A tailored course, built for your situation
Mastering PCI DSS for Senior Data Scientists in Financial Services
A structured path to faster compliance integration without sacrificing data innovation velocity
$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.
Spending too long translating compliance mandates into working data systems?
The situation this course is for
Data scientists in regulated financial institutions often face delays when translating PCI DSS requirements into operational pipelines. Misalignment between security controls and model deployment creates rework loops, slows time-to-value, and increases the burden on already-busy teams.
Who this is for
Senior data scientists in financial services who own or contribute to compliant data system delivery under PCI DSS
Who this is not for
Entry-level analysts, developers outside regulated finance, or professionals focused only on non-data compliance roles
What you walk away with
- Produce compliant data artefacts faster, reducing validation cycles by up to 50%
- Integrate PCI DSS controls directly into development workflows
- Reduce rework caused by late-stage compliance feedback
- Ship compliant models without slowing innovation pace
- Build reusable templates aligned with control expectations
The 12 modules (with all 144 chapters)
Module 1. Understanding PCI DSS Scope in Data Science Workflows
Clarify which data pipelines, storage layers, and model outputs fall under PCI DSS requirements. Learn to map cardholder data flows across ingestion, transformation, and reporting layers to define precise compliance boundaries.
12 chapters in this module
- Identifying cardholder data in structured and unstructured sources
- Mapping data flows through ETL and ML pipelines
- Determining scope based on storage, transmission, and processing
- Differentiating PCI-relevant from non-relevant datasets
- Applying segmentation strategies to isolate compliant pipelines
- Documenting scope decisions for audit readiness
- Avoiding over-scope that slows development
- Recognizing indirect access points in analytics systems
- Handling third-party data integrations under PCI
- Using metadata tagging to track regulated fields
- Aligning data lineage tools with PCI scope definitions
- Establishing scope review checkpoints in sprints
Module 2. Building Data Protection Controls into ML Pipelines
Embed encryption, masking, and access controls directly into data science workflows. Ensure sensitive fields are protected at rest and in transit without disrupting model performance or experimentation.
12 chapters in this module
- Applying encryption to training datasets at rest
- Masking cardholder data in development environments
- Implementing tokenization for test datasets
- Configuring access controls for Jupyter environments
- Securing model outputs containing regulated data
- Validating PII removal in automated feature engineering
- Using role-based access in data labs
- Logging access attempts to sensitive datasets
- Protecting data during cross-environment sharing
- Integrating DLP tools with data science platforms
- Auditing pipeline steps for data exposure risks
- Benchmarking control overhead on training speed
Module 3. Automating Compliance Checks in CI/CD for Data Projects
Integrate automated validation of PCI DSS requirements into continuous integration workflows. Catch misconfigurations early and prevent non-compliant code from progressing to production.
12 chapters in this module
- Embedding PCI rule checks in pre-commit hooks
- Scanning for hardcoded secrets in notebooks
- Validating data masking in pull request pipelines
- Running automated scope validation on DAG updates
- Enforcing encryption policy in deployment scripts
- Blocking merges that expose regulated data
- Integrating with enterprise secrets management
- Alerting on policy deviations in real time
- Versioning compliance rules alongside code
- Using linting to enforce data handling standards
- Testing compliance automation in staging
- Maintaining audit logs for CI/CD actions
Module 4. Streamlining Audit Evidence Collection for Data Teams
Generate required documentation and logs efficiently, reducing manual effort during audits. Focus on producing the exact artefacts PCI assessors expect, without over-documenting.
12 chapters in this module
- Identifying required evidence for each PCI control
- Automating log collection from data pipelines
- Producing data flow diagrams on demand
- Documenting access control configurations
- Generating encryption implementation reports
- Compiling change management records
- Using templates to standardize evidence formats
- Reducing evidence collection from weeks to hours
- Aligning logs with assessor review patterns
- Versioning compliance documentation
- Preparing evidence packages before audit cycles
- Validating completeness with internal checklists
Module 5. Integrating Security Requirements into Data Model Design
Design data models and schemas with compliance built in. Avoid retrofitting controls by incorporating encryption, access, and retention rules at the architecture phase.
12 chapters in this module
- Designing schemas with encrypted field patterns
- Incorporating access tiers into data models
- Defining retention rules in table metadata
- Using database-level controls for PCI fields
- Structuring feature stores for regulated data
- Applying least privilege to model outputs
- Documenting design decisions for compliance
- Validating model compliance before training
- Using schema validation in deployment
- Aligning data contracts with PCI rules
- Building compliance review into design sprints
- Iterating on models without violating controls
Module 6. Reducing Rework with Early Compliance Validation
Shift compliance checks left in the development lifecycle. Provide instant feedback to data scientists so issues are resolved before peer review or deployment.
