A tailored course, built for your situation
Mastering SOC 2 for Machine Learning Engineers
Build compliance-native ML systems with confidence and clarity
$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.
ML systems fail audits not because of code quality, but because compliance was bolted on too late
The situation this course is for
Engineers build powerful models, only to see them delayed or reworked when compliance teams step in. The gap isn't technical, it's alignment. Without early integration of control expectations, even the most advanced pipelines face scrutiny, rework, and visibility gaps at leadership level.
Who this is for
Machine Learning Engineers in large-scale tech environments who are increasingly accountable for system compliance but lack structured guidance on how SOC 2 applies to their work
Who this is not for
Compliance auditors, security generalists without technical depth, or engineers who only work on non-production research models
What you walk away with
- Map SOC 2 controls directly to ML pipeline stages
- Anticipate audit questions before they’re asked
- Design access and logging layers that satisfy compliance reviewers
- Communicate system trustworthiness to non-technical stakeholders
- Turn compliance requirements into forward-compatible engineering decisions
The 12 modules (with all 144 chapters)
Module 1. Why SOC 2 Matters for ML Systems
Understand how SOC 2 is evolving beyond traditional IT into data-intensive engineering domains like ML. Learn the real-world audit triggers that bring ML pipelines into scope.
12 chapters in this module
- Defining SOC 2 in engineering terms
- When ML systems become compliance-relevant
- Common misconceptions about scope
- How Meta's scale increases scrutiny
- The shift from reactive to embedded compliance
- Audit trends impacting AI teams
- Key stakeholders in SOC 2 reviews
- How ICs influence control outcomes
- The role of evidence in automated systems
- Differences between SOC 2 and model governance
- Why logs alone are not enough
- Building compliance-aware development habits
Module 2. SOC 2 Trust Principles and ML Applications
Break down each of the five SOC 2 principles, Security, Availability, Processing Integrity, Confidentiality, and Privacy, and map them to specific components of ML pipelines.
12 chapters in this module
- Security as system integrity
- Availability in training pipelines
- Processing Integrity defined
- Model inputs and confidentiality
- Privacy considerations in feature data
- Mapping controls to pipeline stages
- False positives in anomaly detection
- When models violate integrity
- How redaction impacts compliance
- Versioning as a control mechanism
- Data lineage and audit trails
- Access reviews for model artifacts
Module 3. Integrating Controls into ML Design
Embed compliance from day one. Learn where to place controls in model development, training, deployment, and monitoring phases.
12 chapters in this module
- Pre-development control planning
- Access governance for datasets
- Authentication in distributed training
- Role-based permissions for fine-tuning
- Secure model checkpoint storage
- Encryption in transit and at rest
- Audit logging at inference time
- Automated policy enforcement
- Model card documentation standards
- Change management for retraining
- Drift detection as a control
- Decommissioning with compliance in mind
Module 4. Evidence Collection for Auditors
Produce the right documentation at the right time. Turn raw system behavior into auditor-ready evidence packages.
12 chapters in this module
- What auditors actually look for
- Logs vs. narratives
- Timestamp consistency across clusters
- Proving access controls were enforced
- Demonstrating change approvals
- Automating evidence generation
- Centralized logging strategies
- Annotating model decisions
- Version-controlled runbooks
- Using CI/CD logs as proof
- Retention policies for audit data
- Packaging evidence for non-technical review
Module 5. Access Governance in ML Workflows
Control who can access what in training, tuning, and deployment. Design identity-aware pipelines that satisfy compliance reviews.
12 chapters in this module
- Principle of least privilege for data access
- Service accounts and their risks
- Role definitions for ML teams
- Temporary access with time limits
- Approval workflows for permissions
- Monitoring for privilege creep
- Access revocation on team changes
- Multi-cloud permission alignment
- Breaking down silos without breaking controls
- Emergency access protocols
- Audit trails for access changes
- Zero standing access models
Module 6. Data Lifecycle and Compliance
Track data from ingestion to deletion with built-in compliance. Ensure every stage satisfies SOC 2 requirements.
12 chapters in this module
- Data classification at intake
- Labeling sensitive features
- Encryption key management
- Data retention schedules
- Deletion workflows for compliance
- Cross-border data flow controls
- Anonymization techniques
- Differential privacy in training
- Data provenance tracking
- Vendor data handling expectations
- Third-party dataset governance
- Data subject rights in ML contexts
Module 7. Model Deployment and Monitoring
Deploy models with compliance embedded. Monitor for control deviations and performance decay.
