A tailored course, built for your situation
Scaling Cyber Resilience Alongside AWS and AI Adoption
Build cyber resilience that scales with cloud and AI, designed for security leaders who need precision, consistency, and audit-ready outputs from day one.
$199 one-time
30-day money-back guarantee
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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.
Revising privacy compliance packages last-minute because cloud control mappings don’t align with ISO 27701 requirements.
The situation this course is for
Security teams invest heavily in AWS and AI governance, only to face delays when privacy documentation fails alignment checks during internal validation or pre-audit reviews. The issue isn’t intent, it’s execution fidelity. Control mappings drift, evidence trails are incomplete, and exception justifications lack defensibility. This creates last-minute scrambles, erodes stakeholder trust, and slows down innovation cycles.
Who this is for
Chief Information Security Officers at technology-driven organizations adopting AWS at scale and deploying AI workloads that process personal data. They own cyber resilience strategy and must ensure compliance frameworks like ISO 27701 integrate seamlessly into engineering velocity.
Who this is not for
Organizations not yet operating in AWS at scale, teams without active AI/ML initiatives, or practitioners focused solely on foundational ISO 27001 implementation without privacy extension needs.
What you walk away with
- Produce ISO 27701-compliant documentation that withstands internal validation without rework
- Align cloud-native security controls with privacy-specific requirements across data lifecycle stages
- Reduce time spent on evidence collection and control reconciliation by over 60%
- Enable repeatable, consistent outputs for audits, vendor reviews, and executive reporting
- Strengthen cross-functional credibility by delivering precise, defensible compliance artefacts ahead of cycle deadlines
The 12 modules (with all 144 chapters)
Module 1. Foundations of ISO 27701 in Cloud-Native Environments
Establish core understanding of ISO 27701 requirements as they apply to AWS-hosted systems and AI data pipelines.
12 chapters in this module
- Understanding the relationship between ISO 27001 and ISO 27701 in practice
- Mapping PII processing activities across serverless and containerized workloads
- Key differences between legacy and cloud-adapted privacy control implementations
- Defining scope for privacy compliance in multi-account AWS environments
- Integrating data protection impact assessments into sprint planning
- Roles and responsibilities for privacy governance in DevSecOps teams
- Common misconceptions about anonymization in AI training datasets
- How GDPR and CCPA expectations shape ISO 27701 control selection
- Using AWS Config rules to enforce baseline privacy configuration
- Documenting lawful basis for processing in automated data flows
- Building evidence trails for accountability without manual intervention
- Creating a living register of PII processors across third-party AI tools
Module 2. Integrating Privacy Controls into AWS Architecture Design
Embed privacy-by-design principles directly into infrastructure-as-code and network topology decisions.
12 chapters in this module
- Applying privacy default settings in AWS IAM role configurations
- Designing VPC architectures that limit unnecessary PII exposure
- Enforcing encryption of PII at rest using KMS key policies and S3 bucket defaults
- Implementing least privilege access for AI model training jobs on sensitive data
- Automating tagging of PII-bearing resources across AWS services
- Configuring CloudTrail and GuardDuty to detect anomalous access to personal data
- Setting up secure transit for PII between on-prem and cloud environments
- Validating control effectiveness through automated compliance checks
- Using Service Control Policies to restrict high-risk actions in member accounts
- Architecting private subnets for ML inference endpoints handling personal data
- Integrating AWS Macie for automated PII discovery and classification
- Designing immutable logging for privacy-relevant events in distributed systems
Module 3. Control Mapping for AI Data Lifecycle Stages
Map ISO 27701 controls precisely to data ingestion, preprocessing, training, inference, and retention phases in AI systems.
12 chapters in this module
- Identifying PII touchpoints in raw data ingestion pipelines from external sources
- Applying purpose limitation controls during dataset curation for AI models
- Ensuring data minimization in feature engineering workflows
- Verifying consent status propagation through AI data transformation layers
- Monitoring access to training datasets containing personal information
- Securing model checkpoints and weights derived from PII-laden data
- Controlling inference API access to prevent unauthorized personal data extraction
- Managing model explainability outputs that may reveal individual data patterns
- Implementing retention schedules for AI-generated synthetic data sets
- Auditing deletion completeness after PII data purge commands
- Handling data subject rights fulfillment within AI system constraints
- Documenting trade-offs between model accuracy and privacy preservation techniques
Module 4. Automated Evidence Collection Frameworks
Build systems that generate real-time, verifiable evidence for ISO 27701 compliance without manual effort.
