A tailored course, built for your situation
AI Governance Mastery: Implementing NIST, ISO 27001, and SOC 2 in High-Trust AI Systems
How to lock down high-trust AI systems with auditable, repeatable control mappings, no rework, no last-minute scrambles
$199 one-time
30-day money-back guarantee
Verified against latest insights, updated access provided within 24h
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.
Stop rebuilding AI governance evidence every audit cycle
The situation this course is for
Security and compliance leaders spend 80+ hours per audit collecting, aligning, and validating control evidence across AI systems, often reworking the same mappings due to inconsistent implementation, unclear ownership, or shifting scope. This drains focus from strategic risk work and creates last-minute scrambles when stakeholders finally engage.
Who this is for
Head of Information Security, Compliance, or Risk at a tech company building or deploying AI systems. They own or co-own audit readiness, control frameworks, and policy enforcement. They work cross-functionally with engineering, product, and legal. They need to prove compliance without blocking velocity.
Who this is not for
Individual contributors not responsible for audit outcomes, consultants selling governance as a service, or teams using AI only as end users (not builders or integrators)
What you walk away with
- Produce AI governance evidence packages in under 6 hours instead of 80+
- Own the final mapping of NIST AI RMF to ISO 27001 and SOC 2 controls
- Sign off on AI system deployment without waiting for external review
- Eliminate rework by standardizing control implementation across models
- Control the scope of AI audits before they begin
The 12 modules (with all 144 chapters)
Module 1. Align NIST AI RMF to Security and Compliance Frameworks
Map NIST AI Risk Management Framework functions to ISO 27001 and SOC 2 controls with precision.
12 chapters in this module
- Understanding the NIST AI RMF core functions and categories
- Mapping Govern function to ISO 27001 leadership and policy controls
- Translating Map function into SOC 2 criteria for risk assessment
- Integrating Measure into existing control testing cadences
- Aligning Manage function with incident response and audit readiness
- Crosswalking NIST subcategories to specific SOC 2 requirements
- Creating a unified control taxonomy across frameworks
- Using NIST AI RMF as a communication layer between teams
- Avoiding duplication in control implementation and evidence
- Documenting alignment for external auditor review
- Handling gaps between NIST guidance and auditor expectations
- Maintaining alignment as NIST and ISO standards evolve
Module 2. Define Control Ownership for AI Development Teams
Assign clear, non-overlapping control responsibilities across AI lifecycle roles.
12 chapters in this module
- Identifying key roles in AI system development and deployment
- Defining control ownership for data scientists and ML engineers
- Assigning evidence responsibilities to platform and DevOps teams
- Clarifying compliance ownership for product managers
- Setting boundaries between security, legal, and ethics teams
- Documenting RACI for AI-specific controls
- Avoiding duplication in control execution across teams
- Creating accountability without creating bottlenecks
- Handling ownership for third-party AI components
- Updating ownership with team and system changes
- Auditing ownership assignments for completeness
- Using ownership maps to accelerate evidence collection
Module 3. Design AI System Boundary Documentation
Create auditable system scoping documents that prevent audit scope creep.
12 chapters in this module
- Defining the scope of an AI system for compliance purposes
- Identifying in-scope components: models, data, infrastructure
- Documenting out-of-scope integrations and dependencies
- Creating data flow diagrams for AI training and inference
- Mapping human-in-the-loop and escalation points
- Specifying model update and retraining boundaries
- Handling multi-tenant AI service architectures
- Defining interfaces with non-AI systems
- Using boundary docs to limit audit requests
- Getting stakeholder sign-off on system scope
- Updating boundary documentation with system changes
- Linking boundary docs to control applicability
Module 4. Implement Automated Control Evidence Collection
Set up systems to generate compliant evidence without manual intervention.
12 chapters in this module
- Identifying evidence types that can be automated
- Using CI/CD pipelines to generate control outputs
- Capturing model versioning and training data provenance
- Automating access review logs for AI platforms
- Integrating logging with SOC 2 evidence requirements
- Setting up real-time monitoring for control deviations
- Generating ISO 27001 annex A control outputs automatically
- Validating automated evidence for auditor acceptance
- Handling exceptions and manual overrides
- Documenting automation for auditor review
- Scaling evidence collection across multiple AI systems
- Maintaining evidence integrity and chain of custody
Module 5. Standardize AI Risk Assessment Methodology
Create a repeatable process for assessing AI-specific risks across projects.
12 chapters in this module
- Defining risk criteria for AI systems
- Identifying common AI failure modes and impacts
- Assessing data quality and bias risks
- Evaluating model explainability and interpretability
- Scoring risks based on likelihood and business impact
- Integrating risk assessments into project intake
- Using risk scores to determine control intensity
- Documenting risk decisions for audit review
- Handling high-risk AI use cases
- Updating risk assessments with model changes
- Aligning risk methodology with NIST and ISO standards
- Training teams to conduct consistent risk assessments
Module 6. Document AI Model Development Lifecycle Controls
Apply security and compliance controls to each phase of model development.
