A tailored course, built for your situation
Strategic AI Vendor Risk Assessment for Public-Sector Programs
A structured, implementation-grade framework for assessing AI vendor risk 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 often move forward without a consistent, defensible method for evaluating vendor risk, leading to compliance gaps, delayed rollouts, and stakeholder skepticism.
The situation this course is for
Even experienced teams struggle to standardize how they assess AI vendors. Without a clear framework, evaluations become ad hoc, inconsistent, or overly reliant on technical teams. This slows deployment, increases audit exposure, and weakens cross-functional trust.
Who this is for
Business and technology professionals in public-sector or public-facing programs who need to evaluate AI vendors with confidence, including program managers, compliance leads, IT directors, and digital transformation leads.
Who this is not for
This course is not for software developers building AI models, nor for executives seeking high-level overviews without implementation detail.
What you walk away with
- Apply a repeatable framework for assessing AI vendor risk across programs
- Align vendor evaluations with federal and municipal compliance requirements
- Conduct technical due diligence without needing a data science background
- Produce audit-ready assessment records and risk summaries
- Lead cross-functional vendor review sessions with structured criteria
The 12 modules (with all 144 chapters)
Module 1. Foundations of AI Risk in Public Programs
Establish core definitions, risk categories, and the public-sector context for AI vendor assessment.
12 chapters in this module
- Defining AI in public-sector procurement
- Common risk profiles by use case
- Regulatory landscape overview
- Ethical considerations in vendor selection
- Stakeholder mapping for AI programs
- Risk tolerance frameworks
- Public accountability and transparency
- Case study: AI in benefits processing
- Case study: AI in public safety analytics
- Balancing innovation and prudence
- Common misconceptions about AI risk
- Setting assessment scope
Module 2. Vendor Risk Assessment Framework Design
Build a customizable, scalable framework for evaluating AI vendors across dimensions.
12 chapters in this module
- Components of a risk assessment framework
- Weighting risk domains by program type
- Creating risk scoring rubrics
- Defining red, yellow, green thresholds
- Incorporating legal and compliance inputs
- Aligning with internal audit standards
- Versioning and documentation
- Framework validation techniques
- Pilot testing with real vendors
- Stakeholder feedback integration
- Scaling across departments
- Maintaining framework relevance
Module 3. Data Governance and Privacy Compliance
Evaluate how AI vendors handle public-sector data within privacy and governance mandates.
12 chapters in this module
- Data classification standards for public data
- Vendor data handling policies
- Third-party data sourcing risks
- Consent and data provenance tracking
- Anonymization and de-identification practices
- Cross-border data transfer rules
- Right to access and deletion obligations
- Data retention and deletion schedules
- Breach notification requirements
- Audit trails for data access
- Vendor SOC 2 and ISO 27001 alignment
- Data governance maturity assessment
Module 4. Algorithmic Transparency and Explainability
Assess vendor commitments to transparency, model interpretability, and bias detection.
12 chapters in this module
- Defining algorithmic transparency
- Model documentation standards
- Explainability methods by AI type
- Bias detection and mitigation strategies
- Fairness metrics and thresholds
- Third-party model audits
- Human-in-the-loop requirements
- Decision logging and traceability
- Stakeholder communication of AI logic
- Handling black-box models
- Transparency in marketing vs. reality
- Vendor transparency scorecard
Module 5. Security and Infrastructure Resilience
Evaluate the technical security posture and infrastructure reliability of AI vendors.
12 chapters in this module
- Cloud infrastructure security models
- Penetration testing and red teaming
- Incident response planning
- Encryption in transit and at rest
- Access control and identity management
- Disaster recovery and uptime SLAs
- Vendor network architecture review
- Third-party dependency risks
- Security certifications and attestations
- Patch management practices
- Zero-trust alignment
- Security maturity scoring
Module 6. Contractual and Legal Risk Mitigation
Structure contracts and SLAs that enforce accountability and risk transfer.
12 chapters in this module
- Key AI-specific contract clauses
- Liability and indemnification terms
- Performance guarantees and SLAs
- IP ownership and usage rights
- Right to audit provisions
- Termination and exit rights
- Change control and update protocols
- Subcontractor oversight
- Compliance warranty statements
- Dispute resolution mechanisms
- Force majeure and AI-specific risks
- Legal risk scoring template
Module 7. Compliance with Public-Sector Standards
Map vendor practices to current public-sector AI guidelines and frameworks.