12 chapters in this module
- Implementing pre-submission compliance checks
- Providing instant feedback on notebook commits
- Using automated tools to flag policy violations
- Integrating compliance into code review workflows
- Training teams on common PCI pitfalls
- Creating self-service compliance validation
- Reducing back-and-forth with governance teams
- Speeding up internal review cycles
- Documenting rework reduction metrics
- Aligning dev workflows with control timelines
- Building confidence in first-time compliance
- Scaling validation across multiple projects
Module 7. Managing Third-Party Risks in Data Integrations
Evaluate and monitor external data sources and vendors for PCI DSS compliance. Ensure third-party connections don't introduce control gaps.
12 chapters in this module
- Assessing vendor PCI compliance status
- Reviewing third-party data handling practices
- Validating encryption in external APIs
- Auditing data sharing agreements
- Monitoring third-party access to systems
- Enforcing compliance in API contracts
- Tracking downstream data usage
- Building exit strategies for non-compliant vendors
- Integrating vendor risk into sprint planning
- Using attestation workflows for partners
- Documenting due diligence for audits
- Automating vendor compliance checks
Module 8. Optimizing Data Retention and Disposal Workflows
Implement automated data lifecycle policies that align with PCI DSS retention limits. Avoid prolonged storage of sensitive information.
12 chapters in this module
- Defining retention periods for cardholder data
- Automating data deletion in pipelines
- Validating disposal completeness
- Auditing retention policy enforcement
- Handling exceptions for legal holds
- Documenting disposal procedures
- Integrating retention rules into ETL jobs
- Using metadata to track data age
- Alerting on overdue retention actions
- Designing archiving workflows for compliance
- Testing disposal processes in staging
- Reporting on data lifecycle compliance
Module 9. Aligning Data Science Governance with Enterprise Risk
Connect team-level compliance with broader risk management objectives. Demonstrate how data controls contribute to organizational resilience.
12 chapters in this module
- Mapping PCI controls to enterprise risk registers
- Reporting compliance metrics to leadership
- Integrating with financial risk frameworks
- Using risk scoring for project prioritization
- Demonstrating control effectiveness to auditors
- Aligning with FFIEC and GLBA expectations
- Documenting risk reduction from data controls
- Participating in enterprise risk assessments
- Translating technical work into business terms
- Building trust with compliance partners
- Influencing risk strategy from data teams
- Measuring risk reduction over time
Module 10. Scaling Compliance Across Data Science Teams
Replicate successful compliance practices across multiple teams. Ensure consistency while allowing for project-specific needs.
12 chapters in this module
- Creating reusable compliance templates
- Standardizing data handling patterns
- Training new team members on PCI rules
- Sharing best practices across squads
- Implementing centralized policy management
- Using version control for compliance assets
- Auditing compliance across projects
- Providing guidance without bottlenecks
- Scaling automation to multiple pipelines
- Measuring compliance maturity across teams
- Improving consistency without slowing pace
- Building internal communities of practice
Module 11. Future-Proofing Data Pipelines for PCI DSS Updates
Design systems to absorb changes in PCI DSS requirements. Reduce adaptation time when new mandates are issued.
12 chapters in this module
- Tracking upcoming PCI DSS revisions
- Designing modular compliance controls
- Using abstraction layers for regulated data
- Reducing coupling to specific control versions
- Building upgradable encryption frameworks
- Planning for deprecation of legacy methods
- Aligning roadmap with compliance cycles
- Allocating buffer for regulatory changes
- Testing adaptability in staging environments
- Documenting change impact assessments
- Engaging early with compliance teams
- Measuring adaptation velocity
Module 12. Documenting and Communicating Compliance Achievements
Articulate compliance work in ways that resonate with stakeholders. Turn technical execution into recognized capability.
12 chapters in this module
- Writing clear compliance narratives
- Highlighting risk reduction in reviews
- Sharing success stories across teams
- Building internal credibility
- Contributing to enterprise reporting
- Presenting outcomes to leadership
- Using metrics to show progress
- Aligning language with business goals
- Gaining recognition without self-promotion
- Sustaining compliance momentum
- Mentoring others on best practices
- Leaving audit-ready documentation trails
How this maps to your situation
- Addressing PCI DSS scope in financial data pipelines
- Reducing rework through early compliance validation
- Automating evidence collection for audits
- Scaling compliant practices across data science teams
Before vs. after
Before
Spending cycles reconciling data innovation with compliance requirements, often resulting in rework and delayed deployments
After
Moving directly from policy intent to compliant artefacts with confidence, reducing review loops and accelerating delivery
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 90 minutes per week over six weeks, designed to fit around core responsibilities.
If nothing changes
Continuing to rely on manual compliance alignment risks longer cycle times, increased audit findings, and missed opportunities to lead in secure data innovation.
How this compares to the alternatives
Unlike generic compliance courses, this program focuses specifically on integrating PCI DSS into data science workflows, delivering actionable, role-specific methods that accelerate both compliance and innovation.
Frequently asked
Who is this course designed for?
Senior data scientists in financial services who are responsible for building or overseeing compliant data systems under PCI DSS.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Does this course cover FFIEC or GLBA?
The primary focus is PCI DSS, but principles align with broader financial regulations including FFIEC and GLBA, especially where data handling intersects.
$199 one-time. Approximately 90 minutes per week over six weeks, designed to fit around core responsibilities..
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