A tailored course, built for your situation
Mastering SOC 2 for Machine Learning Practitioners in Regulated Platforms
A 90-minute course to expand your remit in governance-critical AI systems
$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.
Most ML engineers never get credited for the compliance work they enable, but you can turn that invisible labor into formal decision authority.
The situation this course is for
Your model documentation, versioning standards, and pipeline audits already feed into SOC 2, but if you don’t own the narrative, someone else gets the mandate.
Who this is for
Senior IC in machine learning at a regulated tech platform, working at the intersection of model development and compliance assurance, aiming to lead without managing people.
Who this is not for
Managers outsourcing compliance, junior engineers learning fundamentals, or practitioners outside regulated AI domains.
What you walk away with
- Own the SOC 2 control narrative for ML systems end to end
- Structure audit-ready evidence flows from model training to deployment
- Lead cross-functional reviews with security and legal teams as the default participant
- Influence budget allocation for AI assurance tooling
- Set internal precedent on model risk classification and retention
The 12 modules (with all 144 chapters)
Module 1. Why SOC 2 is now a machine learning team responsibility
How SOC 2 scope has expanded into data lineage, model logging, and inference monitoring , and why ML teams now own core control domains.
12 chapters in this module
- How Shopify’s growth phase triggered deeper SOC 2 scrutiny
- The three ML-specific control domains added right now
- Why traditional IT compliance can’t handle model drift
- How data scientists became first-line control owners
- The shift from reactive audits to proactive control design
- When engineering teams became accountable for attestation
- How AI governance differs from general data compliance
- The role of model cards in evidence preparation
- Why version control systems are now audit interfaces
- How CI/CD pipelines trigger control validation
- The rise of automated attestation in cloud environments
- How ML teams now lead cross-functional control mapping
Module 2. Mapping SOC 2 controls to model development lifecycles
How to align training, validation, and deployment stages with Trust Services Criteria without slowing innovation.
12 chapters in this module
- Aligning model development sprints with control deadlines
- How to track access controls in feature stores
- Versioning pipelines as evidence of change management
- Logging inference requests against confidentiality criteria
- Training data provenance and integrity checks
- How model monitoring satisfies availability controls
- Integrating SOC 2 controls into MLOps checklists
- Automating control validation in CI/CD gates
- Documentation standards that pass auditor review
- Handling model updates under stability requirements
- The role of shadow deployments in control testing
- How rollback procedures satisfy recovery criteria
Module 3. Structuring model-specific evidence packages
What auditors actually look for in ML systems , and how to package it so it scales across reviews.
12 chapters in this module
- The five artefacts every ML audit requires
- How to structure model cards for compliance teams
- Version control logs as proof of change history
- Feature store access policies and audit trails
- Logging inference requests with consent flags
- Model drift reports as continuity evidence
- How bias assessments support fairness criteria
- Data retention policies for training datasets
- Exporting signed attestations from model registries
- Integrating security scanning into model packaging
- How explainability reports satisfy transparency standards
- Template: ML evidence package for SOC 2 reviewers
Module 4. Designing model access and authentication flows
How to implement and document access controls that satisfy SOC 2 without slowing development.
12 chapters in this module
- Role-based access for model development teams
- Service account management in MLOps pipelines
- How to track model deployment permissions
- Authentication controls for inference APIs
- Managing secrets in containerized environments
- Audit logging for model access events
- Temporary access for incident investigations
- Multi-factor enforcement for production changes
- How to log access review cycles for auditors
- Integrating IAM with model registry systems
- Handling contractor access in agile teams
- Template: Access control matrix for ML systems
Module 5. Documenting model risk classifications
How to define and justify risk tiers for models based on impact, usage, and data sensitivity.
12 chapters in this module
- Defining criteria for high-risk model classification
- How financial exposure informs risk tiers
- Customer-facing models and reputational risk
- Data sensitivity levels in model training
- Regulatory exposure by use case category
- How to document risk escalation thresholds
- Versioning risk assessments with model updates
- Stakeholder review cycles for risk classification
- Template: Model risk classification form
- How to justify low-risk classification to auditors
- Risk tiering across experimental vs. production models
- Integrating risk classification into CI/CD gates
Module 6. Integrating SOC 2 into MLOps pipelines
How to automate evidence generation and control validation without adding friction.
12 chapters in this module
- Embedding control checks in CI/CD pipelines
- Automated model card generation at deployment
- How to trigger attestation workflows from Git events
- Version locking for audit-ready models
- Integrating security scanning into model packaging
- Automated drift detection as control validation
- Logging model lineage in Kubernetes environments
- How to flag unapproved changes in staging
- Integrating SOC 2 checklists into pull requests
- Automated evidence bundling at release time
- How rollback procedures satisfy recovery criteria
- Template: MLOps SOC 2 integration checklist
Module 7. Leading cross-functional control reviews
How to run efficient, evidence-based sessions with security, legal, and compliance teams.
