A tailored course, built for your situation
Mastering SOC 2 for CIO Advisors in Agentic AI Environments
Build authoritative, implementation-ready SOC 2 controls tailored to agentic AI and generative 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.
SOC 2 for AI systems is still new, and most frameworks don’t account for dynamic agent behavior or emergent risk patterns.
The situation this course is for
Generalist compliance teams apply legacy SOC 2 templates to AI systems, creating audit gaps and over-simplified controls that don’t reflect actual risk. Practitioners like Fred need to lead with technically accurate, defensible mappings, but often lack the structured method to do so independently.
Who this is for
Senior technical advisor or CIO-level practitioner guiding AI governance in complex enterprise environments, especially where agentic AI, RPA, and LLMs interact at scale.
Who this is not for
Entry-level auditors, non-technical compliance staff, or teams looking for checkbox compliance.
What you walk away with
- Design SOC 2 control objectives that reflect actual decision boundaries in agentic AI workflows
- Own the system boundary definition for AI-driven processes without senior review
- Select and justify evidence types specific to generative AI outputs and autonomous actions
- Map NIST CSF and ISO 27001 principles into SOC 2 frameworks without duplication
- Produce control narratives that stand up to AICPA scrutiny in hybrid human-agent environments
The 12 modules (with all 144 chapters)
Module 1. Foundations of SOC 2 in AI Systems
Understand how SOC 2 applies uniquely to autonomous and generative systems, including scope boundaries for agent-based decisioning.
12 chapters in this module
- Defining SOC 2 relevance for AI
- Key differences from traditional IT
- Types of AI in scope
- Controlled vs emergent behavior
- Regulatory traction points
- AICPA expectations update
- Evidence lifecycle basics
- Control sufficiency thresholds
- Common misapplications
- Scope creep warning signs
- Vendor-managed AI considerations
- First principles of trust services
Module 2. System Boundary Definition for Agentic Workflows
Accurately isolate components that fall under SOC 2 scrutiny when agents make unsupervised decisions.
12 chapters in this module
- Agent initiation triggers
- Human-in-the-loop thresholds
- Autonomous escalation paths
- Input validation controls
- Output distribution protocols
- Agent-to-agent handoffs
- Temporal scope of control
- Versioning and drift
- Environment isolation
- Change detection tolerance
- Recovery agent scope
- Boundary documentation format
Module 3. Control Objectives for Generative Outputs
Write control objectives that hold for unpredictable LLM-generated content, not just deterministic systems.
12 chapters in this module
- Output consistency standards
- Bias detection triggers
- Hallucination response protocols
- Context leakage prevention
- Citation integrity checks
- Template override logging
- Feedback loop monitoring
- Scoring threshold rules
- Human review thresholds
- Redaction automation
- Audit trail completeness
- Control durability over model updates
Module 4. Evidence Strategy for Autonomous Actions
Specify what counts as acceptable evidence when no human directly approves each action.
12 chapters in this module
- Automated logging fidelity
- Agent decision trees
- Confidence scoring capture
- Exception classification
- Peer validation mechanisms
- Sampling strategies for high-volume output
- Anomaly correlation logs
- Time-stamped execution records
- Role-based override tracking
- Incident rollback documentation
- Third-party validation hooks
- Evidence retention by agent role
Module 5. Trust Services Criteria in Dynamic Environments
Apply Security, Availability, Processing Integrity, Confidentiality, and Privacy in settings where AI adapts in real time.
12 chapters in this module
- Dynamic access control mapping
- Availability under load shifts
- Processing fidelity metrics
- Data handling in transient memory
- Privacy-preserving agent design
- PII detection in generated text
- Security event escalation paths
- Encryption of agent state
- Authentication of agent identities
- Integrity checks for fine-tuned models
- Change approval workflows
- Service commitment alignment
Module 6. Integration with ISO 27001 and NIST CSF
Leverage existing frameworks without duplicating effort or creating conflicting control narratives.
12 chapters in this module
- ISO 27001 clause crosswalk
- NIST CSF function alignment
- Control overlap resolution
- Single source of truth setup
- Audit efficiency gains
- Unified reporting structure
- Risk register synchronization
- Policy harmonization
- Control ownership matrix
- Exception tracking integration
- Tooling compatibility
- Cross-framework training requirements
Module 7. Vendor-Managed AI and Shared Responsibility
Clarify control ownership when third-party platforms host agentic AI components.
