A tailored course, built for your situation
Mastering PCI DSS for Senior AI Engineers in Global Automotive Systems
Build compliant, secure AI systems that meet payment security standards across global teams and architectures
$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.
AI systems blocked at integration due to payment compliance gaps
The situation this course is for
Even advanced AI models stall when they process payment data without embedded PCI DSS controls, creating friction between innovation teams and compliance stakeholders. Teams end up retrofitting security, delaying deployment and diluting impact.
Who this is for
Senior AI/ML engineer in automotive or industrial tech, responsible for designing scalable, production-grade AI systems that interact with transactional data
Who this is not for
Junior developers, non-technical compliance staff, or professionals outside AI system design and deployment
What you walk away with
- Architect AI systems with PCI DSS controls embedded from inception
- Reduce friction between AI teams and security/compliance partners
- Enable faster approval for AI deployments in payment-adjacent systems
- Become the internal reference for secure AI patterns across business units
- Document repeatable design templates for audit-ready AI implementations
The 12 modules (with all 144 chapters)
Module 1. Introduction to PCI DSS in AI System Contexts
Understand how PCI DSS applies to AI systems handling cardholder data, including edge cases in automotive environments.
12 chapters in this module
- Scope of PCI DSS in non-traditional payment systems
- Data flow mapping for AI-driven transaction processing
- Role of AI engineers in compliance boundary setting
- Common misconceptions about AI and PCI scope
- How payment authentication layers interact with ML models
- Key terminology: CDE, SAQ, P2PE, tokenization
- Jurisdictional overlap with RBI Master Directions
- Industry-specific interpretations in mobility tech
- Compliance as a performance enabler
- Case study: In-vehicle payment system rollout
- Why one-size-fits-all templates fail in AI contexts
- Setting expectations for cross-team alignment
Module 2. Data Handling in Machine Learning Pipelines
Secure data ingestion, storage, and preprocessing while maintaining model integrity.
12 chapters in this module
- Identifying cardholder data in raw inputs
- Tokenization versus masking in training sets
- Secure feature engineering for compliance
- Labeling pipelines without exposing PANs
- Anonymization techniques that preserve utility
- Data lifecycle controls from capture to disposal
- Logging restrictions when handling CHD
- Model debugging without violating segmentation
- Versioning sensitive datasets securely
- Audit trail requirements for data access
- Balancing data utility and compliance
- Worked example: Fleet card usage prediction
Module 3. Secure Model Training Environments
Establish compliant infrastructure for training AI models on protected data.
12 chapters in this module
- Isolating development environments from public clouds
- Network segmentation for ML workstations
- Access control policies for data scientists
- Encryption standards for model checkpoints
- Securing Jupyter notebooks in shared labs
- Role-based permissions in MLOps platforms
- Monitoring insider activity during training
- Container security in ML pipelines
- Hardening Kubernetes clusters for AI workloads
- Compliance checks in CI/CD for ML
- Audit readiness for model development phases
- Template: Secure ML lab setup checklist
Module 4. Model Deployment and Inference Security
Ensure deployed models comply with PCI DSS requirements during real-time operation.
12 chapters in this module
- Securing API endpoints for payment inference
- Authentication for model serving platforms
- Input validation to prevent data leakage
- Real-time monitoring for anomalous queries
- Encryption in transit for inference calls
- Rate limiting and abuse prevention
- Model explainability within compliance audits
- Version control for deployed models
- Failover systems and compliance impact
- Edge deployment challenges in vehicles
- Securing OTA updates for AI modules
- Case study: Telematics-based payment scoring
Module 5. Log Management and Monitoring
Implement audit-compliant logging practices for AI systems processing payment data.
12 chapters in this module
- What logs to retain under PCI DSS Requirement 10
- Masking CHD in application and system logs
- Automated log aggregation strategies
- Retention periods aligned with Indian data laws
- Centralized monitoring for distributed AI services
- Alerting on policy violations
- Time synchronization across vehicle networks
- Secure storage of log archives
- Access controls for log review
- Integrating logs into SIEM tools
- Audit preparation with log evidence
- Template: Log policy for AI inference systems
Module 6. Vulnerability Management for AI Systems
Apply PCI DSS vulnerability scanning and patching to AI-specific infrastructure.
12 chapters in this module
- Identifying AI-specific attack surfaces
- Scanning containers used in ML pipelines
- Patch management for GPU drivers
- Third-party library risk in open-source AI tools
- SBOM generation for AI model packages
- Dynamic analysis of model APIs
- Static analysis for prompt injection risks
- Penetration testing for AI gateways
- Managing findings across global teams
- Remediation timelines for critical flaws
- Reporting to compliance teams
- Worked example: Vulnerability response plan
Module 7. Change and Configuration Management
Maintain compliance through structured changes in AI system environments.
