A tailored course, built for your situation
Mastering ISO 27701; A Step-by-Step Guide to Privacy Implementation
Build privacy-by-design systems with confidence and become the internal reference for compliant data architecture
$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 isn't slowing innovation, it's becoming a competitive edge when led by engineers who can bridge compliance and architecture.
The situation this course is for
Data engineers are increasingly caught between rapid AI-driven feature development and rising privacy expectations. Without a clear implementation path for standards like ISO 27701, even well-designed systems face rework, delayed launches, or shadow governance. The gap isn't intent, it's having structured, actionable guidance that aligns with real infrastructure demands.
Who this is for
Senior Data Engineer in a global tech firm, working at the intersection of data architecture, privacy compliance, and AI systems. Focused on scalable design and reliable implementation. Seeks recognition as a strategic contributor, not just a builder. Values precision, frameworks, and quiet authority.
Who this is not for
Entry-level engineers still mastering core pipelines; professionals outside data infrastructure; those seeking high-level compliance overviews without technical depth.
What you walk away with
- Lead privacy implementation with confidence using ISO 27701 as a design scaffold
- Produce documentation and control mappings that pass internal review the first time
- Become the go-to internal resource for privacy-forward data architecture
- Accelerate AI feature delivery with pre-validated privacy controls
- Design systems that anticipate regulatory scrutiny, not react to it
The 12 modules (with all 144 chapters)
Module 1. Understanding ISO 27701 and Its Role in Data Engineering
Lay the foundation by exploring how ISO 27701 extends ISO 27001 to privacy-specific controls, with emphasis on data lifecycle stages relevant to engineering workflows.
12 chapters in this module
- Defining personally identifiable information in distributed systems
- How ISO 27701 differs from general data protection regulations
- Mapping privacy principles to data schema design decisions
- The role of data minimisation in API architecture planning
- Linking consent mechanisms to storage tier selection
- Engineering implications of purpose limitation in pipelines
- Accountability controls relevant to data ownership models
- Privacy by design as a technical requirement, not just policy
- Integrating data protection impact assessments into sprints
- Tracking data lineage for audit-ready transparency
- Balancing model performance with anonymisation techniques
- Documenting system design choices for compliance reviewers
Module 2. Scoping Personal Data in Complex Architectures
Learn how to accurately identify and document personal data across microservices, data lakes, and AI training environments.
12 chapters in this module
- Identifying PII in unstructured image and text outputs
- Tracing data flow across serverless computing layers
- Defining data controllers versus processors in engineering terms
- Classifying personal data at rest and in transit
- Using metadata tagging to auto-identify sensitive fields
- Scoping data collected through generative AI interfaces
- Documenting jurisdictional boundaries in storage paths
- Mapping data sharing across internal platforms
- Identifying third-party processors in API chains
- Logging data access patterns for compliance monitoring
- Setting thresholds for data retention in streaming pipelines
- Automating data inventory updates with CI/CD hooks
Module 3. Designing for Consent and Purpose Limitation
Implement technical controls that enforce user consent and ensure data is only used for declared purposes.
12 chapters in this module
- Storing consent status in low-latency user tables
- Enabling opt-out propagation across data pipelines
- Validating purpose alignment in feature flag systems
- Masking data fields based on user election
- Designing fallback modes for withdrawn consent
- Auditing access to data beyond stated purpose
- Tagging data exports with purpose-specific labels
- Integrating consent status into model training gates
- Using schema validation to block off-purpose writes
- Automating data deletion triggers on opt-out
- Logging consent changes for regulatory timelines
- Building consent-aware caching layers
Module 4. Implementing Data Minimisation in Practice
Apply strategies to collect and retain only necessary data, reducing privacy risk and storage costs.
12 chapters in this module
- Right-sizing event tracking pipelines
- Applying differential privacy in aggregated metrics
- Using sampling strategies to limit training data volume
- Masking non-essential fields at ingestion
- Designing schema to exclude unnecessary personal data
- Validating data necessity before pipeline writes
- Reducing retention windows by data type
- Automating data truncation by policy
- Enforcing minimisation in A/B testing frameworks
- Measuring data footprint reduction over time
- Benchmarking minimisation against peer systems
- Documenting minimisation decisions for auditors
Module 5. Building Data Subject Rights Workflows
Create scalable systems to fulfill access, correction, and deletion requests efficiently and securely.
12 chapters in this module
- Indexing personal data for rapid retrieval
- Validating identity before data disclosure
- Orchestrating cross-system deletion jobs
- Handling data portability in nested JSON formats
- Encrypting data exports in transit and at rest
- Logging access request fulfillment for compliance
- Designing idempotent correction pipelines
- Auditing data change history by subject
- Scaling workflows for high-volume request periods
- Integrating with customer support ticketing systems
- Using durable queues to manage backpressure
- Testing end-to-end rights fulfillment in staging
Module 6. Privacy in AI and Machine Learning Systems
Adapt privacy controls to AI/ML workflows, including training data, inference, and model output.
