A tailored course, built for your situation
Mastering SOC 2 for Senior Data Science Leaders
Turn model governance into a strategic asset with structured compliance outputs
$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.
Data science leaders stuck executing model tasks without shaping the governance framework
The situation this course is for
High-impact models get delayed because compliance workflows aren’t aligned with data science delivery timelines. Teams lack a repeatable way to generate audit-ready documentation, leading to rework and last-minute scrambles. The result? Missed opportunities to scale models enterprise-wide.
Who this is for
Director-level data science leaders in regulated financial services environments who own model delivery and are expected to demonstrate control maturity
Who this is not for
Individual contributors focused only on model building without ownership of workflow governance or compliance handoffs
What you walk away with
- Produce SOC 2-aligned model documentation that passes internal review without revision
- Establish reusable templates for data provenance, model drift monitoring, and access controls
- Lead the design of model risk controls without needing legal or compliance to initiate
- Position model governance as a core function under your existing scope
- Earn measurable expansion of decision rights around model deployment and monitoring
The 12 modules (with all 144 chapters)
Module 1. SOC 2 Foundations for Data Science Practitioners
Understand how SOC 2 maps to data science workflows, including scope boundaries, trust principles, and evidence requirements specific to modeling pipelines.
12 chapters in this module
- Mapping SOC 2 trust services criteria to model development stages
- Identifying which data science activities fall within SOC 2 scope
- Understanding auditor expectations for model documentation
- Differentiating SOC 2 Type I and Type II in practice
- How model risk management aligns with security and availability criteria
- Common misconceptions about SOC 2 applicability to ML systems
- Integrating compliance thinking into early model design phases
- Role of data lineage in meeting SOC 2 evidence requirements
- Defining system boundaries for model-intensive workflows
- Tracking changes to models and environments over time
- Documenting controls for data access and processing integrity
- Linking SOC 2 requirements to existing data governance practices
Module 2. Building Model Governance Within SOC 2 Boundaries
Design governance structures that operate within compliance scope while expanding influence over model lifecycle decisions.
12 chapters in this module
- Creating governance charters aligned with SOC 2 control domains
- Assigning accountability for model documentation upkeep
- Establishing review cycles tied to audit timelines
- Integrating model risk thresholds into control frameworks
- Documenting decision trails for algorithm changes
- Standardizing version control practices for compliance readiness
- Embedding control checks into model deployment pipelines
- Training team members on SOC 2 evidence expectations
- Auditing internal adherence to model governance standards
- Reporting on control effectiveness to oversight bodies
- Managing exceptions and remediations transparently
- Scaling governance as model portfolio grows
Module 3. Data Provenance and Lineage for Audit Readiness
Ensure every input, transformation, and output in a model workflow is traceable and defensible under SOC 2 scrutiny.
12 chapters in this module
- Defining minimum viable data lineage for SOC 2 compliance
- Automating metadata capture from database sourcing steps
- Mapping raw data sources to final model outputs
- Documenting preprocessing logic and assumptions
- Validating transformations across pipeline stages
- Storing lineage information in auditor-accessible formats
- Handling missing or incomplete provenance data
- Integrating lineage checks into CI/CD workflows
- Using visualization tools to simplify auditor review
- Maintaining lineage documentation between audits
- Responding to auditor follow-up on data paths
- Scaling lineage practices across multiple model teams
Module 4. Access Control Design for Model Systems
Architect role-based access that satisfies SOC 2 security requirements while supporting agile data science work.
12 chapters in this module
- Classifying model system components by sensitivity level
- Defining roles and permissions for data scientists and reviewers
- Implementing least privilege access in development environments
- Managing access to production model endpoints
- Tracking user activity for audit trail completeness
- Integrating SSO and MFA without disrupting workflows
- Automating access revocation for offboarded personnel
- Conducting periodic access reviews efficiently
- Documenting access control policies for SOC 2 reviewers
- Balancing security with experimentation needs
- Handling emergency access scenarios securely
- Scaling access frameworks across cloud and on-prem systems
Module 5. Change Management for Model Infrastructure
Implement structured change controls that meet SOC 2 availability and security criteria without slowing innovation.
12 chapters in this module
- Defining what constitutes a controlled change to models
- Establishing change approval workflows for model updates
- Documenting rationale for algorithmic and data changes
- Maintaining version history for model inputs and code
- Integrating change logs with monitoring systems
- Conducting pre-deployment testing within compliance bounds
- Notifying stakeholders of model changes systematically
- Auditing change records during SOC 2 preparation
- Managing rollback procedures for failed deployments
- Handling emergency changes under compliance rules
- Automating change documentation from version control
- Scaling change controls across model portfolios
Module 6. Monitoring for Model Drift and Performance
Design ongoing monitoring that satisfies SOC 2 requirement for continued control effectiveness.
