A tailored course, built for your situation
Mid-Market AI Vendor Risk Assessment for Public-Sector Programs
A 12-module implementation-grade course for business and technology leaders advancing AI governance in public-sector delivery
$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.
Gaps in vendor risk assessment can delay deployment, inflate compliance costs, and erode stakeholder trust, even when technology performs well.
The situation this course is for
Public-sector programs increasingly rely on mid-market AI vendors, yet standard risk frameworks aren't calibrated for their operating models. This creates blind spots in procurement, integration, and audit readiness, leading to rework, compliance friction, and missed delivery windows.
Who this is for
Business and technology professionals leading AI governance, risk, compliance, or delivery in public-sector programs or their vendor partners.
Who this is not for
Executives seeking high-level AI overviews, developers building core models, or individuals focused solely on consumer AI applications.
What you walk away with
- Apply a structured methodology to assess mid-market AI vendor risk in public-sector contexts
- Align vendor capabilities with regulatory and program-specific compliance requirements
- Identify hidden operational and data governance risks in AI procurement
- Lead cross-functional risk evaluation teams with confidence
- Deploy an implementation-ready playbook tailored to public-sector constraints
The 12 modules (with all 144 chapters)
Module 1. AI Risk in Public-Sector Ecosystems
Introduces the evolving role of AI vendors in government-adjacent programs and the unique risk profile of mid-market providers.
12 chapters in this module
- Defining public-sector AI dependencies
- Mid-market vs. enterprise vendor landscapes
- Risk dimensions in AI procurement
- Regulatory expectations by jurisdiction
- Stakeholder alignment challenges
- Case for proactive vendor assessment
- Common integration pitfalls
- Due diligence maturity models
- Ethical deployment benchmarks
- Data sovereignty considerations
- Performance vs. compliance trade-offs
- Emerging standards in AI governance
Module 2. Vendor Risk Assessment Frameworks
Covers established and emerging frameworks applicable to AI vendor evaluation in public programs.
12 chapters in this module
- Overview of NIST AI RMF alignment
- Mapping to ISO 38507 principles
- OCED AI principles in practice
- Sector-specific compliance overlays
- Risk tiering by program impact
- Third-party audit readiness
- Continuous monitoring models
- Assessment scoring methodologies
- Benchmarking against peer programs
- Documenting evaluation rationale
- Legal defensibility of findings
- Reporting to oversight bodies
Module 3. Due Diligence for AI Vendors
Details the step-by-step process for evaluating technical, operational, and governance readiness of mid-market AI vendors.
12 chapters in this module
- Technical architecture review
- Model transparency requirements
- Training data provenance checks
- Bias and fairness assessment
- Explainability and interpretability
- Validation and testing protocols
- Change management processes
- Incident response planning
- Human-in-the-loop safeguards
- Performance monitoring design
- Fail-safe and fallback mechanisms
- Post-deployment audit trails
Module 4. Compliance Mapping and Alignment
Teaches how to map vendor capabilities to public-sector compliance mandates and reporting requirements.
12 chapters in this module
- Identifying applicable regulations
- Mapping controls to obligations
- Privacy impact assessment integration
- Accessibility compliance checks
- Procurement regulation alignment
- Export control considerations
- Cybersecurity certification review
- Third-party dependency tracking
- Subcontractor oversight protocols
- Data residency and transfer rules
- Retention and deletion policies
- Audit documentation standards
Module 5. Data Governance and Sovereignty
Focuses on data lifecycle risks in AI vendor relationships and jurisdictional compliance.
12 chapters in this module
- Data classification frameworks
- Sovereignty by design principles
- Cross-border data flow rules
- Encryption and access controls
- Data minimization enforcement
- Anonymization effectiveness
- Retention and deletion workflows
- Breach notification readiness
- Data subject rights fulfillment
- Vendor data handling audits
- Chain of custody documentation
- Data stewardship roles
Module 6. Model Risk Management
Covers specialized risk assessment techniques for AI/ML models deployed by vendors.
