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Risk-Managed AI Vendor Risk Assessment for Public-Sector Programs

$199.00
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A tailored course, built for your situation

Risk-Managed AI Vendor Risk Assessment for Public-Sector Programs

A practical, implementation-grade framework for secure and compliant AI adoption in public-sector environments

$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.
Deploying AI through third parties without a structured risk assessment can lead to compliance gaps, operational friction, and reputational exposure.

The situation this course is for

Public-sector teams are under pressure to adopt AI quickly, but vendor promises often outpace accountability. Without a standardized way to assess risk across technical, legal, and operational dimensions, teams face delays, audit findings, and misaligned expectations.

Who this is for

Business and technology professionals in public-sector organizations responsible for AI procurement, risk oversight, compliance, or technology governance.

Who this is not for

This is not for vendors selling AI solutions or consultants focused solely on private-sector applications.

What you walk away with

  • Apply a repeatable framework to evaluate AI vendor risk across 12 key domains
  • Align vendor assessments with federal AI governance expectations
  • Reduce time spent coordinating between legal, IT, and procurement teams
  • Document due diligence in a way that satisfies auditors and oversight bodies
  • Build internal capacity to lead AI risk assessments without external consultants

The 12 modules (with all 144 chapters)

Module 1. Foundations of AI Risk in Public Programs
Introduce core concepts of AI risk, public-sector constraints, and the role of vendor assessment.
12 chapters in this module
  1. Defining AI risk in regulated environments
  2. Public-sector vs private-sector risk profiles
  3. The lifecycle of AI vendor engagement
  4. Legal and ethical guardrails
  5. Understanding algorithmic accountability
  6. Roles and responsibilities in AI oversight
  7. Federal guidance landscape overview
  8. Procurement policy intersections
  9. Stakeholder alignment strategies
  10. Common misconceptions about AI safety
  11. Baseline assessment criteria
  12. Integrating risk into project intake
Module 2. Vendor Due Diligence Framework
Establish a structured approach to pre-contract evaluation of AI vendors.
12 chapters in this module
  1. Designing a vendor screening checklist
  2. Assessing company stability and track record
  3. Evaluating technical documentation quality
  4. Verifying AI development methodologies
  5. Third-party audit rights and access
  6. Reference site validation process
  7. Financial health indicators
  8. Insurance and liability coverage review
  9. Past performance in government contracts
  10. Compliance with data sovereignty rules
  11. Handling sub-contractor relationships
  12. Documenting due diligence decisions
Module 3. Data Governance and Privacy Compliance
Ensure AI vendor practices meet public-sector data handling standards.
12 chapters in this module
  1. Mapping data flows in AI systems
  2. Classifying data sensitivity levels
  3. Consent and data provenance tracking
  4. Anonymization and de-identification standards
  5. Cross-border data transfer protocols
  6. Data retention and deletion policies
  7. Access control enforcement mechanisms
  8. Audit logging requirements
  9. Demonstrating compliance with privacy laws
  10. Handling data subject requests
  11. Vendor breach response coordination
  12. Data stewardship roles and responsibilities
Module 4. Algorithmic Transparency and Explainability
Evaluate how AI vendors provide visibility into model behavior.
12 chapters in this module
  1. Defining transparency in AI systems
  2. Model cards and system documentation
  3. Explainability techniques by use case
  4. Performance benchmarking standards
  5. Bias detection and mitigation plans
  6. Human oversight integration
  7. Model validation procedures
  8. Testing under edge conditions
  9. Accuracy reporting formats
  10. Drift detection and retraining cycles
  11. Error explanation frameworks
  12. Public communication strategies
Module 5. Security and Cyber Resilience
Assess vendor cybersecurity practices specific to AI deployments.
12 chapters in this module
  1. Secure development lifecycle adherence
  2. Penetration testing and red teaming
  3. Vulnerability disclosure policies
  4. Secure API design and access
  5. Model inversion and extraction risks
  6. Adversarial attack resistance
  7. Supply chain software integrity
  8. Zero-trust architecture alignment
  9. Incident response readiness
  10. Encryption in transit and at rest
  11. Privileged access management
  12. Security certification validation
Module 6. Legal and Regulatory Alignment
Verify vendor compliance with public-sector legal frameworks.
12 chapters in this module
  1. Understanding applicable statutes and regulations
  2. Demonstrating compliance with civil rights laws
  3. Accessibility requirements for AI outputs
  4. Intellectual property ownership clarity
  5. Licensing terms and usage rights
  6. Export control considerations
  7. Recordkeeping obligations
