A tailored course, built for your situation
Compliance-Ready AI Vendor Risk Assessment for Public-Sector Programs
Master implementation-grade risk governance for AI vendors in public-sector technology programs
$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.
Public-sector AI initiatives are expanding faster than risk oversight can keep up, leaving teams to navigate compliance gaps without clear frameworks.
The situation this course is for
Teams are expected to approve AI vendor solutions quickly, yet lack standardized, compliance-ready methods to assess risk across data, security, ethics, and performance. Without structured guidance, assessments become inconsistent, reactive, or overly reliant on legal teams, slowing delivery and increasing exposure.
Who this is for
Business and technology professionals in public-sector or public-facing technology programs who own or influence AI vendor evaluation, procurement, or compliance governance.
Who this is not for
This is not for individuals seeking introductory AI concepts or general cybersecurity training. It is not focused on consumer AI tools or private-sector-only use cases.
What you walk away with
- Apply a standardized, compliance-aware framework to assess AI vendor risk
- Align cross-functional stakeholders using shared risk language and criteria
- Accelerate due diligence without compromising accountability
- Design audit-ready documentation for AI procurement workflows
- Lead vendor engagement with confidence in regulatory alignment
The 12 modules (with all 144 chapters)
Module 1. Foundations of AI Vendor Risk in Public Programs
Introduces core risk domains, regulatory touchpoints, and the evolution of AI oversight in public-sector contexts.
12 chapters in this module
- Defining AI vendor risk in public-sector delivery
- Key regulatory drivers shaping vendor oversight
- Differences between commercial and public-sector AI risk
- The role of transparency and public accountability
- Risk maturity models for AI procurement
- Stakeholder mapping in vendor assessment
- Common failure patterns in AI vendor rollout
- Ethical procurement principles for AI systems
- Lifecycle view of vendor engagement and exit
- Balancing innovation speed with due diligence
- Case study: Municipal AI chatbot deployment
- Self-assessment: Organizational readiness
Module 2. Compliance Frameworks and Public Accountability
Explores how existing compliance standards apply to AI vendors and how public-sector expectations amplify accountability.
12 chapters in this module
- Mapping AI risk to existing compliance regimes
- GDPR, CCPA, and privacy-preserving AI vendors
- Sector-specific regulations: Health, education, justice
- Public records and audit trail requirements
- Accessibility standards in AI interface design
- AI and algorithmic transparency laws
- Vendor alignment with open data policies
- Handling FOIA and public inquiry readiness
- Third-party certification benchmarks
- Interpreting guidance from oversight bodies
- Compliance as a procurement gate
- Checklist: Compliance alignment by domain
Module 3. Risk Categorization for AI Vendors
Provides a structured method to classify AI vendors by risk level based on data, autonomy, and impact.
12 chapters in this module
- High, medium, low: Defining risk tiers
- Data sensitivity as a risk driver
- Autonomy level and human-in-the-loop design
- Impact on public services and equity
- Vendor lock-in and long-term dependency
- Scoring model for AI risk categorization
- Dynamic reclassification over time
- Risk escalation triggers and thresholds
- Cross-walk with NIST AI Risk Framework
- Sector-based risk weighting (e.g., benefits vs. permits)
- Documentation for risk classification decisions
- Worked example: Risk tiering an AI document processor
Module 4. Due Diligence Workflows for AI Procurement
Covers structured processes to evaluate AI vendors before procurement, including documentation and stakeholder coordination.
12 chapters in this module
- Stages of AI vendor due diligence
- Pre-RFP risk screening checklist
- Request for Information (RFI) design for AI vendors
- Security questionnaire customization
- Data governance expectations from vendors
- Model validation and testing requirements
- Bias and fairness assessment protocols
- Vendor transparency and explainability standards
- Third-party audit report evaluation
- Reference and case study verification
- Legal and intellectual property red flags
- Workflow integration with procurement teams
Module 5. Contractual Risk Mitigation Strategies
Equips learners with key contractual terms and negotiation levers for AI vendor agreements.
12 chapters in this module
- Right-to-audit clauses for AI systems
- Performance guarantees and SLAs for AI models
- Model drift monitoring and reporting obligations
- Data ownership and reuse restrictions
- Explainability and documentation requirements
- Liability frameworks for AI-generated outcomes
- Termination clauses for underperformance
- Penalties for non-compliance with standards
- Subcontractor and supply chain transparency
- Indemnification for algorithmic harm
- Dispute resolution mechanisms
- Template: AI vendor contract addendum
Module 6. Ongoing Monitoring and Performance Validation
Details how to maintain oversight after AI vendor onboarding and ensure continued compliance.
