A tailored course, built for your situation
Deeper command of AI governance frameworks
Master the architecture, standards, and compliance layers shaping federal AI deployments
$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.
The situation this course is for
Who this is for
Senior governance practitioner in federal services with decision influence across risk, compliance, and technology delivery
Who this is not for
Junior analysts, tool-specific administrators, or consultants without federal program exposure
What you walk away with
- Cold fluency in NIST AI RMF and sector-specific extensions
- Precise control mapping between AI policies and audit evidence requirements
- Internal framework design authority without escalation
- Repeatable templates for AI impact assessments and risk tiering
- First-mover credibility on emerging OMB and OIRA guidance applications
The 12 modules (with all 144 chapters)
Module 1. AI Governance Foundations in Federal Context
Establish the core pillars of AI governance as applied to federal program delivery, including accountability chains, lifecycle oversight, and sector-specific compliance expectations.
12 chapters in this module
- Defining AI governance scope
- Federal decision rights by phase
- NIST AI 100-1 baseline mapping
- Risk tiers in public-sector AI
- Lifecycle checkpoints
- Ethical review integration
- OMB A-130 alignment
- Auditability from design
- Vendor oversight boundaries
- Stakeholder mapping
- Documentation standards
- Compliance timing gates
Module 2. Control Framework Integration
Map AI-specific controls to existing federal control environments including FISMA, FedRAMP, and internal risk frameworks.
12 chapters in this module
- FISMA control overlay
- FedRAMP AI extensions
- Control gap analysis
- Inherited vs new controls
- Automated evidence paths
- Continuous monitoring design
- Third-party attestation paths
- Control ownership models
- Exception handling
- Policy exception workflow
- Audit trail structure
- Review cycle alignment
Module 3. Risk Assessment and Tiering
Implement scalable risk tiering that aligns AI system impact with governance intensity and documentation requirements.
12 chapters in this module
- Impact dimension selection
- Harm likelihood modeling
- Tier assignment rules
- Human oversight thresholds
- Redress mechanisms
- Bias testing integration
- Data lineage checks
- Model monitoring scope
- Adaptive review frequency
- Cross-system interdependence
- Emergency response triggers
- Public transparency levels
Module 4. AI Impact Assessment Design
Build defensible, reusable AI impact assessments that satisfy compliance and stakeholder review.
12 chapters in this module
- Assessment scope definition
- Stakeholder input methods
- Bias and fairness metrics
- Transparency requirements
- Explainability thresholds
- Data provenance checks
- Model drift triggers
- Human-in-the-loop rules
- Documentation templates
- Review sign-off roles
- Version control approach
- Public release criteria
Module 5. Model Lifecycle Governance
Embed governance into each stage of the model lifecycle from ideation to retirement.
12 chapters in this module
- Project intake gates
- Pre-development review
- Data sourcing rules
- Development standards
- Validation rigor levels
- Deployment checklists
- Monitoring KPIs
- Incident response plan
- Retraining triggers
- Model versioning
- Decommissioning rules
- Archival requirements
Module 6. Cross-Agency Framework Alignment
Navigate overlapping guidance from NIST, OMB, OSTP, and sector-specific regulators.
12 chapters in this module
- NIST AI RMF structure
- OMB directives mapping
- OSTP principles integration
- Sector-specific overrides
- Agency variation tracking
- Priority gap analysis
- Compliance roadmapping
- Policy exception requests
- Interagency review prep
- Guidance change monitoring
- Internal update process
- Training rollout plan
Module 7. Ethical Review Integration
Operationalize ethical review as a repeatable function within AI governance workflows.
12 chapters in this module
- Ethics board composition
- Review submission process
- Criteria scoring rubric
- Bias mitigation review
- Human oversight rules
- Public harm assessment
- Transparency evaluation
- Stakeholder consultation
- Documentation standards
- Appeal process design
- Review timing gates
- Follow-up verification
Module 8. Audit and Oversight Readiness
Prepare for internal and external reviews with consistent, evidence-backed artefacts.
12 chapters in this module
- Audit scope definition
- Evidence package structure
- Control mapping format
- Documentation completeness
- Stakeholder alignment
- Pre-audit walkthroughs
- Response drafting
- Deficiency tracking
- Remediation workflows
- Follow-up evidence
- Review history archive
- Lessons learned integration
Module 9. Vendor and Third-Party Oversight
Extend governance to external partners and commercial AI tools with clear boundaries and expectations.
12 chapters in this module
- Vendor risk classification
- Contractual obligations
- Due diligence process
- Transparency requirements
- Audit rights negotiation
- Supply chain checks
- Model card standards
- Bias assessment sharing
- Incident notification
- Compliance attestation
- Performance monitoring
- Exit planning
Module 10. Workforce and Training Strategy
Scale governance awareness and capability across technical and non-technical teams.
12 chapters in this module
- Role-based training paths
- AI literacy baseline
- Developer guidelines
- Procurement training
- Ethics committee onboarding
- Manager responsibilities
- Incident reporting path
- Refresher intervals
- Competency assessment
- Certification paths
- Mentor network design
- Knowledge retention
Module 11. Incident Response and Escalation
Define clear pathways for identifying, assessing, and resolving AI-related incidents.
12 chapters in this module
- Incident definition
- Detection mechanisms
- Triage process
- Escalation criteria
- Stakeholder notification
- Public disclosure rules
- Remediation tracking
- Root cause analysis
- Policy update triggers
- Legal coordination
- Reporting format
- Post-mortem workflow
Module 12. Continuous Improvement and Evolution
Build feedback loops that update governance in response to technical, regulatory, and operational changes.
12 chapters in this module
- Change detection
- Stakeholder input channels
- Policy review cycle
- Lessons learned integration
- Benchmarking approach
- Peer review process
- Framework update workflow
- Change communication
- Training update rollout
- Version control
- Archival policy
- Stakeholder feedback
How this maps to your situation
- New AI project intake
- Pre-audit preparation
- Interagency guidance shift
- Incident response
Before vs. after
Before
Reliance on fragmented guidance and reactive compliance responses
After
Systematic command over AI governance frameworks and decision 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 3-4 hours per module, designed for integration into ongoing program cycles.
How this compares to the alternatives
Unlike generic AI ethics courses or tool-specific training, this program delivers structured mastery of federal AI governance frameworks with direct application to compliance, audit, and cross-functional leadership.
Frequently asked
Who is this course for?
Senior practitioners leading AI governance in federal contracting environments who need structured command over compliance, risk, and framework decisions.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Will this help me lead audits or reviews?
Yes, each module builds evidence-backed artefacts used in federal oversight, from AI impact assessments to control mappings and audit packages.
$199 one-time. Approximately 3-4 hours per module, designed for integration into ongoing program cycles..
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