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SEC0410 Mastering SOC 2 for AI Infrastructure Engineers

$199.00
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A tailored course, built for your situation

Mastering SOC 2 for AI Infrastructure Engineers

Build audit-ready controls that scale with AI system complexity

$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.
Spending cycles defending controls instead of advancing architecture?

Who this is for

AI Engineer at mid-to-large tech firm, involved in SOC 2 audits, control design, or system governance. Works across security, compliance, and infrastructure teams. Has exposure to audit findings or control mapping but wants stronger technical grounding.

Who this is not for

Entry-level auditors, compliance generalists without technical AI experience, or practitioners focused solely on financial controls.

What you walk away with

  • Articulate SOC 2 control intent with technical precision in cross-functional settings
  • Produce evidence flows that match AI system architecture patterns
  • Anticipate auditor questions with source-backed rationale and real-world examples
  • Influence vendor selection and integration design with control-by-design input
  • Reduce rework cycles in audit preparation through first-time-right documentation

The 12 modules (with all 144 chapters)

Module 1. SOC 2 Trust Principles in AI Systems Context
Grounds the five SOC 2 trust service criteria, security, availability, processing integrity, confidentiality, and privacy, within the operational reality of AI infrastructure. Explores how model inference, data pipelines, and access controls intersect with auditor expectations. Builds a shared language between engineering and compliance teams.
12 chapters in this module
  1. Translating security criteria into AI system access controls
  2. Mapping availability requirements to model uptime SLAs
  3. Processing integrity in low-latency inference environments
  4. Confidentiality controls for prompt data and embeddings
  5. Privacy considerations in training data retention policies
  6. How audit scope differs for generative vs. predictive AI
  7. Integrating logging standards with trust principle evidence
  8. Model versioning as part of processing integrity
  9. Third-party API risk within SOC 2 scope boundaries
  10. Data lineage tracking for confidentiality assertions
  11. Distinguishing security from compliance in incident response
  12. Common misalignments between SOC 2 language and AI design
Module 2. Control Design for Distributed AI Architectures
Covers control implementation patterns in microservices, serverless, and Kubernetes-based AI deployments. Focuses on boundary definition, evidence automation, and auditor traceability. Addresses gaps that emerge when controls are applied post-deployment.
12 chapters in this module
  1. Defining control scope in containerized AI pipelines
  2. Network segmentation strategies for model serving layers
  3. Identity and access management in multi-tenant AI platforms
  4. Logging consistency across ephemeral inference nodes
  5. Automated evidence capture for dynamic workloads
  6. Control mapping for serverless function triggers
  7. Resilience testing within availability control requirements
  8. Configuration drift detection in AI model endpoints
  9. Patch management workflows for inference containers
  10. Secrets management in distributed AI environments
  11. Audit trail completeness in event-driven architectures
  12. Zero-trust patterns applicable to model access
Module 3. Evidence Mapping for AI Data Flows
Demonstrates how to align data lifecycle stages, from ingestion to inference, with SOC 2 evidence requirements. Emphasizes traceability from control statements to actual system behavior. Includes patterns for unstructured data and vector stores.
12 chapters in this module
  1. Data classification levels in AI input pipelines
  2. Evidence trails for prompt data handling
  3. Retention policies aligned with confidentiality commitments
  4. Data masking techniques in development environments
  5. Audit logging for embedding generation processes
  6. Access review evidence in AI data lakes
  7. Data subject rights fulfillment in model contexts
  8. Data provenance tracking for training sets
  9. Cross-border data flow documentation
  10. Metadata tagging for compliance automation
  11. Storage encryption evidence for vector databases
  12. Data deletion workflows in model caches
Module 4. Automating Control Validation in CI/CD
Integrates SOC 2 control checks into development pipelines using infrastructure-as-code and policy-as-code tools. Enables continuous compliance for AI model deployment. Reduces last-minute audit scrambles.
12 chapters in this module
  1. IaC scanning for security baseline compliance
  2. Policy-as-code gates in model promotion pipelines
  3. Automated access review reminders in DevOps tools
  4. Static analysis for secrets in AI training scripts
  5. Dynamic testing of authentication in inference APIs
  6. Compliance-as-code frameworks for SOC 2
  7. Automated evidence package generation
  8. Version control for control configuration drift
  9. Canary deployments and control validation
  10. Rollback criteria based on compliance failures
  11. Integration testing for privacy-preserving features
  12. Audit trail capture in deployment automation
Module 5. Vendor Risk in AI Supply Chains
Addresses SOC 2 dependencies on third-party AI tools, APIs, and cloud providers. Covers how to assess vendor compliance posture and document reliance. Includes real-world examples from hyperscaler integrations.
12 chapters in this module
  1. Evaluating SOC 2 reports from AI API providers
  2. Subservice organization scoping for model hosting
  3. Right-to-audit clauses in AI service contracts
  4. Evidence retention for external model inference
  5. Vendor incident response coordination protocols
  6. Data ownership clarity in third-party training
  7. Risk scoring for open-source AI components
  8. Compliance alignment with MLOps platform vendors
  9. Model provenance tracking from external sources
  10. API rate limiting as an availability control
  11. Penetration testing boundaries with third-party models
  12. Shared responsibility models for AI infrastructure
Module 6. Incident Response Planning for AI Systems
Aligns SOC 2 incident response requirements with AI-specific threats such as model poisoning, prompt injection, and data leakage. Builds response playbooks that satisfy auditor expectations while preserving system integrity.
12 chapters in this module
  1. Defining security incidents in model behavior
  2. Detection patterns for adversarial attacks
  3. Incident classification for AI-specific events
  4. Model rollback procedures as response action
  5. Forensic data preservation for inference logs
  6. Breach notification thresholds for AI systems
  7. Coordination between security and ML teams