12 chapters in this module
- Defining phases of the AI model lifecycle
- Implementing access controls for data scientists
- Securing training data storage and processing
- Validating data preprocessing and feature engineering
- Controlling model architecture and hyperparameter selection
- Ensuring reproducibility of training runs
- Implementing version control for models and code
- Securing model artifacts and checkpoints
- Validating model performance before deployment
- Controlling deployment to production environments
- Documenting lifecycle controls for auditors
- Auditing adherence to development controls
Module 7. Enforce AI System Monitoring and Logging
Design monitoring that detects AI-specific risks and generates compliance evidence.
12 chapters in this module
- Identifying key monitoring requirements for AI systems
- Logging model inputs, outputs, and metadata
- Detecting data drift and concept drift
- Monitoring for model degradation and performance drops
- Logging human review and override actions
- Capturing feedback loops and model corrections
- Setting up alerts for anomalous behavior
- Integrating logs with SIEM and security monitoring
- Retaining logs for audit and investigation
- Ensuring log integrity and non-repudiation
- Using logs to demonstrate control effectiveness
- Aligning monitoring with SOC 2 and ISO 27001 requirements
Module 8. Conduct AI System Attestation and Review
Run internal reviews that validate compliance before external audits.
12 chapters in this module
- Defining attestation scope for AI systems
- Scheduling regular review cycles
- Preparing evidence packages for reviewers
- Conducting cross-functional review meetings
- Documenting findings and remediation actions
- Tracking issues to resolution
- Verifying control effectiveness post-remediation
- Using attestations to improve control design
- Training reviewers on AI-specific risks
- Standardizing review checklists and templates
- Reporting results to leadership
- Using attestation data for continuous improvement
Module 9. Prepare for AI Audit Fieldwork and Requests
Respond to auditor inquiries quickly and confidently with pre-built evidence.
12 chapters in this module
- Anticipating common auditor questions for AI systems
- Preparing evidence packages before audit starts
- Creating a single source of truth for audit evidence
- Handling auditor requests for model access
- Responding to questions about model fairness and bias
- Providing evidence of training data controls
- Demonstrating model monitoring and incident response
- Handling requests for code and algorithm details
- Managing auditor access to systems and data
- Coordinating responses across technical and compliance teams
- Tracking and closing auditor findings
- Using audit feedback to improve future readiness
Module 10. Implement AI Incident Response and Escalation
Define processes for responding to AI system failures and compliance issues.
12 chapters in this module
- Defining AI-specific incident types
- Setting up detection for model failures
- Creating escalation paths for AI incidents
- Documenting incident response procedures
- Conducting root cause analysis for model issues
- Implementing corrective actions and model updates
- Notifying stakeholders of AI incidents
- Reporting incidents to regulators when required
- Documenting incidents for audit review
- Conducting post-mortems for AI failures
- Updating controls based on incident learnings
- Testing incident response plans regularly
Module 11. Maintain AI Compliance Documentation
Keep system documentation current and audit-ready at all times.
12 chapters in this module
- Identifying required compliance documents for AI systems
- Creating and maintaining a system security plan
- Documenting control implementation details
- Updating documentation with system changes
- Versioning and controlling document changes
- Storing documents in a secure, accessible repository
- Ensuring documentation meets auditor expectations
- Linking documents to control evidence
- Conducting regular documentation reviews
- Training teams on documentation requirements
- Using templates to ensure consistency
- Auditing documentation completeness and accuracy
Module 12. Scale AI Governance Across Multiple Systems
Extend governance practices consistently across an AI portfolio.
12 chapters in this module
- Assessing governance maturity across AI projects
- Prioritizing systems for governance rollout
- Creating reusable control implementations
- Standardizing documentation and evidence formats
- Training teams on governance practices
- Implementing centralized monitoring and reporting
- Managing governance for third-party AI services
- Handling custom vs. prebuilt model differences
- Scaling automation across multiple environments
- Consolidating audit evidence for group reviews
- Measuring governance effectiveness and efficiency
- Continuous improvement of AI governance program
How this maps to your situation
- Audit evidence flow
- Control ownership clarity
- System boundary control
- Automated compliance
Before vs. after
Before
Spending 80+ hours rebuilding AI governance evidence for each audit, chasing down attestations, and defending scope changes
After
Producing audit-ready evidence in 6 hours, owning the control mapping, and signing off on AI system deployment without escalation
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 12 weeks, or binge-ready in 18 focused hours.
If nothing changes
Without a standardized, automated approach, AI governance will continue to consume security and compliance bandwidth, delay deployments, and increase audit risk due to inconsistent evidence and last-minute fixes.
How this compares to the alternatives
Unlike generic AI ethics courses or high-level policy guides, this course delivers implementation-grade control mappings, evidence templates, and automation blueprints used by security leads at AI-first companies.
Frequently asked
Is this course focused on theory or implementation?
Implementation. Every module delivers concrete templates, control mappings, and execution steps used by compliance leads at AI companies.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help with upcoming audits?
Yes. The course includes templates and checklists designed to produce auditor-accepted evidence for NIST, ISO 27001, and SOC 2 reviews.
$199 one-time. Approximately 90 minutes per week over 12 weeks, or binge-ready in 18 focused hours..
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