A tailored course, built for your situation
Mastering SOC 2 for Senior Data Scientists and ML Analysts
Build audit-ready AI/ML systems with confidence and precision
$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.
Avoid last-minute audit rework in AI/ML projects
The situation this course is for
Data science teams waste days reconciling model outputs with SOC 2 control expectations because compliance was bolted on, not built in.
Who this is for
Senior data scientist or ML analyst in a global systems integrator; works across AI/ML delivery with compliance-adjacent stakeholders; needs to embed controls without slowing innovation
Who this is not for
Entry-level analysts, pure software developers, or compliance auditors without hands-on AI/ML delivery experience
What you walk away with
- Design AI/ML systems with SOC 2 controls embedded by default
- Produce audit-ready evidence packages in under 72 hours
- Reduce cross-functional chasing during compliance cycles
- Gain ownership of control mapping within AI projects
- Deliver with higher confidence when regulators ask follow-ups
The 12 modules (with all 144 chapters)
Module 1. Why SOC 2 Matters for AI and Machine Learning Today
Understand how SOC 2 is evolving to cover algorithmic systems and why it's no longer just for IT departments. Explore real cases where controls made the difference in client delivery.
12 chapters in this module
- How SOC 2 audits now include AI and data pipeline scrutiny
- The shift from infrastructure-only to model-behavior controls
- Why regulators are asking about model training data provenance
- How the firm teams are adapting to compliance-integrated AI delivery
- What a 'reasonable assurance' finding means for your project
- When SOC 2 scope expands to include third-party AI components
- How client procurement teams now demand SOC 2 alignment
- The role of data lineage in control design for ML systems
- Where model drift intersects with control effectiveness
- How to anticipate control gaps before audit season
- Why documentation maturity affects SOC 2 outcomes
- How to map model KPIs to control objectives
Module 2. Mapping SOC 2 Trust Services Criteria to ML Workflows
Translate abstract control criteria into concrete actions for data ingestion, preprocessing, model training, and deployment.
12 chapters in this module
- Aligning TSC Security with data access governance in ML pipelines
- Mapping Availability to model uptime and alerting design
- Processing Integrity in the context of prediction drift
- Confidentiality controls for sensitive training data
- Privacy Criteria and data anonymization traceability
- How fairness metrics support Processing Integrity claims
- Linking model monitoring to Availability reporting
- Data masking as a Privacy and Security control
- Control boundaries between data scientists and MLOps
- How feature store access fits into Security scope
- Model card documentation as evidence for Privacy
- Training data provenance for Processing Integrity
Module 3. Designing Control-Embedded ML Pipelines
Build pipelines that generate compliance evidence automatically as part of execution, not as a retrofit.
12 chapters in this module
- Automating data validation at ingestion with control logging
- Versioning models as a Security control
- Using CI/CD hooks to enforce control checks
- Audit trail generation for model retraining events
- Role-based access control in ML environments
- Logging data drift detections as control events
- Triggering alerts based on threshold violations
- Integrating model monitoring with compliance dashboards
- Tagging pipeline runs with control relevance
- Using metadata to auto-populate SoA sections
- Embedding control checks in data preprocessing steps
- Runtime validation of data schema against baseline
Module 4. From Model Output to Audit-Ready Evidence
Turn model behavior, logs, and documentation into organized, reviewer-friendly packages that pass scrutiny the first time.
12 chapters in this module
- Structuring model evidence packages for SOC 2 reviewers
- Selecting samples that demonstrate control effectiveness
- Documenting edge case handling in testing logs
- Creating traceable links between code and control claims
- Using model cards to support Privacy and Fairness assertions
- Generating control narratives from pipeline run data
- How to show consistency across model versions
- Presenting monitoring data in executive summary format
- Including drift detection results as part of evidence
- Capturing model performance metrics for reviewers
- Version control logs as proof of change management
- How to package artifact lineage for audit teams
Module 5. Automating Control Testing in ML Systems
Replace manual checklists with automated tests that run continuously and generate verifiable logs for auditors.
12 chapters in this module
- Unit testing for data schema compliance
- Automated fairness checks in model evaluation
- Testing data drift detection thresholds
- Validating logging output for audit trails
- Automated role check for control access
- Scripting control effectiveness tests for retraining
- Using synthetic data to test edge case handling
- Testing model monitoring alert thresholds
- Validating data masking rules in preprocessing
- Testing pipeline rollback procedures automatically
- Logging test results for SOC 2 evidence
- Scheduling recurring control validation runs
Module 6. Managing Third-Party AI Components Under SOC 2
Ensure vendor models, APIs, and open-source components don’t become compliance blind spots.
12 chapters in this module
- Assessing third-party AI model compliance posture
- Vendor questionnaires tailored to ML systems
- Evaluating API security and data handling practices
- Mapping external components into control scope
- Documenting reliance on third-party SOC 2 reports
- Handling model updates from external providers
- Data flow tracking across internal and external systems
- Using API logs as evidence for control effectiveness
- Ensuring compliance across model fine-tuning layers
- Capturing dependencies in model architecture diagrams
- Managing open-source library risks in ML pipelines
- Auditing vendor access to internal training data
Module 7. Articulating the Control Narrative for Stakeholders
Communicate how your ML systems meet SOC 2 standards clearly to non-technical reviewers and client executives.
