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Practical AI Vendor Risk Assessment for Public-Sector Programs

$199.00
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A tailored course, built for your situation

Practical AI Vendor Risk Assessment for Public-Sector Programs

A step-by-step framework for evaluating AI vendor risk with confidence and compliance

$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.
AI vendors promise transformation, but public-sector leaders face real risks around compliance, transparency, and long-term accountability.

The situation this course is for

Procurement teams are under pressure to adopt AI quickly, yet lack standardized methods to assess vendor integrity, data handling, model governance, and alignment with public-sector mandates. Without a structured approach, programs risk delays, audit findings, or public trust erosion.

Who this is for

Compliance officers, technology leads, program managers, and risk specialists in public-sector institutions overseeing AI procurement or implementation.

Who this is not for

This is not for vendors selling AI tools, academic researchers, or professionals focused solely on private-sector commercial deployments.

What you walk away with

  • Apply a repeatable framework to assess AI vendor risk across technical, legal, and operational domains
  • Align vendor evaluations with federal and state compliance requirements
  • Build audit-ready documentation for procurement decisions
  • Negotiate stronger contract terms using risk-based prioritization
  • Establish ongoing monitoring practices for deployed AI systems

The 12 modules (with all 144 chapters)

Module 1. Foundations of AI Vendor Risk in the Public Sector
Understand the unique risk landscape for public-sector AI adoption and the role of vendor assessment.
12 chapters in this module
  1. Defining AI vendor risk in government contexts
  2. Key differences from private-sector risk models
  3. The lifecycle of AI procurement and integration
  4. Stakeholder roles in risk evaluation
  5. Regulatory drivers shaping vendor expectations
  6. Emerging standards and policy guidance
  7. Case study: Municipal chatbot rollout
  8. Case study: State workforce analytics platform
  9. Risk classification frameworks
  10. Mapping AI use cases to risk tiers
  11. Public accountability and transparency obligations
  12. Building a risk-aware procurement culture
Module 2. Legal and Compliance Readiness
Prepare for legal scrutiny by aligning vendor assessments with current compliance mandates.
12 chapters in this module
  1. Federal and state data protection rules
  2. Accessibility requirements for AI interfaces
  3. Procurement law and AI vendor selection
  4. FOIA and public records implications
  5. Privacy impact assessment integration
  6. Handling sensitive populations and data
  7. Vendor liability and indemnification
  8. Compliance documentation standards
  9. Third-party audit rights and access
  10. Ethics review board coordination
  11. Sector-specific regulations (education, health, justice)
  12. Compliance gap analysis techniques
Module 3. Technical Due Diligence Framework
Evaluate AI vendors’ technical infrastructure, security, and model integrity.
12 chapters in this module
  1. Assessing model development lifecycle maturity
  2. Data sourcing and provenance verification
  3. Bias detection and mitigation strategies
  4. Model performance reporting standards
  5. Security posture of AI platforms
  6. API security and integration risks
  7. Cloud infrastructure compliance
  8. Incident response and breach protocols
  9. System uptime and reliability metrics
  10. Vendor change management processes
  11. Red teaming and penetration testing access
  12. Technical debt and scalability risks
Module 4. Contractual Risk Mitigation
Structure agreements that protect public interests and enforce accountability.
12 chapters in this module
  1. Key clauses for AI vendor contracts
  2. Defining performance benchmarks and SLAs
  3. Data ownership and portability rights
  4. Model update and version control terms
  5. Termination and exit strategies
  6. Subcontractor and third-party oversight
  7. Audit rights and transparency obligations
  8. Liability caps and insurance requirements
  9. Dispute resolution mechanisms
  10. Force majeure and continuity planning
  11. Renewal and pricing lock-in risks
  12. Contract language templates and examples
Module 5. Vendor Transparency and Explainability
Ensure AI systems are understandable and justifiable to stakeholders.
12 chapters in this module
  1. Right to explanation in public decision-making
  2. Model interpretability standards
  3. Documentation requirements for AI systems
  4. User-facing explanations and notices
  5. Stakeholder communication strategies
  6. Public reporting templates
  7. Handling model uncertainty and errors
  8. Explainability in high-stakes domains
  9. Transparency scorecard development
  10. Vendor transparency self-assessments
  11. Third-party explainability audits
  12. Balancing transparency with IP protection
Module 6. Equity and Bias Risk Assessment
Proactively identify and mitigate algorithmic bias in vendor systems.
12 chapters in this module
  1. Defining equity in public-sector AI
  2. Common sources of algorithmic bias
  3. Bias testing methodologies
  4. Disaggregated performance analysis
  5. Historical data and structural bias
  6. Stakeholder input in bias evaluation
  7. Bias mitigation techniques by vendor
