A tailored course, built for your situation
Mastering AI Governance Frameworks for Technical Program Managers
Build auditable, scalable AI governance systems with confidence
$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 mappings that survive cross-functional scrutiny and audit cycles
The situation this course is for
Technical program managers in high-velocity AI environments often face last-minute rework on governance documentation, especially when control evidence needs to align across engineering, compliance, and product teams. This creates drag during critical review windows and slows down deployment timelines.
Who this is for
Technical Program Manager leading AI/ML initiatives in a large tech organization, responsible for translating governance requirements into executable plans and deliverables
Who this is not for
Individual contributors focused only on model development, or executives seeking high-level policy overviews without implementation detail
What you walk away with
- Design AI governance control mappings that pass internal review the first time
- Translate abstract compliance requirements into technical implementation plans
- Lead cross-functional alignment sessions with confidence using framework-backed reasoning
- Produce auditable documentation packages in under two weeks
- Anticipate auditor questions and embed evidence collection into program workflows
The 12 modules (with all 144 chapters)
Module 1. Foundations of AI Governance Frameworks
Understand the core components of leading AI governance standards including NIST AI RMF, OECD Principles, and internal platform guardrails. Learn how these map to technical program milestones.
12 chapters in this module
- Introduction to AI governance as a program management discipline
- Key differences between AI governance and traditional data governance
- Overview of NIST AI Risk Management Framework structure
- OECD AI Principles and their operational implications
- EU AI Act requirements relevant to platform development
- Meta’s public stance on responsible AI and internal expectations
- How governance frameworks align with product development lifecycles
- Role of the technical program manager in governance enforcement
- Common gaps in AI governance implementation at scale
- Mapping high-level principles to technical controls
- Establishing governance baselines for new AI initiatives
- Integrating ethical considerations into program planning
Module 2. Translating Policy into Program Plans
Convert abstract governance requirements into actionable program roadmaps with clear ownership, milestones, and deliverables.
12 chapters in this module
- Decoding compliance language into technical actions
- Identifying responsible parties for each governance control
- Building Gantt charts that reflect governance milestones
- Creating dependency maps between policy and engineering tasks
- Defining measurable success criteria for governance activities
- Scheduling evidence collection points in sprint cycles
- Aligning governance deliverables with product launch timelines
- Documenting assumptions and risk acceptances early
- Versioning governance plans alongside product iterations
- Managing scope changes without compromising compliance
- Using RACI matrices for governance accountability
- Communicating governance timelines to non-technical stakeholders
Module 3. Control Mapping for AI Systems
Develop comprehensive control mappings that link governance requirements to technical implementations across data, models, and infrastructure.
12 chapters in this module
- Structure of a complete AI control mapping document
- Linking NIST AI RMF functions to technical safeguards
- Mapping data provenance requirements to pipeline design
- Model card integration with governance frameworks
- Documentation requirements for training data curation
- Version control practices for model deployment tracking
- Bias detection mechanisms as auditable controls
- Performance monitoring as an ongoing governance activity
- Incident response planning within AI governance
- Human oversight protocols in automated decision systems
- Third-party model risk assessment procedures
- Finalizing control mappings for stakeholder review
Module 4. Evidence Collection Workflows
Design efficient, repeatable processes for gathering and organizing evidence required for internal and external audits.
12 chapters in this module
- Types of evidence required for AI governance validation
- Automating log collection from model serving infrastructure
- Capturing model performance metrics for compliance
- Storing documentation in audit-ready formats
- Establishing evidence ownership across teams
- Scheduling periodic evidence refreshes
- Version control for governance documentation
- Redacting sensitive information while preserving audit trail
- Using metadata to enhance evidence discoverability
- Integrating evidence collection into CI/CD pipelines
- Validating completeness of evidence packages
- Preparing evidence binders for external reviewers
Module 5. Cross-Functional Alignment Tactics
Lead effective collaboration between engineering, legal, product, and compliance teams to ensure governance requirements are met without slowing innovation.
12 chapters in this module
- Identifying key stakeholders in AI governance programs
- Conducting alignment workshops with technical teams
- Translating legal requirements for engineering audiences
- Facilitating trade-off discussions between speed and safety
- Documenting decisions and rationale for future reference
- Managing conflicting priorities across functions
- Building trust through transparency in governance processes
- Creating shared dashboards for governance status
- Running governance checkpoint meetings efficiently
- Escalating unresolved issues with clear context
- Onboarding new team members to governance expectations
- Sustaining alignment throughout long-term projects
Module 6. Audit Preparation and Response
Prepare for internal and external audits with confidence by organizing documentation, anticipating questions, and leading response efforts.
12 chapters in this module
- Understanding different types of AI audits and reviewers
- Preparing for internal compliance reviews
- Responding to external auditor information requests
- Conducting pre-audit readiness assessments
- Organizing documentation for easy retrieval
- Anticipating common auditor questions on AI systems
- Developing consistent answers to technical inquiries
- Coordinating team availability during audit periods
- Addressing findings and creating remediation plans
- Tracking open items to closure with evidence
- Improving processes based on audit feedback
- Building institutional memory from audit experiences
Module 7. Governance Automation Strategies
Identify opportunities to automate repetitive governance tasks and integrate controls directly into development workflows.