12 chapters in this module
- Translating model metrics into control language
- Explaining drift detection as a control function
- Framing fairness tests as Processing Integrity
- Describing data governance for non-technical reviewers
- Using visuals to show control coverage in ML systems
- Writing control narratives that auditors trust
- Tailoring explanations for client leadership
- Turning technical logs into story-driven evidence
- Highlighting proactive risk mitigation in summaries
- Clarifying roles in control execution and monitoring
- Connecting model KPIs to business risk outcomes
- Avoiding jargon while preserving technical accuracy
Module 8. Integrating SOC 2 into Agile ML Development
Fit compliance requirements into sprint cycles without slowing innovation or creating rework.
12 chapters in this module
- Defining SOC 2 acceptance criteria in user stories
- Including control checks in definition of done
- Planning control documentation in sprints
- Synchronizing audit prep with sprint reviews
- Using backlog grooming to prioritize control work
- Tracking control implementation in Jira
- Assigning control ownership in stand-ups
- Using retrospectives to improve compliance practices
- Aligning sprint goals with audit timelines
- Managing debt in control implementation
- Estimating effort for compliance-related tasks
- Balancing speed and rigor in fast-moving projects
Module 9. Scaling Control Patterns Across AI Projects
Reuse proven control designs and automate documentation to handle multiple engagements efficiently.
12 chapters in this module
- Creating template pipelines with embedded controls
- Developing standard control narratives for reuse
- Building shared libraries for compliance testing
- Using pattern libraries for common ML architectures
- Documenting control design decisions for reuse
- Automating evidence package generation
- Standardizing data lineage tracking across teams
- Creating modular control components for reuse
- Sharing audit feedback to improve future projects
- Establishing internal review checkpoints
- Scaling compliance knowledge across delivery teams
- Using playbooks to accelerate onboarding
Module 10. Handling Model Updates and Retraining Under Controls
Manage model refreshes, fine-tuning, and data updates while maintaining compliance continuity.
12 chapters in this module
- Change control for model version updates
- Validating retraining against original scope
- Testing updated models for drift and fairness
- Updating control evidence after retraining
- Logging model updates for audit trails
- Ensuring data provenance remains intact
- Reviewing updated model cards for compliance
- Managing schema changes in training data
- Handling emergency model updates under controls
- Updating runbooks for new model behavior
- Communicating changes to compliance stakeholders
- Archiving previous model versions for audit
Module 11. Working Effectively with Compliance and Audit Teams
Bridge the gap between technical ML work and formal compliance processes with clarity and mutual respect.
12 chapters in this module
- Understanding auditor goals and timelines
- Anticipating common review questions on ML systems
- Providing timely, complete responses to requests
- Collaborating on control scope definition
- Clarifying technical realities to non-technical reviewers
- Building trust through consistent documentation
- Using plain language in control narratives
- Managing reviewer feedback efficiently
- Coordinating access to logs and systems
- Preparing for walkthroughs and evidence requests
- Following up on findings with actionable plans
- Sharing best practices across teams
Module 12. Future-Proofing AI Systems Against Evolving Standards
Stay ahead of changes in SOC 2, AI governance, and regulatory expectations with adaptable designs.
12 chapters in this module
- Tracking updates to AICPA guidance on AI
- Adapting to new TSC interpretations for machine learning
- Designing for regulatory changes in AI use cases
- Building flexibility into control frameworks
- Monitoring global AI regulation trends
- Preparing for ISO 42001 alignment with SOC 2
- Designing systems to accommodate new fairness standards
- Planning for explainability requirements
- Staying compliant as model complexity increases
- Using modular design to adapt to new controls
- Engaging early with evolving client expectations
- Anticipating new auditor focus areas in AI systems
How this maps to your situation
- Initial control design for AI/ML systems
- Ongoing compliance during model lifecycle
- Cross-functional collaboration with auditors
- Scaling and future-proofing across engagements
Before vs. after
Before
Spending days reconciling model outputs with SOC 2 expectations, scrambling during audit cycles, and losing ownership of compliance narrative in AI projects
After
Confidently delivering AI systems with controls embedded from the start, producing audit-ready evidence quickly, and expanding influence over compliance scope in current role
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, designed to be consumed in focused sessions over 3, 4 weeks.
If nothing changes
Without embedding controls early, data scientists risk late-cycle rework, audit findings, and losing ownership of compliance decisions to external teams.
How this compares to the alternatives
Unlike generic SOC 2 courses, this program is tailored to data scientists and ML analysts working in complex delivery environments. It focuses on practical implementation, not theory, and builds skills directly applicable to real-world AI projects under audit scrutiny.
Frequently asked
Is this course suitable for someone without a compliance background?
Yes. It's designed for technical practitioners who need to meet compliance requirements without becoming auditors.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Can I access the course materials after completion?
Yes. You'll have ongoing access to all content and updates.
$199 one-time. Approximately 3 hours per module, designed to be consumed in focused sessions over 3, 4 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