A tailored course, built for your situation
Mastering SOC 2 for Data Science Practitioners in High-Growth Tech
Build audit-ready data systems with confidence and consistency
$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 cycles explaining data pipelines to auditors or rebuilding systems to meet compliance last-minute
The situation this course is for
Data scientists in fast-moving tech environments often find their work reevaluated during audits, not because it's flawed, but because it wasn't structured with compliance visibility in mind. This leads to rework, delayed launches, and diluted impact.
Who this is for
Senior data scientist in a high-growth tech company facing increasing compliance interlocks, especially around data access, pipeline integrity, and system documentation
Who this is not for
Entry-level analysts, pure research scientists, or engineers focused solely on infrastructure without data governance exposure
What you walk away with
- Design data systems with embedded SOC 2 readiness from day one
- Produce documentation that satisfies auditor expectations without rework
- Anticipate control requirements before sprint planning begins
- Collaborate fluently with compliance and security teams using shared frameworks
- Increase the reuse of your data models across regulated business units
The 12 modules (with all 144 chapters)
Module 1. Why SOC 2 Matters for Data Science Beyond Security Teams
Understand how SOC 2 principles extend to data handling, access logs, and pipeline integrity, areas where data scientists directly influence compliance outcomes.
12 chapters in this module
- How data science decisions trigger SOC 2 control requirements
- The difference between security-owned and data-owned controls
- Real-world audit findings tied to undocumented transformations
- Case study: Data scientist prevents failed SOC 2 renewal
- Mapping data roles to Trust Service Criteria domains
- When engineering velocity clashes with compliance expectations
- How non-security teams get pulled into attestation cycles
- Patterns of misalignment between audit teams and data workflows
- Three common assumptions data teams make about compliance
- The hidden cost of post-audit model documentation
- Why 'we already log everything' is not an auditor answer
- Building traceability into every data pipeline from the start
Module 2. Structuring Data Work for Audit-Ready Outputs
Learn to design deliverables that satisfy both technical standards and compliance reviewers without slowing innovation.
12 chapters in this module
- Designing pipeline logs that serve developers and auditors
- Documenting data lineage without extra effort
- Using metadata tagging to satisfy control evidence needs
- Automating compliance-relevant summaries from code comments
- Version control practices that double as audit trails
- When to formalize a data dictionary for external review
- Integrating control evidence into CI/CD pipelines
- Avoiding last-minute evidence requests during audits
- How to structure Jupyter notebooks for compliance review
- Embedding data classification into schema definitions
- Linking model decisions to documented business justifications
- Creating reusable templates for control narratives
Module 3. SOC 2 Trust Service Criteria Through a Data Lens
Decode the five TSC categories by focusing on tangible data science scenarios, not abstract controls.
12 chapters in this module
- Security criterion: Access logs for data pipelines and models
- Availability: Monitoring uptime for prediction APIs
- Processing integrity: Validating data transformations in production
- Confidentiality: Handling PII in training data sets
- Privacy: Data retention policies in feature engineering
- How anonymization techniques meet or fall short of TSC
- Real examples of failed processing integrity audits
- Distinguishing system-level vs. data-level confidentiality
- When data drift becomes a compliance issue
- Documenting data deletion workflows for auditor review
- Handling model retraining within privacy boundaries
- Proving data inputs haven't been tampered with pre-inference
Module 4. From Code to Control: Mapping Pipelines to Requirements
Translate technical work into language and structure that compliance teams and auditors recognize.
12 chapters in this module
- Identifying which pipeline stages trigger SOC 2 controls
- Documenting data source authenticity for external validators
- Proving data transformation consistency across versions
- Mapping access controls to model endpoints and datasets
- Using role-based permissions to satisfy audit checks
- Tracking changes to training data with version control
- Logging inference requests to demonstrate system integrity
- Demonstrating that model outputs align with stated purpose
- Handling third-party data inputs under compliance scrutiny
- How to document ETL jobs for non-technical reviewers
- Proving data freshness meets stated service level
- Linking model update frequency to control review cycles
Module 5. Building Compliance-Aware Data Architectures
Design systems that are technically sound and structurally audit-ready, reducing future rework.
12 chapters in this module
- Choosing databases with built-in compliance features
- Architecting access logs that serve dual purposes
- Designing schema changes with backward compatibility
- Implementing data retention policies at the source
- Using monitoring tools that generate audit trails
- Structuring model registries for compliance visibility
- Enabling access reviews without manual data dumps
- Automating data classification at ingestion time
- Integrating data quality checks into pipeline steps
- Creating immutable logs for high-risk data flows
- Documenting data dependencies before integration
- Designing disaster recovery plans for data services
Module 6. Collaborating Across Compliance, Security, and Engineering
Bridge communication gaps using shared frameworks and precise artifacts.