12 chapters in this module
- Assessing automation potential in governance processes
- Integrating model cards into MLOps pipelines
- Automated bias testing in continuous integration
- Policy-as-code approaches for AI governance
- Using infrastructure-as-code to enforce controls
- Automated documentation generation from code
- Real-time monitoring dashboards for governance metrics
- Alerting on policy violations in development environments
- Automated evidence collection triggers
- Version-controlled governance rule sets
- Testing automated controls before deployment
- Maintaining human oversight in automated systems
Module 8. Stakeholder Communication Frameworks
Develop clear, consistent messaging strategies for communicating governance status and decisions to technical and non-technical audiences.
12 chapters in this module
- Tailoring governance updates for executive audiences
- Creating technical deep dives for engineering teams
- Developing FAQ documents for common questions
- Visualizing governance maturity over time
- Reporting on risk mitigation progress
- Communicating trade-offs in plain language
- Handling sensitive governance issues discreetly
- Building trust through proactive disclosure
- Creating standardized update templates
- Measuring stakeholder understanding of governance
- Adjusting communication style based on audience
- Archiving communications for audit purposes
Module 9. Incident Response and Governance
Integrate governance considerations into AI incident response planning and execution.
12 chapters in this module
- Defining AI incidents versus system outages
- Including governance leads in incident response teams
- Documenting incidents for compliance purposes
- Assessing regulatory reporting requirements
- Conducting post-incident governance reviews
- Updating controls based on incident findings
- Communicating incidents to external parties
- Preserving evidence during incident investigations
- Training teams on governance aspects of incidents
- Testing incident response plans with governance focus
- Integrating lessons learned into program improvements
- Maintaining transparency while protecting legal position
Module 10. Scaling Governance Across Programs
Extend successful governance approaches from individual projects to organization-wide standards and practices.
12 chapters in this module
- Identifying reusable components across AI initiatives
- Creating governance playbooks for common use cases
- Standardizing documentation templates enterprise-wide
- Training program managers on governance best practices
- Establishing centers of excellence for AI governance
- Developing certification programs for governance readiness
- Measuring governance maturity across teams
- Benchmarking against industry standards
- Sharing lessons learned across projects
- Adapting governance for different risk levels
- Managing exceptions with proper oversight
- Ensuring consistency without stifling innovation
Module 11. Third-Party and Supply Chain Governance
Extend governance controls to external vendors, open-source components, and partner integrations.
12 chapters in this module
- Assessing AI risk in third-party solutions
- Conducting due diligence on external model providers
- Reviewing open-source model licenses and provenance
- Establishing contractual governance requirements
- Monitoring third-party performance and compliance
- Integrating external models into internal governance
- Managing data sharing agreements securely
- Auditing partner compliance with your standards
- Handling incidents involving third-party components
- Documenting supply chain dependencies
- Creating exit strategies for third-party solutions
- Maintaining governance continuity during transitions
Module 12. Continuous Governance Improvement
Implement feedback loops and improvement cycles to keep governance practices current and effective.
12 chapters in this module
- Collecting feedback from audit and review processes
- Analyzing governance metrics for trends
- Soliciting input from cross-functional partners
- Benchmarking against evolving regulatory requirements
- Updating control mappings based on new threats
- Incorporating lessons from industry incidents
- Adjusting governance intensity by project risk level
- Training teams on updated policies and procedures
- Documenting changes to governance approach
- Communicating improvements to stakeholders
- Planning for future governance challenges
- Graduating from compliance to competitive advantage
How this maps to your situation
- Control mapping under audit pressure
- Cross-functional alignment on governance requirements
- Evidence collection during product launch cycles
- Scaling governance practices across AI initiatives
Before vs. after
Before
Spending weeks assembling control mappings that still require rework during reviews
After
Producing audit-ready governance packages in days with confidence
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 total, designed to be completed in short sessions over a few weeks.
If nothing changes
Without structured governance practices, even well-intentioned programs face delays during compliance reviews, increased rework, and potential reputational risk if AI systems encounter scrutiny without proper documentation.
How this compares to the alternatives
Unlike generic AI ethics courses or high-level policy overviews, this program focuses specifically on the implementation work of technical program managers, translating governance into action, creating auditable artifacts, and leading cross-functional execution.
Frequently asked
Is this course focused on Meta-specific policies?
No. While the content acknowledges Meta's public commitments to responsible AI, the course focuses on industry-standard frameworks like NIST AI RMF and OECD principles that apply across organizations.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help me with internal audits?
Yes. The course provides templates and methods specifically designed to meet the demands of internal compliance reviews and cross-functional scrutiny.
$199 one-time. Approximately 6-8 hours total, designed to be completed in short sessions over a few weeks..
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