A tailored course, built for your situation
Mid-Market AI Vendor Risk Assessment for Public-Sector Programs
A structured, implementation-grade approach to evaluating AI vendors in public-sector contexts
$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.
Public-sector programs are adopting AI faster, but mid-market vendor risk remains inconsistently assessed.
The situation this course is for
Teams lack standardized, scalable methods to evaluate AI vendors, especially mid-market providers, leading to delayed decisions, compliance gaps, and inconsistent risk reporting.
Who this is for
Business or technology professionals in public-sector-adjacent roles who evaluate, approve, or oversee AI vendor solutions.
Who this is not for
Executives seeking high-level overviews or vendors promoting their own tools. This is for practitioners doing hands-on risk assessment.
What you walk away with
- Apply a structured framework to assess AI vendor risk across 12 critical dimensions
- Navigate public-sector compliance requirements specific to AI procurement
- Use due diligence templates to accelerate assessment cycles
- Produce audit-ready documentation for oversight bodies
- Anticipate and mitigate risks unique to mid-market AI vendors
The 12 modules (with all 144 chapters)
Module 1. Foundations of AI Vendor Risk in Public-Sector Contexts
Introduces core concepts, key stakeholders, and risk categories specific to public-sector AI adoption.
12 chapters in this module
- Defining AI vendor risk in public programs
- Key differences: public vs private-sector risk profiles
- Stakeholder mapping: oversight, procurement, legal
- Public-sector procurement lifecycle stages
- Vendor lifecycle stages and risk touchpoints
- Regulatory expectations for transparency and accountability
- Common misconceptions about AI risk
- The role of due diligence in early procurement
- Establishing risk tolerance thresholds
- Documenting risk assumptions and constraints
- Integrating risk assessment with existing frameworks
- Case study: early-stage public AI procurement
Module 2. Mid-Market Vendor Landscape and Risk Profiles
Analyzes the unique risk characteristics of mid-market AI vendors versus large or open-source alternatives.
12 chapters in this module
- Defining mid-market AI vendors
- Funding models and financial stability indicators
- Organizational maturity and staffing risks
- Support structure limitations
- Patch update and versioning cadence
- Third-party dependency mapping
- Data handling commitments
- Geographic jurisdiction implications
- Insurance and liability coverage norms
- Exit strategy and data portability
- Vendor lock-in risk indicators
- Case study: mid-market vendor selection failure
Module 3. Compliance and Regulatory Alignment
Covers key regulations, standards, and public-sector mandates affecting AI vendor evaluation.
12 chapters in this module
- Identifying applicable laws and directives
- Mapping requirements to vendor capabilities
- GDPR, CCPA, and similar privacy frameworks
- Accessibility standards for public services
- Algorithmic transparency obligations
- Recordkeeping and audit trail requirements
- Data sovereignty and localization rules
- Sector-specific compliance (health, education, justice)
- Certifications and attestations to validate
- Interpreting vendor compliance claims
- Gap analysis techniques
- Case study: compliance-driven vendor rejection
Module 4. Due Diligence Workflow Design
Teaches how to structure and scale due diligence processes for AI vendor assessment.
12 chapters in this module
- Scoping assessment boundaries
- Designing intake and triage processes
- Standardizing vendor questionnaires
- Evidence collection protocols
- Reference check best practices
- On-site vs remote assessment planning
- Time and resource estimation models
- Stakeholder communication templates
- Risk scoring rubric development
- Weighting criteria by program sensitivity
- Documenting assessment rationale
- Case study: streamlined due diligence rollout
Module 5. Data Governance and Security Review
Focuses on assessing vendor data practices, security posture, and governance maturity.
12 chapters in this module
- Data classification alignment
- Encryption standards in transit and at rest
- Access control and identity management
- Incident response readiness
- Penetration testing and audit history
- Subprocessor transparency
- Data retention and deletion policies
- Anonymization and re-identification risks
- Logging and monitoring capabilities
- Third-party security certifications
- Supply chain integrity checks
- Case study: data governance red flag
Module 6. Model Transparency and Explainability
Covers techniques to evaluate AI model behavior, documentation, and interpretability.
12 chapters in this module
- Model documentation expectations
- Feature importance and input transparency
- Explainability methods by model type
- Bias detection and mitigation reporting
- Performance monitoring in production
- Drift detection and retraining schedules
- Model lineage and version control
- Validation dataset quality
- Human-in-the-loop requirements
- Auditability of decision logic
- Vendor accountability for model outcomes
- Case study: lack of explainability derailing deployment
Module 7. Ethical and Societal Impact Assessment
Guides evaluation of ethical risks and broader societal implications of AI vendor solutions.