12 chapters in this module
- Baseline configuration for AI servers
- Change approval workflows
- Automated drift detection
- Version control for AI models and code
- Configuration as code for reproducibility
- Rollback procedures after failed deployments
- Impact assessment for infrastructure changes
- Documentation standards for auditors
- Segregation of duties in MLOps
- Audit trails for configuration changes
- Managing exceptions securely
- Template: Change control record for AI updates
Module 8. AI-Specific Aspects of Network Security
Design secure network architectures for AI systems handling payment data.
12 chapters in this module
- Segmentation of AI training clusters
- Firewall rules for model serving endpoints
- Zero-trust architecture for ML platforms
- Securing inter-node communication in training jobs
- Private link usage in cloud AI services
- Vehicle-to-cloud data transmission security
- DNS protection for AI microservices
- Network monitoring for data exfiltration
- Compliance with Requirement 1 and 12
- Designing secure OTA update channels
- Hybrid cloud network layouts
- Case study: Secure AI gateway for parking payments
Module 9. Building Cross-Functional Compliance Workflows
Lead collaboration between AI, security, and compliance teams effectively.
12 chapters in this module
- Translating PCI DSS controls into engineering tasks
- Creating shared vocabulary between teams
- Compliance handoff points in MLOps
- Running joint design reviews
- Documenting control ownership
- Aligning sprint planning with audit cycles
- Escalation paths for control conflicts
- Building trust with risk assessors
- Internal advocacy for secure-by-design AI
- Training peers on compliance basics
- Facilitating cross-region alignment
- Worked example: Quarterly compliance sync
Module 10. Documentation and Audit Preparation
Produce clear, sufficient evidence for PCI DSS assessments.
12 chapters in this module
- What assessors expect from AI teams
- Writing effective implementation statements
- Gathering artifacts for control 11 testing
- Preparing system architecture diagrams
- Demonstrating control effectiveness
- Responding to assessor findings
- Maintaining up-to-date ROC documentation
- Internal pre-audit checklists
- Handling follow-up questions
- Leveraging automation for evidence
- Streamlining annual re-certification
- Template: AI system narrative for auditors
Module 11. Emerging Challenges in AI and Payment Systems
Stay ahead of evolving threats and regulatory expectations.
12 chapters in this module
- Prompt injection in payment reasoning models
- Model poisoning via compromised data
- Adversarial attacks on transaction classifiers
- Privacy-preserving ML in compliant environments
- AI-driven fraud detection and its limits
- Regulatory expectations in India and EU
- Explaining AI decisions under audit
- Bias considerations in payment scoring
- Future of decentralized identity in vehicles
- Integration with UPI and RuPay ecosystems
- Preparing for PCI SSC AI guidance
- Strategic roadmap for next-gen systems
Module 12. Capstone: Designing a PCI-Compliant AI Pipeline
Apply all concepts to a realistic automotive use case.
12 chapters in this module
- Defining project scope and boundaries
- Mapping data flows with CHD exposure
- Selecting appropriate SAQ type
- Designing secure training infrastructure
- Implementing inference safeguards
- Configuring monitoring and alerts
- Establishing change control process
- Planning audit evidence collection
- Creating team onboarding materials
- Documenting control implementation
- Presenting design to virtual assessor
- Finalizing playbook for future projects
How this maps to your situation
- AI system design with built-in compliance
- Cross-team rollout of secure AI patterns
- Audit-ready deployment in regulated environments
- Scaling secure AI practices across regions
Before vs. after
Before
AI systems require retrofitting after compliance reviews, slowing deployment and increasing rework.
After
AI models are designed with PCI DSS compliance embedded, enabling faster approvals and cross-business adoption.
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 2.5 hours per module, designed to be completed alongside ongoing projects over 6, 8 weeks.
If nothing changes
Continuing to treat compliance as a post-development checklist increases project risk, delays time-to-market, and limits your influence beyond core AI teams.
How this compares to the alternatives
Unlike generic compliance courses, this program focuses specifically on AI engineers in industrial systems, with real-world templates and implementation guidance tailored to payment data environments.
Frequently asked
Is this course relevant if I don't work directly on payment systems?
Yes. If your AI systems could ever process or influence transaction data (even indirectly), PCI DSS applies. This course prepares you for those intersections before they become blockers.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help me in audits?
Yes. You'll learn how to generate the right evidence, document control implementation, and speak the language of assessors, making audits smoother and less disruptive.
$199 one-time. Approximately 2.5 hours per module, designed to be completed alongside ongoing projects over 6, 8 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