A tailored course, built for your situation
Implementing AI Governance within Enterprise Data and Security Frameworks
Build authoritative control designs that withstand auditor scrutiny and scale with AI deployment
$199 one-time
30-day money-back guarantee
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Each order is checked and updated against the latest insights before delivery. That is why access takes up to 24 hours rather than being instant.
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.
Control narratives that get kicked back during SOC 2 evidence cycles
The situation this course is for
Security leaders spend weeks reworking AI governance controls to meet auditor expectations, often due to misalignment between technical implementation and compliance framing. The cost isn't just time, it's credibility during review cycles.
Who this is for
Senior security and IT executives (CISOs, SVPs) responsible for aligning AI deployments with compliance requirements, particularly in data-intensive environments. They own control design, audit readiness, and cross-functional alignment with data and engineering teams.
Who this is not for
Individual contributors focused only on technical AI development without compliance ownership, or auditors looking to assess controls rather than build them.
What you walk away with
- Design AI governance controls that align with SOC 2 Trust Services Criteria from day one
- Produce traceable, evidence-ready control narratives without rework
- Standardize control patterns across multiple AI models and data pipelines
- Reduce audit preparation time by structuring controls for clarity and coverage
- Lead cross-functional alignment between security, data governance, and AI engineering teams
The 12 modules (with all 144 chapters)
Module 1. Foundations of AI Governance in SOC 2 Context
Establish the link between AI system risks and SOC 2 control objectives, focusing on relevance to enterprise security leadership.
12 chapters in this module
- Defining AI governance scope within SOC 2 Trust Services Criteria
- Mapping AI risk domains to compliance control families
- Understanding auditor expectations for AI-related controls
- Differentiating ethical AI from compliance-grade control design
- The role of the CISO in shaping AI governance narratives
- Integrating AI governance into existing SOC 2 control frameworks
- Common gaps in AI control documentation observed in audits
- Aligning AI governance with enterprise data classification policies
- Control ownership models for AI systems across engineering teams
- Balancing innovation speed with compliance readiness
- Establishing governance boundaries for generative AI tools
- Documenting control intent for AI-specific risk scenarios
Module 2. Control Design for AI Data Lineage and Provenance
Build controls that track data flow through AI systems, ensuring auditability from source to output.
12 chapters in this module
- Designing controls for end-to-end data lineage in AI pipelines
- Verifying data provenance for training and inference stages
- Mapping data sources to SOC 2 data integrity requirements
- Control specifications for synthetic data usage in AI models
- Audit evidence requirements for data transformation steps
- Ensuring data retention policies apply to AI system outputs
- Controls for third-party data dependencies in AI training
- Validating data quality checks within AI preprocessing stages
- Documenting data access permissions for AI model training
- Designing automated data lineage verification workflows
- Handling PII and sensitive data in AI model inputs
- Control testing procedures for data provenance claims
Module 3. AI Model Access and Authentication Controls
Secure model endpoints and access paths with controls that satisfy SOC 2 access requirements.
12 chapters in this module
- Defining user roles and permissions for AI model access
- Implementing MFA and identity verification for model API calls
- Control design for service accounts accessing AI models
- Session management and timeout policies for AI interfaces
- Logging and monitoring access to AI model endpoints
- Segregation of duties for model deployment and access
- Controls for third-party integrations with AI systems
- Authentication audit trails for AI model usage
- Securing model weights and configuration files
- Access control for AI model retraining workflows
- Enforcing least privilege in AI development environments
- Reviewing access logs for anomalous AI system behavior
Module 4. Monitoring and Logging for AI System Behavior
Create monitoring controls that detect anomalies and ensure accountability in AI operations.
12 chapters in this module
- Designing logs for AI model inputs, outputs, and decisions
- Capturing metadata for AI inference and prediction events
- Control specifications for real-time anomaly detection
- Ensuring log integrity and immutability for AI systems
- Audit trail requirements for AI model updates and versioning
- Monitoring for model drift and performance degradation
- Logging explainability requests and outcomes
- Control design for AI system restart and recovery events
- Integrating AI logs with SIEM and security monitoring tools
- Retention policies for AI operational logs
- Controls for redaction and anonymization in AI logs
- Validating log completeness for audit evidence
Module 5. AI Risk Assessment and Control Mapping
Conduct AI-specific risk assessments and map findings to SOC 2 controls.
12 chapters in this module
- Scoping AI risk assessments for compliance relevance
- Identifying high-risk AI use cases by impact and likelihood
- Linking AI risks to SOC 2 Trust Services Criteria
- Documenting risk treatment decisions for audit review
- Control mapping for AI model bias and fairness concerns
- Risk assessment for AI dependencies on third-party APIs
- Evaluating model explainability requirements for controls
- Assessing AI system resilience to adversarial attacks
- Control mapping for AI model retraining frequency
- Risk treatment through technical, process, and policy controls
- Maintaining risk register alignment with control updates
- Reviewing risk assessment completeness with auditors
Module 6. AI Governance Documentation and Evidence Packaging
Produce clear, auditor-ready documentation for AI governance controls.
