A tailored course, built for your situation
Mastering SOC 2 for Senior AI & ML Practitioners
Build authority in compliance-critical AI delivery with structured, auditable frameworks.
$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.
Who this is for
Senior AI/ML engineer or technical lead working in regulated environments who needs to own compliance alignment without sacrificing delivery speed.
Who this is not for
Entry-level developers, non-technical compliance staff, or consultants focused solely on audit execution without technical implementation.
What you walk away with
- Own end-to-end SOC 2 control mapping for AI workloads without dependency on compliance teams
- Produce auditable artefacts that satisfy trust service criteria out of the gate
- Lead internal reviews and sign off on control evidence for AI projects
- Influence architecture decisions by embedding compliance into design patterns
- Expand project leadership scope to include data governance, access controls, and system integrity by default
The 12 modules (with all 144 chapters)
Module 1. SOC 2 Fundamentals for Technical Teams
Understand the five trust service criteria through the lens of AI system design, focusing on practical interpretation for engineers.
12 chapters in this module
- What SOC 2 really means for AI teams
- Security principle in model deployment
- Availability and uptime commitments
- Processing integrity in generative workflows
- Confidentiality of training data
- Privacy controls for inference outputs
- Difference between SOC 1, 2, and 3
- Role of auditor vs implementer
- Common misconceptions in tech orgs
- Mapping controls to development phases
- How reports are structured
- When SOC 2 applies to AI projects
Module 2. Control Mapping for Machine Learning Pipelines
Apply SOC 2 controls directly to data ingestion, model training, serving, and monitoring workflows.
12 chapters in this module
- Data lineage and provenance tracking
- Access controls on datasets
- Versioning model artifacts
- Audit logging in training jobs
- Output consistency guarantees
- Input validation for generative models
- Model card documentation standards
- Bias disclosure as control
- Human review escalation paths
- Anomaly detection integration
- Retraining trigger controls
- Model deprecation procedures
Module 3. Automating Evidence Collection
Turn operational telemetry into automatic compliance artefacts using Python tooling.
12 chapters in this module
- Logging framework alignment
- Auto-generating control reports
- Timestamp accuracy validation
- Immutable log storage patterns
- Role-based access reviews
- Scheduled access recertification
- Event correlation across systems
- Failure mode documentation
- Incident response logging
- Auto-updating policy attestations
- Integration with Jira workflows
- Evidence packaging for auditors
Module 4. Designing for Audit Readiness
Embed compliance into architecture decisions before development begins.
12 chapters in this module
- Pre-deployment control checklist
- Schema for model risk logs
- Access approval workflows
- Environment segregation
- Change management gates
- Backup and recovery testing
- Disaster recovery documentation
- Vendor risk input templates
- Third-party dependency tracking
- Model performance thresholds
- Fallback mechanism design
- Escalation path documentation
Module 5. SOC 2 and Generative AI Specifics
Address unique risks in LLMs, synthetic data, and prompt engineering.
12 chapters in this module
- Prompt injection prevention
- PII redaction in outputs
- Training data provenance
- Copyright compliance in outputs
- Hallucination logging
- Content filtering systems
- Moderation workflow design
- Bias testing protocols
- Synthetic data traceability
- Model watermarking
- Usage monitoring by tenant
- Rate limiting and abuse detection
Module 6. Stakeholder Communication Frameworks
Translate technical controls into clear, auditor-friendly narratives.
12 chapters in this module
- Writing control descriptions
- Evidence sufficiency thresholds
- Risk rating documentation
- Control exception handling
- Remediation workflows
- Management assertion drafting
- Audit preparation timeline
- Q&A preparation for interviews
- Clarifying scope boundaries
- Version control of documents
- Change logs for controls
- Cross-team alignment meetings
Module 7. Policy as Code Implementation
Turn written policies into executable checks and automated enforcement.
12 chapters in this module
- Defining policy rules
- Static analysis integration
- CI/CD gate checks
- Automated configuration validation
- Drift detection alerts
- Policy version tracking
- Change approval automation
- Remediation playbooks
- Compliance dashboard design
- Alert prioritization logic
- Escalation routing rules
- Review cycle automation
Module 8. Access Governance in AI Systems
Implement least privilege and just-in-time access across model platforms.
12 chapters in this module
- Role-based access design
- Attribute-based access controls
- Just-in-time provisioning
- Time-bound access grants
- Access request workflows
- Session recording
- Credential rotation policies
- Service account management
- Privileged access monitoring
- Break-glass access controls
- Access review automation
- Segregation of duties rules
Module 9. Vendor Risk and Third-Party AI Tools
Assess and monitor compliance posture of external AI platforms and APIs.
12 chapters in this module
- Third-party due diligence
- Contractual control commitments
- API security validation
- Data processing agreements
- Subprocessor tracking
- Audit rights negotiation
- Independent assessment review
- Security questionnaire design
- Continuous monitoring tools
- Risk tier classification
- Exit strategy documentation
- Fallback capability planning
Module 10. Incident Response for AI Systems
Define and document SOC 2-aligned response procedures specific to AI incidents.
12 chapters in this module
- Incident classification schema
- Model drift detection
- Output anomaly response
- Data poisoning response
- Adversarial attack mitigation
- Escalation path definition
- Forensic data preservation
- Post-mortem documentation
- Regulatory notification triggers
- Customer communication templates
- System recovery procedures
- Lessons learned integration
Module 11. Continuous Compliance Monitoring
Maintain compliance posture in dynamic, frequently updated AI environments.
12 chapters in this module
- Real-time control validation
- Automated drift detection
- Change impact assessment
- Control effectiveness metrics
- Threshold alerting
- Dashboard for stakeholders
- Monthly control reviews
- Quarterly attestation flows
- Audit trail completeness checks
- System integration testing
- Documentation update triggers
- Compliance health scoring
Module 12. Scaling Compliance Across AI Portfolios
Repeat and standardize compliance practices across multiple teams and projects.
12 chapters in this module
- Compliance playbook creation
- Template control documentation
- Centralized artefact repository
- Cross-team review cycles
- Compliance champion network
- Training materials for engineers
- Onboarding checklists
- Standard operating procedures
- Metrics for leadership reporting
- Lessons learned repository
- Framework evolution planning
- Future audit prep cycles
How this maps to your situation
- Starting a new AI project with compliance requirements
- Preparing for SOC 2 audit with AI components
- Responding to internal compliance review findings
- Expanding leadership scope over technical governance
Before vs. after
Before
Reliant on compliance teams to define controls, reactive to audit findings, limited discretion over governance decisions in AI projects.
After
Leads SOC 2 integration from project inception, owns control design and evidence, expands leadership scope within 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 for implementation alongside ongoing projects.
If nothing changes
Continued dependency on external teams for compliance decisions limits autonomy and slows innovation velocity in regulated environments.
How this compares to the alternatives
Unlike generic compliance courses, this program is tailored to AI/ML workflows and focuses on technical implementation, not just policy interpretation. Compared to vendor-specific training, it provides independent, actionable frameworks applicable across platforms.
Frequently asked
Is this course technical or managerial?
It's designed for technical practitioners leading AI projects who need to own compliance integration.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will I be able to apply this directly to my work?
Yes, every module includes templates and examples tailored to AI/ML implementations in regulated environments.
$199 one-time. Approximately 3 hours per module, designed for implementation alongside ongoing projects..
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