A tailored course, built for your situation
Mastering SOC 2 for Senior Data Scientists in Generative AI
Become the recognized authority on SOC 2 compliance in data-intensive AI environments
$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.
Frustrated by fragmented compliance advice that doesn't apply to AI systems?
The situation this course is for
Most SOC 2 guidance is written for IT or security teams, not data scientists building Generative AI pipelines. As a result, critical control decisions get delayed, reworked, or escalated unnecessarily, even when you have the deepest understanding of the data flow.
Who this is for
Senior Data Scientists leading Generative AI initiatives in regulated environments who need to align innovation with compliance without losing technical credibility
Who this is not for
Junior analysts, non-technical compliance staff, or practitioners outside data-intensive AI roles
What you walk away with
- Lead SOC 2 control mapping for AI/ML systems with confidence
- Produce audit-ready documentation that passes internal and external review
- Anticipate and resolve data access and retention control gaps before assessment
- Communicate effectively with audit, security, and engineering teams using shared frameworks
- Build reusable templates for future SOC 2 engagements in AI contexts
The 12 modules (with all 144 chapters)
Module 1. Introduction to SOC 2 in Data Science Contexts
Understand how SOC 2 trust principles apply specifically to Generative AI systems and data pipelines.
12 chapters in this module
- What SOC 2 means for data scientists
- The five trust service criteria
- Why AI creates unique compliance challenges
- Mapping data flows to compliance needs
- Common misconceptions among engineers
- How audits differ in machine learning environments
- The role of documentation in AI systems
- Understanding auditor expectations
- Key differences from ISO 27001
- SOC 2 Type I vs Type II in practice
- When to involve legal and risk teams
- Setting compliance goals early
Module 2. Data Integrity Controls for LLM Systems
Design controls that ensure reliable, auditable data handling in large language models.
12 chapters in this module
- Defining data integrity for AI
- Input validation strategies
- Logging prompt data securely
- Versioning training datasets
- Detecting data drift in production
- Access logging for audit trails
- Ensuring reproducibility
- Metadata tagging standards
- Handling PII in embeddings
- Secure pipeline checkpoints
- Model card documentation
- Audit readiness for data flows
Module 3. Access Control Design in AI Platforms
Implement role-based access that satisfies auditors while supporting development velocity.
12 chapters in this module
- Principle of least privilege in ML
- User provisioning workflows
- Authentication in notebook environments
- Service account governance
- API key management
- Role definitions for data teams
- Segregation of duties
- Emergency access protocols
- Just-in-time access models
- Audit logging for access events
- Reviewing access quarterly
- Integrating with IAM systems
Module 4. Confidentiality Frameworks for AI Outputs
Protect sensitive information in AI-generated content and downstream applications.
12 chapters in this module
- Classifying AI output sensitivity
- Redaction techniques
- Output filtering rules
- Secure delivery mechanisms
- Customer data isolation
- Encryption in transit and at rest
- Data residency constraints
- Third-party sharing risks
- Usage tracking for compliance
- Anonymization methods
- Retention policies
- Legal hold procedures
Module 5. Availability Assurance for AI Services
Ensure uptime and disaster recovery for AI systems without over-engineering.
12 chapters in this module
- Defining acceptable uptime
- Monitoring key endpoints
- Incident response workflows
- Failover strategies
- Resource scaling under load
- Dependency mapping
- Disaster recovery testing
- Backup strategies for models
- Capacity planning
- Notification systems
- Post-mortem documentation
- Linking availability to SLAs
Module 6. Security Controls for Model Deployment
Integrate security into CI/CD pipelines for Generative AI models.
12 chapters in this module
- Threat modeling for AI APIs
- Vulnerability scanning in pipelines
- Secure container practices
- Model signing and verification
- Tamper detection mechanisms
- Dependency audits
- Secrets management
- Network segmentation
- Rate limiting for APIs
- DDoS protection
- Penetration testing
- Zero trust principles
Module 7. Policy Documentation That Sticks
Write policies that are actually used in AI engineering workflows.
12 chapters in this module
- Writing actionable policies
- Version control for policy docs
- Linking policies to code
- Automated policy checks
- Training requirements
- Audit evidence collection
- Change management
- Policy review cycles
- Enforcement tracking
- Exception handling
- Stakeholder alignment
- Living documentation
Module 8. Control Mapping for AI Workloads
Translate SOC 2 requirements into technical controls specific to Generative AI.
12 chapters in this module
- Mapping TSC to AI systems
- Identifying control owners
- Determining control effectiveness
- Documenting control design
- Testing control operation
- Evidence gathering
- Control automation
- Exception tracking
- Third-party control reliance
- Vendor management
- Subservice organization oversight
- Control maturity assessment
Module 9. Audit Preparation and Evidence Gathering
Streamline SOC 2 audits with pre-built artefacts and team coordination.
12 chapters in this module
- Understanding auditor timelines
- Preparing evidence packets
- Assigning evidence owners
- Tracking open items
- Common findings in AI audits
- Remediation workflows
- Interview preparation
- Documenting compensating controls
- Change logs for audit
- System diagrams
- User access reports
- Final review checklist
Module 10. Cross-Functional Collaboration for Compliance
Lead compliance efforts without authority over other teams.
12 chapters in this module
- Building credibility with auditors
- Influencing security teams
- Partnering with legal
- Aligning with engineering leads
- Managing upward communication
- Running cross-functional meetings
- Conflict resolution
- Negotiating timelines
- Documenting agreements
- Escalation paths
- Tracking shared deliverables
- Creating shared ownership
Module 11. Continuous Compliance in Agile AI Teams
Maintain SOC 2 alignment in fast-moving development environments.
12 chapters in this module
- Integrating compliance into sprints
- Automated control monitoring
- Alerting on policy drift
- Change approval workflows
- Regular self-assessments
- Updating documentation
- Auditor communication cadence
- Handling new features
- Model retraining compliance
- Third-party updates
- Patch management
- Year-round readiness
Module 12. Strategic Positioning as a Compliance Leader
Become the internal reference point for SOC 2 in AI innovation.
12 chapters in this module
- Establishing thought leadership
- Mentoring junior team members
- Presenting at internal forums
- Writing internal whitepapers
- Hosting brown bags
- Contributing to standards
- Networking across departments
- Tracking industry trends
- Sharing best practices
- Building a personal brand
- Documenting impact
- Planning next steps
How this maps to your situation
- Leading SOC 2 implementation for AI systems
- Responding to auditor inquiries
- Designing compliant AI architectures
- Guiding cross-functional teams
Before vs. after
Before
Reactive participation in SOC 2 discussions, relying on others to define compliance for AI systems
After
Proactive leadership in SOC 2 design, with peers and stakeholders seeking your input on control decisions
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 of focused study, designed to fit around project commitments.
If nothing changes
Remaining a passive participant in compliance conversations means missed opportunities to shape AI systems from the start, leading to rework, delayed launches, and diminished influence in strategic discussions.
How this compares to the alternatives
Unlike generic compliance courses, this program is tailored to senior data scientists in Generative AI roles, with real-world examples, technical depth, and implementation tools that apply directly to your work.
Frequently asked
Is this course relevant if my organization hasn’t started SOC 2 yet?
Yes. This course prepares you to lead the initiative when the time comes, positioning you as the internal expert from day one.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Can I use this to support ISO 27001 or other frameworks as well?
Yes. While focused on SOC 2, the control design principles apply broadly to other standards like ISO 27001 and NIST CSF.
$199 one-time. Approximately 6-8 hours of focused study, designed to fit around project commitments..
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