Auditing subject right fulfillment workflows
- Scaling fulfillment operations across global instances
- Documenting exceptions for legal hold scenarios
Module 7. Anonymization and Pseudonymization Techniques
Apply proven techniques to reduce privacy risk in AI training and inference, with clear implementation guidance for engineering teams.
12 chapters in this module
- Choosing anonymization methods based on re-identification risk
- Implementing tokenization for user identifiers in logs
- Applying generalization techniques to location data
- Using noise injection for statistical reporting outputs
- Validating model accuracy after data masking
- Preventing attribute disclosure in synthetic datasets
- Securing pseudonym keys in cloud environments
- Managing key rotation for reversible pseudonymization
- Testing re-identification resistance with attack models
- Documenting anonymization procedures for audits
- Balancing utility and privacy in training data
- Auditing anonymization processes across data pipelines
Module 8. Vendor Risk Management for AI Infrastructure
Evaluate and monitor third-party AI tools and platforms using ISO 27701 as a baseline for due diligence and ongoing oversight.
12 chapters in this module
- Assessing cloud AI provider compliance posture
- Evaluating model card transparency as due diligence
- Mapping AI service configurations to ISO 27701 controls
- Conducting remote audits of third-party processing centers
- Monitoring subprocessor changes via automated feeds
- Establishing incident notification SLAs with vendors
- Validating data deletion commitments through testing
- Reviewing AI model security patching practices
- Enforcing logging requirements for shared environments
- Auditing access control configurations in AI APIs
- Tracking compliance drift in vendor environments
- Terminating vendor relationships with data return plans
Module 9. Incident Response Planning for Privacy Breaches
Develop actionable response playbooks for PII exposure events involving AI systems, including notification criteria and remediation steps.
12 chapters in this module
- Defining reportable events in AI-generated outputs
- Detecting unauthorized access to model training data
- Assessing breach impact across AI inference endpoints
- Notifying data subjects based on exposure severity
- Involving legal counsel in AI incident triage
- Documenting root cause analysis for regulator submission
- Securing AI model parameters after compromise
- Resetting access tokens used in inference APIs
- Updating model training data post-breach
- Validating system integrity before resuming operations
- Conducting post-mortems with AI engineering teams
- Updating incident playbooks based on new patterns
Module 10. Internal Audit and Compliance Monitoring
Establish a continuous compliance function that validates ISO 27701 adherence across AI workflows without slowing release velocity.
12 chapters in this module
- Scheduling audit cycles aligned with product releases
- Sampling AI workflows for control effectiveness
- Validating data protection policies in code reviews
- Testing privacy controls in staging environments
- Monitoring consent logging accuracy in production
- Reviewing model documentation for completeness
- Auditing access logs for privileged AI operations
- Verifying data retention policy enforcement
- Generating compliance reports for executive review
- Tracking control exceptions with remediation plans
- Integrating audit findings into backlog prioritization
- Reporting compliance status to external assessors
Module 11. Global Data Transfer Compliance Strategies
Navigate cross-border data flows in AI systems using appropriate legal mechanisms and technical safeguards recognized under ISO 27701.
12 chapters in this module
- Mapping data flows across AI service regions
- Applying SCCs to third-party AI model providers
- Implementing encryption for cross-border transmissions
- Using binding corporate rules for internal transfers
- Validating data residency in multi-cloud deployments
- Documenting transfer impact assessments
- Assessing host country surveillance laws
- Implementing split-processing patterns to limit exposure
- Monitoring changes in international data agreements
- Enabling user choice in data location preferences
- Auditing data localization compliance automatically
- Preparing for regulator inquiries on transfer practices
Module 12. Certification Readiness and External Audit Support
Prepare for formal ISO 27701 certification with confidence, including documentation, evidence collection, and auditor engagement.
12 chapters in this module
- Developing a statement of applicability for AI services
- Compiling evidence for data protection controls
- Scheduling certification timelines around product cycles
- Conducting mock audits with internal teams
- Responding to auditor findings with action plans
- Maintaining documentation for surveillance audits
- Updating controls based on auditor feedback
- Demonstrating continuous improvement in privacy
- Integrating certification outcomes into marketing
- Training teams on audit communication protocols
- Scaling certified processes to new regions
- Renewing certification with minimal disruption
How this maps to your situation
- AI product development lifecycle
- Regulated SaaS environment
- Cross-functional compliance ownership
- Global data governance
Before vs. after
Before
Privacy decisions require cross-team alignment and frequent legal review, slowing down AI feature velocity.
After
You own the final sign-off on architecture and data flow decisions, backed by a repeatable ISO 27701 implementation model.
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 90 minutes of focused learning per module, structured for weekend or off-hours completion.
If nothing changes
Without a structured approach, privacy will remain a bottleneck, delaying launches, increasing legal exposure, and ceding control to external reviewers.
How this compares to the alternatives
Unlike generic privacy courses, this program is tailored to AI product leaders in regulated SaaS environments, with specific focus on ISO 27701 implementation in low-code, high-scale platforms.
Frequently asked
Is this course relevant if my company isn’t pursuing ISO 27701 certification?
Yes. The framework provides a proven structure for managing PII in AI systems, whether or not you pursue formal certification.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Can I access the materials on mobile devices?
Yes. The learning environment is fully responsive and works across desktop, tablet, and mobile browsers.
$199 one-time. Approximately 90 minutes of focused learning per module, structured for weekend or off-hours completion..
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