A tailored course, built for your situation
Mastering SOC 2 for Senior AI Scientists in Life Sciences
Build compliance-ready AI systems with documented control frameworks that earn executive trust
$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.
AI projects stalling in audit review due to undocumented controls
The situation this course is for
High-performing AI teams ship models that work, but too often, they hit friction in assurance cycles. Without clear control mappings to SOC 2 criteria, even robust systems get delayed. The gap isn’t technical, it’s visibility. Work that meets standards never clears review because it wasn’t presented the right way.
Who this is for
Senior AI Scientist in regulated industries, delivering end-to-end machine learning systems with growing accountability for compliance readiness
Who this is not for
Entry-level data analysts, non-technical compliance staff, or practitioners focused solely on model accuracy without deployment context
What you walk away with
- Map AI system components directly to SOC 2 trust services criteria
- Document controls with evidence trails that satisfy auditor requests
- Produce a working System and Organization Controls (SoC) report for AI workloads
- Anticipate and resolve control gaps before audit cycle begins
- Position your technical work as leadership-ready assurance content
The 12 modules (with all 144 chapters)
Module 1. SOC 2 Fundamentals for AI Workloads
Establish core terminology and relevance of SOC 2 to AI systems in regulated environments.
12 chapters in this module
- What SOC 2 means for AI scientists
- Trust Services Criteria overview
- Type I vs Type II distinctions
- Why AI systems trigger scrutiny
- Common misalignments in model deployment
- Control objectives for data integrity
- Evidence expectations from auditors
- How AI fits into organizational scope
- Defining system boundaries
- The role of documentation in assurance
- Mapping model lifecycle to controls
- Case study: AI in clinical decision support
Module 2. Control Design for Model Pipelines
Design controls specific to data ingestion, preprocessing, and model training workflows.
12 chapters in this module
- Input validation controls
- Data lineage tracking methods
- Version control for training sets
- Model access restriction patterns
- Secure key management
- Environment segregation strategies
- Logging training parameters
- Audit trail retention policies
- Anomaly detection triggers
- Training data provenance
- Model drift detection controls
- Case study: batch retraining pipeline
Module 3. Security Controls for AI Systems
Implement access, authentication, and infrastructure safeguards aligned with SOC 2 Security principle.
12 chapters in this module
- Role-based access design
- Multi-factor enforcement points
- Network segmentation for AI workloads
- Endpoint protection integration
- Encryption at rest and in transit
- Key rotation schedules
- Secrets management tools
- Session timeout configuration
- Remote access controls
- Asset inventory for AI components
- Patch management tracking
- Case study: cloud-hosted inference API
Module 4. Availability and Monitoring Design
Ensure AI systems meet uptime and performance benchmarks expected in assurance reports.
12 chapters in this module
- Uptime SLA definition
- Monitoring stack integration
- Alert triage workflows
- Incident response coordination
- Failover mechanism validation
- Disaster recovery testing
- Capacity planning alignment
- Downtime documentation
- Third-party dependency tracking
- Performance benchmarking
- Load testing cadence
- Case study: real-time diagnostics engine
Module 5. Processing Integrity Validation
Verify outputs remain accurate and complete across operational cycles.
12 chapters in this module
- Output accuracy sampling
- Bias detection cadence
- Model calibration routines
- Input-output consistency checks
- Error rate thresholds
- Feedback loop integration
- Audit logging for decisions
- Reconciliation with ground truth
- Model rollback procedures
- Validation against regulatory rules
- Performance drift monitoring
- Case study: automated assay interpretation
Module 6. Confidentiality Frameworks
Apply encryption, access, and data handling rules to protect sensitive datasets.
12 chapters in this module
- Data classification schema
- Encryption key management
- Data masking techniques
- Access approval workflows
- Data retention rules
- Secure deletion methods
- Third-party data agreements
- Confidentiality training content
- Data sharing controls
- Anonymization vs pseudonymization
- Jurisdictional data flow mapping
- Case study: PHI in diagnostic models
Module 7. Privacy Controls for Personal Data
Implement lifecycle management for personal information used in training and inference.