12 chapters in this module
- Preparing agendas that focus on control gaps
- Presenting model evidence without technical overload
- How to lead review meetings as the IC owner
- Handling legal team concerns about model use
- Addressing security team findings on access controls
- Documenting action items and ownership clearly
- Integrating feedback into model updates
- How to manage scope creep in compliance reviews
- Running time-boxed control validation sessions
- Using standardized templates to reduce rework
- How to escalate unresolved control issues
- Template: Cross-functional control review agenda
Module 8. Managing model incident response under SOC 2
How to document and report model incidents while maintaining compliance posture.
12 chapters in this module
- Defining what constitutes a model incident
- Classification of severity levels for model failures
- How to log incidents in audit-ready formats
- Communicating with stakeholders during investigations
- Documenting root cause analysis for auditors
- Integrating incident findings into model updates
- How to handle regulator inquiries about failures
- Maintaining confidentiality during public incidents
- Defining recovery criteria for model rollback
- Template: Model incident response playbook
- How to report patterns, not just single events
- Timing evidence submission after incident resolution
Module 9. Building reusable control patterns across models
How to scale compliance practices without duplicating effort.
12 chapters in this module
- Identifying common control domains across use cases
- Creating template evidence packages by model type
- Standardizing model card structures across teams
- How to reuse access control patterns
- Template: Model classification and control mapping
- Developing internal review checklists
- How to document precedent-setting decisions
- Sharing approved patterns across engineering pods
- Maintaining a living control library
- How to onboarding new teams to existing standards
- Versioning control templates with framework updates
- Template: Reusable control pattern repository
Module 10. Negotiating scope with auditors and legal teams
How to defend your control design and avoid over-compliance.
12 chapters in this module
- How to justify control boundaries for ML systems
- Presenting evidence that satisfies without over-sharing
- Handling auditor requests for unnecessary data
- Balancing privacy and compliance requirements
- How to push back on scope creep from legal teams
- Documenting rationale for control exceptions
- Using precedent to avoid redundant reviews
- How to align with business risk appetite
- Template: Scope negotiation response kit
- When to escalate control disputes to leadership
- Maintaining auditor trust through transparency
- How to close audit cycles efficiently
Module 11. Influencing tooling and budget decisions
How your control leadership translates into influence on AI platform investments.
12 chapters in this module
- How control ownership creates budget influence
- Building business cases for MLOps tooling
- Aligning tool selection with SOC 2 readiness
- How to justify investments in model monitoring
- Presenting cost-benefit analysis to finance teams
- Influencing vendor selection for AI infrastructure
- How to prioritize roadmap items with compliance impact
- Template: Tooling investment justification memo
- Documenting ROI for control automation
- How to position reliability as a growth enabler
- Linking control maturity to incident reduction
- Making the case for dedicated assurance roles
Module 12. Setting internal precedent as an IC leader
How to document and scale your approach so others follow.
12 chapters in this module
- How to formalize ad hoc control decisions
- Creating internal standards from successful patterns
- Documenting rationale for future reference
- How to share playbooks across engineering teams
- Presenting best practices as org-wide standards
- Gaining recognition without formal authority
- How to get leadership to endorse your approach
- Using peer influence to spread control practices
- Template: Internal standards submission package
- How to maintain ownership as teams grow
- Building legacy through reusable frameworks
- Scaling your mandate beyond single projects
How this maps to your situation
- After the first SOC 2 audit cycle involving ML systems
- When new data privacy regulations impact model training
- Before launching customer-facing AI features
- During platform-wide control standardization
Before vs. after
Before
Responsible for model development with compliance as a side outcome.
After
Mandate over the assurance framework for ML systems , shaping standards others follow.
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: 90 minutes total , designed to be completed in a single Sunday morning.
If nothing changes
Continue doing the work without claiming the ownership , let others define the controls for your systems and gain the influence you could have earned.
How this compares to the alternatives
Unlike generic SOC 2 courses, this is built specifically for ML practitioners in regulated platforms , no fluff, no theory, just the artefacts and decisions that expand your remit.
Frequently asked
Is this course technical or compliance-focused?
It’s both , it teaches ML engineers how to own the compliance narrative for their systems using technical artefacts and documented decisions.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help me lead without becoming a manager?
Yes , it’s designed for senior ICs who want to expand their influence through technical leadership and control ownership.
$199 one-time. 90 minutes total , designed to be completed in a single Sunday morning..
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