A tailored course, built for your situation
Become the go-to ML security practitioner for PCI DSS compliance in AI systems
A practitioner’s path to being first called when ML systems must meet payment security
$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
ML engineers working in high-compliance environments who want to become the recognized internal expert when AI systems intersect with financial regulations
Who this is not for
Engineers focused only on model accuracy without security context, or those not involved in systems touching regulated data
What you walk away with
- Recognized as the internal expert on ML implications for PCI DSS
- First invited to design-phase discussions for AI systems handling payment data
- Confidently lead conversations between security teams and ML squads
- Produce audit-ready documentation linking model behaviour to control requirements
- Anticipate compliance needs before they become blockers
The 12 modules (with all 144 chapters)
Module 1. Mapping PCI DSS controls to ML system boundaries
Define where PCI DSS applies in model training, inference, and data pipelines. Identify scope boundaries for cardholder data in AI systems.
12 chapters in this module
- Control scope definition
- Data flow tagging
- System boundary analysis
- Tokenization touchpoints
- Encryption zones
- Logging requirements
- Access control mapping
- Third-party vendor hooks
- Real-time monitoring constraints
- Compliance touchpoints
- Risk tier assignment
- Audit trail alignment
Module 2. Designing ML systems with PCI DSS built in
Embed compliance into architecture decisions. Use patterns that satisfy controls without sacrificing model performance.
12 chapters in this module
- Secure by design patterns
- Data minimization tactics
- Feature engineering within scope
- Model explainability for auditors
- Secure inference design
- Training data provenance
- Pipeline encryption layering
- Anonymization techniques
- Access logging integration
- Model monitoring controls
- Change control alignment
- Versioning compliance
Module 3. Documenting ML for auditor review
Create clear, concise artefacts that demonstrate compliance. Translate technical choices into control-language the audit team accepts.
12 chapters in this module
- Control mapping templates
- Narrative writing for auditors
- Evidence packaging
- Model card formatting
- System diagrams
- Data lineage charts
- Access logs review
- Change logs submission
- Exception rationale
- Remediation tracking
- Gap reporting
- Sign-off workflows
Module 4. Leading cross-functional PCI DSS reviews
Facilitate discussions between ML teams, security, and compliance. Own the agenda and drive consensus on control implementation.
12 chapters in this module
- Stakeholder mapping
- Meeting facilitation
- Control ownership assignment
- Risk escalation paths
- Decision logging
- Timeline coordination
- Gap resolution tracking
- Feedback integration
- Compliance champion role
- Executive summary prep
- Escalation protocols
- Follow-up cadences
Module 5. Anticipating PCI DSS evolution in AI contexts
Stay ahead of compliance changes. Interpret new guidance and assess impact on existing and future ML systems.
12 chapters in this module
- Standards monitoring
- Draft review techniques
- Internal impact assessment
- Cross-team communication
- Roadmap alignment
- Gap analysis
- Phased compliance planning
- Vendor coordination
- Training updates
- Policy drafting
- Model revalidation cycles
- Audit preparation
Module 6. Building a repeatable compliance playbook
Turn one successful project into a reusable framework. Document patterns that survive team changes and scale across initiatives.
12 chapters in this module
- Playbook structure
- Template creation
- Version control
- Team onboarding
- Review cycles
- Feedback loops
- Success metrics
- Adaptation triggers
- Knowledge transfer
- Ownership handoff
- Compliance audits
- Lessons learned
Module 7. Managing third-party ML components under PCI DSS
Evaluate vendor models, APIs, and platforms for compliance readiness. Own the due diligence process.
12 chapters in this module
- Vendor risk assessment
- Contract clause review
- Data handling checks
- Audit rights negotiation
- Compliance evidence request
- Model transparency
- Patch management
- Incident response
- Fallback planning
- Exit strategy
- Renewal reviews
- Performance audits
Module 8. Handling incident response with ML systems
Respond to breaches or anomalies in PCI-scoped ML systems. Document root cause and corrective action for auditors.
12 chapters in this module
- Incident classification
- Data exposure assessment
- Model impact analysis
- Forensic data capture
- Timeline reconstruction
- Root cause identification
- Corrective action
- Audit trail review
- Reporting requirements
- Remediation testing
- Post-mortem writing
- Control updates
Module 9. Training teams on PCI DSS for ML
Teach compliance concepts to ML engineers. Create role-specific guidance that sticks.
12 chapters in this module
- Needs assessment
- Curriculum design
- Hands-on labs
- Case studies
- Compliance checklists
- Onboarding integration
- Refresh cycles
- Feedback collection
- Knowledge checks
- Mentorship setup
- Documentation updates
- Certification tracking
Module 10. Scaling compliance across ML portfolios
Extend one system’s success to the broader AI landscape. Prioritize efforts and allocate resources effectively.
12 chapters in this module
- Portfolio mapping
- Risk-based prioritization
- Resource allocation
- Cross-team alignment
- Compliance debt tracking
- Automation opportunities
- Tooling integration
- Metrics reporting
- Leadership updates
- Budget justification
- Hiring needs
- External support
Module 11. Earning recognition as a compliance enabler
Position yourself as the go-to person. Build credibility through consistent delivery and clear communication.
12 chapters in this module
- Success storytelling
- Visibility tactics
- Internal networking
- Knowledge sharing
- Mentorship roles
- Conference talks
- Whitepapers
- Cross-org impact
- Awards and recognition
- Leadership visibility
- Board-level mentions
- Career growth
Module 12. Sustaining authority through changing standards
Maintain your position as regulations evolve. Turn updates into opportunities to deepen influence.
12 chapters in this module
- Change tracking
- Impact forecasting
- Stakeholder alerts
- Planning cycles
- Resource requests
- Project initiation
- Team coordination
- Vendor alignment
- Training updates
- Documentation refresh
- Audit preparation
- Lessons applied
How this maps to your situation
- When designing a new ML system handling payment data
- Before an internal PCI DSS audit cycle
- After a compliance gap is identified
- When onboarding a third-party ML vendor
Before vs. after
Before
Invited to compliance discussions late, translating requirements after design decisions are made
After
First called when ML systems touch payment data, shaping architecture from day one
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, recommended over 12 weeks to allow for integration into active projects.
If nothing changes
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How this compares to the alternatives
Generic PCI DSS courses focus on IT systems and network security, not ML-specific risks. This course is built specifically for engineers working with AI systems in payment environments , no abstractions, no fluff, only actionable patterns that apply directly to your work.
Frequently asked
Is this course about passing PCI DSS audits?
It’s about being the person who makes passing audits possible. You’ll learn how to design, document, and lead ML systems so they meet compliance from the start.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help me if I’m not in payments?
Yes , the patterns apply to any regulated ML system. PCI DSS is the most detailed framework for data security, so mastering it positions you for other standards too.
$199 one-time. Approximately 3 hours per module, recommended over 12 weeks to allow for integration into 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