12 chapters in this module
- Defining thresholds for acceptable model drift
- Tracking input data distribution shifts over time
- Implementing automated alerts for performance degradation
- Logging model predictions for retrospective analysis
- Scheduling regular model retraining cycles
- Validating new training data against compliance standards
- Documenting monitoring outcomes for auditors
- Linking model monitoring to access and change controls
- Using dashboards to maintain operational awareness
- Responding to drift alerts within compliance timelines
- Archiving monitoring records between audits
- Scaling monitoring across enterprise modeling efforts
Module 7. Risk Assessment Integration with Model Workflows
Embed formal risk assessments into model development cycles to meet SOC 2 proactive oversight expectations.
12 chapters in this module
- Conducting initial risk assessments for new models
- Classifying models by risk level based on impact
- Documenting risk mitigation strategies for each tier
- Integrating risk reviews into model approval gates
- Updating risk assessments after major changes
- Aligning model risk categories with SOC 2 domains
- Involving compliance teams at defined intervention points
- Using risk assessments to prioritize testing efforts
- Reporting risk posture to governance committees
- Maintaining risk documentation between audits
- Scaling risk frameworks across model pipelines
- Adapting risk approach based on regulatory feedback
Module 8. Compliance Documentation for Model Artifacts
Generate consistent, auditor-ready documentation for models and supporting infrastructure.
12 chapters in this module
- Creating standardized templates for model descriptions
- Documenting data sources and transformation logic
- Writing clear control narratives for SOC 2 reviewers
- Assembling evidence packs for each control point
- Linking documentation to actual system configurations
- Maintaining up-to-date system diagrams
- Describing backup and recovery procedures for models
- Documenting disaster recovery testing outcomes
- Storing documentation in auditor-accessible locations
- Versioning compliance artifacts alongside code
- Preparing documentation for external auditor access
- Scaling documentation practices across teams
Module 9. Audit Preparation and Response Tactics
Prepare for SOC 2 audits by aligning model governance outputs with reviewer expectations.
12 chapters in this module
- Understanding auditor timelines and request patterns
- Anticipating follow-up questions on model controls
- Organizing evidence to minimize auditor back-and-forth
- Conducting internal mock audits for readiness
- Training team members on auditor interaction protocols
- Responding to findings without overcommitting
- Tracking open items to closure efficiently
- Using audit feedback to improve controls
- Preparing executive summaries of compliance posture
- Coordinating responses across technical and compliance roles
- Maintaining composure during challenging review sessions
- Scaling audit readiness across multiple business units
Module 10. Cross-Functional Alignment on Model Governance
Lead collaboration between data science, compliance, and IT without ceding control.
12 chapters in this module
- Identifying key stakeholders in model compliance
- Establishing regular sync points with compliance teams
- Communicating technical details to non-technical partners
- Negotiating scope boundaries with legal and risk units
- Integrating feedback without losing momentum
- Building trust through consistent delivery
- Escalating blockers constructively
- Maintaining ownership while welcoming input
- Documenting agreements to prevent rework
- Scaling alignment across geographically distributed teams
- Adapting communication style for different functions
- Measuring success of cross-functional initiatives
Module 11. Scaling Model Governance Across Teams
Extend SOC 2-aligned practices across growing data science organizations.
12 chapters in this module
- Identifying common patterns across modeling projects
- Creating reusable governance components
- Onboarding new teams to standard practices
- Enabling self-service compliance documentation
- Maintaining consistency without central bottlenecks
- Adapting frameworks for domain-specific models
- Training leads to propagate best practices
- Monitoring adherence across decentralized teams
- Sharing learnings across model initiatives
- Optimizing resource allocation for compliance
- Evolving governance as organization scales
- Balancing standardization with innovation
Module 12. Long-Term Evolution of Model Compliance Programs
Position your team to lead ongoing improvements in model governance.
12 chapters in this module
- Tracking maturity of model compliance practices
- Benchmarking against industry peers
- Identifying opportunities for automation
- Advocating for investment in governance tools
- Incorporating lessons from audits into planning
- Staying ahead of evolving SOC 2 expectations
- Contributing to industry best practices
- Mentoring next-generation leaders in compliance
- Balancing short-term demands with long-term vision
- Measuring business impact of governance improvements
- Planning for future regulatory changes
- Establishing your team as a center of excellence
How this maps to your situation
- Model development lifecycle
- Compliance integration points
- Cross-functional coordination
- Audit and review readiness
Before vs. after
Before
Managing model delivery as a technical task with reactive compliance support
After
Leading model governance as a strategic function with formal authority and repeatable processes
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, or 36 hours total, designed for integration with existing workflows.
If nothing changes
Without structured governance, your team risks delayed model deployments, repeated auditor requests, and missed opportunities to expand your influence into adjacent domains.
How this compares to the alternatives
Unlike generic compliance trainings or vendor-led workshops, this course delivers role-specific, actionable guidance rooted in real-world data science leadership challenges and SOC 2 audit realities.
Frequently asked
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Who is this course best suited for?
Senior data science leaders in regulated industries who want to expand their governance authority within current roles.
Can I access templates and examples offline?
Yes, all templates and worked examples are downloadable for offline use.
$199 one-time. Approximately 3 hours per module, or 36 hours total, designed for integration with existing workflows..
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