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Cross-Functional AI Vendor Risk Assessment for Public-Sector Programs

$199.00
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A tailored course, built for your situation

Cross-Functional AI Vendor Risk Assessment for Public-Sector Programs

Master implementation-grade risk governance for AI procurement 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.
AI procurement in the public sector is accelerating, but inconsistent risk assessment practices create delays, compliance gaps, and stakeholder misalignment.

The situation this course is for

Teams are under pressure to adopt AI solutions quickly while ensuring fairness, transparency, and accountability. Without a unified framework, legal, IT, and program leaders often work in silos, leading to fragmented evaluations, repeated work, and elevated oversight risk.

Who this is for

Business and technology professionals in public-sector programs, compliance leads, risk analysts, procurement specialists, IT governance officers, and program managers, involved in AI vendor evaluation and deployment.

Who this is not for

This course is not for software developers building AI models or vendors marketing AI tools. It is designed for those responsible for assessing and approving third-party AI systems within regulated environments.

What you walk away with

  • Apply a structured, cross-functional framework to evaluate AI vendor risk
  • Align technical, legal, and operational assessments across teams
  • Navigate compliance requirements specific to public-sector AI use
  • Deploy standardized templates for vendor scoring and documentation
  • Lead confident, auditable AI procurement decisions

The 12 modules (with all 144 chapters)

Module 1. Foundations of AI Risk in Public Programs
Establish core concepts of AI risk, public-sector constraints, and the role of cross-functional coordination.
12 chapters in this module
  1. Defining AI risk in government contexts
  2. Key differences between commercial and public-sector AI use
  3. Stakeholder landscape in public AI procurement
  4. Regulatory drivers shaping AI governance
  5. Case study: AI in benefits eligibility systems
  6. Ethical principles in public AI deployment
  7. Risk taxonomy for algorithmic systems
  8. Common failure modes in vendor-led AI projects
  9. The lifecycle of public AI procurement
  10. Balancing innovation and accountability
  11. Emerging expectations from oversight bodies
  12. Setting program-level risk tolerance
Module 2. Cross-Functional Team Alignment
Design collaboration models that integrate legal, technical, and program teams into a unified risk assessment process.
12 chapters in this module
  1. Mapping roles and responsibilities across functions
  2. Creating shared language for AI risk discussions
  3. Facilitating joint assessment workshops
  4. Establishing decision rights and escalation paths
  5. Aligning risk appetite across departments
  6. Building trust between technical and non-technical stakeholders
  7. Managing conflicting priorities in AI evaluations
  8. Documenting consensus and dissent
  9. Integrating DEI considerations into team dynamics
  10. Onboarding new team members to the framework
  11. Maintaining alignment across procurement cycles
  12. Measuring team effectiveness in risk assessment
Module 3. Vendor Due Diligence Frameworks
Implement standardized due diligence processes tailored to AI vendor capabilities and transparency.
12 chapters in this module
  1. Scope of AI vendor due diligence
  2. Assessing vendor organizational maturity
  3. Evaluating AI development lifecycle practices
  4. Reviewing data provenance and lineage
  5. Auditing model training and validation processes
  6. Testing for bias and fairness in vendor models
  7. Reviewing model documentation standards
  8. Assessing explainability and interpretability
  9. Vendor third-party audit readiness
  10. Evaluating incident response and model monitoring
  11. Reviewing AI safety and containment protocols
  12. Benchmarking vendor practices against peers
Module 4. Compliance Mapping and Regulatory Alignment
Translate evolving regulatory expectations into actionable assessment criteria for AI vendors.
12 chapters in this module
  1. Overview of AI-related public-sector regulations
  2. Mapping requirements to vendor evaluation criteria
  3. Aligning with data protection and privacy laws
  4. Incorporating algorithmic accountability mandates
  5. Addressing accessibility and digital inclusion
  6. Meeting public transparency and disclosure rules
  7. Preparing for external audit and oversight
  8. Documenting compliance rationale and decisions
  9. Handling jurisdiction-specific legal constraints
  10. Tracking regulatory changes and updates
  11. Integrating ethics review board requirements
  12. Demonstrating due care in procurement decisions
Module 5. Technical Risk Assessment Protocols
Conduct in-depth technical evaluations of AI systems without requiring data science expertise.
12 chapters in this module
  1. Understanding model architecture at a program level
  2. Assessing data quality and representativeness
  3. Evaluating model performance metrics
  4. Reviewing testing and validation approaches
  5. Assessing robustness and adversarial resilience
  6. Evaluating model drift and retraining processes
  7. Reviewing system integration and dependencies
  8. Assessing API security and data handling
  9. Evaluating fallback and human-in-the-loop design
  10. Testing for edge case behavior
  11. Reviewing model versioning and change control
  12. Conducting technical interviews with vendor teams
Module 6. Operational Risk and Service Reliability
Evaluate the operational stability and long-term supportability of AI vendor offerings.
12 chapters in this module
  1. Assessing vendor service level agreements
  2. Reviewing incident management and response
  3. Evaluating system uptime and reliability
  4. Assessing disaster recovery and business continuity
  5. Reviewing support structure and escalation paths
  6. Evaluating training and knowledge transfer
  7. Assessing documentation completeness
  8. Measuring vendor responsiveness and communication
  9. Reviewing update and patch management
  10. Evaluating scalability and performance under load
  11. Assessing long-term vendor viability
  12. Planning for exit and data portability
Module 7. Stakeholder Engagement and Communication
Design communication strategies that build trust and transparency across internal and external stakeholders.
12 chapters in this module
  1. Identifying key internal and external stakeholders
  2. Developing clear messaging about AI risk
  3. Creating transparency reports for public release
  4. Engaging community and advocacy groups
  5. Managing media and public inquiries
  6. Documenting stakeholder feedback
  7. Reporting to oversight and audit bodies
  8. Preparing executive summaries for leadership
  9. Communicating risk trade-offs clearly
  10. Handling concerns about bias or fairness
  11. Facilitating public consultation processes
  12. Building ongoing stakeholder trust
Module 8. Risk Scoring and Decision Frameworks
Apply structured scoring models to compare vendors and support defensible procurement decisions.
12 chapters in this module
  1. Designing risk scoring criteria
  2. Weighting technical, legal, and operational factors
  3. Normalizing scores across assessment teams
  4. Creating visual dashboards for decision-makers
  5. Documenting rationale for high-risk ratings
  6. Setting thresholds for acceptable risk
  7. Conducting peer review of assessments
  8. Integrating scoring into procurement workflows
  9. Handling borderline or contested evaluations
  10. Archiving assessments for audit purposes
  11. Updating scores based on new information
  12. Benchmarking against historical procurement data
Module 9. Implementation Playbook Development
Build a customized implementation playbook to operationalize the risk assessment framework.
12 chapters in this module
  1. Customizing the framework for your agency
  2. Selecting pilot programs for initial rollout
  3. Training assessors and coordinators
  4. Integrating with existing procurement systems
  5. Setting up templates and documentation flows
  6. Establishing review and approval workflows
  7. Configuring access and permissions
  8. Onboarding stakeholders to the playbook
  9. Running dry-run assessments
  10. Collecting feedback and refining processes
  11. Scaling across programs and departments
  12. Maintaining version control and updates
Module 10. Third-Party Audit and Oversight Readiness
Prepare for internal and external scrutiny of AI vendor risk assessment practices.
12 chapters in this module
  1. Understanding auditor expectations
  2. Documenting assessment processes and decisions
  3. Creating audit trails for evaluations
  4. Preparing responses to common audit questions
  5. Conducting internal readiness reviews
  6. Engaging with oversight bodies proactively
  7. Handling audit findings and recommendations
  8. Demonstrating continuous improvement
  9. Publishing transparency and accountability reports
  10. Integrating feedback from auditors
  11. Aligning with government-wide audit standards
  12. Maintaining compliance posture over time
Module 11. Scaling and Institutionalizing the Framework
Embed the risk assessment process into ongoing operations and culture.
12 chapters in this module
  1. Building a center of excellence for AI risk
  2. Developing training programs for new staff
  3. Creating communities of practice
  4. Integrating with enterprise risk management
  5. Establishing metrics and KPIs
  6. Reporting on program impact and outcomes
  7. Securing leadership buy-in and resources
  8. Sharing best practices across agencies
  9. Updating the framework based on lessons learned
  10. Recognizing and rewarding strong assessments
  11. Maintaining momentum and engagement
  12. Planning for long-term sustainability
Module 12. Future-Proofing and Adaptive Governance
Design governance models that evolve with advancing AI capabilities and regulatory landscapes.
12 chapters in this module
  1. Monitoring emerging AI technologies
  2. Tracking regulatory and policy shifts
  3. Updating assessment criteria proactively
  4. Incorporating lessons from incidents
  5. Engaging with research and innovation teams
  6. Participating in cross-agency working groups
  7. Anticipating public expectations and concerns
  8. Building adaptive review cycles
  9. Integrating feedback loops into governance
  10. Preparing for new types of AI risk
  11. Balancing agility and rigor
  12. Leading responsible AI adoption in government