  8. Post-mortem documentation for control gaps
  9. Simulating model integrity failures
  10. Legal hold processes for model artifacts
  11. Disclosure requirements for AI incident summaries
  12. Training data contamination response workflows
Module 7. Change Management for Model Deployment
Establishes SOC 2-compliant workflows for AI model updates, retraining, and rollback. Focuses on auditability, authorization, and version control. Prevents compliance gaps during rapid iteration.
12 chapters in this module
  1. Model version control as change management
  2. Approval workflows for production promotions
  3. Baseline configuration for inference endpoints
  4. Automated drift detection in model behavior
  5. Rollback validation against prior control state
  6. Change advisory board integration for AI
  7. Documentation requirements for model updates
  8. Retraining triggers and audit trail capture
  9. Model drift as a control failure indicator
  10. Peer review evidence in model governance
  11. Schema change impact on data controls
  12. Emergency bypass procedures with audit logging
Module 8. Access Governance in Multi-Model Environments
Designs role-based and attribute-based access controls for AI platforms supporting multiple models and teams. Ensures SOC 2 compliance while enabling collaboration. Addresses privilege creep in research settings.
12 chapters in this module
  1. Role definitions for AI developer access
  2. Attribute-based access for model endpoints
  3. Just-in-time access for data scientists
  4. Access review automation for AI teams
  5. Segregation of duties in model deployment
  6. Break-glass access with audit escalation
  7. Service account management in inference APIs
  8. Model access logging for confidentiality reviews
  9. Project-based access boundaries in AI platforms
  10. Federated identity in cross-org AI collaborations
  11. Temporary access with automatic expiration
  12. Access certification evidence for auditors
Module 9. SOC 2 Readiness for Generative AI
Adapts SOC 2 expectations to generative AI workloads including prompt engineering, content moderation, and hallucination risk. Builds controls that address novel compliance challenges while maintaining deployability.
12 chapters in this module
  1. Prompt input validation as a security control
  2. Content filtering evidence for regulated outputs
  3. Hallucination detection and mitigation logging
  4. Output review workflows for compliance assurance
  5. Model fine-tuning data provenance tracking
  6. Bias assessment integration into control design
  7. Copyright risk in AI-generated content
  8. Human-in-the-loop requirements for high-risk models
  9. Model watermarking for origin tracing
  10. Prompt injection attack resilience testing
  11. Contextual misuse detection in deployment
  12. Feedback loop logging for model correction
Module 10. Performance Monitoring and Availability Controls
Aligns system observability with SOC 2 availability and processing integrity criteria. Builds evidence from real user monitoring, error rates, and scalability testing. Supports uptime commitments.
12 chapters in this module
  1. Defining uptime for AI inference APIs
  2. Error rate tracking as processing integrity
  3. Latency benchmarks for availability compliance
  4. Auto-scaling evidence during traffic spikes
  5. Model cold-start detection and mitigation
  6. Failover testing documentation
  7. Dependency monitoring for third-party APIs
  8. Resource exhaustion prevention controls
  9. Performance degradation alerts
  10. Capacity planning evidence for auditor review
  11. Model degradation over time monitoring
  12. Degraded mode operation logging
Module 11. Audit Preparation and Evidence Packaging
Streamlines auditor engagement by structuring evidence around control objectives. Focuses on clarity, completeness, and traceability. Reduces follow-up cycles and documentation churn.
12 chapters in this module
  1. Control-to-evidence mapping templates
  2. Organizing evidence by trust service criterion
  3. Standardized naming for AI system components
  4. Versioning control documentation artifacts
  5. Cross-reference tables for auditor navigation
  6. Evidence sufficiency checklists
  7. Common auditor questions and prepared responses
  8. Artifact packaging for external review
  9. Gap analysis prior to formal audit
  10. Internal review workflows for completeness
  11. Evidence retention schedules
  12. Audit trail completeness validation
Module 12. Continuous Compliance for AI Evolution
Embeds SOC 2 thinking into ongoing AI system evolution. Builds feedback loops between audit findings, control updates, and engineering practices. Ensures long-term compliance without manual overhead.
12 chapters in this module
  1. Integrating audit findings into backlog prioritization
  2. Automated control testing for model retraining
  3. Compliance metrics in engineering dashboards
  4. Control ownership assignment in teams
  5. Update cadence for control documentation
  6. Lessons learned from past audits
  7. Cross-team knowledge transfer protocols
  8. Regulatory change impact assessment
  9. Control maturity benchmarking
  10. Compliance debt tracking and reduction
  11. Stakeholder communication templates
  12. Roadmap integration of compliance milestones

How this maps to your situation

  • Designing controls for new AI platform rollout
  • Preparing for first SOC 2 audit on generative AI service
  • Responding to auditor findings on evidence gaps
  • Scaling AI infrastructure with compliance automation

Before vs. after

Before
Spending cycles explaining control rationale and revising evidence after auditor feedback
After
Walking into reviews with structured, source-backed responses and pre-validated artefacts

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 90 minutes per module, designed for completion over 12 weeks with weekend study.

If nothing changes
Without structured control knowledge, AI projects face delays during audits, require costly rework, and expose teams to reputation risk when findings become public. Engineers lose influence when unable to respond confidently to compliance challenges.

How this compares to the alternatives

Unlike generic SOC 2 courses, this program focuses exclusively on AI infrastructure challenges, control mapping for dynamic workloads, evidence for model behavior, and compliance in MLOps pipelines. It’s built for engineers, not auditors.

Frequently asked

How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is this relevant if I'm not in security or compliance?
Yes. This is designed for engineers who own AI system design and deployment and need to meet audit requirements without slowing innovation.
$199 one-time. Approximately 90 minutes per module, designed for completion over 12 weeks with weekend study..

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
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