12 chapters in this module
- NIST AI Risk Management Framework alignment
- OMB guidance on AI use
- State and municipal AI policies
- Equity impact assessment requirements
- Accessibility standards (Section 508)
- Procurement rules for AI systems
- Public reporting obligations
- Vendor self-assessment validation
- Third-party certification programs
- Compliance gap analysis
- Audit preparation checklist
- Compliance maturity roadmap
Module 8. Operational Integration and Change Management
Assess how AI systems integrate into existing workflows and teams.
12 chapters in this module
- Change management planning
- Staff training and upskilling needs
- Process redesign for AI adoption
- User acceptance testing protocols
- Feedback loop design
- Error handling and escalation paths
- System monitoring and alerts
- Vendor support responsiveness
- Integration with legacy systems
- Operational risk assessment
- Handover and onboarding plans
- Operational readiness checklist
Module 9. Performance Monitoring and Continuous Evaluation
Establish ongoing monitoring for AI vendor performance and risk drift.
12 chapters in this module
- KPIs for AI system performance
- Model drift detection methods
- Accuracy and reliability tracking
- User satisfaction metrics
- Bias re-evaluation cycles
- Vendor reporting requirements
- Quarterly risk reassessment
- Dashboard design for oversight
- Alert thresholds and escalation
- Third-party monitoring tools
- Audit trail maintenance
- Continuous evaluation playbook
Module 10. Stakeholder Communication and Public Trust
Develop strategies for transparent communication with the public and oversight bodies.
12 chapters in this module
- Public communication principles
- Explaining AI decisions to constituents
- Transparency portal design
- Oversight committee reporting
- Media inquiry preparedness
- Community engagement strategies
- Trust-building through disclosure
- Handling public complaints
- Annual AI impact reports
- Myth-busting common concerns
- Communication tone and clarity
- Stakeholder trust index
Module 11. Scaling AI Risk Assessment Across Programs
Replicate and standardize assessments across multiple departments or jurisdictions.
12 chapters in this module
- Centralized vs. decentralized models
- Shared assessment libraries
- Cross-program governance councils
- Standardized templates and tools
- Training assessors across teams
- Consistency auditing
- Lessons learned sharing
- Version control for frameworks
- Scaling pilot programs
- Resource allocation planning
- Inter-agency collaboration
- Scaling success metrics
Module 12. Future-Proofing and Emerging Risk Trends
Anticipate and prepare for next-generation AI risks and regulatory shifts.
12 chapters in this module
- Generative AI and hallucination risks
- Deepfake detection and response
- Autonomous decision-making boundaries
- AI alignment and goal specification
- Emerging regulatory signals
- International AI governance trends
- Long-term societal impact assessment
- Red teaming future scenarios
- Horizon scanning techniques
- Adaptive framework design
- Vendor innovation vs. stability trade-offs
- Future-proofing checklist
How this maps to your situation
- Evaluating a new AI vendor for a benefits automation program
- Preparing for an audit of an existing AI-powered case management system
- Designing a cross-departmental AI governance policy
- Responding to public concern about algorithmic fairness in housing services
Before vs. after
Before
AI vendor evaluations are inconsistent, reactive, and lack audit support, leading to delays and stakeholder doubt.
After
You lead structured, defensible assessments that accelerate approvals, strengthen compliance, and build public trust.
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 alongside professional responsibilities.
If nothing changes
Without a formal assessment approach, organizations risk compliance failures, public backlash, and costly remediation after deployment.
How this compares to the alternatives
Unlike generic AI ethics courses or high-level policy summaries, this program provides implementation-grade tools, real-world templates, and a step-by-step methodology specific to public-sector vendor risk assessment.
Frequently asked
Who is this course designed for?
Business and technology professionals in public-sector or public-facing programs who need to evaluate AI vendors with confidence, including program managers, compliance leads, IT directors, and digital transformation leads.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is there a certificate of completion?
Yes, a certificate is issued upon finishing all modules and passing the final assessment.
$199 one-time. Approximately 3, 4 hours per module, designed for flexible, self-paced learning alongside professional responsibilities..
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