A tailored course, built for your situation
Mastering PCI DSS for Data and ML Engineers
Build compliant machine learning systems with full decision authority on control implementation.
$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.
Who this is for
Senior data and ML engineers who own or influence compliance-critical design decisions in regulated environments.
Who this is not for
Junior developers without system design responsibilities, auditors, or non-technical compliance staff.
What you walk away with
- Own final sign-off on control architecture for ML pipelines handling cardholder data
- Deploy pre-validated control templates that meet PCI DSS 3.2.1 requirements out of the gate
- Document control ownership boundaries that prevent scope creep during audits
- Make live updates to logging, encryption, and segmentation without requiring senior review
- Lead cross-functional alignment on control implementation using standardized artifacts
The 12 modules (with all 144 chapters)
Module 1. Understanding PCI DSS Scope in ML Systems
Map PCI DSS scope boundaries to data pipelines, feature stores, and model inference endpoints handling cardholder data.
12 chapters in this module
- Identifying cardholder data touchpoints
- Scoping training versus inference
- Control applicability for synthetic data
- Boundary rules for third-party APIs
- Data flow diagramming standards
- Logging requirements for access paths
- Tokenization impact on model inputs
- Encryption in transit versus at rest
- Shared responsibility in cloud environments
- Model retraining triggers
- Versioning control gates
- Documenting scope exclusions
Module 2. Control Mapping for ML Architecture
Assign specific PCI DSS controls to ML system components with clear ownership and validation paths.
12 chapters in this module
- Matching controls to ingestion layers
- Authentication for batch pipelines
- Access controls for model registries
- Audit logging for prediction APIs
- Network segmentation for GPU clusters
- Change management for model updates
- Penetration testing machine learning endpoints
- Vulnerability scans in containerized models
- File integrity monitoring for model weights
- PCI DSS requirement 6.3 mapping
- Control ownership templates
- Evidence collection workflows
Module 3. Designing Compliant Data Pipelines
Build data preprocessing workflows that maintain compliance without sacrificing model performance.
12 chapters in this module
- Masking sensitive fields pre-training
- Tokenization in feature engineering
- Data minimization techniques
- Anonymization versus pseudonymization
- Validation rules for synthetic data
- Audit trail requirements
- Data lineage for compliance
- Retention policies for training sets
- Cross-region data transfer controls
- Logging access to raw data sets
- Compliance checks in CI/CD
- Automated policy enforcement
Module 4. Securing Model Deployment Environments
Apply PCI DSS controls to model serving infrastructure with minimal latency impact.
12 chapters in this module
- Container hardening standards
- Runtime protection for inference
- API gateway security controls
- Mutual TLS for model endpoints
- Rate limiting for prediction APIs
- Payload inspection for model inputs
- Model version rollback procedures
- Environment segregation
- Compliance checks in canary releases
- Zero-trust access to model hosts
- Patch management for inference servers
- Session timeout configurations
Module 5. Implementing Access Control Frameworks
Design role-based access for ML systems that satisfies PCI DSS while enabling collaboration.
12 chapters in this module
- RBAC for feature stores
- Attribute-based access controls
- Just-in-time access for data scientists
- Privileged account management
- Access reviews for model deployment
- Break-glass procedures
- Multi-factor authentication integration
- SSO integration patterns
- Access logging standards
- Role definition templates
- Emergency access controls
- Access revocation automation
Module 6. Building Audit-Ready Documentation
Create living documentation that satisfies PCI DSS requirements without slowing down iteration.
12 chapters in this module
- Automated control evidence generation
- Living system architecture diagrams
- Control mapping spreadsheets
- Narrative descriptions for auditors
- Version-controlled policy documents
- Compliance dashboards
- Automated gap detection
- Stakeholder communication templates
- Audit response workflows
- Evidence retention policies
- Cross-team alignment records
- Continuous monitoring reports
Module 7. Integrating Security into CI/CD
Embed compliance checks into ML model development lifecycle without creating bottlenecks.
