A tailored course, built for your situation
Mastering SOC 2 for Data Science and Analytics Practitioners
Turn compliance into a strategic advantage with precise, audit-ready implementation.
$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
Senior data scientist or ML engineer working in regulated enterprise environments where compliance intersects with advanced analytics and client-facing deliverables.
Who this is not for
Entry-level analysts, engineers outside regulated domains, or practitioners focused solely on model tuning without governance context.
What you walk away with
- Produce SOC 2-compliant data handling documentation that stands up to regulator-facing review
- Lead control design for data access, retention, and classification in analytics environments
- Anticipate auditor questions and build evidence packs proactively
- Position yourself as the go-to practitioner for M&A due diligence involving data systems
- Convert technical work into board-prep-ready narratives without rework
The 12 modules (with all 144 chapters)
Module 1. Understanding SOC 2 in Data-Intensive Environments
Lay the foundation for how SOC 2 principles apply specifically to data science workflows, including model inputs, data pipelines, and output reporting.
12 chapters in this module
- What SOC 2 means for analytics teams
- Trust services criteria and data handling
- Difference between SOC 1, SOC 2, and SOC 3
- When SOC 2 applies in client engagements
- How it intersects with AI governance
- Regulatory expectations by industry
- Common misconceptions among data teams
- Control scope vs. data scope
- Types of SOC 2 reports: Type I and Type II
- The role of the practitioner in scoping
- Audit timelines and evidence cycles
- How control design differs from engineering design
Module 2. Scoping the Right Boundaries for Analytics Systems
Learn how to define system boundaries that reflect real data flows in marketing and capital markets analytics, avoiding over- or under-scoping.
12 chapters in this module
- Mapping data sources to control domains
- Identifying reportable systems
- Excluding non-relevant infrastructure
- Documenting API integrations
- Handling third-party data tools
- Boundary decisions for LLM/NLP pipelines
- How auditors test scope accuracy
- Avoiding common boundary drift
- Using data lineage diagrams
- Version control for system documentation
- Glossary standards for auditors
- Common pitfalls in cloud-based analytics
Module 3. Designing Access Controls for Data Science Teams
Build role-based access frameworks that satisfy SOC 2 while supporting agile collaboration.
12 chapters in this module
- Principle of least privilege in practice
- User access review cadence
- Temporary access workflows
- Just-in-time access implementation
- Segregation of duties for data roles
- Managing service accounts securely
- Authentication vs. authorization
- Multi-factor requirements
- Logging access decisions
- Handling contractor access
- Documenting access policies
- Audit trail expectations
Module 4. Data Classification and Handling Procedures
Establish classification rules tailored to analytics outputs and model training data.
12 chapters in this module
- Defining data sensitivity tiers
- Labeling structured vs unstructured data
- Handling PII in NLP pipelines
- Encryption at rest and in transit
- Retention schedules by data class
- Secure archival methods
- Data disposal verification
- Cross-border data transfers
- Vendor obligations for data handling
- Logging classification decisions
- Reviewer sign-off process
- Audit evidence for data handling
Module 5. Building Evidence Packs That Pass First-Time Review
Turn technical work into audit-ready documentation with precision and consistency.
12 chapters in this module
- Types of evidence by control type
- Screenshot standards for system logs
- Sampling methodology for testing
- Documenting control operation
- Timestamping and version control
- Using standardized templates
- Avoiding narrative drift
- Linking evidence to test procedures
- Common auditor pushbacks
- Preparing for walkthroughs
- Organizing evidence by domain
- Version comparison for renewals
Module 6. Narrative Development for Auditor-Facing Deliverables
Write clear, defensible descriptions of control design and operation.
12 chapters in this module
- Structure of a control description
- Avoiding vague language
- Using active voice consistently
- Referencing control frameworks correctly
- Explaining compensating controls
- Describing automated vs manual checks
- Tone for regulator-facing docs
- How much detail is enough
- Peer review checklist
- Common rejection reasons
- Using diagrams to support narrative
- Maintaining version history
Module 7. Control Mapping for Data Pipelines and Models
Align technical architecture with SOC 2 control domains using proven mapping techniques.