  8. Freedom of information request readiness
  9. Liability allocation in contracts
  10. Indemnification clauses evaluation
  11. Dispute resolution mechanisms
  12. Termination rights and data exit
Module 7. Operational Integration and Support
Plan for seamless integration and ongoing support from AI vendors.
12 chapters in this module
  1. Defining service level expectations
  2. Uptime and availability commitments
  3. Support response time standards
  4. Training and knowledge transfer
  5. System interoperability requirements
  6. Change management processes
  7. Patch and update frequency
  8. Documentation completeness
  9. Vendor escalation paths
  10. Incident coordination protocols
  11. Performance monitoring integration
  12. Exit strategy and data portability
Module 8. Ethical Review and Oversight
Implement ethical guardrails for AI vendor systems.
12 chapters in this module
  1. Establishing ethical review boards
  2. Defining prohibited use cases
  3. Human-in-the-loop requirements
  4. Monitoring for unintended consequences
  5. Community impact assessment
  6. Bias impact reporting
  7. Whistleblower protection alignment
  8. Ethical training for vendor staff
  9. Public justification of AI use
  10. Oversight body reporting formats
  11. Independent audit access
  12. Ethics-by-design integration
Module 9. Financial and Performance Accountability
Ensure AI vendor performance is measurable and fiscally responsible.
12 chapters in this module
  1. Cost structure transparency
  2. Unit pricing and consumption models
  3. Budget overruns prevention
  4. Performance-based payment terms
  5. ROI measurement frameworks
  6. Cost-benefit analysis templates
  7. Hidden fee identification
  8. Scalability cost projections
  9. Vendor financial reporting
  10. Audit rights for billing
  11. Multi-year cost forecasting
  12. Value realization tracking
Module 10. Stakeholder Engagement and Communication
Manage internal and external expectations around AI vendor programs.
12 chapters in this module
  1. Identifying key stakeholders
  2. Developing communication plans
  3. Managing public inquiries
  4. Internal change management
  5. Training frontline staff
  6. Addressing community concerns
  7. Media engagement protocols
  8. Transparency reporting
  9. Feedback loop integration
  10. Public consultation frameworks
  11. Managing misinformation
  12. Reporting to oversight bodies
Module 11. Monitoring, Audit, and Continuous Improvement
Build systems to monitor vendor performance over time.
12 chapters in this module
  1. Designing ongoing monitoring plans
  2. Key risk indicator selection
  3. Automated alerting systems
  4. Audit trail maintenance
  5. Third-party audit coordination
  6. Performance dashboard design
  7. Trend analysis techniques
  8. Corrective action workflows
  9. Lessons learned documentation
  10. Continuous improvement cycles
  11. Regulatory change adaptation
  12. Reporting to executive leadership
Module 12. Implementation and Institutionalization
Embed the risk assessment framework into organizational practice.
12 chapters in this module
  1. Customizing the framework for your agency
  2. Integrating with existing procurement
  3. Training internal teams
  4. Documenting policies and procedures
  5. Securing leadership buy-in
  6. Pilot program design
  7. Scaling across departments
  8. Vendor performance scorecards
  9. Knowledge transfer planning
  10. Internal audit alignment
  11. Sustaining momentum
  12. Measuring long-term impact

How this maps to your situation

  • Evaluating a new AI vendor for a public health initiative
  • Conducting due diligence before renewing an AI contract
  • Responding to an oversight body's inquiry about AI use
  • Building internal capacity to assess AI risk without consultants

Before vs. after

Before
Uncertain about how to assess AI vendor risk systematically or document due diligence to oversight bodies.
After
Confidently lead AI vendor assessments using a repeatable, compliant framework that aligns with public-sector expectations.

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 45, 60 hours of self-paced learning, designed to fit around professional responsibilities.

If nothing changes
Continuing without a standardized approach increases the likelihood of compliance findings, audit delays, and reputational risk when AI systems underperform or cause unintended harm.

How this compares to the alternatives

Unlike generic AI ethics courses or academic overviews, this program provides implementation-grade tools tailored to public-sector procurement cycles, compliance requirements, and oversight expectations.

Frequently asked

Who is this course designed for?
It's for professionals in public-sector organizations involved in AI procurement, risk management, compliance, or technology oversight.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is this course technical or policy-focused?
It balances both, providing actionable guidance for cross-functional teams including legal, IT, procurement, and program leadership.
$199 one-time. Approximately 45, 60 hours of self-paced learning, designed to fit around 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
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