12 chapters in this module
- Speaking the language of auditors without becoming one
- Asking better questions of compliance teams early
- When to escalate data concerns to security partners
- Creating shared documentation standards across teams
- Running joint readiness reviews before audit cycles
- Translating technical decisions into risk narratives
- Avoiding blame games when controls fail audit review
- Using diagrams to align on data flow boundaries
- Managing scope differences between teams
- Facilitating cross-functional control mapping sessions
- Creating a single source of truth for data policies
- Establishing regular syncs with compliance leads
Module 7. Documenting Controls Without Slowing Down
Embed compliance documentation into existing workflows instead of treating it as a separate task.
12 chapters in this module
- Generating control narratives from code comments
- Using READMEs to satisfy auditor evidence needs
- Automatically extracting control-relevant metadata
- Integrating documentation into pull request templates
- Versioning control narratives alongside code
- Creating living runbooks for data systems
- Using tags to flag compliance-critical components
- Building documentation templates for recurring tasks
- Linking Jira tickets to control requirements
- Proving consistency between development and production
- Validating access controls through automated checks
- Using linting rules to enforce documentation standards
Module 8. Preparing for Audit Cycles Without Panic
Turn audit preparation from a reactive scramble into a predictable rhythm.
12 chapters in this module
- Creating a calendar of compliance touchpoints
- Running internal mock audits on data systems
- Identifying high-risk areas before auditor arrival
- Organizing evidence folders by control domain
- Responding to auditor inquiries with precision
- Avoiding over-documentation while meeting standards
- Using past findings to prioritize current efforts
- Coordinating evidence collection across teams
- Handling follow-up questions efficiently
- Demonstrating improvement year over year
- Knowing when to involve legal counsel
- Exiting audits with fewer corrective actions
Module 9. Scaling Audit-Ready Patterns Across Projects
Extend compliance-aware practices beyond one-off projects to create lasting impact.
12 chapters in this module
- Identifying reusable compliance components
- Creating internal libraries for common controls
- Standardizing data documentation formats
- Onboarding new team members to compliance norms
- Sharing templates across data science pods
- Measuring adoption of audit-ready practices
- Recognizing compliance champions in technical teams
- Linking promotion criteria to cross-functional impact
- Demonstrating ROI of proactive compliance design
- Reducing audit prep time across quarters
- Scaling lessons from one system to another
- Building institutional muscle for future standards
Module 10. Anticipating Regulator and Customer Questions
Stay ahead of inquiries by designing systems that inherently answer scrutiny.
12 chapters in this module
- Common questions customers ask about data use
- Explaining model fairness in compliance contexts
- Documenting data sourcing for external validation
- Proving data isn't used beyond intended scope
- Handling requests for data deletion or correction
- Demonstrating model accuracy over time
- Responding to third-party security questionnaires
- Preparing for on-site customer audits
- Answering questions about third-party vendors
- Showing due diligence in algorithmic decision-making
- Proving ongoing monitoring of production models
- Communicating risk posture to non-technical clients
Module 11. Designing for Future Frameworks Beyond SOC 2
Lay foundations that support upcoming standards like ISO 27701 or GDPR.
12 chapters in this module
- How SOC 2 practices support future privacy audits
- Building data systems ready for ISO 27701 alignment
- Preparing for cross-border data transfer scrutiny
- Designing for CCPA and similar privacy laws
- Extending access logs to cover new regulations
- Using existing controls as a base for DORA readiness
- Anticipating AI-specific compliance frameworks
- Mapping current practices to NIST AI standards
- Preparing for algorithmic transparency laws
- Adapting to evolving definitions of 'sensitive data'
- Creating flexible classification schemes
- Future-proofing documentation with modular design
Module 12. Leading Without Authority in Compliance Conversations
Become the de facto reference point by combining technical depth with structured clarity.
12 chapters in this module
- Influencing peers through clear documentation
- Setting norms by example, not mandate
- Volunteering to lead cross-team readiness reviews
- Mentoring others on compliance-aware design
- Proposing improvements without overstepping
- Balancing velocity and rigor in sprint planning
- Earning trust from compliance and security teams
- Being the first called when audits begin
- Shaping internal best practices
- Extending influence beyond direct reports
- Measuring impact through reduced audit friction
- Becoming the practical authority on data compliance
How this maps to your situation
- High-growth tech compliance scrutiny
- Data science at intersection of innovation and control
- Cross-functional influence without formal authority
- Future-proofing systems against evolving standards
Before vs. after
Before
Building data systems that work technically but require rework when audited.
After
Shipping data solutions that are both innovative and inherently audit-ready, trusted across teams.
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 eight weeks, with flexibility to accelerate or pause.
If nothing changes
Without proactive design, even the most advanced systems face delays during compliance reviews, reducing your ability to scale work across departments.
How this compares to the alternatives
Unlike generic compliance trainings, this course is built specifically for data scientists in high-growth environments, focusing on real audit scenarios, reusable templates, and cross-functional credibility, not abstract rules.
Frequently asked
Is this course only for security or compliance roles?
No, it’s designed specifically for data scientists and machine learning engineers who need to meet compliance standards without sacrificing technical agility.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help me pass an SOC 2 audit?
Yes, by teaching you how to build systems and documentation that align with auditor expectations from the start.
$199 one-time. Approximately 90 minutes per week over eight weeks, with flexibility to accelerate or pause..
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