12 chapters in this module
- Pre-deployment compliance checklist
- Canary releases and control validation
- Rate limiting as a security control
- Input validation at endpoint level
- Model explainability for auditors
- Monitoring for bias drift
- Logging prediction patterns
- Alerting on anomalous outputs
- Version rollback with audit trail
- Performance vs. compliance trade-offs
- Scaling guardrails with traffic
- Incident response for model faults
Module 8. Change Management for ML Systems
Ensure every update to code, data, or infrastructure is tracked, approved, and auditable.
12 chapters in this module
- Change types that trigger reviews
- Automated approvals for low-risk changes
- Human review thresholds
- Version control for datasets
- Model registry as source of truth
- Backward compatibility obligations
- Rollback plans as compliance artifacts
- Documentation standards for changes
- Peer review integration
- Emergency change protocols
- Change tracking across microservices
- Integration with incident management
Module 9. Third-Party and Vendor Risk
Manage external dependencies in your ML stack while maintaining compliance accountability.
12 chapters in this module
- Vendor due diligence basics
- ML APIs and SOC 2 dependencies
- Contractual control expectations
- Subprocessor disclosures
- Auditing vendor compliance claims
- Open source model risks
- Pre-trained model provenance
- Supply chain integrity checks
- Firewalls around external data
- Rate limiting for vendor APIs
- Fallback strategies when vendors fail
- Exit strategies for vendor lock-in
Module 10. Communicating with Compliance Teams
Bridge the gap between engineering and compliance. Speak a shared language grounded in system reality.
12 chapters in this module
- Translating code into control language
- Avoiding auditor jargon
- What compliance teams really need
- Anticipating follow-up questions
- Preparing for walkthroughs
- Documenting design decisions
- Handling scope disagreements
- Negotiating control interpretations
- Using diagrams to clarify systems
- Writing audit-friendly summaries
- Managing conflicting priorities
- Building trust over time
Module 11. Incident Response for ML Systems
Respond to security events without compromising compliance posture.
12 chapters in this module
- Defining incidents in ML terms
- Detection of unauthorized access
- Model poisoning scenarios
- Data leakage indicators
- Alert triage workflows
- Containment without data loss
- Forensic data collection
- Chain of custody basics
- Notifying stakeholders
- Regulatory reporting triggers
- Post-mortem for compliance
- Lessons learned documentation
Module 12. Sustainable Compliance Practices
Turn one-off efforts into repeatable, team-wide patterns that compound over time.
12 chapters in this module
- Building internal playbooks
- Onboarding new engineers
- Mentoring on compliance topics
- Integrating checks into CI/CD
- Automated policy as code
- Template reuse across projects
- Knowledge transfer strategies
- Updating controls with tech changes
- Lessons from past audits
- Scaling practices across teams
- Measuring compliance efficiency
- Continuous improvement cycles
How this maps to your situation
- Designing a new ML pipeline with compliance in mind
- Responding to an internal SOC 2 scoping request
- Preparing for an external audit cycle
- Explaining model system compliance to non-technical reviewers
Before vs. after
Before
ML systems are audited reactively, with compliance gaps identified late, leading to rework and limited visibility beyond engineering
After
ML systems are built with compliance embedded, reducing audit friction and elevating engineering contributions to strategic level
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 total, designed to fit around core engineering responsibilities
If nothing changes
Without intentional integration of SOC 2 principles, even high-performing ML systems face rework, delayed deployment, and missed opportunities for leadership recognition. The trend is clear: compliance is no longer siloed, it’s part of the engineer’s domain.
How this compares to the alternatives
Unlike generic compliance courses, this program is built specifically for ML engineers working in high-scale environments. No theory without implementation. No auditor-first framing. Just precise, actionable integration of SOC 2 into real systems.
Frequently asked
Do I need prior compliance experience?
No. The course is designed for engineers who understand ML systems but want to speak confidently about compliance alignment.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help me in my current role at a large tech company?
Yes. The content is tailored for engineers at organizations like Meta where scale increases compliance scrutiny and visibility opportunities.
$199 one-time. Approximately 6-8 hours total, designed to fit around core engineering responsibilities.
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