12 chapters in this module
- Anonymising training data at scale
- Tracking provenance of synthetic data
- Validating model outputs for PII leakage
- Implementing output filtering in generative AI
- Auditing model access to personal data
- Using watermarking to trace AI-generated images
- Logging prompt data with privacy safeguards
- Enforcing access controls on model endpoints
- Assessing re-identification risk in embeddings
- Building privacy-preserving federated learning
- Monitoring model drift for bias and leakage
- Documenting AI training data sources
Module 7. Data Protection by Design and by Default
Embed privacy into system architecture decisions from the start, not as an add-on.
12 chapters in this module
- Including privacy criteria in RFC templates
- Requiring privacy assessments in design docs
- Setting default configurations to minimise data exposure
- Using secure defaults in schema migrations
- Enforcing encryption in new service templates
- Requiring authentication for internal data APIs
- Automating security group reviews in CI
- Integrating static analysis for PII leaks
- Validating data flow diagrams pre-deployment
- Setting up automated policy compliance gates
- Documenting design trade-offs for reviewers
- Creating checklists for privacy-ready sprints
Module 8. Third-Party and Vendor Risk Management
Ensure external partners comply with your privacy standards through technical and contractual controls.
12 chapters in this module
- Mapping data sharing with third-party SDKs
- Reviewing vendor DPAs from an engineering lens
- Auditing API access patterns for overreach
- Enforcing data minimisation in partner integrations
- Validating encryption in transit with partners
- Monitoring for unauthorised downstream sharing
- Building sandbox environments for vendor testing
- Requiring audit rights in integration contracts
- Documenting data flow boundaries with vendors
- Automating vendor compliance checks in pipelines
- Creating escalation paths for data misuse
- Terminating access on contract expiry
Module 9. Incident Response and Breach Notification
Prepare systems and processes to detect, respond to, and report privacy incidents effectively.
12 chapters in this module
- Configuring alerting for unauthorised access
- Logging data exfiltration attempts
- Setting up incident triage runbooks
- Classifying breach severity by data type
- Automating data preservation on detection
- Coordinating with security and legal teams
- Assessing notification timelines by jurisdiction
- Generating regulator-ready incident reports
- Managing public communications discreetly
- Conducting post-mortems with compliance input
- Updating controls based on findings
- Testing response workflows in simulations
Module 10. Ongoing Monitoring and Compliance Verification
Establish continuous controls to maintain compliance and demonstrate adherence over time.
12 chapters in this module
- Automating compliance checks in CI/CD
- Scanning repositories for hardcoded keys
- Monitoring for unauthorised data exports
- Validating retention policy enforcement
- Auditing access logs for anomalies
- Generating control-specific compliance reports
- Using dashboards to track compliance KPIs
- Scheduling periodic data inventory reviews
- Integrating with internal audit tools
- Updating documentation after system changes
- Benchmarking control effectiveness quarterly
- Planning for certification readiness
Module 11. Documentation and Audit Preparation
Produce clear, technical documentation that satisfies auditors and accelerates certification.
12 chapters in this module
- Writing system descriptions for compliance
- Mapping technical controls to ISO 27701 clauses
- Generating evidence for data processing activities
- Creating data flow diagrams with context
- Documenting control implementation details
- Organising evidence in audit-ready formats
- Using version control for policy documents
- Automating evidence collection from logs
- Preparing responses to common auditor questions
- Linking architecture diagrams to control mappings
- Redacting sensitive details without losing clarity
- Maintaining documentation across team changes
Module 12. Scaling Privacy Across Engineering Teams
Extend privacy practices across teams through tooling, templates, and shared ownership.
12 chapters in this module
- Embedding privacy linting in IDEs
- Creating reusable service templates
- Offering internal consulting hours
- Developing onboarding materials for new hires
- Hosting brown bags on recent implementations
- Building self-service documentation hubs
- Measuring adoption across product areas
- Recognising team privacy champions
- Integrating privacy KPIs into team goals
- Gathering feedback for framework improvements
- Scaling tooling via internal developer platforms
- Tracking maturity across engineering domains
How this maps to your situation
- Implementing privacy in AI-generated content systems
- Scaling compliance across global data infrastructure
- Reducing rework from late-stage privacy reviews
- Establishing engineering-led privacy leadership
Before vs. after
Before
Privacy reviews happen late, leading to rework and delayed launches. Compliance feels like a burden imposed by non-technical teams.
After
Privacy-by-design is embedded from day one. You lead with structured implementation, accelerate delivery, and earn recognition as the go-to expert.
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 for 4 weeks, or 12 hours total. Designed for engineers with existing workloads.
If nothing changes
Without a clear path to implementing privacy frameworks like ISO 27701, engineering teams risk delays, compliance gaps, and reactive design, missing the chance to lead innovation with integrity.
How this compares to the alternatives
Unlike generic online courses or certification prep, this course is tailored to real-world engineering challenges, focused on implementation, not memorisation. You get actionable playbooks, not abstract theory.
Frequently asked
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is this relevant if I'm not in privacy or compliance?
Yes. This is designed for engineers who build systems where privacy matters, especially in AI, data platforms, and infrastructure. It’s about building correctly, not just complying.
$199 one-time. 90 minutes per week for 4 weeks, or 12 hours total. Designed for engineers with existing workloads..
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