12 chapters in this module
- Post-deployment monitoring frameworks
- Establishing performance baselines
- Model drift detection and alerting
- Quarterly review processes for AI vendors
- Automated compliance check-ins
- Human oversight in AI decision chains
- Incident reporting and response protocols
- Public complaint intake and triage
- Audit trail retention and access
- Performance dashboards for leadership
- Escalation workflows for underperformance
- Case study: Correcting AI-driven denial errors
Module 7. Cross-Functional Coordination Models
Shows how legal, IT, compliance, and program teams can collaborate effectively in AI vendor risk assessment.
12 chapters in this module
- RACI matrix for AI vendor evaluation
- Legal team engagement without bottlenecks
- IT security review integration
- Privacy officer involvement timing
- Program manager as risk integrator
- Finance and procurement alignment
- Internal audit as a validation partner
- Stakeholder communication cadence
- Shared documentation platforms
- Conflict resolution in risk scoring
- Training for cross-functional teams
- Meeting framework: AI vendor review board
Module 8. Bias, Fairness, and Equity in AI Vendor Systems
Provides tools to assess and mitigate algorithmic bias in vendor-provided AI solutions.
12 chapters in this module
- Defining fairness in public-sector AI
- Common sources of algorithmic bias
- Demographic parity and equity metrics
- Bias testing in training data
- Vendor-provided fairness documentation
- Third-party bias audit requirements
- Community impact assessment methods
- Redress mechanisms for AI errors
- Bias mitigation reporting expectations
- Transparency in model decision logic
- Case study: Addressing bias in benefits eligibility
- Checklist: Equity review for AI vendors
Module 9. Data Governance and Security in AI Vendors
Focuses on evaluating vendor data practices, security posture, and resilience.
12 chapters in this module
- Data lifecycle in AI vendor systems
- Data minimization and retention policies
- Encryption standards for data in transit and at rest
- Access control and authentication protocols
- Incident response readiness
- Penetration testing and red teaming expectations
- Cloud infrastructure compliance (FedRAMP, etc.)
- Data sovereignty and jurisdiction risks
- Subprocessor transparency
- Breach notification timelines
- Data portability and exit planning
- Security questionnaire template
Module 10. Public Trust and Communication Strategy
Covers how to build and maintain public confidence in AI vendor-supported programs.
12 chapters in this module
- Public communication principles for AI use
- Transparency reporting frameworks
- Stakeholder engagement in AI rollout
- Handling media inquiries on AI decisions
- Public education on AI-assisted services
- Disclosure of AI use in public interfaces
- Feedback loops from service users
- Trust indicators in AI system design
- Crisis communication for AI failures
- Case study: Rebuilding trust after AI error
- Messaging framework for leadership
- Template: Public FAQ on AI vendor use
Module 11. Scaling AI Risk Practices Across Programs
Shows how to institutionalize AI vendor risk assessment across multiple teams and initiatives.
12 chapters in this module
- Centralized vs. decentralized risk models
- AI risk office or center of excellence design
- Standard operating procedures for assessment
- Training programs for procurement staff
- Knowledge sharing across departments
- Risk dashboard for leadership
- Version control for assessment criteria
- Lessons learned integration
- External benchmarking
- Continuous improvement cycle
- Scaling without overburdening teams
- Playbook: Institutionalizing AI risk governance
Module 12. Future-Proofing AI Vendor Engagement
Prepares learners for emerging trends and evolving standards in AI compliance and risk.
12 chapters in this module
- Anticipating new regulatory developments
- AI legislation tracking methods
- Adaptive framework design
- Scenario planning for regulatory shifts
- Vendor innovation vs. compliance stability
- AI explainability advancements
- Public expectations evolution
- International alignment trends
- Preparing for AI incident audits
- Building organizational learning
- Long-term vendor relationship strategy
- Final checklist: AI vendor risk maturity
How this maps to your situation
- Assessing AI vendors for public-sector programs
- Designing compliance-aware procurement workflows
- Leading cross-functional AI risk coordination
- Institutionalizing accountable AI vendor practices
Before vs. after
Before
Uncertain how to systematically assess AI vendor risk in public-sector programs, relying on ad-hoc reviews and fragmented guidance.
After
Confidently lead structured, compliance-ready AI vendor assessments with reusable frameworks, documentation, and stakeholder alignment.
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 flexible, self-paced learning over 6-8 weeks.
If nothing changes
Without a structured approach, organizations risk inconsistent evaluations, delayed deployments, compliance gaps, and public trust erosion when AI vendors underperform or introduce unintended harm.
How this compares to the alternatives
Unlike generic AI ethics courses or high-level compliance overviews, this course delivers implementation-grade workflows, templates, and cross-functional coordination models specific to public-sector AI vendor risk, making it actionable from day one.
Frequently asked
Who is this course designed for?
Business and technology professionals involved in AI vendor assessment, procurement, compliance, or governance within public-sector or public-facing programs.
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 flexible, self-paced learning over 6-8 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