12 chapters in this module
- Static analysis of model code
- Dependency scanning for training scripts
- Secrets detection in notebooks
- Compliance gates in model promotion
- Automated data classification
- Model signing procedures
- Provenance tracking for artifacts
- Policy enforcement with OPA
- Automated remediation workflows
- Vulnerability scanning frequency
- Compliance status badges
- CI/CD pipeline hardening
Module 8. Managing Third-Party Risk
Extend PCI DSS control ownership to vendor-supplied components and cloud services.
12 chapters in this module
- Vendor risk assessment criteria
- Third-party model validation
- API security for external services
- Data processing agreements
- Subprocessor disclosure rules
- Cloud provider compliance
- Shared responsibility models
- Evidence collection from vendors
- Penetration testing vendor APIs
- Incident response coordination
- Vendor termination procedures
- Compliance monitoring for SaaS tools
Module 9. Designing Resilient Monitoring Systems
Implement continuous monitoring that detects compliance deviations in real time.
12 chapters in this module
- Log aggregation for ML systems
- Anomaly detection for data access
- Real-time alerting on policy violations
- SIEM integration patterns
- Correlation rules for model behavior
- Audit trail retention policies
- Log integrity verification
- Centralized logging architecture
- Compliance dashboarding
- Incident triage workflows
- Automated evidence collection
- False positive reduction techniques
Module 10. Leading Cross-Functional Compliance Efforts
Drive alignment between data science, security, and compliance teams using standardized frameworks.
12 chapters in this module
- Bridging technical and compliance language
- Facilitating control design workshops
- Translating regulations into technical specs
- Conflict resolution strategies
- Stakeholder communication plans
- Escalation path definition
- Cross-team documentation standards
- Compliance sprint planning
- Shared ownership models
- Feedback loops with auditors
- Training materials for engineers
- Compliance champion networks
Module 11. Optimizing for Continuous Validation
Shift from periodic audits to always-on compliance validation through automation.
12 chapters in this module
- Automated control testing
- Continuous control monitoring
- Compliance scorecards
- Automated evidence generation
- Policy-as-code frameworks
- Compliance dashboards
- Remediation automation
- Control drift detection
- Validation frequency tuning
- Exception management workflows
- Audit readiness metrics
- Compliance debt tracking
Module 12. Owning Control Implementation End to End
Take full responsibility for PCI DSS control design, implementation, and validation in ML systems.
12 chapters in this module
- Final sign-off authority on control design
- Documentation ownership standards
- Change approval workflows
- Incident response leadership
- Audit coordination responsibilities
- Stakeholder updates
- Control improvement cycles
- Lessons learned documentation
- Compliance innovation initiatives
- Mentoring junior engineers
- Cross-team collaboration
- Continuous learning practices
How this maps to your situation
- Designing ML systems with cardholder data
- Leading compliance for model deployment
- Responding to audit findings
- Scaling ML systems under compliance constraints
Before vs. after
Before
Waiting for approvals on control design decisions and reacting to compliance findings after the fact.
After
Taking direct sign-off on control architecture for ML systems and driving compliance proactively.
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 3 hours per module, designed for engineers to complete alongside active projects.
If nothing changes
Continuing to rely on cross-team approvals for routine control decisions slows down innovation and positions you as an implementer rather than a decision-maker in compliance-critical work.
How this compares to the alternatives
Unlike general compliance courses, this program is tailored to the specific technical decisions ML engineers make when implementing PCI DSS controls in production systems.
Frequently asked
Is this course relevant if my ML systems don't directly handle cardholder data?
Yes. The frameworks apply to any system in the PCI DSS scope boundary, including supporting infrastructure and indirect data flows.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help me pass a PCI DSS audit?
The course equips you with the knowledge and documentation practices to confidently lead control implementation and respond to auditor inquiries.
$199 one-time. Approximately 3 hours per module, designed for engineers to complete alongside active projects..
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