A tailored course, built for your situation
Mastering PCI DSS for Senior ML Engineers in High-Throughput Data Environments
A structured path to owning compliance-critical AI systems without slowing innovation
$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.
Model validation packages that stall during compliance review cycles
The situation this course is for
ML engineers in regulated environments often face rework when compliance teams question model behavior, especially around data handling and access controls. The gap isn't technical capability, it's structured documentation that maps model logic to control requirements. Without it, even robust models face delays, extra cycles, and cross-team friction during audits or reviews.
Who this is for
Senior ML Engineer at a high-growth tech company processing sensitive user data; works across model development, deployment, and compliance interface points; owns end-to-end model integrity but lacks formal frameworks to justify design choices under regulatory scrutiny.
Who this is not for
Junior data scientists, developers outside compliance-adjacent domains, or practitioners working in non-data-intensive environments.
What you walk away with
- Produce model validation packages that require zero rework during PCI DSS reviews
- Own the narrative around model data handling without deferring to compliance teams
- Design compliance-ready pipelines from day one, not as an afterthought
- Earn mandate over ML system boundaries in payment-adjacent infrastructure
- Reduce cross-team back-and-forth by delivering self-attesting documentation
The 12 modules (with all 144 chapters)
Module 1. Understanding PCI DSS Scope in ML Systems
Clarify which parts of ML pipelines fall under PCI DSS scrutiny, focusing on data flow boundaries, tokenization points, and inference-time access.
12 chapters in this module
- Mapping PCI DSS scope to model training data sources
- Identifying cardholder data in feature engineering pipelines
- When model inputs trigger compliance obligations
- Data lineage requirements for PCI-bound models
- Storage of temporary features in compliant workflows
- Access controls for model debugging in PCI environments
- Logging model predictions without exposing sensitive data
- The role of anonymization in PCI-scoped ML systems
- Model registry requirements for audit readiness
- Boundary decisions between PCI and non-PCI systems
- Handling third-party data in compliant pipelines
- Designing for scope containment from day one
Module 2. Model Design for Compliance by Construction
Embed compliance requirements into model architecture decisions to reduce rework and accelerate review cycles.
12 chapters in this module
- Choosing algorithms based on interpretability needs
- Feature selection with data minimization in mind
- Designing models to avoid cardholder data touchpoints
- Architectural patterns for PCI-safe inference
- Model size and complexity trade-offs under compliance
- Input validation layers to block non-compliant data
- Using proxy variables to avoid direct data use
- Model versioning aligned with control requirements
- The impact of model drift on compliance status
- Designing for auditability from the first prototype
- Embedding compliance checks in training scripts
- Documentation as code in ML pipelines
Module 3. Data Pipeline Controls for ML Systems
Implement control points in data workflows to ensure ongoing compliance without sacrificing model performance.
12 chapters in this module
- Tokenization before model ingestion
- Secure data transfer between staging and training
- Masking sensitive attributes in development copies
- Access logs for data pipeline runs
- Automated detection of PII in training data
- Data retention policies in model datasets
- Audit trails for pipeline modifications
- Version-controlled data schemas for compliance
- Testing data sanitization steps
- Handling synthetic data in PCI environments
- Data provenance tracking from source to model
- Pipeline rollback procedures under review
Module 4. Model Documentation as Compliance Artefact
Transform model documentation into a credible, reusable evidence package that satisfies compliance reviewers.
12 chapters in this module
- Building a model factsheet for auditors
- Documenting data sources and transformations
- Explaining model logic in non-technical terms
- Versioning model documentation with code
- Including bias and fairness considerations
- Linking model outputs to control requirements
- Creating visual data flow diagrams
- Storing documentation in auditable repositories
- Using templates for consistency across models
- Updating documentation with retraining cycles
- Cross-referencing controls in PCI DSS scope
- Preparing documentation for regulator review
Module 5. Access and Authentication in ML Infrastructure
Secure model development and deployment environments to meet authentication and access control standards.
12 chapters in this module
- Role-based access for ML teams
- Multi-factor authentication for production access
- Separation of duties in model workflows
- Audit logging for model deployment actions
- Service account management for pipelines
- Temporary access provisioning for debugging
- Monitoring for anomalous access patterns
- Credential rotation in CI/CD for ML
- Access reviews for model repositories
- Handling contractor access securely
- Session timeout policies in development tools
- Just-in-time access for compliance tasks
Module 6. Logging and Monitoring for Model Behavior
Design observability systems that detect compliance deviations in real time without overwhelming engineers.
