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Implementation-Focused AI Vendor Risk Assessment for Public-Sector Programs

$199.00
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A tailored course, built for your situation

Implementation-Focused AI Vendor Risk Assessment for Public-Sector Programs

A structured, actionable framework for assessing and managing 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.
AI adoption in public-sector programs is accelerating, but vendor risk practices remain inconsistent and reactive.

The situation this course is for

Teams are under pressure to validate AI vendor claims around security, fairness, and compliance, without standardized tools or clear accountability. This leads to delayed deployments, compliance exposure, and erosion of public trust.

Who this is for

Business and technology professionals in public-sector organizations responsible for technology procurement, risk management, compliance, or digital transformation who need to assess AI vendors with precision and confidence.

Who this is not for

This is not for vendors marketing AI tools, academic researchers, or individuals seeking high-level AI policy overviews without implementation detail.

What you walk away with

  • Apply a repeatable framework to evaluate AI vendors across technical, legal, and ethical dimensions
  • Identify red flags in vendor documentation, data handling, and model governance
  • Align vendor assessments with federal and state compliance requirements
  • Build stakeholder confidence through transparent, defensible evaluation processes
  • Reduce time-to-deployment by standardizing pre-contract risk review workflows

The 12 modules (with all 144 chapters)

Module 1. Foundations of AI Vendor Risk in Public Programs
Establish core concepts, regulatory context, and risk categories specific to public-sector AI adoption.
12 chapters in this module
  1. Defining AI vendor risk in government contexts
  2. Key differences between commercial and public-sector risk profiles
  3. Regulatory landscape overview: federal, state, and local expectations
  4. Ethical frameworks guiding public AI use
  5. Common failure modes in AI vendor deployments
  6. Stakeholder mapping: who needs to be involved
  7. Risk tolerance and public accountability
  8. Overview of procurement constraints and opportunities
  9. Case study: AI rollout in a municipal service platform
  10. Integrating risk assessment into acquisition lifecycle
  11. Baseline assessment tools introduction
  12. Building internal alignment on risk criteria
Module 2. Structuring the Vendor Evaluation Framework
Design a scalable, consistent framework to assess AI vendors across multiple dimensions.
12 chapters in this module
  1. Core components of an evaluation framework
  2. Weighting risk domains by program impact
  3. Developing scoring rubrics for objectivity
  4. Creating evaluation workflows for procurement teams
  5. Aligning with existing IT governance structures
  6. Documenting assumptions and decision rationale
  7. Version control for assessment templates
  8. Integrating feedback loops from operations
  9. Benchmarking against peer agency practices
  10. Handling vendor-specific customization requests
  11. Managing conflicts of interest in evaluations
  12. Training cross-functional assessors
Module 3. Technical Due Diligence for AI Systems
Assess the technical integrity, model performance, and system architecture of AI vendors.
12 chapters in this module
  1. Reviewing model documentation and MLOps practices
  2. Evaluating training data provenance and bias mitigation
  3. Assessing model explainability and interpretability
  4. Testing for robustness and adversarial vulnerability
  5. Infrastructure and deployment architecture review
  6. API security and integration points
  7. Performance monitoring and drift detection
  8. Versioning and update management processes
  9. Third-party dependency audits
  10. Penetration testing expectations
  11. Incident response readiness for AI components
  12. Scalability and load-handling verification
Module 4. Data Governance and Privacy Compliance
Ensure vendor practices align with public-sector data protection standards and privacy laws.
12 chapters in this module
  1. Data classification and handling expectations
  2. Mapping data flows in AI-enabled systems
  3. Vendor compliance with FERPA, HIPAA, and state privacy laws
  4. Consent mechanisms and opt-out processes
  5. Data minimization and retention policies
  6. Encryption standards in transit and at rest
  7. Access controls and role-based permissions
  8. Third-party data sharing disclosures
  9. Breach notification protocols
  10. Audit logging and monitoring requirements
  11. Data subject rights fulfillment processes
  12. Vendor data processing agreements (DPAs) review
Module 5. Legal and Contractual Risk Mitigation
Identify and address legal exposures through contract design and vendor accountability clauses.
12 chapters in this module
  1. Key contractual terms for AI vendor agreements
  2. Liability allocation for model errors and harm
  3. Indemnification clauses for third-party claims
  4. Intellectual property ownership of models and outputs
  5. Warranties around performance and fairness
  6. Service level agreements (SLAs) for AI components
  7. Termination rights and exit strategies
  8. Audit rights and transparency obligations
  9. Subcontractor oversight requirements
  10. Insurance requirements for AI deployments
  11. Dispute resolution mechanisms
  12. Governing law and jurisdiction considerations
Module 6. Ethical and Equity Impact Assessment
Evaluate AI vendors for fairness, bias, and potential disparate impact on protected populations.
12 chapters in this module
  1. Defining equity goals for public programs
  2. Bias detection methods in training and inference
  3. Disaggregated performance testing by demographic
  4. Community impact assessment protocols
  5. Stakeholder engagement in ethical review
  6. Transparency requirements for model decision-making
  7. Redress mechanisms for affected individuals
  8. Oversight body formation and roles
