A tailored course, built for your situation
Mastering ISO 27701 for GenAI and Agentic AI Practitioners
Build privacy-compliant AI architectures with precision and 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.
Avoid rework and audit delays in AI deployments due to missing or weak privacy controls
The situation this course is for
Teams deploying agentic AI often overlook granular PII handling requirements until audit time, leading to costly revisions and delayed time-to-compliance.
Who this is for
Senior AI architect or platform engineer working on autonomous systems with customer data exposure, focused on scalability and compliance readiness
Who this is not for
Junior developers without system design authority or practitioners outside AI and data governance domains
What you walk away with
- Produce auditable privacy compliance artifacts for GenAI systems on first submission
- Map data processing flows in agentic architectures to ISO 27701 Annex A controls with precision
- Document lawful basis, data retention rules, and consent handling in autonomous workflows
- Integrate DPIA requirements directly into AI model deployment pipelines
- Accelerate approval cycles with regulators and internal review boards
The 12 modules (with all 144 chapters)
Module 1. Foundations of ISO 27701 in AI Systems
Establish core understanding of ISO 27701 principles and how they apply specifically to AI-driven data processing environments.
12 chapters in this module
- Defining personally identifiable information in AI-generated data streams
- Understanding the scope of data controllers and processors in agentic systems
- Key differences between GDPR and ISO 27701 in AI contexts
- Privacy by design as a non-functional requirement for agents
- Mapping autonomous decision-making to privacy impact thresholds
- Integrating data subject rights into agent loop design
- Establishing accountability frameworks for AI behaviors
- Role of consent in self-modifying AI workflows
- Data minimization challenges in adaptive agent training
- Temporal aspects of PII handling in long-running AI sessions
- Jurisdictional boundaries in distributed AI inference
- Versioning privacy controls alongside model updates
Module 2. Data Flow Mapping for Autonomous Agents
Learn to diagram complex data pathways in agentic systems to ensure visibility and compliance with privacy standards.
12 chapters in this module
- Tracing PII from intake through agent reasoning layers
- Identifying shadow data copies in autonomous memory systems
- Logging data access without compromising agent performance
- Detecting data leakage risks in prompt engineering patterns
- Mapping cross-agent data sharing in collaborative AI teams
- Data retention policies across ephemeral and persistent AI states
- External API calls and third-party data exposure tracking
- Session boundary definition for AI customer interactions
- Data portability requirements in agent output formats
- Audit trail requirements for autonomous data modification
- Capturing context data that may infer PII indirectly
- Automating data flow discovery in dynamic AI environments
Module 3. Privacy Impact Assessment for Agentic AI
Conduct rigorous DPIAs tailored to AI systems that learn, adapt, and make autonomous decisions.
12 chapters in this module
- Scoping a DPIA for an evolving AI agent architecture
- Assessing necessity and proportionality in agent actions
- Evaluating transparency risks in black-box agent logic
- Measuring potential harm from AI misclassification of personal data
- Determining thresholds for mandatory DPIA submission
- Stakeholder consultation methods for AI-specific concerns
- Risk mitigation strategies for autonomous data processing
- Integrating DPIA updates into agent retraining cycles
- Documenting assumptions about agent behavior under drift
- Validating DPIA findings against real-world agent logs
- Version control for DPIA documentation alongside models
- Automation opportunities in continuous DPIA monitoring
Module 4. Consent Architecture in Agent Workflows
Design robust consent mechanisms that work seamlessly within dynamic, conversational AI systems.
12 chapters in this module
- Explicit consent capture in natural language interfaces
- Handling implied consent in ongoing agent conversations
- Granular opt-in controls for multi-purpose agent data use
- Consent inheritance across agent handoff scenarios
- Withdrawal mechanisms that propagate across agent memory
- Temporal scope definition for AI data processing consent
- Consent logging formats aligned with audit requirements
- Re-consent triggers after agent capability upgrades
- Multi-jurisdictional consent harmonization strategies
- Consent verification in agent-to-agent data exchange
- Fallback behaviors when consent status is ambiguous
- Automated consent expiry and renewal workflows
Module 5. Data Subject Rights Fulfillment
Enable reliable execution of data subject requests within AI systems that process and generate personal data.
12 chapters in this module
- Right to access implementation in agent-generated content
- Right to erasure across agent memory and training data
- Handling rectification requests in AI-generated narratives
- Portability of personal data from agent interaction logs
- Detecting and responding to automated decision challenges
- Data subject verification in voice and biometric AI systems
- Time-bound fulfillment tracking for AI workflows
- Logging data subject interactions for compliance proof
- Cascading data deletion across agent knowledge graphs
- Partial erasure handling when data is aggregated
- Exemptions based on legal grounds in AI decision systems
- Automated response templating for common DSAR types
Module 6. Anonymization and Pseudonymization Techniques
Apply effective de-identification methods to personal data used in AI training and inference.
12 chapters in this module
- Risk-based selection of anonymization vs pseudonymization
- Tokenization strategies for agent memory storage
- Differential privacy integration in agent learning
- k-anonymity thresholds for AI-generated data sets
- Re-identification risk assessment in agent outputs
- Synthetic data generation for agent training
- Data masking rules in real-time agent responses
- Preserving utility while minimizing privacy risk
- Context-aware anonymization in conversational AI
- Evaluating anonymization effectiveness post-processing
- Data minimization in agent observation logs
- Automated de-identification in agent output pipelines
Module 7. Third-Party Data Sharing Controls
Govern data flows between autonomous agents and external systems while maintaining privacy compliance.
