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Audit-Tested AI Vendor Risk Assessment for Public-Sector Programs

$199.00
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A tailored course, built for your situation

Audit-Tested AI Vendor Risk Assessment for Public-Sector Programs

A 12-module implementation-grade course for business and technology professionals advancing AI governance in public-sector engagements

$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 vendor risk assessments often lack audit readiness, resulting in delayed deployments and compliance rework

The situation this course is for

Teams are expected to validate AI vendors against evolving standards, yet most lack a structured, evidence-based methodology that holds up under review. This leads to inconsistent evaluations, last-minute remediation, and stakeholder distrust. The gap isn't awareness , it's implementation rigor.

Who this is for

Business and technology professionals in compliance, risk, procurement, IT, or program management roles supporting public-sector AI initiatives

Who this is not for

This course is not for executives seeking high-level overviews or technical researchers focused on model architecture. It is designed for implementers, not observers.

What you walk away with

  • Apply a standardized, audit-ready framework to assess AI vendors
  • Collect and organize evidence that satisfies compliance reviewers
  • Align vendor controls with public-sector program requirements
  • Reduce time spent on risk assessment rework by 50% or more
  • Build stakeholder confidence through transparent, defensible evaluations

The 12 modules (with all 144 chapters)

Module 1. Foundations of AI Vendor Risk in Public-Sector Contexts
Establish the core principles, regulatory drivers, and stakeholder expectations shaping AI vendor risk today.
12 chapters in this module
  1. Defining AI vendor risk in public programs
  2. Key differences: commercial vs public-sector AI procurement
  3. Regulatory landscape overview
  4. Stakeholder mapping: who needs what
  5. Risk tolerance thresholds by program type
  6. The role of transparency and accountability
  7. Public trust as a design constraint
  8. Common misconceptions about AI audits
  9. Lifecycle view of vendor engagement
  10. Baseline expectations for due diligence
  11. How standards bodies are evolving
  12. Setting your assessment scope
Module 2. Audit-Ready Assessment Design
Learn how to structure an assessment that produces evidence acceptable to internal and external reviewers.
12 chapters in this module
  1. What auditors look for in AI vendor reviews
  2. Designing for traceability and reproducibility
  3. Building an evidence collection plan
  4. Control mapping fundamentals
  5. Using checklists without losing nuance
  6. Version control for assessment artifacts
  7. Documenting assumptions and exceptions
  8. Creating an assessment runbook
  9. Preparing for peer review
  10. Integrating feedback loops
  11. Timeboxing evaluation phases
  12. Balancing rigor with pace
Module 3. Vendor Onboarding and Scoping
Master the initial engagement phase to set clear boundaries and expectations with AI vendors.
12 chapters in this module
  1. Request for information (RFI) best practices
  2. Defining program-specific risk criteria
  3. Tailoring assessment scope by use case
  4. Engaging legal and procurement early
  5. Setting data access expectations
  6. Managing vendor resistance proactively
  7. Establishing communication protocols
  8. Documenting vendor claims vs commitments
  9. Classifying AI system types
  10. Determining assessment depth by risk tier
  11. Using pre-assessment questionnaires effectively
  12. Securing leadership alignment
Module 4. Data Governance and Provenance Validation
Evaluate how vendors handle data sourcing, quality, lineage, and compliance with public-sector standards.
12 chapters in this module
  1. Assessing training data provenance
  2. Evaluating data bias mitigation strategies
  3. Data quality assurance processes
  4. Compliance with data protection frameworks
  5. Third-party data sourcing risks
  6. Data retention and deletion policies
  7. Data lineage documentation standards
  8. Cross-border data flow considerations
  9. Labeling accuracy and oversight
  10. Synthetic data use and disclosure
  11. Vendor data governance maturity models
  12. Validating data processing agreements
Module 5. Model Transparency and Explainability Requirements
Determine whether a vendor’s AI system meets public-sector demands for interpretability and accountability.
12 chapters in this module
  1. Defining explainability by use case
  2. Types of model interpretability methods
  3. Evaluating vendor-provided explanations
  4. User-facing transparency needs
  5. Documentation of model behavior
  6. Handling black-box model trade-offs
  7. Stakeholder communication strategies
  8. Audit trails for model decisions
  9. Performance under edge cases
  10. Monitoring for model drift explanations
  11. Third-party model validation options
  12. Balancing IP protection and disclosure
Module 6. Bias Detection and Fairness Assurance
Implement structured techniques to uncover and mitigate algorithmic bias in vendor systems.
12 chapters in this module
  1. Defining fairness metrics for public programs
  2. Disaggregated performance testing
  3. Bias audit methodologies
  4. Evaluating demographic parity
  5. Vendor claims vs empirical testing
  6. Bias mitigation techniques in practice
  7. Ongoing monitoring requirements
  8. Community impact assessment integration
  9. Handling proxy variables
  10. Intersectional analysis approaches
  11. Bias reporting standards
  12. Remediation pathways when bias is found
Module 7. Security and Resilience Evaluation
Assess the robustness of vendor AI systems against adversarial attacks and operational failures.
12 chapters in this module
  1. Threat modeling for AI systems
  2. Adversarial attack resistance testing
  3. Model inversion and membership inference risks
  4. Secure deployment configurations
  5. Access control for model endpoints
  6. Monitoring for anomalous behavior
  7. Incident response planning with vendors
  8. Redundancy and failover capabilities
  9. Penetration testing coordination
  10. Secure update and patching processes
  11. Supply chain integrity checks
  12. Resilience under load and stress
Module 8. Performance Monitoring and Accountability
Ensure ongoing vendor accountability through continuous performance tracking and reporting.
12 chapters in this module
  1. Defining success metrics for AI outcomes
  2. Establishing baseline performance thresholds
  3. Monitoring for degradation over time
  4. Vendor reporting frequency and format
  5. Automated alerting mechanisms
  6. Handling performance disputes
  7. Independent validation techniques
  8. User feedback integration
  9. Escalation pathways for underperformance
  10. Contractual enforcement levers
  11. Service level agreement alignment
  12. Public reporting obligations
Module 9. Compliance Alignment and Regulatory Mapping
Map vendor practices to current and emerging public-sector compliance requirements.
12 chapters in this module
  1. NIST AI RMF alignment strategies
  2. EU AI Act implications for procurement
  3. U.S. federal AI guidance integration
  4. Sector-specific regulations (health, justice, etc.)
  5. Local and municipal policy considerations
  6. Ethics board and review panel coordination
  7. Documentation for regulatory submissions
  8. Handling evolving compliance landscapes
  9. Cross-jurisdictional compliance challenges
  10. Vendor compliance self-assessment review
  11. Gap analysis techniques
  12. Remediation planning with vendors
Module 10. Third-Party Audit Coordination
Prepare for and manage external audit engagements involving AI vendor assessments.
12 chapters in this module
  1. Selecting qualified audit firms
  2. Defining audit scope and objectives
  3. Preparing audit evidence packages
  4. Coordinating vendor participation
  5. Responding to auditor inquiries
  6. Managing findings and recommendations
  7. Corrective action plan development
  8. Follow-up verification processes
  9. Audit communication protocols
  10. Maintaining independence and objectivity
  11. Budgeting for audit activities
  12. Building institutional audit memory
Module 11. Stakeholder Communication and Reporting
Develop clear, credible reports that inform decision-makers and build public trust.
12 chapters in this module
  1. Audience segmentation for risk reporting
  2. Translating technical findings for leaders
  3. Creating executive summaries
  4. Visualizing risk exposure
  5. Public disclosure considerations
  6. Managing sensitive findings
  7. Board-level presentation strategies
  8. Interagency coordination reporting
  9. Vendor performance dashboards
  10. Version-controlled report archives
  11. Feedback collection from stakeholders
  12. Improving reporting over time
Module 12. Scaling and Institutionalizing the Assessment Process
Turn one-off evaluations into a repeatable, organization-wide capability.
12 chapters in this module
  1. Building an internal center of excellence
  2. Knowledge transfer strategies
  3. Training new assessors
  4. Maintaining assessment templates
  5. Tooling and platform selection
  6. Integrating with procurement workflows
  7. Performance metrics for the function
  8. Continuous improvement cycles
  9. Sharing best practices across teams
  10. Budgeting for ongoing assessments
  11. Leadership sponsorship models
  12. Measuring program impact

