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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

Master compliant, defensible AI procurement with implementation-grade frameworks

$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.
Avoid rework, delays, and failed audits in AI vendor onboarding

The situation this course is for

Public-sector AI initiatives are stalling due to inconsistent risk documentation, lack of audit alignment, and vendor accountability gaps. Teams are forced to rebuild assessments from scratch, leading to delays, compliance exposure, and eroded stakeholder trust.

Who this is for

Compliance officers, technology risk leads, and procurement specialists in public-sector or public-facing technology programs who need to validate AI vendor trustworthiness with audit-ready rigor.

Who this is not for

Individuals seeking introductory AI awareness or general cybersecurity hygiene without a focus on vendor assessment or public-sector compliance.

What you walk away with

  • Apply a standardized, audit-tested framework to assess AI vendor risk
  • Produce documentation that passes third-party and internal audits
  • Align AI procurement with current public-sector compliance expectations
  • Reduce vendor onboarding time with reusable templates and checklists
  • Build stakeholder confidence through transparent, defensible risk decisions

The 12 modules (with all 144 chapters)

Module 1. Foundations of AI Vendor Risk in Public Programs
Establish core principles and compliance context for AI vendor assessments.
12 chapters in this module
  1. Defining AI vendor risk in public-sector contexts
  2. Key regulatory drivers shaping vendor expectations
  3. Lifecycle overview of AI procurement and risk touchpoints
  4. Roles and responsibilities in vendor governance
  5. Differences between commercial and public-sector risk thresholds
  6. Building cross-functional assessment teams
  7. Risk categorization frameworks for AI systems
  8. Mapping AI use cases to risk levels
  9. Understanding vendor accountability models
  10. Common pitfalls in early-stage vendor selection
  11. Integrating risk assessment into procurement workflows
  12. Setting success metrics for vendor due diligence
Module 2. Audit Expectations and Compliance Alignment
Decode what auditors look for in AI vendor risk programs.
12 chapters in this module
  1. Auditor priorities in AI-related procurement reviews
  2. Mapping controls to common compliance frameworks
  3. Demonstrating due diligence in vendor selection
  4. Documentation standards for audit readiness
  5. Evidence types accepted by internal and external auditors
  6. Preparing for surprise audits and spot checks
  7. Common findings in AI vendor risk audits
  8. How to respond to auditor recommendations
  9. Building repeatable assessment patterns
  10. Versioning and retention of assessment records
  11. Crosswalking controls across multiple standards
  12. Proving continuous improvement in vendor oversight
Module 3. Vendor Risk Categorization and Tiering
Classify AI vendors by risk impact and operational criticality.
12 chapters in this module
  1. Developing a risk tiering matrix for AI vendors
  2. Assessing data sensitivity in vendor workflows
  3. Evaluating system autonomy and decision impact
  4. Scoring model for vendor risk classification
  5. Handling third-party dependencies in vendor stacks
  6. Geopolitical and supply chain risk factors
  7. Financial stability and vendor longevity checks
  8. Incident history and breach disclosure review
  9. Service-level agreement enforceability
  10. Exit strategy and data portability readiness
  11. Human oversight requirements by risk tier
  12. Adjusting scrutiny based on deployment scale
Module 4. Due Diligence Questionnaire Design
Build effective, targeted questionnaires for AI vendors.
12 chapters in this module
  1. Structuring multi-tier due diligence forms
  2. Writing unambiguous, audit-friendly questions
  3. Validating vendor self-reporting accuracy
  4. Incorporating technical verification steps
  5. Handling incomplete or evasive responses
  6. Benchmarking responses against industry norms
  7. Automating response validation where possible
  8. Managing vendor fatigue during assessments
  9. Version control for questionnaires
  10. Translating technical answers into risk ratings
  11. Integrating legal and compliance review steps
  12. Maintaining chain of custody for submissions
Module 5. Technical Validation and Proof-of-Concept Design
Design hands-on evaluations to verify vendor claims.
12 chapters in this module
  1. Scoping technical validation exercises
  2. Designing secure proof-of-concept environments
  3. Testing model performance under real conditions
  4. Validating data handling and privacy safeguards
  5. Assessing model explainability and documentation
  6. Reviewing training data provenance and bias checks
  7. Evaluating model drift detection capabilities
  8. Stress-testing vendor support and incident response
  9. Measuring system uptime and reliability
  10. Auditing vendor change management processes
  11. Verifying security patching timelines
  12. Documenting validation outcomes for auditors
Module 6. Third-Party Audit and Attestation Review
Evaluate vendor-provided audit reports and certifications.
12 chapters in this module
  1. Interpreting SOC 2, ISO 27001, and other reports
  2. Spotting gaps in third-party attestations
  3. Assessing scope alignment with AI services
  4. Validating report recency and coverage
  5. Cross-checking controls with actual vendor practices
  6. Identifying reliance risks in subcontracted functions
  7. Handling expired or lapsed certifications
  8. Requesting supplemental evidence from vendors
  9. Managing discrepancies between reports and reality
  10. Documenting reliance decisions for auditors
  11. Updating assessments when reports expire
  12. Building a vendor attestation tracking system