12 chapters in this module
- Designing evidence triggers based on AWS resource state changes
- Using EventBridge rules to capture control-relevant system events
- Generating automatic screenshots of console configurations for attestation
- Exporting IAM policy versions with timestamps for change tracking
- Capturing network flow logs associated with PII transfers
- Pulling compliance reports from AWS Security Hub on scheduled intervals
- Storing evidence in tamper-evident S3 buckets with versioning enabled
- Using Lambda functions to package evidence into auditor-friendly formats
- Integrating evidence generation into CI/CD pipelines for infrastructure code
- Tagging evidence artifacts with control ID, date, and responsible team
- Validating evidence completeness against ISO 27701 annex A requirements
- Preparing automated evidence bundles for internal review cycles
Module 5. Exception Management and Justification Workflows
Create defensible, time-bound exceptions that maintain compliance posture while enabling innovation.
12 chapters in this module
- Defining acceptable risk thresholds for temporary control deviations
- Structuring exception requests with clear business justification and duration
- Requiring compensating controls for any approved deviation
- Automatically flagging expired exceptions using CloudWatch alarms
- Linking exception records to specific AWS resources and deployment IDs
- Maintaining an auditable log of approvals and reviewer comments
- Escalating unresolved exceptions to senior leadership before go-live
- Reporting outstanding exceptions in monthly security dashboards
- Conducting retrospective reviews of frequently requested exceptions
- Using exceptions to identify gaps in standard control baselines
- Balancing agility with accountability in rapid AI experimentation phases
- Transitioning from temporary exceptions to permanent control enhancements
Module 6. Cross-Functional Alignment on Privacy Scope
Ensure consistent interpretation of ISO 27701 scope across engineering, legal, product, and security teams.
12 chapters in this module
- Facilitating joint scoping workshops with cloud architects and data scientists
- Translating legal privacy obligations into technical control requirements
- Creating shared visual maps of PII flows across microservices and AI components
- Establishing escalation paths for scope boundary disputes
- Publishing a single source of truth for current compliance coverage
- Conducting regular walkthroughs of updated system diagrams
- Aligning sprint goals with incremental privacy control rollout plans
- Clarifying ownership for hybrid controls split across teams
- Resolving conflicts between data utility and privacy enforcement needs
- Training product managers on privacy implications of new feature designs
- Incorporating feedback from red team exercises into scope refinement
- Updating scope documentation automatically when new services go live
Module 7. Validation Playbooks for Internal Review Cycles
Run efficient, predictable validation processes that confirm readiness before formal audits.
12 chapters in this module
- Scheduling quarterly validation cycles aligned with AWS deployment calendars
- Assigning peer reviewers across independent teams to reduce bias
- Using standardized checklists tied directly to ISO 27701 control statements
- Running automated scans to verify technical control implementation
- Conducting sample testing of evidence packages for completeness
- Hosting dry-run review meetings with mock auditor questions
- Tracking findings in a centralized backlog with SLA-based resolution
- Measuring validation pass rates over time to assess maturity
- Benchmarking results against industry peers using public benchmarks
- Identifying systemic weaknesses from recurring validation issues
- Adjusting control design based on validation feedback loops
- Celebrating clean validation outcomes to reinforce quality culture
Module 8. Privacy Control Automation Using Infrastructure-as-Code
Codify ISO 27701 controls directly into Terraform, CloudFormation, and CDK templates.
12 chapters in this module
- Writing Terraform modules that enforce encrypted storage by default
- Parameterizing privacy controls for reuse across multiple environments
- Including mandatory tags for PII handling in root module definitions
- Using Sentinel or Open Policy Agent to validate IaC before merge
- Automatically injecting logging and monitoring configurations
- Creating reusable components for secure API gateways handling personal data
- Versioning control implementations alongside application code
- Testing control behavior in isolated staging environments
- Documenting architectural decisions affecting privacy compliance
- Integrating code scanning tools to catch hardcoded secrets in AI projects
- Enabling self-service provisioning within defined privacy guardrails
- Deprecating old control versions with automated migration guides
Module 9. Incident Response Integration with Privacy Obligations
Align security incident workflows with mandatory breach notification timelines and data subject rights.