12 chapters in this module
- Model validation fundamentals
- Performance decay monitoring
- Drift detection strategies
- Bias testing frequency
- Accuracy vs. fairness balance
- Model version control
- Retraining and rollback plans
- Model card completeness
- Systemic risk indicators
- Scenario stress testing
- Model inventory requirements
- Independent validation paths
Module 7. Operational Resilience Planning
Addresses business continuity, incident response, and service reliability in AI vendor relationships.
12 chapters in this module
- SLA and SLO evaluation
- Uptime and availability tracking
- Disaster recovery readiness
- Incident escalation paths
- Root cause analysis protocols
- Vendor resilience documentation
- Dependency failure modeling
- Redundancy and failover design
- Support response benchmarks
- Crisis communication plans
- Post-mortem review standards
- Resilience testing frequency
Module 8. Ethical and Social Impact Review
Guides assessment of AI vendor alignment with ethical AI principles and societal impact expectations.
12 chapters in this module
- Fairness and equity benchmarks
- Community impact assessment
- Stakeholder consultation practices
- Transparency and disclosure standards
- Accountability mechanisms
- Grievance redress systems
- Human oversight design
- Autonomy and consent principles
- Cultural sensitivity checks
- Long-term societal implications
- Public trust indicators
- Ethical audit frameworks
Module 9. Procurement and Contract Strategy
Teaches how to structure procurement processes and contracts to embed risk assessment outcomes.
12 chapters in this module
- RFP design for AI vendors
- Evaluation criteria weighting
- Risk-based selection scoring
- Contractual risk allocation
- Liability and indemnity terms
- Penalty and incentive structures
- Performance guarantee design
- Exit strategy planning
- Knowledge transfer requirements
- Audit rights and access
- Renewal and termination clauses
- Dispute resolution mechanisms
Module 10. Cross-Functional Team Leadership
Equips professionals to lead risk assessment across legal, technical, procurement, and program teams.
12 chapters in this module
- Stakeholder identification
- Role clarity and RACI design
- Communication cadence planning
- Conflict resolution strategies
- Consensus building techniques
- Documentation standards
- Decision traceability
- Escalation protocols
- Vendor engagement rules
- Negotiation preparation
- Reporting to leadership
- Team performance metrics
Module 11. Implementation Playbook Development
Guides creation of a customized, ready-to-deploy risk assessment playbook for ongoing use.
12 chapters in this module
- Template selection and adaptation
- Workflow integration planning
- Toolchain alignment
- Customization for program type
- Stakeholder onboarding
- Training and enablement
- Pilot testing design
- Feedback loop integration
- Version control process
- Scaling considerations
- Maintenance schedule
- Continuous improvement cycle
Module 12. Program Sustainability and Evolution
Ensures long-term effectiveness of vendor risk practices amid changing technology and policy landscapes.
12 chapters in this module
- Trend monitoring systems
- Policy change impact analysis
- Technology refresh planning
- Stakeholder expectation shifts
- Regulatory horizon scanning
- Capability maturity tracking
- Benchmarking against peers
- Lessons learned integration
- Succession planning
- Knowledge retention strategies
- Community of practice development
- Innovation adoption criteria
How this maps to your situation
- Assessing a new AI vendor for a public-sector pilot
- Scaling an existing AI program with multiple vendors
- Responding to an audit or compliance review
- Designing a new procurement process for AI-enabled services
Before vs. after
Before
Uncertainty in evaluating AI vendor claims, inconsistent assessment approaches, and reactive compliance efforts.
After
Confidence in leading structured, defensible, and repeatable AI vendor risk assessments aligned to public-sector demands.
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 self-paced learning with immediate applicability.
If nothing changes
Continuing without a structured approach can lead to vendor lock-in, compliance gaps, reputational exposure, and missed opportunities to shape ethical AI deployment.
How this compares to the alternatives
Unlike generic AI ethics courses or enterprise-focused vendor risk training, this program is specifically calibrated for mid-market AI vendors in public-sector contexts, combining technical depth with governance pragmatism.
Frequently asked
Who is this course designed for?
Business and technology professionals responsible for AI governance, risk assessment, compliance, or delivery in public-sector programs or their vendor partners.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is there a money-back guarantee?
Yes, a 30-day money-back guarantee is included.
$199 one-time. Approximately 3, 4 hours per module, designed for self-paced learning with immediate applicability..
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