12 chapters in this module
- Consent tracking mechanisms
- Right to be forgotten workflows
- Data minimization enforcement
- Purpose limitation alignment
- Subject access request handling
- Privacy notice integration
- Data sharing disclosures
- Vendor privacy assessments
- Children's data safeguards
- Cross-border transfer controls
- Privacy by design integration
- Case study: patient-reported outcomes
Module 8. Documentation and Evidence Assembly
Create audit-ready packages with consistent, standards-aligned artefacts.
12 chapters in this module
- Control description templates
- Evidence collection checklists
- Testing methodology design
- Sampling plans for auditors
- Remediation tracking systems
- Management assertions drafting
- Version control for documents
- Review cycles with legal
- Stakeholder sign-off workflows
- Control mapping matrices
- Artifacts for automated review
- Case study: internal audit prep
Module 9. SOC 2 Readiness Assessment
Evaluate readiness across all Trust Services Criteria using a structured gap analysis.
12 chapters in this module
- Readiness scoring rubric
- Internal control testing
- Control operating effectiveness
- Remediation prioritization
- Audit timing considerations
- Vendor control dependency
- Management review cycles
- Risk ranking of findings
- Preparation timeline design
- Stakeholder alignment points
- Final evidence collection
- Case study: pre-audit walkthrough
Module 10. Working with Auditors
Navigate audit engagements with confidence and precision.
12 chapters in this module
- Auditor selection criteria
- Scope definition negotiation
- Request list response strategy
- Evidence submission workflows
- Interview preparation
- Finding resolution process
- Management letter drafting
- Follow-up timing
- Coordination with legal
- Post-audit review steps
- Improvement planning
- Case study: first-time SOC 2 audit
Module 11. Scaling Compliance Across AI Projects
Replicate compliant patterns across multiple initiatives efficiently.
12 chapters in this module
- Template reuse strategy
- Centralized control registry
- Playbook distribution
- Team training delivery
- Compliance tracking dashboard
- Standard operating procedures
- Cross-project consistency
- Version updates and change control
- Lessons learned integration
- Maturity model progression
- External benchmarking
- Case study: enterprise AI governance
Module 12. Executive Communication and Influence
Present technical work in a way that earns leadership attention and sponsorship.
12 chapters in this module
- Translating controls to business risk
- Executive summary drafting
- Board-level narrative shaping
- Sponsor update templates
- Budget justification for compliance
- Strategic roadmap alignment
- Cross-functional influence tactics
- Credibility through consistency
- Positioning as go-to expert
- Thought leadership content
- Media and speaking opportunities
- Case study: executive visibility lift
How this maps to your situation
- Developing AI systems in regulated life sciences
- Facing internal audit or external assurance cycles
- Needing to demonstrate control maturity
- Positioning technical work for leadership recognition
Before vs. after
Before
Delivering technically sound AI systems that get questioned in assurance reviews due to undocumented controls
After
Producing SOC 2-ready AI systems with clear evidence trails and leadership visibility
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 alongside active project work.
If nothing changes
Without structured control documentation, even high-performing AI systems face delays, rework, and missed opportunities for recognition, despite meeting technical standards.
How this compares to the alternatives
Generic SOC 2 courses focus on IT controls. This course is built specifically for AI scientists who must embed compliance into model design and deployment, making it actionable where standard training falls short.
Frequently asked
Is this course relevant if I’m not in finance or SaaS?
Yes. SOC 2 applies to any organization processing data with systems. Life sciences, diagnostics, and lab automation are seeing rising assurance demands.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help me pass an actual SOC 2 audit?
Yes. The course teaches how to build systems and documentation that meet auditor expectations for AI workloads.
$199 one-time. Approximately 3 hours per module, designed to be completed alongside active project work..
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