  8. Ongoing bias monitoring plans
  9. Equity impact assessment templates
  10. Community review and feedback loops
  11. Bias reporting and correction timelines
  12. Public disclosure of bias findings
Module 7. Data Governance and Privacy
Evaluate how vendors handle public-sector data with care and compliance.
12 chapters in this module
  1. Data minimization and retention policies
  2. Consent and lawful basis verification
  3. De-identification and re-identification risks
  4. Cross-border data transfer rules
  5. Data access and role-based controls
  6. Logging and audit trail requirements
  7. Data breach notification timelines
  8. Vendor data stewardship practices
  9. Third-party data sharing disclosures
  10. Data subject rights fulfillment
  11. Data governance maturity models
  12. Privacy-by-design integration
Module 8. Operational Resilience and Continuity
Ensure AI vendor services remain reliable and recoverable under stress.
12 chapters in this module
  1. Disaster recovery and backup plans
  2. Business continuity testing results
  3. Single points of failure analysis
  4. Vendor financial stability indicators
  5. Workforce continuity and key person risk
  6. Supply chain resilience for AI services
  7. Failover and redundancy capabilities
  8. Incident escalation and response times
  9. Service degradation protocols
  10. Public communication during outages
  11. Redundant vendor strategies
  12. Resilience scorecard development
Module 9. Stakeholder Engagement and Communication
Design communication strategies that build trust across constituents.
12 chapters in this module
  1. Identifying key stakeholder groups
  2. Tailoring messages by audience type
  3. Managing public expectations of AI
  4. Handling media inquiries and scrutiny
  5. Internal change management planning
  6. Training materials for frontline staff
  7. Feedback collection and response loops
  8. Community advisory board engagement
  9. Transparency portal setup
  10. Public reporting cadence
  11. Crisis communication planning
  12. Success story documentation
Module 10. Monitoring and Ongoing Oversight
Establish post-deployment practices to maintain risk control.
12 chapters in this module
  1. Performance tracking dashboards
  2. Model drift detection methods
  3. Regular audit schedules
  4. Third-party monitoring tools
  5. Vendor reporting requirements
  6. Automated alert systems
  7. Escalation pathways for issues
  8. Periodic risk reassessment
  9. Contract compliance checks
  10. Public reporting updates
  11. Lessons learned integration
  12. Sunset planning for AI systems
Module 11. Scaling and Replicability
Design assessments that can be reused across programs and agencies.
12 chapters in this module
  1. Creating standardized vendor scorecards
  2. Template-based evaluation workflows
  3. Cross-agency collaboration models
  4. Shared risk libraries and databases
  5. Interoperability with existing systems
  6. Modular assessment design
  7. Training teams on consistent evaluation
  8. Version control for assessment tools
  9. Centralized oversight models
  10. Decentralized implementation support
  11. Scaling pilot programs responsibly
  12. Replication success metrics
Module 12. Implementation and Leadership Strategy
Lead successful AI vendor risk programs with confidence and clarity.
12 chapters in this module
  1. Building internal risk assessment teams
  2. Securing executive sponsorship
  3. Budgeting for ongoing oversight
  4. Change management for new processes
  5. Measuring program effectiveness
  6. Integrating with enterprise risk management
  7. Board and council reporting
  8. Public trust and legitimacy metrics
  9. Continuous improvement cycles
  10. Benchmarking against peer agencies
  11. Developing internal expertise
  12. Sustaining momentum over time

How this maps to your situation

  • Evaluating a new AI vendor for a public service platform
  • Scaling an existing AI tool across multiple departments
  • Responding to public or legislative questions about AI use
  • Designing a reusable vendor assessment process for future procurements

Before vs. after

Before
Uncertain about how to assess AI vendors beyond surface-level promises, relying on ad-hoc checklists and inconsistent standards.
After
Equipped with a comprehensive, repeatable framework to evaluate AI vendors with confidence, compliance, and long-term oversight.

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 36 hours of total engagement, designed for self-paced learning with practical application between modules.

If nothing changes
Without a structured approach, organizations risk compliance gaps, public trust erosion, and costly remediation after deployment.

How this compares to the alternatives

Unlike general AI ethics courses or high-level policy summaries, this program delivers actionable, implementation-grade tools tailored specifically to public-sector procurement and risk workflows.

Frequently asked

Who is this course designed for?
Public-sector professionals involved in AI procurement, risk management, compliance, or technology leadership.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is there a certificate upon completion?
Yes, a certificate of completion is issued after finishing all modules and assessments.
$199 one-time. Approximately 36 hours of total engagement, designed for self-paced learning with practical application between modules..

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
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