Module 7. Contractual Risk Mitigation Clauses
Incorporate enforceable risk controls into vendor agreements.
12 chapters in this module
  1. Defining acceptable AI behavior in contracts
  2. Including audit rights and access provisions
  3. Data ownership and usage limitations
  4. Incident notification and response timelines
  5. Liability caps and indemnification terms
  6. Model performance guarantees and SLAs
  7. Right-to-exit and data return clauses
  8. Penalties for non-compliance with controls
  9. Change control and version approval processes
  10. Subcontractor approval requirements
  11. Dispute resolution mechanisms
  12. Termination triggers for ethical violations
Module 8. Ongoing Monitoring and Reassessment
Maintain continuous risk oversight post-contract award.
12 chapters in this module
  1. Designing periodic reassessment schedules
  2. Tracking vendor performance against SLAs
  3. Monitoring public disclosures and news
  4. Reviewing updated audit reports and certifications
  5. Assessing incident trends across vendor portfolios
  6. Updating risk ratings based on new data
  7. Automating risk signal detection
  8. Managing vendor relationship changes
  9. Conducting surprise audits and spot checks
  10. Documenting ongoing due diligence
  11. Escalation paths for emerging risks
  12. Revisiting risk tiering based on operational changes
Module 9. Incident Response and Vendor Accountability
Ensure vendors meet obligations during security or ethical incidents.
12 chapters in this module
  1. Defining incident types requiring vendor action
  2. Validating vendor response plans
  3. Testing communication protocols under pressure
  4. Assessing root cause analysis quality
  5. Verifying corrective action implementation
  6. Tracking vendor post-incident improvements
  7. Managing public relations coordination
  8. Enforcing penalties for delayed responses
  9. Documenting lessons learned
  10. Updating risk models based on incidents
  11. Handling data breach disclosures
  12. Termination considerations after repeated failures
Module 10. Cross-Agency and Interoperability Risk
Manage risk when AI systems interact across organizational boundaries.
12 chapters in this module
  1. Mapping data flows across agency lines
  2. Assessing interoperability standards compliance
  3. Validating secure API integrations
  4. Handling jurisdictional differences in data rules
  5. Coordinating risk assessments with partner agencies
  6. Establishing shared accountability frameworks
  7. Managing consent and data lineage across systems
  8. Auditing multi-vendor solution stacks
  9. Resolving conflicting control requirements
  10. Building federated risk oversight models
  11. Documenting cross-agency dependencies
  12. Designing exit strategies for shared systems
Module 11. Public Trust and Ethical Assurance
Incorporate ethical review into vendor risk practices.
12 chapters in this module
  1. Defining ethical AI use in public contexts
  2. Assessing vendor alignment with public values
  3. Reviewing model fairness and bias mitigation
  4. Evaluating transparency and explainability
  5. Incorporating community feedback mechanisms
  6. Validating human oversight in high-risk decisions
  7. Auditing model impact on vulnerable populations
  8. Handling complaints about AI decisions
  9. Publishing vendor accountability reports
  10. Balancing innovation with public trust
  11. Managing perception risks in AI adoption
  12. Documenting ethical review outcomes
Module 12. Scaling Risk Programs Across Portfolios
Expand vendor risk practices across multiple AI initiatives.
12 chapters in this module
  1. Building centralized risk assessment teams
  2. Standardizing templates across programs
  3. Creating shared vendor risk databases
  4. Automating risk scoring at scale
  5. Training non-specialists in risk basics
  6. Integrating risk tools with procurement systems
  7. Reporting portfolio risk to leadership
  8. Benchmarking performance across agencies
  9. Managing resource constraints in scaling
  10. Ensuring consistency without stifling innovation
  11. Adapting frameworks to new technology types
  12. Sustaining program quality during growth

How this maps to your situation

  • New AI procurement initiative in public-sector program
  • Post-incident review requiring stronger vendor controls
  • Audit finding related to vendor risk documentation
  • Scaling AI adoption across multiple departments

Before vs. after

Before
Manual, inconsistent vendor assessments with limited audit support and high rework risk.
After
Standardized, defensible process for AI vendor risk that passes audits and scales across programs.

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 self-paced learning with practical application between modules.

If nothing changes
Continuing with ad-hoc vendor assessments increases the likelihood of audit findings, project delays, and reputational exposure when AI systems underperform or violate compliance expectations.

How this compares to the alternatives

Unlike generic AI ethics courses or high-level compliance overviews, this program delivers implementation-grade frameworks specifically for public-sector AI vendor risk, complete with audit-tested documentation patterns and field-validated playbooks.

Frequently asked

Who is this course designed for?
Compliance leads, risk officers, and technology procurement professionals in public-sector or public-facing programs who need to validate AI vendors with audit-ready rigor.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is this relevant for non-US public-sector programs?
Yes. The frameworks are designed to align with international compliance expectations and can be adapted to local regulatory environments.
$199 one-time. Approximately 45, 60 hours total, 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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