12 chapters in this module
- Defining processor versus controller roles in agent networks
- Data processing agreements for AI service providers
- Audit rights provisions specific to AI systems
- Security requirements for agent API integrations
- Cross-border data transfer mechanisms in agent workflows
- Sub-processor oversight in multi-agent ecosystems
- Data use limitation enforcement in external APIs
- Monitoring third-party compliance with agent data
- Incident response coordination with AI partners
- Contractual liability allocation for AI data breaches
- Standardized data sharing metadata for audit
- Automated compliance checks in agent-to-external integrations
Module 8. AI-Specific Breach Response Planning
Prepare incident response protocols tailored to the unique risks of agentic AI systems.
12 chapters in this module
- Defining personal data breach in AI context
- Detection mechanisms for unauthorized agent behavior
- Agent memory forensic collection procedures
- Notification timelines aligned with AI system tempo
- Root cause analysis for autonomous decision failures
- Escalation paths for AI-generated privacy violations
- Breach impact assessment in adaptive agent environments
- Regulatory reporting thresholds for AI incidents
- Containment strategies for self-propagating agents
- Recovery validation for agent data access rights
- Lessons learned integration into agent training
- Automated breach logging and reporting templates
Module 9. Continuous Compliance Monitoring
Implement ongoing oversight of privacy controls in production AI systems.
12 chapters in this module
- Automated control testing in agent workflows
- Real-time policy violation detection in agent actions
- Agent behavior drift monitoring and alerting
- Privacy KPIs for autonomous system performance
- Audit log analysis for compliance gaps
- Version comparison of privacy control implementations
- Compliance dashboard design for AI systems
- Internal audit preparation for AI deployments
- Regulator inquiry simulation exercises
- Control remediation tracking in agile AI teams
- Automated evidence collection for auditors
- Compliance health scoring across agent fleet
Module 10. Documentation and Audit Readiness
Generate complete, defensible records for internal and external privacy audits of AI systems.
12 chapters in this module
- Building the register of AI data processing activities
- Documenting lawful basis for each agent data use
- Control mapping to ISO 27701 Annex A clauses
- Evidence collection for autonomous decision-making
- Version-controlled privacy documentation sets
- Audit trail preparation for agent behavior review
- Response drafting for common auditor questions
- Pre-audit checklist validation for AI systems
- Stakeholder interview preparation for privacy reviews
- Gap analysis against ISO 27701 certification requirements
- Remediation tracking for audit findings
- Certification roadmap development for AI products
Module 11. Privacy Governance for AI Teams
Establish oversight structures that align AI innovation with organizational privacy commitments.
12 chapters in this module
- Role definition in AI privacy governance board
- Privacy review gates in agent development lifecycle
- Training requirements for AI developers on privacy
- Change control for agent logic updates
- Vendor oversight for third-party AI components
- Ethics review integration with privacy assessments
- KPIs for privacy compliance in AI delivery
- Incident learning integration into team practice
- Knowledge sharing across AI product teams
- Privacy champion networks in engineering groups
- Budget allocation for AI privacy tooling
- Maturity model advancement for AI governance
Module 12. Certification Preparation and Roadmap
Navigate the path to formal ISO 27701 certification for AI-driven products and services.
12 chapters in this module
- Pre-certification gap assessment methodology
- Engagement with accredited certification bodies
- Stage 1 audit preparation for AI systems
- Evidence portfolio assembly for auditors
- Internal audit simulation for certification readiness
- Corrective action planning for non-conformities
- Stage 2 audit coordination and support
- Surveillance audit scheduling and follow-up
- Scope definition for initial certification
- Post-certification maintenance planning
- Marketing certified AI systems to customers
- Continuous improvement planning after certification
How this maps to your situation
- Agentic AI development lifecycle
- GenAI product compliance requirements
- Privacy governance in tech-forward enterprises
- Audit preparation for autonomous systems
Before vs. after
Before
Spending cycles revising privacy documentation after auditor feedback and struggling to prove compliance in autonomous systems
After
Submitting complete, accurate privacy artifacts on first attempt with clear evidence trails for all AI-driven data processing
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-minute weekly commitment over three months, designed for working practitioners
If nothing changes
Without structured privacy implementation, AI deployments face delayed approvals, regulatory exposure, and rework costs that slow time-to-market and erode stakeholder trust.
How this compares to the alternatives
Unlike generic privacy training, this course delivers AI-specific control patterns, implementation templates, and audit-proof documentation workflows tailored to agentic systems.
Frequently asked
Is this course specific to ISO 27701 or does it cover other frameworks?
The course focuses on ISO 27701 implementation but includes cross-references to GDPR, CCPA, and NIST Privacy Framework where applicable.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help with actual certification?
Yes, Module 12 provides a detailed roadmap and evidence requirements for pursuing formal ISO 27701 certification.
$199 one-time. 90-minute weekly commitment over three months, designed for working practitioners.
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