12 chapters in this module
- Responsibility boundary mapping
- SLA enforcement mechanisms
- Subprocessor visibility
- Audit rights negotiation
- Evidence portability
- Performance benchmark tracking
- Change notification protocols
- Security control validation
- Data location tracking
- Incident response coordination
- Exit strategy documentation
- Compliance communication templates
Module 8. Policy Design for Autonomous Escalation
Define policies that allow agents to escalate while maintaining compliance boundaries.
12 chapters in this module
- Escalation trigger thresholds
- Authorized recipient roles
- Context bundling rules
- Urgency classification
- Review queue prioritization
- Human acknowledgment logging
- Fallback response protocols
- Escalation fatigue prevention
- Multi-path resolution
- Time-bound auto-closure
- Escalation rejection handling
- Post-incident analysis integration
Module 9. Change Management for AI Models
Maintain SOC 2 continuity when models are retrained, updated, or replaced.
12 chapters in this module
- Model version tracking
- Retraining approval workflow
- Drift detection thresholds
- Control revalidation process
- Staging environment requirements
- Rollback readiness
- User notification protocols
- Impact assessment criteria
- Version documentation standards
- Model registry integration
- Baseline performance comparison
- Change audit trail structure
Module 10. Audit-Ready Documentation Pack
Assemble a defensible, concise package that satisfies external auditors and internal stakeholders.
12 chapters in this module
- Executive summary structure
- Control matrix formatting
- Narrative clarity standards
- Evidence indexing system
- Risk rating documentation
- Exception handling logs
- Stakeholder communication plan
- Q&A preparation
- Regulator-facing summary
- Internal distribution list
- Version control process
- Maintenance schedule setup
Module 11. Stakeholder Communication Framework
Communicate SOC 2 outcomes clearly to executives, legal, and technical teams without oversimplifying.
12 chapters in this module
- Executive briefing format
- Legal team alignment
- Technical team feedback loop
- Board-level summary
- Risk committee reporting
- Audit team coordination
- Vendor update protocol
- Cross-functional review cycle
- Status dashboard design
- Incident escalation comms
- Annual update planning
- Training material distribution
Module 12. Continuous Control Monitoring
Implement live oversight that ensures ongoing compliance without manual re-audits.
12 chapters in this module
- Automated control checks
- Anomaly detection setup
- Threshold alerting
- Daily control snapshot
- Weekly validation report
- Monthly attestation
- Quarterly deep-dive
- AI model behavior tracking
- User feedback integration
- External audit prep cycle
- Tooling integration points
- Team accountability rhythm
How this maps to your situation
- Designing first SOC 2 framework for AI system
- Responding to auditor questions on agent autonomy
- Justifying control scope to vendor partners
- Preparing internal stakeholders for audit
Before vs. after
Before
Relying on generic SOC 2 templates not built for AI autonomy, deferring control decisions to compliance teams, struggling to justify evidence strategies for generative outputs.
After
Owning end-to-end SOC 2 control design for AI systems, making final decisions on scope, evidence, and narrative, and leading audits with technical authority.
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 learning, plus 2, 3 hours implementing the playbook across your current project.
If nothing changes
Continuing to apply traditional SOC 2 approaches to AI systems increases audit risk, creates unnecessary rework, and cedes control to generalist teams unfamiliar with agent behavior.
How this compares to the alternatives
Unlike generic SOC 2 courses, this program is built specifically for AI-driven systems and focuses on tactical control design, not theory. Compared to vendor-specific training, it’s framework-agnostic and decision-focused, giving you independence from platform lock-in.
Frequently asked
Is this relevant if I’m not in a compliance role?
Yes. This course is designed for technical leaders, advisors, and architects who shape AI governance but don’t own compliance outright.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Does this cover SOC 1 or SOC 3?
No. This course focuses exclusively on SOC 2 for AI systems, with deep integration points into NIST CSF and ISO 27001 where relevant.
$199 one-time. Approximately 6, 8 hours of focused learning, plus 2, 3 hours implementing the playbook across your current project..
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