12 chapters in this module
- Mapping inputs to control objectives
- Identifying processing integrity controls
- Documenting model validation steps
- Linking pipeline stages to criteria
- Using control matrices
- Automated monitoring points
- Exception handling procedures
- Logging for auditability
- Version control integration
- Change management alignment
- Vendor tool configurations
- Third-party review expectations
Module 8. Vendor Risk Management in Analytics Ecosystems
Evaluate third-party tools and platforms through a SOC 2 lens.
12 chapters in this module
- Assessing vendor compliance posture
- Reviewing SOC 2 reports from vendors
- Understanding vendor responsibilities
- Managing multi-cloud environments
- Due diligence for SaaS tools
- Contractual obligations for data
- Subservice organization mapping
- Vendor exception tracking
- Ongoing monitoring frequency
- Using CAIQ questionnaires
- Managing shadow IT
- Documenting reliance on vendor controls
Module 9. Incident Response and Data Breach Readiness
Prepare incident workflows that meet SOC 2 expectations for timely response.
12 chapters in this module
- Defining reportable incidents
- Detection mechanisms for data exfiltration
- Response team roles and escalation
- Documentation requirements
- Forensic readiness
- Legal and regulator notification
- Mock breach exercises
- Post-incident review process
- Updating controls after events
- Logging incident response
- Coordination with external counsel
- Public disclosure alignment
Module 10. Change Management for Analytics Platforms
Implement formal change workflows that satisfy SOC 2 without slowing innovation.
12 chapters in this module
- Defining change types
- Approval workflows by risk level
- Emergency change protocols
- Backout plans documentation
- Testing requirements pre-deployment
- Version control for models
- Configuration management databases
- Post-implementation review
- Auditor access to change logs
- Handling hotfixes
- Separation of duties in deployment
- Automating change tracking
Module 11. Continuous Monitoring and Control Testing
Design automated checks and review cycles to ensure ongoing compliance.
12 chapters in this module
- Frequency by control type
- Automated log reviews
- User access recertification
- Security scanning cadence
- Vulnerability management
- Penetration testing alignment
- Control exception tracking
- Remediation timelines
- Reporting to leadership
- Using dashboards for visibility
- Metrics that matter to auditors
- Benchmarking maturity
Module 12. From Audit Preparation to Strategic Influence
Evolve from compliance executor to trusted advisor on data governance.
12 chapters in this module
- Positioning compliance as an enabler
- Advising on new project scoping
- Influencing architecture early
- Escalation pathways for risk
- Building trusted relationships
- Speaking the language of leadership
- Documenting strategic contributions
- Creating repeatable playbooks
- Succession planning for knowledge
- Measuring downstream impact
- Refining practice over time
- Becoming the default reviewer
How this maps to your situation
- Preparing for first SOC 2 audit
- Supporting M&A due diligence
- Responding to client compliance questionnaires
- Scaling analytics teams with governance
Before vs. after
Before
Compliance is something handed off to another team, requiring rework and clarification.
After
You own the narrative , producing audit-ready artefacts and guiding peers with confidence.
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 6, 8 hours of focused learning, designed to be completed in short sprints alongside active projects.
If nothing changes
Without structured knowledge of SOC 2, data science teams risk being bypassed in high-stakes engagements, losing influence on M&A, client onboarding, and strategic projects where trust signals are required.
How this compares to the alternatives
Unlike generic compliance courses, this focuses exclusively on data science workflows, using real artefacts from capital markets and marketing analytics environments. No theory , only what’s needed to pass audit and gain influence.
Frequently asked
Is this course technical enough for a data scientist?
Yes. It uses real data pipeline examples, control mappings for ML models, and templates for documenting AI/LLM workflows.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help me lead audit responses?
Yes. You’ll learn how to write auditor-facing narratives, build evidence packs, and anticipate follow-up questions , all from a practitioner’s perspective.
$199 one-time. Approximately 6, 8 hours of focused learning, designed to be completed in short sprints alongside 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