How this maps to your situation

  • Assessing AI vendors for public health programs
  • Evaluating AI tools for benefits processing
  • Procuring AI systems for transportation infrastructure
  • Deploying AI in public safety and emergency response

Before vs. after

Before
Unstructured evaluations, inconsistent documentation, and siloed team input lead to delayed decisions and elevated oversight risk.
After
Confident, coordinated assessments using standardized tools that produce auditable, defensible outcomes aligned across functions.

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 be completed over 6, 8 weeks with practical application between modules.

If nothing changes
Without a formalized approach, organizations risk inconsistent evaluations, compliance gaps, delayed procurements, and reputational exposure when AI systems underperform or generate public concern.

How this compares to the alternatives

Unlike general AI ethics courses or technical AI training, this program focuses specifically on the cross-functional risk assessment process for public-sector procurement, offering actionable frameworks, real-world templates, and an implementation playbook tailored to regulated environments.

Frequently asked

Who is this course designed for?
It's for business and technology professionals in public-sector programs, compliance, risk, procurement, IT, and program management, who need to assess AI vendors across technical, legal, and operational domains.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is prior AI experience required?
No. The course is designed to be accessible to non-technical professionals while providing depth for technical stakeholders involved in cross-functional teams.
$199 one-time. Approximately 45, 60 hours of self-paced learning, designed to be completed over 6, 8 weeks 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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