12 chapters in this module
- Structuring AI control narratives for auditor clarity
- Writing control descriptions that reflect actual implementation
- Including technical specifications in control documentation
- Designing evidence matrices for AI governance controls
- Compiling run books and operational procedures for review
- Documenting control testing methodologies and results
- Preparing AI-specific evidence for SOC 2 review cycles
- Versioning and change management for control documentation
- Using diagrams and flowcharts to illustrate AI controls
- Ensuring consistency between policy and practice in documentation
- Preparing for auditor walkthroughs of AI systems
- Responding to auditor inquiries on AI control design
Module 7. Third-Party AI Vendor Risk and Oversight
Manage vendor risks for AI tools and platforms with enforceable controls.
12 chapters in this module
- Assessing SOC 2 coverage for third-party AI vendors
- Defining contractual obligations for AI vendor controls
- Conducting due diligence on AI model training data sources
- Control design for API security in vendor AI integrations
- Monitoring vendor AI system uptime and performance
- Reviewing vendor update and patch management practices
- Ensuring data isolation in multi-tenant AI platforms
- Auditing vendor access to customer data in AI systems
- Managing sub-processor risks in AI vendor ecosystems
- Conducting on-site assessments of critical AI vendors
- Documenting vendor risk treatment decisions
- Termination and data extraction controls for AI vendors
Module 8. AI Incident Response and Breach Preparedness
Develop incident response plans that address AI-specific failure modes.
12 chapters in this module
- Identifying AI system failure scenarios for incident response
- Defining escalation paths for AI model malfunctions
- Incident classification criteria for AI-related events
- Containment strategies for compromised AI models
- Eradicating malicious inputs in adversarial attack scenarios
- Recovery procedures for corrupted model weights
- Communication protocols for AI incident disclosure
- Post-incident review processes for AI system flaws
- Integrating AI incidents into enterprise IR playbooks
- Testing AI incident response plans through tabletop exercises
- Maintaining logs for AI incident investigation
- Reporting AI incidents to regulators and stakeholders
Module 9. Model Validation and Testing Controls
Implement validation processes that ensure AI models operate as intended.
12 chapters in this module
- Designing test plans for AI model accuracy and fairness
- Control specifications for model validation environments
- Testing for bias in training data and model outputs
- Validating model performance across diverse input sets
- Ensuring reproducibility of AI model training runs
- Control design for model version comparison and rollback
- Testing explainability mechanisms for audit readiness
- Validating model security against adversarial inputs
- Documenting test cases and results for auditor review
- Automating regression testing for AI model updates
- Ensuring test data reflects production data patterns
- Reviewing test coverage completeness with QA teams
Module 10. AI System Change and Configuration Management
Control changes to AI systems with structured approval and tracking.
12 chapters in this module
- Defining change control scope for AI model updates
- Requiring approval workflows for AI system modifications
- Documenting configuration settings for AI environments
- Tracking model version deployments across environments
- Ensuring rollback capabilities for failed AI updates
- Reviewing change logs for unauthorized AI system edits
- Integrating AI changes into enterprise change management
- Testing updates in staging before production deployment
- Managing configuration drift in AI infrastructure
- Controlling access to AI model retraining pipelines
- Auditing change requests for compliance relevance
- Reporting on change success and failure rates
Module 11. AI Governance Automation and Tooling
Leverage automation to maintain consistent, auditable AI governance.
12 chapters in this module
- Automating control checks for AI data pipelines
- Using code scanning to enforce AI security policies
- Implementing policy-as-code for AI governance rules
- Automating evidence collection for SOC 2 reviews
- Integrating AI governance tools with CI/CD pipelines
- Monitoring AI model behavior with automated alerts
- Generating compliance reports from AI system logs
- Automating risk assessment updates based on new data
- Using version control for AI governance documentation
- Enabling self-service compliance for AI engineering teams
- Centralizing AI governance artefacts in a compliance hub
- Evaluating AI governance tooling against SOC 2 requirements
Module 12. Scaling AI Governance Across the Enterprise
Extend governance practices to support multiple AI initiatives.
12 chapters in this module
- Establishing a center of excellence for AI governance
- Standardizing control templates across AI projects
- Onboarding new AI use cases into governance frameworks
- Training engineering teams on compliance expectations
- Measuring AI governance maturity across business units
- Aligning AI governance with enterprise risk management
- Integrating AI controls into overall SOC 2 programs
- Reporting on AI governance performance to leadership
- Updating governance policies based on audit feedback
- Managing resource allocation for AI governance teams
- Scaling documentation and evidence processes
- Continuous improvement of AI governance practices
How this maps to your situation
- Control design for AI systems under SOC 2
- Evidence packaging for audit readiness
- Cross-functional alignment on AI governance
- Scaling governance across multiple AI deployments
Before vs. after
Before
AI governance controls are built reactively, require rework during audits, and lack consistency across teams.
After
AI governance is structured, auditable, and repeatable , producing clean evidence packages on demand.
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 week over six weeks, or bingeable in three focused sessions.
If nothing changes
Without structured governance, AI initiatives risk audit findings, delayed deployments, and increased remediation costs during compliance reviews.
How this compares to the alternatives
Unlike generic AI ethics courses or high-level compliance overviews, this program delivers implementation-grade control designs tailored to SOC 2 requirements and real audit cycles.
Frequently asked
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is this course technical or strategic?
It's implementation-focused , written for senior leaders who need to deliver auditable controls, not abstract frameworks.
Will this help with other frameworks like ISO 27001 or NIST CSF?
The control design principles transfer, but the course is optimized for SOC 2 audit readiness in AI systems.
$199 one-time. Approximately 90 minutes per week over six weeks, or bingeable in three focused sessions..
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