12 chapters in this module
- Ethical framework selection
- Stakeholder impact mapping
- Equity and fairness considerations
- Community engagement expectations
- Reputational risk scenarios
- Whistleblower and reporting channels
- Historical bias in training data
- Environmental impact of AI systems
- Long-term societal consequences
- Public trust and perception management
- Ethics review board alignment
- Case study: ethical misalignment in public rollout
Module 8. Contractual and Licensing Risk
Teaches how to identify and negotiate high-risk contract terms with AI vendors.
12 chapters in this module
- License scope and usage rights
- Restrictive clauses and limitations
- Liability caps and indemnification
- IP ownership and derivative works
- Audit rights and compliance verification
- Renewal and termination terms
- Price escalation mechanisms
- Service level agreements
- Data ownership and portability
- Warranties and disclaimers
- Force majeure and continuity
- Case study: contract-driven vendor exit
Module 9. Implementation and Integration Risk
Covers risks related to deploying and integrating AI systems in public-sector environments.
12 chapters in this module
- System compatibility assessment
- API security and stability
- Data pipeline reliability
- Change management planning
- Legacy system integration risks
- Performance under load
- Scalability constraints
- Disaster recovery readiness
- Monitoring and alerting setup
- Rollback and fallback procedures
- User training and adoption planning
- Case study: integration failure causing service disruption
Module 10. Oversight and Audit Readiness
Prepares practitioners to document assessments for auditors and oversight bodies.
12 chapters in this module
- Audit trail structure
- Evidence retention policies
- Version control for assessment docs
- Stakeholder approval workflows
- Independent review requirements
- Regulatory reporting templates
- Corrective action tracking
- Continuous monitoring setup
- Internal audit coordination
- External auditor engagement
- Documentation standardization
- Case study: successful audit outcome
Module 11. Stakeholder Communication and Decision Support
Teaches how to translate technical risk assessments into actionable insights for leadership.
12 chapters in this module
- Tailoring messages by audience
- Risk communication frameworks
- Visualization of risk scores
- Executive summary templates
- Board-level reporting formats
- Balancing risk and innovation
- Scenario planning for decision support
- Managing conflicting stakeholder views
- Presenting alternatives and trade-offs
- Documenting decision rationale
- Post-decision review processes
- Case study: consensus-building in high-stakes procurement
Module 12. Sustained Risk Management and Vendor Lifecycle
Covers ongoing risk monitoring and management across the vendor relationship lifecycle.
12 chapters in this module
- Post-deployment risk reassessment
- Ongoing compliance monitoring
- Performance tracking and KPIs
- Incident response coordination
- Renewal risk evaluation
- Exit planning and transition
- Knowledge transfer requirements
- Lessons learned documentation
- Updating assessment frameworks
- Scaling assessment across multiple vendors
- Building internal capability
- Case study: long-term vendor lifecycle success
How this maps to your situation
- Evaluating a new AI vendor for a public-sector pilot
- Scaling an existing AI solution across departments
- Responding to an audit finding related to vendor risk
- Designing a standard due diligence process for future procurements
Before vs. after
Before
Uncertain, inconsistent, or reactive approaches to AI vendor evaluation in public-sector programs.
After
Confident, structured, and repeatable risk assessment that supports compliant, accountable, and effective 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 2, 3 hours per module, designed for working professionals to complete at their own pace.
If nothing changes
Continuing without a structured approach may lead to compliance gaps, delayed decisions, and increased exposure to operational and reputational risks in public-sector AI programs.
How this compares to the alternatives
Unlike generic AI ethics courses or high-level risk overviews, this program delivers implementation-grade tools and public-sector-specific workflows not found in broader commercial offerings.
Frequently asked
Who is this course designed for?
Professionals in public-sector or public-facing roles who evaluate, approve, or oversee AI vendor solutions and need practical, repeatable assessment methods.
How is the course structured?
12 modules, each containing 12 chapters (144 chapters total).
Is this course technical or strategic?
It bridges both, providing technical depth for assessment while aligning with strategic governance and compliance objectives.
$199 one-time. Approximately 2, 3 hours per module, designed for working professionals to complete at their own pace..
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