How this maps to your situation

  • You're evaluating your first AI vendor for a public-sector pilot
  • You're scaling AI procurement and need consistent assessment methods
  • You've faced audit questions about past vendor decisions
  • You're building a governance framework from the ground up

Before vs. after

Before
Uncertain, ad-hoc evaluations that leave teams exposed to audit findings and delays
After
Confident, structured, and defensible assessments that accelerate trusted AI adoption

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 total, designed for flexible, self-paced completion over 6, 8 weeks.

If nothing changes
Without a standardized approach, teams risk inconsistent evaluations, increased remediation costs, and diminished stakeholder trust , especially under audit scrutiny.

How this compares to the alternatives

Unlike generic AI ethics guides or high-level compliance overviews, this course delivers a step-by-step, implementation-focused methodology specifically for public-sector AI vendor risk , with templates, examples, and a playbook you can apply immediately.

Frequently asked

Who is this course designed for?
It's for business and technology professionals responsible for assessing, procuring, or governing AI vendors in public-sector programs , including roles in compliance, risk, IT, procurement, and program management.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is this course technical or policy-focused?
It bridges both , providing practical, implementation-grade guidance for professionals who need to apply policy requirements to real-world vendor assessments.
$199 one-time. Approximately 45, 60 hours total, designed for flexible, self-paced completion over 6, 8 weeks..

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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