A tailored course, built for your situation
Mastering PCI DSS for Senior ML Engineers in High-Volume Transaction Systems
A structured path to full command of payment security frameworks in AI-driven environments
$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.
Pre-audit crunch caused by unclear mapping between ML features and PCI DSS controls
The situation this course is for
ML systems that process or influence payment data must meet strict audit requirements, but model opacity and dynamic data flows often delay evidence collection. Teams waste cycles reverse-engineering lineage when the auditor knocks. The gap isn't technical ability, it's a missing framework for aligning model architecture with control objectives.
Who this is for
Senior ML Engineer working on systems that interface with or influence financial transactions, needing to demonstrate compliance readiness without slowing innovation
Who this is not for
Junior data scientists, non-technical compliance staff, or engineers working exclusively on non-transactional AI models
What you walk away with
- Map every ML feature to a specific PCI DSS control with documented rationale
- Automate evidence generation for requirements 8, 10, and 11 using model metadata pipelines
- Anticipate auditor questions about model drift and data provenance with pre-built responses
- Reduce pre-audit workload by 85% through standardized templates and validation scripts
- Speak confidently with security teams using precise control-language, not approximations
The 12 modules (with all 144 chapters)
Module 1. PCI DSS Scope in Machine Learning Systems
Define which ML components fall within PCI DSS scope based on data flow, model purpose, and integration points with transaction systems.
12 chapters in this module
- Identifying payment-related data in feature stores
- Mapping model inference paths to cardholder data environment
- Determining CDE boundaries for real-time scoring systems
- Classifying ML services as in-scope or out-of-scope
- Documenting data processing activities under Article 30
- Using data tagging to automate scope detection
- Validating scope with network diagrams and logs
- Handling edge cases: fallback models and shadow deployments
- Integrating scope checks into CI/CD pipelines
- Versioning scope definitions across model iterations
- Collaborating with security teams on boundary agreements
- Maintaining scope documentation for auditor review
Module 2. Requirement 3 Deep Dive: Protecting Stored Cardholder Data
Apply encryption, tokenization, and masking strategies to model inputs and outputs that involve cardholder data.
12 chapters in this module
- Locating cardholder data in training datasets
- Implementing field-level encryption in feature pipelines
- Using tokenization proxies for model scoring
- Masking sensitive data in debug and logging outputs
- Validating encryption strength in inference environments
- Managing keys for encrypted model features
- Auditing data masking rules across versions
- Handling data exports for model validation
- Preventing accidental storage in cache layers
- Designing models to minimize data retention
- Testing decryption paths under failure conditions
- Documenting data protection for requirement 3.4
Module 3. Requirement 4: Securing Transmission of Cardholder Data
Ensure encrypted transport of data between ML services and transaction systems, including API calls and batch feeds.
12 chapters in this module
- Enforcing TLS 1.2+ for model inference APIs
- Validating certificate chains in production
- Securing internal service-to-service communication
- Configuring mTLS for model serving endpoints
- Auditing network paths for data leakage
- Using service mesh for encrypted data flows
- Monitoring for unencrypted legacy connections
- Handling certificate rotation in autoscaling groups
- Validating encryption in staging and shadow traffic
- Documenting transmission security for requirement 4.1
- Testing fail-open vs fail-closed behavior
- Integrating with central PKI infrastructure
Module 4. Requirement 7: Role-Based Access Control in ML Pipelines
Implement fine-grained access policies for model development, training, and inference environments.
12 chapters in this module
- Defining roles for ML engineers and data scientists
- Enforcing least privilege in feature stores
- Auditing access to sensitive training data
- Managing service account permissions
- Integrating with IAM systems for access reviews
- Automating access revocation on role change
- Logging access attempts to model artifacts
- Using attribute-based access control
- Validating access controls in staging environments
- Documenting access policies for requirement 7.2
- Handling emergency access procedures
- Integrating with HR systems for automated provisioning
Module 5. Requirement 8: Strong Authentication for ML Systems
Implement multi-factor authentication and secure credential management for model deployment and access.
12 chapters in this module
- Requiring MFA for production model deployment
- Using hardware tokens for root access
- Managing API keys with rotation policies
- Integrating with centralized auth providers
- Auditing authentication events across environments
- Enforcing password complexity for service accounts
- Using short-lived tokens for CI/CD pipelines
- Monitoring for brute-force attempts
- Implementing account lockout policies
- Documenting authentication methods for requirement 8.5
- Testing recovery procedures for locked accounts
- Integrating with identity governance tools
Module 6. Requirement 10: Logging and Monitoring ML Activities
Generate comprehensive audit logs for model training, deployment, and inference activities.