12 chapters in this module
- Defining normal vs. anomalous model behavior
- Logging model inputs and outputs at scale
- Alerting on data leakage patterns
- Monitoring for unauthorized access attempts
- Tracking model performance degradation
- Integrating logs with security information systems
- Setting thresholds for model drift detection
- Reviewing logs during compliance cycles
- Automated reporting for compliance teams
- Handling log retention under PCI rules
- Masking sensitive data in log streams
- Correlating model events with access logs
Module 7. Model Validation and Testing Frameworks
Build repeatable validation processes that prove model integrity and data handling compliance.
12 chapters in this module
- Unit testing for data sanitization steps
- Integration testing across pipeline stages
- Testing model behavior on edge cases
- Validating output consistency across versions
- Penetration testing for ML APIs
- Fuzz testing for model resilience
- Performance testing under load conditions
- Bias testing in production data
- Replaying historical data for validation
- Automated regression testing for models
- Testing access control enforcement
- Documenting test results for auditors
Module 8. Change Management for ML Systems
Implement structured change workflows that maintain compliance across model updates and redeployments.
12 chapters in this module
- Version control for model code and config
- Change approval workflows for production
- Rollback procedures for failed deployments
- Impact assessment for model updates
- Change documentation for compliance
- Scheduling changes outside peak hours
- Peer review requirements for model changes
- Automated deployment checks
- Handling emergency fixes securely
- Communicating changes to compliance teams
- Audit trails for change approvals
- Change freeze periods during audits
Module 9. Vendor and Third-Party Risk in ML
Manage external dependencies in ML systems to ensure they don’t introduce compliance gaps.
12 chapters in this module
- Assessing vendor compliance posture
- Third-party model usage and risks
- Data sharing agreements with vendors
- Auditing vendor access to systems
- Managing open-source dependencies
- Tracking license compliance in ML tools
- Vendor incident response planning
- Right-to-audit clauses in contracts
- Monitoring vendor-supplied models
- Managing vendor access lifecycle
- Performance monitoring for third-party services
- Exit strategies for non-compliant vendors
Module 10. Incident Response for ML Systems
Prepare for and respond to incidents involving ML models in compliance-sensitive environments.
12 chapters in this module
- Defining ML-related incident types
- Detecting model misuse or abuse
- Responding to data leakage via model outputs
- Containment strategies for compromised models
- Forensic analysis of model behavior
- Notifying stakeholders during incidents
- Coordinating with compliance teams
- Documenting incident root causes
- Updating models post-incident
- Testing incident response plans
- Reporting to regulators when required
- Learning from incidents to improve controls
Module 11. Compliance Review Preparation
Streamline preparation for internal and external compliance reviews with ML systems.
12 chapters in this module
- Assembling model audit packages
- Preparing responses to common questions
- Scheduling walkthroughs with auditors
- Providing access to logs and documentation
- Rehearsing compliance interviews
- Updating artefacts before review cycles
- Tracking open findings and remediation
- Using past reviews to improve processes
- Aligning with internal audit timelines
- Preparing evidence for control testing
- Coordinating across engineering and compliance
- Closing findings efficiently
Module 12. Sustaining Compliance Over Time
Build systems that maintain compliance as ML models evolve and scale.
12 chapters in this module
- Automating compliance checks in pipelines
- Continuous monitoring for control drift
- Updating documentation with model changes
- Revalidating models after updates
- Scaling compliance practices across teams
- Training new engineers on requirements
- Maintaining control ownership over time
- Updating practices with framework changes
- Benchmarking against industry standards
- Reducing manual effort through tooling
- Sharing best practices across organizations
- Evolving practices with model complexity
How this maps to your situation
- Model validation rework during compliance cycles
- Lack of structured documentation for auditors
- Cross-team friction on data access decisions
- Delays in deployment due to compliance reviews
Before vs. after
Before
Spending cycles rewriting model documentation, reacting to compliance feedback, and defending design choices without a structured framework.
After
Proactively delivering self-attesting model packages that pass review cycles with minimal rework, earning mandate over ML system boundaries.
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 module (approximately 18 hours total), designed to be completed at your pace over 4-6 weeks.
If nothing changes
Continuing to treat compliance as a downstream gate creates bottlenecks, delays innovation, and limits your influence over system design decisions that should fall within your technical remit.
How this compares to the alternatives
Unlike generic compliance courses, this is tailored to ML engineers working in data-intensive environments, with concrete templates, real-world examples, and a focus on actionable integration into existing workflows.
Frequently asked
Is this course only for engineers in financial services?
No. It's designed for ML engineers in any company handling payment or sensitive user data, including tech platforms with transaction systems.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help me pass a PCI DSS audit?
Yes. The course teaches how to build and document ML systems so they support audit success, not just survive it.
$199 one-time. 90 minutes per module (approximately 18 hours total), designed to be completed at your pace over 4-6 weeks..
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