12 chapters in this module
- Detecting potential PII breaches using AWS-native monitoring tools
- Classifying incidents based on type and volume of exposed personal data
- Activating cross-functional response teams within one hour of detection
- Preserving evidence in accordance with legal hold requirements
- Assessing likelihood of harm to affected individuals within 72 hours
- Coordinating communications with legal, PR, and customer support leads
- Filing required notifications to regulators within mandated windows
- Providing data subjects with clear information about impacted records
- Logging all response actions for post-incident review and audit
- Updating runbooks based on lessons learned from real events
- Simulating breach scenarios involving AI model data leaks
- Testing integration between SOAR platforms and privacy case management
Module 10. Vendor Risk Management for Third-Party AI Services
Extend ISO 27701 expectations to external providers processing personal data through APIs or managed models.
12 chapters in this module
- Evaluating third-party AI vendors for privacy compliance maturity
- Negotiating DPAs that reflect actual data flows and processing purposes
- Verifying subprocessor transparency in vendor supply chains
- Assessing model training data sources for PII contamination risks
- Monitoring API usage patterns for potential misuse of personal data
- Requiring evidence of certification or audit reports from vendors
- Conducting on-site assessments for critical AI infrastructure partners
- Managing contract expiration and data return/deletion obligations
- Tracking vendor compliance status in a dynamic risk register
- Responding to vendor breaches that involve your organization’s data
- Terminating relationships with non-compliant AI service providers
- Building fallback plans for decommissioned third-party AI tools
Module 11. Executive Reporting and Stakeholder Communication
Deliver concise, accurate updates on privacy compliance posture to senior leaders and board-level committees.
12 chapters in this module
- Summarizing ISO 27701 status in non-technical language for executives
- Highlighting trends in control effectiveness and exception rates
- Visualizing progress toward full coverage of cloud and AI systems
- Reporting on recent validation outcomes and audit findings
- Communicating strategic initiatives to strengthen privacy resilience
- Presenting risk appetite alignment with current control investments
- Sharing metrics on time-to-remediate identified gaps
- Demonstrating ROI of automation efforts in compliance operations
- Discussing emerging threats to personal data in AI applications
- Aligning privacy roadmap with broader cybersecurity strategy
- Preparing Q&A briefings for leadership inquiries on data practices
- Positioning the security team as an enabler of trusted innovation
Module 12. Continuous Improvement and Maturity Advancement
Evolve your ISO 27701 implementation from compliance necessity to strategic advantage.
12 chapters in this module
- Measuring maturity using NIST Privacy Framework tiers
- Benchmarking against peer organizations in your sector
- Identifying opportunities to exceed minimum regulatory requirements
- Leveraging strong privacy posture as a competitive differentiator
- Incorporating user feedback into privacy experience improvements
- Exploring privacy-enhancing technologies like federated learning
- Contributing to open standards and industry best practices
- Training next-generation security leaders in modern privacy engineering
- Publishing transparency reports to build public trust
- Aligning with evolving guidance from standards bodies like ISO
- Scaling proven patterns to new regions and jurisdictions
- Making privacy resilience a core element of brand integrity
How this maps to your situation
- Pre-audit preparation
- Cloud migration oversight
- AI governance rollout
- Compliance automation initiative
Before vs. after
Before
Spending weeks assembling privacy documentation, chasing down evidence, and revising control mappings before audits.
After
Producing precise, defensible ISO 27701 outputs that pass validation the first time, built into daily workflows.
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 per week over six weeks, designed for completion during off-peak hours.
If nothing changes
Without structured integration of ISO 27701 into cloud and AI operations, organizations face delayed launches, repeated audit findings, increased rework, and erosion of trust among regulators and customers.
How this compares to the alternatives
Unlike generic compliance courses, this program delivers implementation-grade knowledge tailored to AWS and AI contexts, with concrete tool integrations, real-world templates, and decision-specific guidance used by leading security teams.
Frequently asked
Is this course relevant if we haven’t started ISO 27701 implementation yet?
Yes. The course supports both initiating and maturing ISO 27701 programs, with step-by-step guidance from scoping through validation.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Can my team access the course together?
Each enrollment is individual, but templates and playbooks are licensed for team use within your organization.
$199 one-time. Approximately 90 minutes per week over six weeks, designed for completion during off-peak hours..
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