A tailored course, built for your situation
Mastering SOC 2 for Lead Data Scientists
Gain full command of compliance frameworks shaping modern AI governance
$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.
Struggling to align advanced AI models with compliance expectations?
The situation this course is for
Data scientists are increasingly asked to justify model decisions within regulated frameworks, but most weren't trained in audit-ready documentation or control design. This gap leads to last-minute scrambles, rework, and diluted impact when governance teams step in.
Who this is for
Lead Data Scientist working at a digital services firm, fluent in LLM modelling and prompt engineering, now expected to deliver systems that meet compliance standards like SOC 2.
Who this is not for
This course is not for junior analysts, auditors, or compliance generalists looking for a high-level overview. It’s for hands-on data science leaders who own the architecture and governance of AI systems end to end.
What you walk away with
- Map SOC 2 controls directly to data pipelines and model outputs
- Produce audit-ready documentation as a byproduct of development
- Anticipate evidence requests before they’re formalized
- Speak confidently with internal audit and security teams using their framework
- Design compliant-by-default workflows for prompt engineering and finetuning
The 12 modules (with all 144 chapters)
Module 1. SOC 2 Fundamentals for Technical Practitioners
Understand SOC 2 beyond audit reports, learn how its five Trust Services Criteria apply directly to data pipelines, model training, and inference workflows.
12 chapters in this module
- What SOC 2 evaluates and why it matters now
- Difference between Type I and Type II in practice
- How AI systems trigger specific criteria
- Common misconceptions among engineers
- Regulator expectations on transparency
- Evidence lifecycle from design to audit
- Mapping controls to technical deliverables
- How LLM usage triggers security criteria
- Data privacy boundaries in training sets
- System availability in generative AI contexts
- Common failure points in documentation
- Preparing for first audit touchpoint
Module 2. Control Mapping for Data Science Workflows
Translate abstract SOC 2 controls into specific actions across model development, deployment, and monitoring phases.
12 chapters in this module
- Identifying control owners in AI teams
- Linking model cards to compliance artefacts
- Version control as evidence trail
- Model registry as control anchor
- Finetuning workflow documentation
- Prompt engineering audit trails
- Input validation and data provenance
- Access control for model endpoints
- Monitoring drift as control failure
- Retraining triggers and documentation
- API security and SOC 2 overlap
- Mapping CI/CD steps to controls
Module 3. Designing Audit-Ready Outputs
Build documentation that satisfies auditors without slowing down innovation, integrated into your existing workflow.
12 chapters in this module
- What auditors look for in AI systems
- Minimal viable documentation set
- SoA writing for technical teams
- Control descriptions that reflect reality
- Evidence collection without disruption
- Timestamping and chain of custody
- Internal review checklists
- Writing control narratives as code comments
- Automating evidence capture
- Storing artefacts for audit access
- Versioning policy for compliance
- Handoff protocols to governance teams
Module 4. Integrating SOC 2 into Prompt Engineering
Apply control thinking to prompt design, testing, and reuse, ensuring compliance without sacrificing agility.
12 chapters in this module
- Prompt versioning and traceability
- Access control for prompt libraries
- Security classification of prompts
- Prompt review and approval workflows
- Output filtering and content moderation
- Compliance tagging for prompt reuse
- Third-party prompt sourcing risks
- Prompt finetuning and IP boundaries
- Monitoring for unauthorized modifications
- Documentation templates for prompt sets
- Audit trail integration with orchestration
- SOC 2 alignment in RAG architectures
Module 5. Model Finetuning and Control Integrity
Ensure that every finetuning cycle maintains compliance boundaries and leaves a defensible trail.
12 chapters in this module
- Data provenance for finetuning sets
- Bias assessment as control activity
- Validation steps before deployment
- Versioning between base and tuned models
- Access control during training runs
- Logging hyperparameters as evidence
- Security review for model artifacts
- Approval gates before production push
- Drift monitoring post-deployment
- Retraining justification documentation
- Model rollback and compliance impact
- Handover from ML team to ops
Module 6. Data Lineage and SOC 2 Compliance
Establish clear, auditable trails from raw data to model output, meeting data integrity and confidentiality criteria.
12 chapters in this module
- Tracking data sources in training sets
- Documenting data transformations
- Classification of sensitive data types
- Encryption in transit and at rest
- Data retention policies in pipelines
- Provenance tagging in feature stores
- Third-party data vendor reviews
- Data anonymization techniques
- Audit trail integration with metadata
- Lineage visualization for non-technical reviewers
- Versioning across pipeline stages
- Control ownership at each data touchpoint
Module 7. Security Controls in AI Infrastructure
Align cloud infrastructure and MLOps tools with SOC 2 requirements, without over-engineering.