  9. Public reporting expectations
  10. Handling contested algorithmic outcomes
  11. Mitigation strategies for identified disparities
  12. Continuous equity monitoring post-deployment
Module 7. Operational Resilience and Continuity Planning
Assess vendor capacity to maintain service continuity and respond to disruptions.
12 chapters in this module
  1. Disaster recovery and backup procedures
  2. Redundancy in model serving infrastructure
  3. Failover and graceful degradation strategies
  4. Incident response plan review
  5. Business continuity planning for vendor organizations
  6. Monitoring and alerting coverage
  7. Service restoration timelines and expectations
  8. Dependency on single points of failure
  9. Vendor financial stability assessment
  10. Workforce continuity and key personnel risks
  11. Supply chain resilience for AI components
  12. Crisis communication protocols
Module 8. Security and Cyber Threat Preparedness
Evaluate vendor cybersecurity posture and preparedness for evolving threat landscapes.
12 chapters in this module
  1. Security certifications and audit history review
  2. Vulnerability management processes
  3. Patch management and update cadence
  4. Threat modeling for AI-specific attack vectors
  5. Secure development lifecycle adherence
  6. Code review and static analysis practices
  7. Identity and access management controls
  8. Network segmentation and zero-trust alignment
  9. Logging, monitoring, and SIEM integration
  10. Phishing and social engineering resilience
  11. Third-party security assessments
  12. Coordination with public-sector CISO teams
Module 9. Compliance with Accessibility Standards
Ensure AI vendor systems meet accessibility requirements for all users.
12 chapters in this module
  1. Section 508 and WCAG compliance expectations
  2. Accessibility in user interfaces and outputs
  3. Assistive technology compatibility testing
  4. Alternative input and output methods
  5. Documentation accessibility
  6. Training materials for staff and public users
  7. User testing with people with disabilities
  8. Accessibility in voice and multimodal interfaces
  9. Captioning and transcription accuracy
  10. Color contrast and readability standards
  11. Keyboard navigation and screen reader support
  12. Ongoing accessibility monitoring
Module 10. Stakeholder Communication and Transparency
Develop strategies to communicate AI vendor risk decisions to internal and public audiences.
12 chapters in this module
  1. Crafting clear risk summaries for non-technical leaders
  2. Public-facing transparency reports
  3. Responding to media and community inquiries
  4. Internal briefing templates for executives
  5. Engaging elected officials and boards
  6. Managing public perception of AI risk
  7. Disclosure of limitations and uncertainties
  8. Building trust through proactive communication
  9. Handling misinformation and concerns
  10. Community advisory board engagement
  11. Transparency in algorithmic decision-making
  12. Balancing security and openness
Module 11. Implementation Playbook Development
Assemble a customized, ready-to-use implementation playbook for your organization.
12 chapters in this module
  1. Customizing the framework to agency context
  2. Integrating with existing procurement workflows
  3. Training materials for evaluation teams
  4. Checklists for each phase of assessment
  5. Template library for RFPs and RFIs
  6. Scoring dashboards and reporting tools
  7. Version control and update protocols
  8. Onboarding new team members
  9. Pilot testing the playbook in real scenarios
  10. Gathering feedback for continuous improvement
  11. Scaling across departments and programs
  12. Documenting lessons learned
Module 12. Continuous Monitoring and Improvement
Establish ongoing oversight to ensure sustained compliance and performance.
12 chapters in this module
  1. Post-deployment monitoring strategies
  2. Key risk indicators for vendor performance
  3. Regular reassessment intervals
  4. Feedback loops from end users
  5. Updating risk criteria as technology evolves
  6. Handling vendor model updates and retraining
  7. Annual review and refresh process
  8. Benchmarking against emerging best practices
  9. Incident-driven reassessment triggers
  10. Public reporting and accountability cycles
  11. Auditor readiness and documentation
  12. Long-term sustainability planning

How this maps to your situation

  • Procuring AI tools for public education or social services
  • Leading digital transformation in state or local government
  • Managing compliance for technology vendors in regulated environments
  • Supporting ethical AI adoption in community-facing programs

Before vs. after

Before
Unstructured evaluations, inconsistent criteria, delayed decisions, and reactive risk management when adopting AI vendors.
After
A standardized, defensible, and efficient process to assess AI vendors with clarity, speed, and stakeholder confidence.

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 12, 15 hours of focused learning, designed for modular completion alongside regular duties.

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

How this compares to the alternatives

Unlike generic AI ethics courses or high-level policy summaries, this program delivers implementation-grade tools, public-sector specific frameworks, and actionable checklists not found in academic or vendor-provided materials.

Frequently asked

Who is this course designed for?
Public-sector business and technology professionals involved in AI procurement, risk assessment, compliance, or digital transformation who need practical, implementation-ready tools.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is this relevant for non-technical leaders?
Yes. While technical depth is included, the framework and playbook are designed for cross-functional use, with clear guidance for non-technical stakeholders.
$199 one-time. Approximately 12, 15 hours of focused learning, designed for modular completion alongside regular duties..

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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