12 chapters in this module
- Capturing model training start and end events
- Logging data versions used in training
- Recording hyperparameter changes across runs
- Monitoring inference request rates and patterns
- Integrating logs with SIEM systems
- Setting up alerts for anomalous behavior
- Validating log integrity with hashing
- Retaining logs for 365 days
- Automating log review for compliance
- Documenting logging practices for requirement 10.2
- Handling log rotation and archival
- Correlating ML events with security incidents
Module 7. Requirement 11: Regular Testing of Security Controls
Implement automated vulnerability scanning and penetration testing for ML infrastructure.
12 chapters in this module
- Scheduling regular vulnerability scans
- Integrating scans into CI/CD pipelines
- Validating container security for model serving
- Testing API endpoints for common flaws
- Running network-level penetration tests
- Assessing model server configuration
- Using static analysis on model code
- Validating security patches in staging
- Documenting test results for requirement 11.2
- Scheduling annual third-party assessments
- Tracking remediation of identified issues
- Integrating test results with GRC platforms
Module 8. Data Lineage and Model Provenance for Audits
Build traceable lineage from raw data to model output for compliance validation.
12 chapters in this module
- Tagging data at ingestion with metadata
- Tracking feature transformations across pipelines
- Versioning datasets used in training
- Linking models to training data versions
- Capturing model hyperparameters and code
- Storing model evaluation metrics
- Documenting deployment environments
- Using lineage graphs for audit trails
- Validating lineage completeness
- Automating lineage extraction
- Integrating with data catalog tools
- Preparing lineage documentation for assessors
Module 9. Automating Evidence Collection for PCI DSS
Build scripts and workflows to automatically generate compliance evidence.
12 chapters in this module
- Identifying required evidence per control
- Building automated data collection scripts
- Validating evidence completeness
- Generating standardized reports
- Storing evidence in secure repositories
- Versioning evidence packages
- Scheduling evidence generation
- Integrating with ticketing systems
- Alerting on missing evidence
- Documenting automation for requirement 10.5
- Testing evidence workflows quarterly
- Auditing evidence access and modification
Module 10. Integrating PCI DSS into ML Development Lifecycle
Embed compliance checks into model design, development, and deployment processes.
12 chapters in this module
- Including compliance requirements in model specs
- Conducting security reviews before training
- Validating data sources for sensitivity
- Checking model architecture for scope
- Reviewing code for secure practices
- Testing models for data leakage
- Obtaining security sign-off before deployment
- Documenting compliance in model cards
- Training teams on PCI DSS basics
- Integrating compliance gates in CI/CD
- Auditing compliance integration annually
- Improving processes based on audit feedback
Module 11. Handling PCI DSS for Real-Time Inference Systems
Apply controls to low-latency ML services processing transaction data.
12 chapters in this module
- Minimizing data retention in inference paths
- Encrypting data in memory during scoring
- Validating input sanitization for security
- Monitoring for abnormal request patterns
- Implementing rate limiting and throttling
- Using secure enclaves for sensitive models
- Auditing real-time decision logs
- Handling model updates without downtime
- Testing failover security configurations
- Documenting real-time controls for requirement 6.5
- Validating performance under encryption
- Integrating with fraud detection systems
Module 12. Preparing for the PCI DSS Assessment
Assemble documentation, coordinate with assessors, and respond to findings.
12 chapters in this module
- Scheduling assessment with QSA
- Compiling evidence packages
- Conducting internal readiness review
- Assigning points of contact
- Documenting control implementation
- Preparing for on-site interviews
- Responding to assessor questions
- Tracking findings and remediation
- Obtaining ROC sign-off
- Publishing AOC to stakeholders
- Archiving assessment materials
- Planning for next cycle improvements
How this maps to your situation
- Pre-audit evidence generation
- ML model deployment in regulated environments
- Cross-functional collaboration with security teams
- Responding to auditor inquiries about data flows
Before vs. after
Before
Spending weeks reconstructing data lineage and control mappings before audits, relying on tribal knowledge and last-minute heroics.
After
Generating complete, accurate compliance evidence in hours, with documented frameworks that survive team changes.
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 12 weeks, or an intensive 18-hour weekend deep dive.
If nothing changes
Continuing with manual, reactive compliance approaches risks audit delays, findings, and last-minute production changes that could impact model reliability and team credibility.
How this compares to the alternatives
Unlike generic compliance courses, this program is tailored to ML engineers working with transaction systems, focusing on actionable implementation rather than theoretical overview. Compared to internal training, it provides an external benchmark and structured path to mastery.
Frequently asked
Is this course relevant if I don't work directly on payment systems?
Yes, if your models process or influence any data in the transaction path, this course provides the framework to assess and document compliance requirements.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help me during an actual PCI DSS audit?
Yes, the course includes templates and documentation practices used by successful assessors, helping you anticipate and respond to auditor requests.
$199 one-time. 90 minutes per week for 12 weeks, or an intensive 18-hour weekend deep dive..
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