12 chapters in this module
- IAM policies for model access
- Network segmentation for inference APIs
- Vulnerability management in containers
- Secrets management for API keys
- Logging and monitoring setup
- Incident response planning for AI systems
- Penetration testing scope definition
- Change management for infrastructure
- Backup and disaster recovery
- Third-party dependency review
- SOC 2 alignment in CI/CD tools
- Cloud provider compliance settings
Module 8. Availability and Performance Monitoring
Meet SOC 2 availability criteria through robust monitoring, alerting, and uptime practices tailored to AI systems.
12 chapters in this module
- Defining uptime for AI services
- SLA tracking and reporting
- Error rate thresholds and alerts
- Model refresh schedules
- Monitoring for inference latency
- Auto-scaling compliance
- Incident logging and review
- Post-mortem documentation
- Capacity planning documentation
- DR testing for model endpoints
- Backup model availability
- Failover process documentation
Module 9. Privacy and Data Use Compliance
Ensure your models comply with confidentiality and privacy expectations under SOC 2 and beyond.
12 chapters in this module
- Identifying personal data in training sets
- Purpose limitation in model use
- Consent tracking in data pipelines
- Right to deletion in embeddings
- PII detection and redaction
- Cross-border data transfer review
- Data minimization in feature engineering
- User access rights to model outputs
- Automated data deletion workflows
- Vendor privacy assessments
- Privacy impact assessment templates
- Documentation for external review
Module 10. Cross-Functional Collaboration with Governance
Bridge the gap between data science and compliance teams, speak the same language and move faster.
12 chapters in this module
- Translating technical work into control terms
- Preparing for auditor interviews
- Collaborative evidence review
- JIRA integration for control tracking
- Aligning sprint planning with audit cycles
- Documentation handoff protocols
- Weekly syncs with compliance leads
- Escalation paths for control conflicts
- Training compliance team on AI specifics
- Building shared glossary
- Joint artifact creation
- Feedback loops from audit findings
Module 11. Continuous Compliance Automation
Embed compliance checks into your toolchain, ensuring ongoing adherence without manual overhead.
12 chapters in this module
- Automated control validation scripts
- Linting for compliance in code
- Policy as code frameworks
- Static analysis for security flaws
- Automated SoA updates
- Control dashboards for leadership
- Alerting on control drift
- Version-controlled documentation
- CI/CD gates for compliance
- Scheduled evidence collection
- Automated gap reporting
- Compliance scorecards
Module 12. From Project to Institutional Knowledge
Turn compliance achievements into repeatable, defensible practices that survive team changes.
12 chapters in this module
- Documenting tribal knowledge
- Creating onboarding materials
- Knowledge transfer sessions
- Standard operating procedures
- Maintaining compliance playbooks
- Succession planning for control owners
- Lessons learned from audits
- Updating templates post-audit
- Building internal training
- Measuring compliance maturity
- Sharing best practices across teams
- Positioning as internal expert
How this maps to your situation
- First SOC 2 audit preparation
- Post-audit compliance improvements
- Scaling AI governance across teams
- Integrating compliance into MLOps
Before vs. after
Before
Reactive documentation, fragmented control ownership, and last-minute scrambles to meet auditor requests
After
Proactive, integrated compliance, where audit-ready outputs emerge naturally from development workflows
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 completed in parallel with ongoing work, about 1.5 hours per week over 3 months.
If nothing changes
Without structured command of SOC 2, technical teams risk repeated audit findings, delayed system rollouts, and increasing friction with governance teams, all of which dilute the impact of advanced AI initiatives.
How this compares to the alternatives
Unlike generic compliance overviews or auditor-focused training, this course is built for data science leaders who must deliver systems that are both innovative and audit-ready. It goes deeper than checklists, giving you command over the framework so you can design with confidence.
Frequently asked
Is this course technical or compliance-focused?
It’s designed for technical leaders who need to master the compliance framework, not auditors. The focus is on applying SOC 2 to real data science work.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help with other frameworks like ISO 27001?
Yes, SOC 2 is foundational, and the control thinking transfers directly to other standards.
$199 one-time. Approximately 3 hours per module, designed to be completed in parallel with ongoing work, about 1.5 hours per week over 3 months..
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